Note: Descriptions are shown in the official language in which they were submitted.
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AN INTELLIGENT DRILLING ADVISOR
BACKGROUND OF THE INVENTION
[00011 This invention is in the field of the drilling of wells, and is more
specifically directed to measurement and control systems for use in such
drilling.
[00021 As is fundamental in the art, the drilling of wells consumes a large
portion of the cost involved in the exploration for and production of oil and
gas.
Drilling costs have increased substantially in recent years, considering that
many of
the easily discovered and accessible fields in the world are already
producing, if not
already tapped out. As such, new wells to reach such less-accessible
reservoirs are
generally much deeper, and otherwise much more complex, than in years past.
New
wells are also often drilled at locations of reduced confidence that a
producing
potential reservoir is present, because of the extreme depth of the remaining
reservoirs. Even when drilling into more certain hydrocarbon reservoirs,
drilling
costs are also often higher than in the past because of the inaccessibility of
the
reservoirs (e.g., at locations far offshore), or other local difficulties.
[0003] Because of these increasing costs involved in modem drilling, it is
ever
more critical that the drilling operation be carried out accurately and
efficiently. The
criticality of accurate drilling is also especially important as smaller
potential
reservoirs, at greater depths into the earth, are being exploited. In
addition, the
extreme depths to which modem wells are now being drilled add many
complications
to the drilling process, including the cost and effort required to address
drilling
problems that may occur at such extreme depths and with such increased well
complexity. A very high level of skill is thus required of the driller or
drilling
engineer, who is the primary decision-maker at the drilling rig, in order to
safely drill
the well as planned. But these skills are in short supply.
[00041 On the other hand, as known in the art, a tremendous amount of
information and computer processing power is available from modem computing
equipment and techniques. The technology available for sensors, and for
communicating and processing signals from sensors, continues to advance; in
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addition, modern techniques for data acquisition have also greatly improved,
due in
large part to the massive computing power now locally available at relatively
modest
cost.
[0005] By way of further background, the failure mechanism of "lost
circulation" is a known concern in the drilling of an oil or gas well. As is
fundamental in the art, drilling "mud" is circulated through the drill string
during
drilling to lubricate and perhaps power the drill bit itself, and to return
cuttings to the
surface; the drilling mud is cleaned to remove the cuttings and other
material, and is
then recycled into the drill string. Lost circulation refers to the situation
in which the
drilling mud is lost into the formation, rather than returning to the surface.
Besides
the obvious economic cost of replacing the relatively expensive drilling mud,
lost
circulation can also cause more catastrophic failures such as stuck drill
pipe, blowout
of the well, damage to the reservoir itself, and loss of the well altogether.
[0006] By way of further background, the term "software agent" is known in
the art as referring to a computer software program or object that is capable
of acting
in a somewhat autonomous manner to carry out one or more tasks on behalf of
another program or object in the system. Software agents can also have one or
more
other attributes, including mobility among computers in a network, the ability
to
cooperate and collaborate with other agents in the system, adaptability, and
also
specificity of function (e.g., interface agents). Some software agents are
sufficiently
autonomous as to be able to instantiate themselves when appropriate, and also
to
terminate themselves upon completion of their task.
[0007] By way of further background, the term "expert system" is known in
the art as referring to a software system that is designed to emulate a human
expert,
typically in solving a particular problem or accomplishing a particular task.
Conventional expert systems commonly operate by creating a "knowledge base"
that
formalizes some of the information known by human experts in the applicable
field,
and by codifying some type of formalism by way the information in the
knowledge
base applicable to a particular situation can be gathered and actions
determined.
Some conventional expert systems are also capable of adaptation, or
"learning", from
one situation to the next. Expert systems are commonly considered to in the
realm of
"artificial intelligence".
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[0008] By way of further background, the term "knowledge base" is known in
the art to refer to a specialized database for the computerized collection,
organization,
and retrieval of knowledge, for example in connection with an expert system.
[0009] By way of further background, the term "rules engine" is known in the
art to refer to a software component that executes one or more rules in a
runtime
environment providing among other functions, the ability to: register, define,
classify,
and manage all the rules, verify consistency of rules definitions, define the
relationships among different rules, and relate some of these rules to other
software
components that are affected or need to enforce one or more of the rules.
Conventional approaches to the "reasoning" applied by such a rules engine in
performing these functions involve the use of inference rules, by way of which
logical
consequences can be inferred from a set of asserted facts or axioms. These
inference
rules are commonly specified by means of an ontology language, and often a
description language. Many reasoners use first-order predicate logic to
perform
reasoning; inference commonly proceeds by forward chaining and backward
chaining.
[0010] By way of further background, the use of automated computerized
system to gather measurement data from an oil or gas well during drilling, and
to
display trend information for those measurements at the rig location, is
known. One
such conventional system gathers such measurement data including bottomhole
pressure, temperature, flow, torque and turn information and the like. In that
conventional system, a display is generated to indicate pressure differences
(i.e.,
differences between bottomhole pressure and formation pressure) versus
drilling
depth.
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BRIEF SUMMARY OF TIE INVENTION
[0011] It is therefore an object of this invention to provide a system,
method,
and a computing architecture, for harnessing the power of modem computing
equipment to assist in the drilling of wellbores for the exploration and
production of
oil and gas, for geothermal wells, and for other purposes.
[0012] It is a further object of this invention to provide such a system,
method,
and architecture that can exploit previously gained knowledge about the
location and
behavior of sub-surface strata, and previously gained knowledge about drilling
equipment and processes, to optimize the drilling operation.
[0013] It is a further object of this invention to provide such a system,
method,
and architecture that can provide usable perception of current drilling
conditions to
the driller, detection and diagnosis of drilling dysfunction events, and
insight into and
recommendations regarding drilling conditions to be encountered, in real-time.
[0014] It is a further object of this invention to provide such a system,
method,
and architecture that can provide expertise to less experienced drillers, and
recommendations for drilling into new fields, or using new drilling equipment,
to both
experienced and inexperienced drillers.
[0015] It is a further object of this invention to provide such a system,
method,
and architecture that adaptively adjusts its results and recommendations based
on
input from drilling personnel and from human experts.
[0016] It is a further object of this invention to provide such a system,
method,
and architecture that is responsive to human or computerized expert
verification and
analysis of possible results and recommendations, to maintain and improve
safety and
success of the drilling operations.
[0017] It is a further object of this invention to provide such a system,
method,
and architecture that is capable of managing multiple drilling sites at
multiple drilling
locations in multiple production fields, while incorporating and using
applicable
information gained from analogous drilling events in an adaptive manner.
[0018] It is a further object of this invention to provide such a system,
method,
and architecture that is capable of providing, to a decision-maker at the
drilling rig, a
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real-time recommendation, based on surface sensor information, for avoiding or
correcting for a down hole vibration event.
[0019] It is a further object of this invention to provide such a system,
method,
and architecture that is capable of providing, to a decision-maker at the
drilling rig, a
real-time recommendation, based on a combination of surface, subsurface and
historical, knowledge-based sensor information, for the optimizing of the
drilling of a
wellbore.
[0020] It is a further object of this invention to provide such a system,
method,
and architecture that is capable of providing, to a decision-maker at the
drilling rig, a
real-time recommendation, based on a combination of surface, subsurface and
historical, knowledge-based sensor information, for avoiding or correcting for
a down
hole vibration event.
[0021] It is a further object of this invention to provide such a system,
method,
and architecture that is capable of providing, to a decision-maker at the
drilling rig, a
real-time recommendation for avoiding or correcting for a loss of circulation
of the
drilling mud.
[0022] Other objects and advantages of this invention will be apparent to
those of ordinary skill in the art having reference to the following
specification
together with its drawings.
[0023] The present invention may be implemented into an expert computer
hardware and software system, implemented and operating on multiple levels, to
derive and apply specific behavioral tools at a drilling site from a common
knowledge
base including information from multiple drilling sites, production fields,
drilling
equipment, and drilling environments. At a highest level, a knowledge base is
developed from attributes and measurements of prior and current wells,
information
regarding the subsurface of the production fields into which prior and current
wells
have been or are being drilled, lithology models for the subsurface at or near
the
drilling site, and the like. In this highest level, an inference engine drives
formulations (in the form of rules, heuristics, calibrations, or a combination
thereof)
based on the knowledge base and on current data; an interface to human expert
drilling administrators is provided for verification of these rules and
heuristics. These
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formulations pertain to drilling states and drilling operations, as well as
recommendations for the driller, and also include a trendologist function that
manages
incoming data based on the quality of that data, such management including the
amount of processing and filtering to be applied to such data, as well as the
reliability
level of the data and of calculations therefrom.
[0024] At a second level, an information integration environment is provided
that identifies the current drilling sites, and drilling equipment and
processes at those
current drilling sites. Based upon that identification, and upon data received
from the
drilling sites, servers access and configure software agents that are sent to
a host client
system at the drilling site; these software agents operate at the host client
system to
acquire data from sensors at the drilling site, to transmit that data to the
information
integration environment, and to derive the drilling state and drilling
recommendations
for the driller at the drilling site. These software agents include one or
more rules,
heuristics, or calibrations derived by the inference engine, and called by the
information integration environment. In addition, the software agents sent
from the
information integration environment to the host client system operate to
display
values, trends, and reliability estimates for various drilling parameters,
whether
measured or calculated.
[0025] The information integration environment is also operative to receive
input from the driller via the host client system, and to act as a knowledge
base server
to forward those inputs and other results to the knowledge base and the
inference
engine, with verification or input from the drilling administrators as
appropriate.
[0026] According to another aspect of this invention, the system includes the
capability of creating a notional "best well" from all available information
for the
production field. The information on which this "best well" is created
includes depth
and time based values, and drilling history including driller reaction, to
encapsulate
rules about how to drill an optimal well, including reaction prior to a
dysfunction.
These rules can indicate the actions to be taken to drill such a "best well",
operational
recommendations including when to operate near the maximum operating
parameters
of the drilling rig, and displayable rationale for recommended actions ahead
of the
driller's perception of an impending down hole vibration event.
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10027] According to another aspect of the invention, the system develops a
knowledge base from attributes and measurements of prior and current wells,
and
from information regarding the subsurface of the production fields into which
prior
and current wells have been or are being drilled. According to this aspect of
the
invention, the system self-organizes and validates historic, real time, and/or
near real
time depth or time based measurement data, including information pertaining to
drilling dynamics, earth properties, drilling processes and driller reactions.
These data
and information are used to create the rules for drilling a notional "best
well". This
drilling knowledge base suggests solutions to problems based on feedback
provided
by human experts, learns from experience, represents knowledge, instantiates
automated reasoning and argumentation for embodying best drilling practices
into the
"best well".
[00281 According to another aspect of the invention, the system includes the
capability of virtualizing information from a well being drilled into a
collection of
metalayers, such metalayers corresponding to a collection of physical
information
about the layer (material properties, depths at a particular location, and the
like) and
also information on how to successfully drill through such a layer, such
metalayers re-
associating as additional knowledge is acquired, to manage real-time feedback
values
in optimizing the drilling operation, and in optimizing the driller response
to
dysfunction. Normalization of the "best well" into a continuum, using a system
of
such metalayers, enables real-time reaction to predicted downhole changes that
are
identified from sensor readings.
[0029] According to another aspect of the invention, the system is capable of
carrying out these functions by creating and managing a network of software
agents
that interact with the drilling environment to collect and organize
information for the
knowledge base, and to deliver that information to the knowledge base. The
software
agents in this network are persistent, autonomous, goal-directed, sociable,
reactive,
non-prescriptive, adaptive, heuristic, distributed, mobile and self-organizing
agents
for directing the driller toward drilling optimization, for collecting data
and
information for creating the "best well", and for creating dynamic
transitional triggers
for metalayer instantiation. These software entities interact with their
environment
through an adaptive rule-base to intelligently collect, deliver, adapt and
organize
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information for the drilling knowledge base. According to this aspect of the
invention, the software agents are created, modified and destroyed as needed
based on
the situation at the drilling rig, within the field or at any feasible
knowledge collection
point or time instance within the control scope of any active agent.
[0030] According to another aspect of the invention, the software agents in
the
network of agents are controlled by the system to provide the recommendations
to the
drillers, using one or more rules, heuristics, and calibrations derived from
the
knowledge base and current sensor signals from the drilling site, and as such
in a
situationally aware manner. In this regard, the software agents interact among
multiple software servers and hardware states in order to provide
recommendations
that assist human drillers in the drilling of a borehole into the earth at a
safely
maximized drilling rate. The software "experts" dispatch agents, initiate
transport of
remote memory resources, and provide transport of knowledge base components
including rules, heuristics, and calibrations according to which a drilling
state or
drilling recommendation is identified responsive to sensed drilling conditions
in
combination with a selected parameter that is indicative of a metalayer of the
earth,
and in combination with selected minimums and maximums of the drilling
equipment
sensor parameters. The software experts develop rules, heuristics, and
calibrations
applicable to the drilling site derived from the knowledge base that are
transmitted via
an agent to a drilling advisor application, located at the drilling site, that
is coupled to
receive signals from multiple sensors at the drilling site, and also to one or
more
servers that configure and service multiple software agents.
[0031] According to another aspect of the invention, the system is applied to
circulation actors to optimize circulation, hydraulics at the drill bit point
of contact
with the medium being drilled, rationalization of distributed pressure and
temperature
measurements and to provide recommendations to avoid or recover from loss of
circulation events.
[0032] In addition, while this invention is summarized in connection with a
multiple level hardware and software architecture system, in combination with
drilling equipment and human operators, it is contemplated that several
portions and
facets of this invention are separately and independently inventive and
beneficial,
whether implemented in this overall system environment or if implemented on a
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stand-alone basis or in other system architectures and environments. Those
skilled in
the art having reference to this specification are thus directed to consider
this
description in such a light.
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BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING
[0033] Figure 1 is a schematic diagram of a system constructed and operating
according to embodiments of the invention.
[0034] Figure 2 is a schematic diagram illustrating the implementation of
embodiments of the invention in connection with multiple production fields.
[0035] Figure 3 is a schematic diagram illustrating functions at a drilling
rig
and their communication of information from the drilling rig to an information
integration environment, according to an embodiment of the invention.
[0036] Figure 4 is a schematic diagram illustrating the arrangement of various
computational resources within an information integration environment and a
formulator, according to an embodiment of the invention, and the relationship
of those
resources to a drilling rig in that embodiment of the invention.
[0037] Figure 5 is a schematic diagram illustrating the arrangement and
operation of software components in the system constructed according to
embodiments of the invention.
[0038] Figure 6 is a schematic diagram illustrating the operation of software
agents in assisting the drilling of a well according to an embodiment of the
invention.
[0039] Figure 7 is a state diagram illustrating an example of the operation of
a
drilling state engine according to an embodiment of the invention.
[0040] Figure 8 is a data flow diagram illustrating an example of the
operation
of data access tools including trendologist and data grinder functions
according to an
embodiment of the invention.
[0041] Figure 9 is a process flow diagram illustrating an example of the
operation of the trendologist and data grinder functions of an embodiment of
the
invention.
[0042] Figure 10 is a schematic diagram illustrating the operation of software
expert functions in assisting the drilling of a well according to an
embodiment of the
invention.
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[0043] Figure 11 is a flow diagram illustrating the operation of the software
expert functions in modifying formulations according to an embodiment of the
invention.
[0044] Figure 12 is a schematic diagram illustrating the operation of
knowledge-based aspects assisting the drilling of a well according to an
embodiment
of the invention.
[0045] Figures 13 through 15 are screen shots illustrating examples of the
visual output at the drilling rig according to an example of the operation of
an
embodiment of this invention.
[0046] Figure 16 is a flow diagram illustrating a generalized example of the
operation of embodiments of the invention.
[0047] Figure 17 is a flow diagram illustrating specific examples of the
operation of embodiments of the invention.
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DETAILED DESCRIPTION OF THE INVENTION
10048] The present invention will be described in connection with its
preferred
embodiment, namely as implemented into a situationally-aware distributed
hardware
and software architecture in communication with operating drilling rigs,
because it is
contemplated that this invention is especially beneficial when implemented in
such an
environment. However, it is also contemplated that this invention may provide
substantial benefits when implemented in systems according to other
architectures,
and that some or all of the benefits of this invention may be applicable in
other
applications. For example, while these embodiments of the invention will be
described in connection with wells used for oil and gas exploration and
production,
this invention is also contemplated to be applicable in connection with other
wells,
such as geothermal wells, disposal wells, injection wells, and many other
types of
wells. Accordingly, it is to be understood that the following description is
provided
by way of example only, and is not intended to limit the true scope of this
invention as
may be claimed herein or in subsequent patents and applications claiming
priority to
this application.
[00491 The preferred embodiments of this invention will be described in
connection with the managing and assisting of the drilling of wellbores into
the earth,
for the exploration, development, and production of hydrocarbons (i.e., oil,
gas, etc.),
and by way of examples in connection with optimizing the drilling operation,
and
with managing reaction to vibration and lost circulation events in such
wellbores. As
will become evident from the following description, these preferred
embodiments of
the invention provide an automated "expert" system that utilizes a body of
previously
obtained information, and rules, heuristics, and calibrations generated from
that body
of knowledge, to perceive current and past conditions of the drilling
operation, to
comprehend the state of the drilling operation based on the perceived
conditions, and
to project future results of the drilling operation, based upon which
recommendations
may be made to the driller and other personnel at the drilling site.
Context of the invention
10050] Figure 1 illustrates the contextual arrangement of a preferred
embodiment of this invention, in connection with a single drilling rig W I.
Drilling rig
W l is a conventional drilling rig, with conventional drilling equipment, as
useful for
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the drilling of an oil well or gas well into the earth. Examples of such
conventional
drilling rigs suitable for use in connection with this preferred embodiment of
the
invention include, without limitation, hydraulic rigs, percussion rigs, air-
drilling rigs,
alternating-current (AC) rigs, and modifications thereof. As is typical for
modem
drilling rigs, several sensors S are implemented at drilling rig WI for
measuring
various parameters useful in connection with the drilling of such a well.
These
sensors S include pressure sensors, mechanical sensors (for sensing rotation,
position,
velocity, acceleration, etc.), temperature sensors, flow sensors, etc.
According to this
embodiment of the invention, these sensors S are in communication with rig
client
computer system TI, for example by way of a data acquisition card or subsystem
(not
shown), which typically includes an analog-to-digital converter (ADC)
function.
10051] According to this embodiment of the invention, rig client TI is
deployed at the site of drilling rig W1. Rig client TI provides the driller
with visual
indicators in various formats corresponding to current conditions at drilling
rig WI, as
based on signals from sensors S and computations as may be carried out by rig
client
Ti itself. In addition, as will be described in further detail below, rig
client TI is
capable of determining and displaying trends in these various measurements,
based on
rules and heuristics that are defined by the overall system of this invention,
as well as
determining and displaying recommendations derived according to such rules,
heuristics, and calibrations, suggesting actions to the driller in operating
drilling rig
W l. Rig client TI is also capable of receiving inputs from the driller in
response to
its indicated trends and recommendations, for example by way of a human-
machine
interface (HMI), and input devices such as a touchscreen, keyboard, and mouse.
10052] In the context of this invention, rig client Ti is connected over a
conventional communications link to an information integration environment
IIE. As
will be described below in further detail, the information integration
environment IIE
manages software "agents" that are sent to and executed on rig client TI,
specifically
configured for drilling rig WI, in the computations and input/output functions
provided by rig client TI to the driller on site at drilling rig WI. In
addition,
information integration environment IIE requests and receives data
corresponding to
the output of sensors S and corresponding to driller input into rig client TI,
so that
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this data can be used in the reformulation of existing rules and heuristics,
and in the
formulation of new rules and heuristics, as will be described in further
detail below.
[00531 Figure 2 illustrates that information integration environment IIE
manages software agents deployed over multiple drilling rigs W, each with a
corresponding rig client T, in multiple production fields F. In the example
shown in
Figure 2, terrestrial production field Fl includes multiple drilling rigs W1
through
W4, and off-shore production field F2 includes multiple off-shore drilling
rigs W5
through W7. Of course, it is contemplated that many more drilling rigs W per
production field F, and many more production fields F, than shown in Figure 2
can be
managed by an installation of information integration environment IIE. Figure
2
illustrates that information integration environment IIE can manage these
multiple
clients at multiple drilling rigs W and fields F, and that information
integration
environment IIE can also select the software agents to be sent to and executed
by each
rig client T according to its particular drilling rig W (to include rules,
heuristics, and
calibrations specific to that drilling rig W), and according to its production
field F (to
include rules, heuristics, and calibrations common to all rigs W at field F).
[00541 Referring back to Figure 1, in the context of the preferred
embodiments of this invention, in the direction further upstream from wells W,
information integration environment LIE is in communication with advanced
drilling
advisor formulator ADA F. Advanced drilling advisor formulator ADA F
corresponds to a remote computing function by way of which formulations to be
used
in connection with the monitoring and advising of the driller via information
integration environment IIE and rig clients T are formulated. For purposes of
this
description, the term "formulations" will refer to protocols or criteria
according to
which the system makes determinations (e.g., drilling state, parameter
estimates, etc.)
and decisions (e.g., recommendations) regarding the drilling operation.
Examples of
formulations include rules or rule sets, such as logical or arithmetic
formulae that
return an inference based on parameter values, heuristics, referring to less-
rigid
automated techniques (as compared with rule sets) for returning an estimate or
drawing an inference using a trial-and-error or some other less-than-rigorous
decision
process, and calibrations, by way of which measurements from various sensors S
can
be correlated or normalized with one another. Accordingly, formulator ADA_F
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includes large data base memory resources, and substantial computing power for
deriving rule sets, heuristics, calibrations, adaptive networks ("neural
nets"), and other
so-called "artificial intelligence" relationships to be incorporated into
software agents
forwarded by information integration environment IIE to rig clients T, and
executed at
rig clients T. In addition, according to the preferred embodiment of the
invention,
formulator ADA_F operates an automated trendologist, by way of which sensor
data
from multiple data sources forwarded from drilling rigs W by information
integration
environment IIE can be intelligently processed and combined, with the
processing
dependent on the nature and quality of the data itself, and so that displays
of these
trends can be derived by rig clients T and displayed by rig clients T in the
most useful
manner for the human drillers on site. According to an embodiment of the
invention,
the processing of data from the various data sources is carried out by data
grinder
functions that apply low-pass filters and other processes to the sensor data,
of a
function and complexity selected for each data source according to
formulations
derived by software experts, in response to the nature of the data, and its
expected and
actual quality.
[0055] Formulator ADA F is coupled to one or more remote administrators
RA1, RA2, etc., as shown in Figure 1. Remote administrators RA1, RA2 represent
remote desktops or computers (i.e., human-machine interfaces, or HMIs) that
can
communicate with formulator ADA F, and that have the computational and
communicative capability appropriate to allow a human drilling expert to
review new,
modified, or potential rules, heuristics, and calibrations formulated by
formulator
ADA_F. This review of these rules, heuristics, and calibrations allows inputs
from a
human drilling expert to verify or veto a proposed rule set or individual
rules within a
rule set, whether new or modified, based on actual drilling experience. In
addition,
remote administrators RA1, RA2 may access formulator ADA_F to carry out
management and monitoring functions regarding the system. Remote
administrators
RAI, RA2 can also optionally communicate with information integration
environment
IIE, so that the human drilling experts can monitor the state of any one of
drilling rigs
W, or indeed mirror the display at the corresponding selected rig client T for
that
drilling rig. Furthermore, remote administrators RA1, RA2 may, assuming proper
security levels are granted, modify the configuration parameters for a given
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rig W, or select different rules, heuristics, or calibrations to be applied to
a particular
drilling rig W.
Construction of the preferred embodiment of the invention
[0056] The construction of an intelligent drilling advisor system according to
an embodiment of this invention will now be described. This particular
embodiment
of the invention is contemplated to provide those of ordinary skill with a
useful
example of the implementation of this invention in sufficient detail that
those readers
can readily realize this invention in a wide range of situations and
applications
without undue experimentation. It is to be understood, however, that the
embodiment
of the invention described in this specification is provided by way of example
only,
and is not to be interpreted as limiting the scope of the invention as
hereinafter
claimed.
[0057] According to this embodiment of the invention, as discussed above
relative to Figures 1 and 2, and as will be apparent from the following
description, the
system of this embodiment of the invention is arranged in a client-server
architecture.
Each rig client T may be implemented in the form of a "thin client" or
alternatively as
a "fat client". As known in the art, a "fat client" computer provides
substantial
functionality to its user independently from the central server or servers,
while a "thin
client" does little processing itself, but rather relies on accesses to the
central server or
servers, which perform the bulk of the processing. In either case, rig client
T includes
sufficient computational capacity to receive and process incoming digital data
from
the data acquisition system, preferably using a conventional data acquisition
application running on rig client T, and also to receive and execute software
agents
forwarded to it from information integration environment IIE. In this regard,
considering the system according to this embodiment as a client/server
architecture,
information integration environment IIE operates as the central server or
servers for
rig clients T.
[0058] As known in the art, the term software "agent" refers to a component
of software that is capable of acting in a defined manner to accomplish a task
on
behalf of a user. Agents are often characterized according to their attributes
describing their capability. For example, some software agents are static, in
that they
reside only on one computer within a network, while other software agents are
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mobile, capable of moving themselves among computers and among applications
within the network (e.g., from server to client, or vice versa). Agents can
also be
classified as either deliberative or reactive: deliberative agents possess
some sort of
internal "reasoning" model according to which they plan and execute
coordination
with other agents, while the behavior of reactive agents follows a stimulus-
response
approach. Autonomous agents are capable of operating on their own, without
requiring guidance or direction from a human user, in effect operating
proactively;
this autonomy often includes the capability of self-termination by an agent
once its
tasks are complete. Cooperative agents are capable of interacting with other
agents to
communicate data and results, and to coordinate their individual actions
within a
larger framework. Adaptive agents react to their external environment to adapt
their
behavior in response to input data and calculations or determinations.
According to
this embodiment of the invention, as will become apparent from the following
description, the distributed software agents operating in the overall system,
including
those agents executed at rig clients T, are autonomous, cooperative, adaptive,
mobile,
and reactive software agents. These agents include goal-directed and
persistent
agents, cooperative with other agents to the extent of being able to self-
organize into a
network of agents. The overall function of these agents will be to interact
with the
drilling environment, and formulations (e.g.. rules, heuristics, calibrations)
that have
been previously developed, in order to intelligently collect, deliver, adapt,
and
organize information about the drilling operation.
[00591 As shown in Figures 1 and 2, sensors S are provided at drilling rig W
in the conventional manner. Some sensors S are provided at the surface, and
others
are provided sub-surface, at the site of drilling rig W. Examples of the
instantaneous
measurements acquired by sensors S and utilized according to this embodiment
of the
invention include, among others:
= weight-on-bit (WOB);
= revolutions per minute (RPM) of the drill bit;
= torque applied at the top of the drill string;
= standpipe pressure;
= mud pump output.
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In addition, it is contemplated that other sensors S may correspond to
pressure, stress,
and temperature measurements, such as those obtained by direct drill string
sensors, and
logging measurements, such as those obtained by way of measurement-while-
drilling
(MWD) technologies, as well known in the art. As will be described below,
software
agents and other functions within the system will calculate averages and other
statistics
from these measurements. These statistics may be reflected as real-time
(instantaneous)
values of the various parameters, or may be running averages over one or more
elapsed
periods of time. In addition, according to this embodiment of the invention,
"reliability"
ratings are computed or otherwise derived from the measurement data by
software
agents. These reliability ratings for a particular sensor or other data source
can be used
in determining the extent of processing to be applied to the measurement data.
[00601 Many of sensors S present their output signals in the analog domain.
These analog output signals from sensors S are collected by way of a
conventional
data acquisition system (not shown), which includes an analog-to-digital
conversion
(ADC) function in this embodiment of the invention, to enable digital data
analysis as
will be described. According to this embodiment of the invention, these
measurement
data from the data acquisition system are acquired through the action of
software
agents instantiated in the system, which are executed at rig client T to poll
or to
receive "pushed" measurements from the data acquisition system at its drilling
rig W.
Some of these software agents may obtain the sensor measurement data
periodically,
at a default or selected frequency; other software agents may be instantiated
and
execute in a "situationally aware" manner, in response to the system
determining, by
way of one or more formulations derived by software experts, that the
operation at
drilling rig W is in a particular drilling state or condition.
10061] Figure 3 illustrates part of the data flow from a drilling rig W to
information integration environment IIE, according to this agent-based
approach.
Sensors St through S3 represent some of the sensors at drilling rig W itself.
Measurements from those sensors S1 through S3 are obtained (in the physical
sense,
via the data acquisition system, A/D conversion, buffering, etc.) by
corresponding
software agents Al through A3, respectively. As discussed above, agents Al
through
A3 are instantiated by information integration environment HE, and are
typically
executed at or through client T, to acquire measurement data from their
corresponding
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sensors Si through S3 on a periodic basis, or on a situationally-aware basis
in
response to previously sensed conditions or intelligently determined drilling
states.
Of course, it is contemplated that many more than three sensors S and
corresponding
data acquisition agents A will be deployed for a given drilling rig Wl within
a typical
drilling operation.
[00621 For example, U.S. Provisional Application No. 61/097,128 filed
September 15, 2008, incorporated herein by reference, describes the
distributed
deployment of multiple sensors along the length of the drill string, rather
than only at
the surface and in the bottomhole assembly as conventional. For example, these
sensors may be deployed in subs along the drill string at which signal
repeaters (for
signals transmitted from the bottomhole assembly to the surface) are placed.
These
multiple sensors provide real-time measurements from locations that are
distributed
along the drill string, both during drilling or while the drill string is
stationary.
Examples of parameters measured in this distributed manner include borehole
measurements such as pressure and temperature, and theological properties or
states
of the drilling fluid or borehole, such as temperature, viscosity, flow rate,
shear rate,
depth, and the like.
[00631 The measurement data are communicated from these distributed
sensors to a computer system at the surface. Processing of these distributed
measurements, along with the bottomhole and surface measurements, provide a
great
deal of additional insight into the optimization of the drilling process, and
also into
diagnosis and corrective action for drilling or circulation dysfunctions. For
example,
anomalous measurement values along the length of the drill string, and the
location of
those anomalies, can be detected from these distributed measurements - such
anomalies can only be detected indirectly or inferentially, if at all, by
conventional
bottomhole and surface sensors. Furthermore, these distributed measurements
can
provide real-time profiles of the measured parameters over time and depth, and
these
profiles can be themselves processed to detect changes in those measurement
profiles
over time and over depth. It is contemplated that this approach of distributed
measurement will be especially valuable when incorporated into the intelligent
drilling advisor system and method of this embodiment of the invention. As
such, it
is contemplated that sensors S may be deployed at drilling rig W 1 at the
surface, in a
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bottomhole assembly, and also in a distributed manner along the length of the
drill
string, according to these embodiments of the invention.
[0064] As shown in Figure 3, rig client T also preferably includes touchscreen
display TDISP, by way of which a human machine interface (HMI) software agent
presents certain current measurements and calculation results, trends of those
measurements and calculations, indications of the current state of the
drilling
operation for its associated drilling rig W, and also recommendations of
drilling
actions to be taken as derived by the applicable rules, heuristics, and
calibrations
according to which one or more of the software agents operate. If the display
is a
touchscreen, as is preferred, the drilling decision-maker (i.e., the driller
at land-based
drilling rigs W, and the drilling engineer at offshore drilling locations) can
enter
inputs to rig client T in response to the displayed recommendation. An
important type
of such inputs is an "ignore" input, by way of which the drilling decision-
maker
indicates a decision to decline following the recommendation, as will be
described in
further detail below. Typically, touchscreen display TDISP at which the
recommendations are displayed to the drilling decision-maker will be
physically
located at drilling rig W; alternatively, or additionally (i.e., if multiple
displays TDISP
are supported), touchscreen display TDISP or another type of HMI may be
deployed
at a location remote from drilling rig W, with telephone, radio, or other
communications being carried out between the remote drilling decision-maker
and
personnel at drilling rig W that are physically carrying out the
recommendation or
other instructions. Other input devices (keyboard, mouse, etc.) beyond
touchscreen
display TDISP are also preferably deployed with rig client T. These
measurements
and inputs are acquired and forwarded by the agents executed at each rig
client T
(e.g., agent A5 shown in Figure 3) to information integration environment IIE.
[0065] In addition, various data regarding the drilling equipment deployed at
drilling rig W are also stored at rig client T, and forwarded to information
integration
environment IIE by way of software agents (e.g., agents A4, A6, A7 of Figure
3).
These data include various attributes of the well being drilled, including a
name for
the well, a text description of the well, projected completion depth of the
well, cost
parameters (fixed and variable costs), and links to the lists of the rig
hardware types
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deployed at drilling rig W and of the layers (lithology) through which the
well is
being drilled, as will now be described relative to Figure 3.
[0066] Among these well properties, the projected completion depth is a
parameter that can be applied to formulations (rules, heuristics, and
calibrations).
This and other such parameters WP1 stored within client T can be acquired by a
corresponding software agent A4, and forwarded to information integration
environment IIE.
[0067] Other well properties WP2 serve as a key by way of which other
objects can be obtained. These properties are in the form of a "root" object
that links
to the types of hardware at drilling rig W, lithological models pertinent to
the location
of drilling rig W, and the like. Rig hardware types stored at rig client T are
not
themselves referenced directly by rules, but rather provide keys to tables or
other data
stores, for example in a database DB at formulator ADA F (Figure 1), from
which
properties of the particular rig hardware can be retrieved. As shown in Figure
3, rig
hardware type values stored as well properties WP2 at client T access a table
or object
in database DB storing identifying information regarding the rig, including
the rig
manufacturer and model, drive unit manufacturer and model, the control system
manufacturer and model, the number of pumps, pump manufacturers and models,
mud system manufacturer and model, drill bit manufacturer and model, and
various
calibration factors derived from the rig properties. These properties WP2 can
also
point to tables or objects storing the information regarding these various
hardware
features. For the case of the drill bit, this information can include the
specific current
bit being used (e.g., serial number), its size, expected remaining life, and
its cutting
properties. Other similar information regarding the rig stored in database DB
and
retrievable by way of a key within well properties WP2 stored at client T
include
operational limits, such as maximum weight-on-bit (WOB), maximum RPM for the
rig, maximum surface torque, maximum standpipe pressure, maximum pump output,
horsepower of the drawworks, and the like. As mentioned above, calibration
factors
may be stored at database DB, by way of which measurement data from sensors S
can
be adjusted during operation.
[0068] According to this embodiment of the invention, each drilling rig W is
also preferably associated with a tithological model that indicates the
expected
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sequence of lithological layers (e.g., from five to twenty layers) to be
encountered in
the drilling operation. In this embodiment of the invention, well properties
WP2
stored at client T also include a key or link to such a model for the well
being drilled
from drilling rig W1, in this example. Within that lithological model, also
stored at a
database DB, for example within formulator ADA F or elsewhere in the network,
data are stored for each layer in the corresponding model that indicate its
particular
characteristics. The following table lists an example of such layer-specific
property
data, by way of example:
Property T (units)
Layer name Text
Layer description Text
Depth of Lop of layer Real (ft.)
Depth of bottom of layer Real (ft.)
Average compressive strength Real (Kpsi)
Layer top status (estimated or Boolean
actual
Layer bottom status (estimated Boolean
or actual)
Thickness uncertainty (zone of Real (ft.)
uncertainty at bottom of layer)
The lithological model also can include expected values of drilling parameters
that will
be encountered within each layer, for example:
Expected values Type (units)
Expected min/max WOB Real (Kpsi)
Expected min/max RPM Real
Expected min/max rate of Real (ft./sec.)
penetration (ROP)
Expected min/max standpipe Real (psi)
pressure
Expected min/max pump rate Real (flow unit)
Expected min/max Real (Kpsi)
compressive strength
As shown in Figure 3, the lithological model can also include calculated
estimated limits
for these drilling parameters, as derived from the rig properties (hardware
types) or as
otherwise may be calculated:
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Limits Type (units)
Maximum WOB limit Real (Kpsi)
Maximum RPM limit Real
Maximum rate of penetration Real (ft./sec.)
(ROP) limit
Maximum standpipe pressure Real (psi)
limit
Maximum pump rate limit Real (flow unit)
Maximum WOB limit near Real (Kpsi)
bottom of layer
Maximum RPM limit near Real (RPM)
bottom of la er
Maximum ROP limit near Real (ft./sec.)
bottom of la er
The lithological model stored in database DB also preferably includes expected
physical
properties. These expected physical properties and layer behavior can be
expressed
qualitatively (on a scale of from 0 to 5, from least-to-most, for example):
Physical nropertv
Ex ected abrasiveness
Expected porosity
Expected rmeabili
Expected propensity for bit balling
Expected propensity for bottom balling
Expected propensity for stick-slip vibration
Expectation of embedded soft layers
Expectation of embedded hard layers
Expected propensity for well control problems
As shown in Figure 3, these keys or links stored at client T as well
properties WP2 can
be acquired by corresponding software agents A6, A7, and forwarded to
information
integration environment IIE for retrieval from database DB within formulator
ADA F or
elsewhere. Indeed, it is contemplated that the rig hardware data and the
lithological
model itself may alternatively be stored within information integration
environment HE,
and called or linked to by rig identifying information stored at rig client T
and forwarded
to information integration environment LIE by agents A6, A7.
10069] From a hardware standpoint, it is contemplated that information
integration environment IIE is preferably configured as a conventional server
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architecture, with one or more server computers (e.g., a bank of rack-mounted
servers,
accessible via one or more high-performance laptop or desktop computers)
serving as
the physical servers in the overall network. The physical locations of these
physical
server computers may also be distributed among data centers, or among
production
fields, or the like, as may be supported by the network infrastructure in
place. As
known in the art, a conventional server architecture is substantially a
passive system,
in that it operates in a request-acknowledge fashion relative to its clients
(e.g., rig
clients T). In addition, although operating as a server, information
integration
environment TIE may also itself execute various software agents, for example
to
request data from rig clients T, or to access formulator ADA F, as will be
described
in further detail below.
10070] As evident from Figure 2, information integration environment IIE is
in communication with many rig clients T, at various locations such as
production
fields F. It is contemplated that the particular communications links can be
realized
by way of any one or more than one communications technologies, including
dedicated or private wired or wireless LAN, MAN, or WAN networks, conventional
Internet protocol (IP) over wired or wireless physical communications links
either
public or by way of a virtual private network (VPN), satellite communications,
etc. It
is contemplated that the particular communications technology and physical
coupling
of rig clients T and information integration environment IIE, as well as
between
information integration environment TIE and formulator ADA F, and to and from
remote administrators RA, according to one or a combination of these
approaches, can
be readily implemented by those skilled in the art having reference to this
specification.
[00711 From a software standpoint, according to this embodiment of the
invention, the software executed and executable by information integration
environment HE is arranged as a "stack" of drilling applications. Figure 4
illustrates
the software architecture of information integration environment TIE and of
formulator ADA F, according to this embodiment of the invention. As shown in
Figure 4, information integration environment TIE is supported by one or more
physical servers 10, at one or more physical locations as described above.
Physical
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servers 10 support and execute the stack of drilling applications, which
includes data
access tools 14 and also multiple logical servers 16.
[0072] Data access tools 14 operate to request and process data from sensors S
for each of the operating drilling rigs W. These data access tools 14 include
standardized data access tools 14a, which refer software applications required
to
receive data in various formats (e.g., WITSML, LAS, CSV, WITS format data) via
software agents and other such applications. In this regard, the term
"standardized" in
connection with standardized data access tools 14a refers to the multiple
standardized
formats in which these data are imported, rather than referring to any
standardization
in the tools themselves; rather, as will be apparent from this specification,
these
standardized data access tools 14a involve new and unique approaches to the
receipt
and processing of these data. For example, as will be described in further
detail
below, standardized data access tools 14a implement data feeds directly to
data
"grinder" functions located at rig clients T. These data grinder functions
process the
data acquired by data acquisition software agents executed at clients T, with
the level
of processing (e.g., filtering) dependent on the quality of the data received,
as
determined by rules, heuristics, and calibrations derived by formulator ADA_F.
These data grinder functions produce filtered data, from the various data
sources, that
are then combined and formatted for display and for use in creating drilling
recommendations, updated or new formulations, and the like by a "trendologist"
function within formulator ADA F.
[0073] It is contemplated that measurement data and other information will be
provided not only from various equipment and computer systems, but also from
various entities, including the leaseholder of the oil or gas production field
(i.e., the
"oil company"), drilling contractors, and other oilfield service companies.
For
example, the oil company will provide information regarding the production
levels
and downstream pipeline infrastructure, as well as other well planning
intelligence.
The drilling contractor will provide information regarding its infrastructure,
including
information related to the subsea support systems and vessel management, power
management, safety management, and the like. Service companies provide
information including logging-while-drilling (LWD) measurements, measurement-
while-drilling (MWD) data, marine management factors, and information
regarding
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such other operations as directional drilling parameters and constraints, and
completion and cementing parameters. This information from these various
entities
influence the situational awareness determinations that are deduced by the
system
according to this invention. As such, drilling applications stack 12 also
includes
drilling state influencer tools 14b within information integration environment
IIE
interface data received from these various data providing "entities", or
sources. It is
contemplated that these drilling state influencer tools 14b can also
incorporate various
data processing operations.
[0074] In addition to these data access tools 14a, various logical servers 16
are
also included within information integration environment IIE, specifically
within
drilling applications stack 12 embodied within information integration
environment
IIE, and executed by the one or more physical servers 10 at that level. As
mentioned
above, logical servers 16 may be realized by server applications being
executed on
individual physical server computers 10, or as distributed server applications
operating on one or more physical server computers 10; it is contemplated that
those
skilled in the art having reference to this description will be able to
readily implement
these and other logical servers that are useful in realizing information
integration
environment TIE according to the preferred embodiment of the invention.
[0075] Agent access server 16a refers to a server application by way of which
various software agents executed by rig clients T, and by information
integration
environment TIE itself, are selected and configured for the particular
computer that is
to execute that software agent, and by way of which, once an agent is
selected, that
agent instantiated and forwarded to the corresponding rig clients T. As will
be
described in further detail below, these agents (e.g., agents Al through A7 of
Figure
3) can be dispatched by agent access server 16a in a situationally-aware
manner,
depending on the particular stage in the drilling operation at each drilling
rig Wl and
its current drilling state, as may be deduced by formulator ADA F.
[0076] Expert tools server 16b, also operating as part of information
integration environment IIE, configures and instantiates software agents that
acquire
data useful for the development of formulations (rules, heuristics, and
calibrations),
and the updating of previously derived formulations. It is contemplated that
these
formulations can include "too] kits" of rules, heuristics, and calibrations
that are
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tailored to each of drilling rigs W served by this system, and the fields F in
which they
are being deployed. In this embodiment of the invention, expert tools server
l6b
instantiates those agents in response to directives from formulator ADA F, and
in a
non-real-time basis, considering that the derivation of new or revised
formulations is
contemplated to not be a real-time exercise. In this manner, agents can be
instantiated
by expert tools server 16b at such time as convenient for clients T, thus
avoiding
resource conflicts with essential real-time processes, or with agents
instantiated by
agent access server 16a. Upon creation or updating of these formulations, by
formulator ADA F, the tool kits including the rules, heuristics, and
calibrations
appropriate for a given rig client T and its well W are then made available to
later-
configured software agents.
[00771 Knowledge tools server 16c is another logical server operating within
information integration environment IIE according to this preferred embodiment
of
the invention. Knowledge tools server 16c provides packages of formulations
(rules,
heuristics, and calibrations) corresponding to a notional "best well" model
for each
specific drilling rig W. This "best well" model includes rules, heuristics,
and
calibrations for "how" to drill the optimum well at the location of each
specific
drilling rig W. According to this embodiment of the invention, the optimum
well is
optimal in a situational sense, such that the applicable rules, heuristics,
and
calibrations can themselves determine the optimization criteria, for current
and
expected drilling conditions. These rules, heuristics, and calibrations are
based on the
entirety of information and intelligence available to formulator ADA F,
including
well parameters and well properties for the specific well, along with
knowledge
gained from other wells including responses of drillers to recommendations,
performance of other wells, information from drilling state influencers
acquired by
drilling state influencer tools 14b, and the like. In addition, human experts
can
provide input into these notional "best wells" via remote administrators RA,
via
human machine interface (HMI) API portal 27 at formulator ADA F. The "best
well" configurations provided by knowledge tools server 16c include the rules,
heuristics, and calibrations corresponding to the "best well" model, and are
configured into one or more software agents that are "pushed" to rig clients T
for the
various drilling rigs W; those pushed software agents are then capable of
receiving the
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sensed measurement data from sensors S at the drilling site and, using the
formulations provided by knowledge tools server 16c in the "best well"
configuration,
provide the driller or drilling engineer with drilling recommendations. It is
contemplated that these recommendations include recommendations on how to
achieve optimal drilling results, recommendations to maximize one or more
operating
parameters of the hardware, and also recommendations with rationale on actions
to be
taken by the driller to avoid impending undesirable events (vibration,
sticking, low of
circulation fluids, well control, etc.). As mentioned above, these
recommendations
are displayed at rig clients T, and the responses from the driller, for
example both
"ignore" and "accept" inputs from the driller in response to a recommendation,
are
also processed and forwarded to formulator ADA_F (along with other
measurements)
for the derivation of new inferences, and thus updated formulations.
100781 According to this embodiment of the invention, the "best well"
configuration for a given drilling rig W, provided by knowledge tools server
16c, is a
virtualization of information regarding the subsurface at the location of that
rig W. In
this embodiment of the invention, this virtualized information is arranged as
a
collection of "metalayers", each metalayer corresponding to an abstraction of
a
combination of distinct lithological layers, a combination of portions of
distinct
lithological layers, or a portion of a single distinct lithological layer. The
metalayer
abstraction includes a collection of information, including that information
contained
within the lithological model for that layer, as described above, as well as
sensed or
calculated measurements made during drilling into that layer at the very
location of
rig W or at the locations of nearby rigs W that have previously drilled
through that
layer. As discussed above, this information includes the depths of the top and
bottom
of this layer at the location of drilling rig W, physical properties of the
layer (e.g.,
compressive strength, etc.), and expected drilling parameters as the metalayer
is
encountered during drilling (e.g., minimum and maximum expected WOB, etc.). In
addition, also according to this embodiment of the invention, it is
contemplated that
each metalayer includes information regarding how to drill through the
physical layer,
including limits on various drilling parameters that are defined for optimum
drilling
performance. This "how-to" information is contemplated to be generated by way
of
formulator ADA F, according to this embodiment of the invention. It is
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contemplated that each of the metalayers will be referred to by way of a
"layer name"
or other indicator (layer number, offsets, etc.), so as to be correlatable
with the drilling
measurements acquired by other rigs W within the same or nearby production
fields.
This abstraction of information into metalayers enable this correlation of
information
among drilling rigs - by referring to a given metalayer by name or some other
"tag"
or link, specific parameters of the metalayer can be adjusted to be rig-
specific, such as
the depth at its top and bottom at the specific location of a given drilling
rig W.
[00791 It is contemplated that these tools 14 and servers 16 implemented at
information integration environment IIE allow the overall system to operate
according
to an observe-orient-decide-act ("OODA") sequence or loop. As known in the
knowledge engineering art, the OODA loop is based on the concept of
situational
awareness, operable at the three hierarchic levels of perception (awareness of
various
situational elements and their current states), comprehension (recognition of
patterns
in system behavior, and interpretation and evaluation of those patterns), and
projection (anticipation of future behavior in situations not previously
encountered).
The OODA loop operates by considering the awareness of a particular situation
(at
one or more of the three hierarchical levels), making a decision of an action
to take
based on that awareness, and then evaluating the result or performance of that
action
to improve the awareness capability. The specific actions performed in an OODA
representation of this intelligent system control includes acquiring
observations about
the current environment, orienting those observations to the system being
controlled
based on previously gained knowledge, deciding on a course of action, and then
acting on the decision, followed by repetition of the observation loop to
evaluate the
effect of the action taken. Information integration environment IIE according
to this
embodiment of the invention is intended to carry out these OODA steps for the
task of
drilling a well, with the higher-level comprehension and projection based on
formulations created by formulator ADA F, the construction and operation of
which
will now be described.
[00801 From a hardware standpoint, formulator ADA F may be constructed as
multiple computers or servers operating cooperatively, or alternatively as
cloud,
utility, or autonomic computing. It is contemplated that formulator ADA F will
include substantial memory resources, for example extremely large disk drive
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systems, for storing large quantities of data over time. As shown in Figure 4,
it is
contemplated that one such memory source 20 (whether realized by a single
dedicated
disk drive, or as distributed among multiple disk drives) will store database
DB, or
another readily-accessible memory arrangement, for receiving and storing data
sensed
at the drilling rigs W currently in operation, and also data as sensed
previously in
other drilling rigs. Database DB in memory source 20 also stores rig
identification
information (hardware type information), lithology models, and the like to
which well
properties WP2 stored at rig clients T link, as discussed above relative to
Figure 3.
Database DB also stores measurement data, calculations, and calibrations from
rig
clients T or calculated values otherwise derived, and the like. Memory
resource 22,
again implemented as a dedicated disk drive or distributed among multiple disk
drive
or other memory resources, stores knowledge base KB in which heuristics
derived by
other functions in formulator ADA F, specifically by the computational (i.e.,
hardware and software) resource of inference engine 24 within formulator ADA
F.
Knowledge base KB may be arranged and operate in the form of a neural net,
from
the standpoint of its development (i.e., "training") and operation. Also as
shown in
Figure 4, formulator ADA F includes WITSML application programming interface
(API) 25, by way of which measurement data and the line are received from
sensors S
via information integration environment IIE.
[0081] As evident in Figure 4, various "engines" are realized as part of
formulator ADA F. The term "engine" in this embodiment of the invention refers
to
hardware and software computational resources that execute particular
functions
useful in connection with the drilling of oil and gas wells. Certain of those
functions
will be described in detail below, in connection with the description of the
operation
of this embodiment of the invention. It is contemplated that these hardware
and
software computational resources involved in a particular engine will
typically be
implemented as one or more software applications or objects, executed by
digital
logic processing circuitry within the computer system or systems in which
formulator
ADA_F is realized. The particular hardware arrangement of formulator ADA F can
vary widely, as discussed above. As such, the specific hardware used to
implement
these functions can also vary widely. In some architectures, the same general
purpose
central processing unit may be used to execute software applications or
objects
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corresponding to each of these functions. In other architectures, dedicated
processing
circuitry may be provided for these functions. Still further in the
alternative, in a
distributed architecture, one or more specific computers may be used to
realize a
particular engine. In addition, some of the functions performed by these
engines may
be executed by application-specific digital logic circuitry (i.e., custom,
semi-custom,
or programmable special purpose logic such as digital signal processors). It
is
contemplated that those skilled in the art having reference to this
specification will be
readily able to realize these engines in various architectures, without undue
experimentation.
[00821 As mentioned above, inference engine 24 is a computational resource,
for example a software component executed by programmable processing circuitry
within formulator ADA F, that defines and updates various formulations (rules,
heuristics, and calibrations) from information stored in knowledge base KB, as
may
be applied to measurements and conditions stored in data base DB, and stores
those
formulations in an accessible form, in knowledge base KB. Rules engine 26 is a
computational resource, again a software component executed by programmable
processing circuitry within formulator ADA F, that selects those formulations
that are
appropriate for a certain situation, and forwards those rules to information
integration
environment lIE for configuration into software agents that apply those rules
at rig
clients T. As known in the art, rules engine 26 has the capability of
inferring logical
consequences from a set of "asserted" facts or axioms, which of course
correspond to
the measurements and conditions stored in database DB. Examples of algorithms
useful in connection with rules engine 26 include a Rete-based algorithm, and
a
forward chaining rule engine, as commercially available. New or modified rule
sets
created by inference engine 24 and accessible to rules engine 26 are available
for
review and verification by a human expert driller, via one of remote
administrators
RA.
[00831 According to this embodiment of the invention, formulator ADA_F
also includes trendologist function 28, which applies specific rules according
to which
data from sensors S are to be processed, combined, and displayed. In this
embodiment of the invention, inference engine 24 generates rules, heuristics,
and
calibrations that are useful to trendologist function 28, which receives data
from
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sensors S, processes those data for coherence and usefulness, and based on
applicable
rules for the current drilling situation, arranges data for display at the
corresponding
rig clients T in a meaningful fashion.
[0084] Other "engines" are also included within formulator ADA F according
to this embodiment of the invention. Agent engine 30 is a computational
resource
within formulator ADA F that adds, modifies, deletes, and monitors all
software
agents that are created within the system. Based upon the results obtained by
inference engine 24 as may be verified by a human expert via remote
administrators
RA, if a new software agent ought to be created, or an existing software agent
ought
to be modified to be more effective, agent engine 30 is the computing resource
that
performs that function. Drilling state engine 32 is a computational resource
within
formulator ADAF in the form of a generic state machine, configurable based on
various rules and heuristics as appropriate, to monitor events and to
determine drilling
"states" (e.g., drilling, sliding, circulating, etc.) for a given drilling rig
W, based on
the sensed data and knowledge stored in knowledge base KB, according to
developed
rules and heuristics.
[00851 As in the case of information integration environment IIE and as
discussed above, formulator ADA F can be implemented by a single installation
of
hardware, for example deployed at a data center or some other location remote
from
the drilling sites. Alternatively, formulator ADA F can be deployed in a
distributed
manner, indeed in a manner that is distributed over the same hardware
resources as
information integration environment IIE if desired. Indeed, it is contemplated
that the
distribution of the hardware in the architecture may itself be situationally-
aware, with
various hardware entities used depending on the sensed and anticipated
conditions for
the well. However it may be implemented, it is preferred that formulator ADA F
operate as a unitary whole, so that each instance of the information
integration
environment IIE and of rig clients T can benefit from the knowledge gained
from all
other instances currently operating, and operated in the past.
[00861 It is contemplated that this invention may be realized in the form of a
software application on a computer readable medium, for example a magnetic
disk
drive, an optical disk, or the like, either directly physically readable by
one of the
computers (i.e., one or more of the server or main-frame computers realizing
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information integration environment IIE or formulator ADA F, and also
including rig
clients T), or stored on such a medium in a location from which one of those
computers may download the software application, or download an installer of
such a
software application which, when executes, downloads and installs the software
application, in either case being communicated to one of those computers by
way of
encoded information on an electromagnetic carrier signal via the appropriate
interface
or input/output device. Further in the alternative, the software application
may be in
the form of a web-based application, in which case the application can reside
and be
executed remotely. Figure 5 represents such a computer readable medium,
directly
readable or downloadable as the case may be, by way of computer readable
medium
CRM. It is contemplated that those skilled in the art will be readily able to
realize the
appropriate computer readable medium and the contents of such a medium by way
of
which the necessary software application can be installed and implemented on a
particular system, based on this description and according to this invention.
Operation of the preferred embodiment of the invention
[0087] The operation of the system according to the preferred embodiments of
this invention will now be described in connection with several examples. It
is
contemplated that those skilled in the art having reference to this
specification will be
readily able to comprehend the operation of this system from those examples,
and to
comprehend the operation of this system in connection with other similar and
analogous tasks and functions beyond those described by example in this
specification. Figure 5, in combination with the other Figures, provides an
overview
of the operation of this system.
[0088] According to this embodiment of the invention, prior to the initiation
of a drilling operation at drilling rig WI at production field Fl, for
example,
formulator ADA F will have previously acquired drilling data, including
measurements and calculations made by one or more rig clients T at various
drilling
rigs W in various production fields F. These data will be stored within
database DB.
As will become evident from this description, it is not essential that
formulator
ADA_F have data from the very production field F1 at which drilling is to
begin, or
from the very equipment of drilling rig WI itself. Although such data, rules,
heuristics, and calibrations will be more accurate if that rig-specific and
field-specific
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information was previously available, drilling can commence at drilling rig W1
based
on rules and heuristics that are deemed to be the best fit for that site, or
generic
drilling rules. Because formulator ADA_F is an adaptive system, the
acquisition of
new data during the operation of drilling rig WI in combination with inputs
from the
driller at rig client TI and also from human experts at remote administrators
RA will
result in the development of formulations that will better apply to the
current
equipment and environment of drilling rig W l.
[0089] By way of a general operational description, during the drilling
operation, rig client Ti executes one or more software agents instantiated,
configured
and forwarded to it by information integration environment TIE to assist the
driller
with control and management of the drilling operation. According to an
embodiment
of the invention, the combination of rig clients T with information
integration
environment HE and with formulator ADA F implements a three-level "situational
awareness" intelligent drilling advisor system, so that attributes of the
drilling
operation are perceived (i.e., measured and current parameter values
evaluated),
comprehended (e.g., by determining current "states" of the drilling operation,
of the
drilling equipment involved, and the subsurface lithology into which the
wellbore is
being drilled), and projected (e.g., by making and presenting recommendations
for
action to the driller). This situational awareness is implemented in the
rules,
heuristics, and calibrations formulated by formulator ADA F, which are
configured
as tools by information integration environment HE into software agents
selected for
particular drilling rigs and clients, and then sent to those rig clients T for
execution at
the drilling site.
[0090] According to an embodiment of the invention, rig clients T present
current measurements, trends and acceptable ranges of those measurements,
indications of the current state of the drilling operation, and
recommendations for
future action, by way of a human machine interface ("HMI") on a visual
display,
preferably a touchscreen so that the driller's inputs are received by way of
the same
graphical user interface (GUI). Figure 13 illustrates an example of the
display from
this human machine interface at rig client TI, at a point in time at which
drilling rig
WI is idle, having drilled 7690 feet into the earth. The human machine
interface
indicates that the current drilling state is "Not Drilling". In Figures 13
through 15,
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"ADA" is a trademark of BP North America Inc., used in connection with its
systems
and software systems for assisting drilling.
[00911 Once drilling begins, measurement data from sensors S at drilling rig
WI are received at rig client Ti. Software agents that have been previously
forwarded by information integration environment IIE to rig client Ti, or
alternatively
new agents that are identified, configured, and sent by information
integration
environment IIE in response to measurements from drilling activity, begin
executing.
As shown via the HMI as illustrated in Figure 5, one of these software agents
(state
agent A -S shown in Figure 5) determines that the current state of the
drilling
operation is "rotating". Another software agent or agents (e.g., trend agent A
T and
display agent A -P of Figure 5) display the current values of measurements and
calculated parameters over time, along with recommended values and a
recommended
range (shown as the shaded portion within each trend display of Figures 14 and
15).
As previously described, trend agent A T includes various rules, heuristics,
and
calibrations developed by formulator ADA F, specifically using its
"trendologist"
function, to determine a preferred frequency with which the measurements or
calculations are derived and displayed, based on the quality of the
measurements and
other variables; a measure of the reliability of each of these measurements
and
calculations is also preferably determined. Figure 14 illustrates these
measurements
and calculations as the rate of penetration (ROP), the mechanical specific
energy
(MSE) corresponding to the energy expended for a given volume of earth
removed,
bit torque, the weight on bit (WOB), the RPM of the drill string at the rotary
table of
drilling rig WI, and the pressure differential (Delta P) between the standpipe
pressure
and a base setting. The base setting of the pressure differential, in this
embodiment of
the invention, is derived by the trendologist function 28 of formulator ADA_F
at
various times and states in the drilling operation, based on sensed
measurements such
as strokes per minute of the mud pumps, and known mud equipment parameters
such
as piston diameter, pipe diameter, etc. will affect this base setting. The HMI
at rig
client Ti also indicates recommended actions to be taken ("Set RPM and WOB to
Recommended Values" in the example of Figure 14) to optimize the drilling
operation, and provides a way for the human driller to enter feedback.
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[0092] One or more drilling state agents A_S instantiated and forwarded by
information integration environment HE to rig client Ti preferably operates,
again
according to rules, heuristics, and calibrations derived by formulator ADA F
for the
particular drilling rig WI based on its equipment, and the lithology and
location of the
site of that drilling rig WI, to determine whether a drilling dysfunction is
occurring
based on current and trended measurements and calculations, and if so, what
the
dysfunction is. Figure 15 illustrates such a situation, in which the HMI
indicates that
a "bit bounce" dysfunction is present; some of the measurements (MSE, Delta P,
RPM) are outside of their recommended ranges at this point, and most likely
(depending on the rules) were at least partially the basis of the identified
dysfunction.
A recommendation is also provided to the driller, as before.
[0093] An input mechanism is provided at touchscreen display TDISP, as
shown in Figures 13 through 15, by way of which the driller or drilling
engineer can
choose to "ignore" the displayed recommendation. If this occurs, that "ignore"
input
will be communicated back to information integration environment TIE and
formulator ADA F, along with measurement data and other information regarding
drilling rig W, for incorporation into new and updated rules, heuristics, or
calibrations
as appropriate. In any event, whatever the action taken by the driller or
drilling
engineer in response to the recommendation, the system will continue to
monitor,
communicate, display, and provide recommendations during the drilling
operation.
10094] Given this generalized description of the operation of the system,
various features, aspects, and components of such a system according to an
embodiment of the invention will now be provided in further detail.
Agent-Based Drilling
[0095] As mentioned above, servers in information integration environment
IIE operate to instantiate, configure, and send software agents to rig clients
T. such
software agents being selected and arranged according to various attributes of
drilling
rig W associated with each such rig client T. In this regard, integration
environment
IIE and its drilling applications stack operate to select, instantiate,
configure, and
manage these software agents.
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[00961 Figure 5 illustrates the overall arrangement of software agents and
resources associated with those agents, within an example of a system
according to
this embodiment of the invention. Rig client Tl, associated with drilling rig
WI in
this example, receives signals from sensor S via analog/digital converter 35
and other
functions within a data acquisition system (not shown). Rig client Ti receives
the
digital measurement data corresponding to sensor S output via data acquisition
application 37 executed at rig client Ti. Data acquisition agent Al,
configured and
instantiated by agent access server 16a at information integration environment
IIE, is
executing on rig client Ti, and is forwarding measurement data according to
that
configuration to information integration environment IIE, via agent access
server 16a.
Other software agents are also being executed at rig client TI, including
trend agent
A -T for deriving trends of measured data. State agent A -S is also executing
at rig
client Tl, for determining the current drilling state of drilling rig W1,
based on the
measurement data and on formulations for determining drilling states. Display
agent
A -D operates to display measurement data, trends of that data, drilling
recommendations, current and upcoming drilling state indications, and other
information at touchscreen display TDISP of rig client T1. Inputs from the
driller
(terrestrial drilling) or drilling engineer (offshore drilling) are received
by touchscreen
display TDISP, and in the case of at least "ignore" inputs responsive to
displayed
recommendations, are forwarded via function 39 to information integration
environment HE, specifically via knowledge tools server I6c therein.
[00971 As shown in Figure 5, information integration environment IIE
includes agent access server 16a. Agent access server 16a has access to a
collection
of "level 1" agents A 1, which include data acquisition agents, trend agents,
display
agents, and the like that are executable at rig client Ti. According to this
embodiment
of the invention, agent access server 16a can select from among these agents
A_1, and
also from among state agents A_S, depending on the current drilling state and
other
conditions at drilling rig W l. This selection, configuring, and instantiation
of agents
A-1, A_S by agent access server 16a in this situationally aware manner will be
described in further detail below relative to Figure 6. Referring still to
Figure 5,
however, information integration environment IIE also includes expert tools
server
16b, described above and which will be further described below. Information
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integration environment IIE processes measurement data received from agent Al
at
rig client Ti, using data access tools 14a, and forwards these processed data
to
formulator ADA F, via data interface 25 (e.g., WITSML API 25, shown in Figure
4).
[0098] Figure 6 illustrates the overall operation of agent-based drilling
according to an embodiment of this invention, in connection with multiple
drilling
rigs W deployed in multiple fields F1 through F4. Agent engine 30 identifies
software agents that may be useful in connection with the drilling of drilling
rigs W in
production fields F1 through F4, modifies those agents based on recent updates
to
formulations (rules, heuristics, and calibrations) by inference engine 24, and
monitors
those software agents that are in use or become in use within the system. This
identification of software agents that may be useful in a particular drilling
state is
made by agent engine 30, in response to drilling state determinations made by
drilling
state engine 32.
[0099] For purposes of agent-based drilling, according to this embodiment of
the invention, agent access server 16a within information integration
environment IIE
selects, configures, and instantiates software agents in a situationally aware
manner,
for each of the wells W of interest in the multiple production fields F 1
through F4. As
evident from Figure 6, the software agents instantiated at rig clients T occur
at
varying points in time, rather than on a periodic basis; this non-uniform
instantiation
(and termination) of software agents by agent access server 16a reflects the
situational
awareness involved in this overall process. In a general sense, drilling state
engine 32
determines a drilling "state" of each specific drilling rig W, based on
measurement
data acquired and forwarded to formulator ADA F from sensors S at that
drilling rig
W, and based on various formulations, and communicates the deduced drilling
state to
agent engine 30. Agent engine 30 in turn forwards software agents appropriate
for
that drilling state for that particular drilling rig W to agent access server
16a, if not
already at and configured within information integration environment HIS. In
turn,
agent access server 16a instantiates the indicated software agents at rig
client T
associated with the drilling rig W of interest.
[0100] For purposes of instantiating and operating software agents such as
data acquisition agents A, display agents AD, trend agents AT, state agents
A_S,
and the like, drilling rig W can be considered to pass through a variety of
drilling
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states. Figure 7 illustrates a state diagram according to which drilling state
engine 32
(Figure 6) can determine the drilling state of drilling rig W. As shown in
Figure 7, the
available states are initialization (INIT), adding stand (ADDING), circulating
drilling
mud (CIRCULATING), rotating the drill string (ROTATING), preparing to add
another stand of drill pipe (PREPARING), sliding of the drill string
(SLIDING), as
well as stopped and paused states. The state diagram of Figure 7 illustrates
the
various transitions that are permissible in this example of the determinations
made by
drilling state engine 32. For example, a transition from state INIT to state
ADDING
is detected by drilling state engine 32 upon receiving data (e.g., alarms and
events
from state agents A_D executing at rig client Ti) indicating that the elevator
of
drilling rig W is at the top. Transition from state ADDING to state
CIRCULATING
is deduced upon the detection of stand length, hookload, and pressure
measurements
being captured, in combination with data indicating that drill string RPM at
the top of
the drill string is greater than zero, in combination with zero (or negative)
weight-on-
bit WOB. Transition to state SLIDING from CIRCULATING is deduced from
positive values of WOB and rate-of-penetration (ROP), in combination with zero
drill
string RPM. The other transitions within the state diagram of Figure 7 in this
example
are evident from the "Legend" of that Figure, and correspond to knowledge of
the
behavior of drilling rigs based on various measurements.
101011 It is contemplated that multiple state transition rule sets may be
implemented at drilling state engine 32. For example, the state transition
diagram
illustrated in Figure 7 corresponds to the available states for a "running"
condition of
drilling rig W. On the other hand, different state transition rule sets may
apply in
difference circumstances, for example if the drilling rig is currently being
"tripped",
or if a "casing" operation is being performed at the drilling rig, or during
particular
test sequences such as a blow-out prevention equipment (BOPE) test.
[0102] As noted above, referring back to Figure 6, drilling state engine 32
deduces a transition to a particular drilling state for each drilling rig W of
interest,
indicating the same to agent engine 30, which in turn controls agent access
server 16a
to instantiate or terminate various software agents at the corresponding rig
client T for
that drilling rig W. Agent access server 16a then forwards the appropriate
software
agents to rig client T over the appropriate physical communications facility
(shown as
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well communication bus 40 in Figure 6), configured as appropriate for the
newly-
detected state at the corresponding drilling rig W.
[01031 For example, one of the software agents that may be configured,
instantiated, and sent to a given rig client Ti for its drilling rig W1 may
include data
acquisition software agent Al (Figure 3), by way of which information is
acquired
from its sensors S. Data acquisition software agent Al will be configured for
rig
client T1 to include those resources applicable to the sensors S that are
installed and
operational at drilling rig W1, such configuration being provided by
information
integration environment IIE. Agent access server 16a will then send the
outfitted
software agent to rig client Ti, via well communication bus 40. In this
example,
various detailed preconditions are to be satisfied before this agent Al begins
its
operation at rig client Ti:
= rig client Ti is operating, and is receiving data from sensors S;
= agent access server 16a has forwarded agent Al to rig client
Ti;
= agent Al is operating and functioning properly (no execution
errors);
= agent Al has obtained the proper security authority, within rig
client TI, to forward data to agent access server 16a.
Upon these preconditions being satisfied, software agent Al begins carrying
out its
assigned task, which in this case is the acquisition of measurement data from
sensor S.
In operation, rig client Ti generally executes a resident application to
receive data from
the data acquisition system, and as determined by that application, writes
blocks of data
to a file in a memory resource such as a disk drive or flash memory within TI.
These
data are then available to data acquisition software agent Al. For example, a
sequence
of operations by agent Al in acquiring such measurement data may include:
= agent Al detects an "end of file write" to a data file at rig client
Ti;
= agent Al "packages" data in the written data file for
transmission. e.g., by converting the data to a WITSML format
in preparing for transmission of the data to information
integration environment IIE;
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= agent Al sends the packaged data file to agent access server
16a via well communication bus 40;
= agent Al receives acknowledgement of valid file receipt from
agent access server 16a;
= agent Al prepares for a next file, and waits for an "end of file
write".
Data acquisition software agent Al can remain instantiated at rig client Ti
until such
time as it is terminated by agent access server 16a in response to a change of
state or
some other indication from agent engine 30, or upon agent Al terminating
itself upon
reaching some post-condition event or state.
[0104] Of course, more than one data acquisition software agent will typically
be active and operating at many rig clients T at any given time, considering
that
multiple data sources (e.g., sensors S, well parameters WP1 and well
properties WP2
stored at rig client) will be receiving and providing measurement and other
data
during drilling operations. Such operation is described above in connection
with
Figure 3. These multiple data acquisition software agents can operate in a
coordinated fashion with one another, and with other software agents carrying
out
other functions, including the trend, display, and drilling state agents being
executed
at rig clients T, as well as in combination with agents being executed within
information integration environment IIE and formulator ADA F.
[0105] As will be described further below, these data acquisition software
agents collect data and information that are used to derive formulations for
creating a
"best well" model specific to the drilling rig W, and for deducing dynamic
transition
triggers, based on that model and on the acquired measurement data and
information,
to provide recommendations to the driller or drilling engineer for achieving
that
optimum well. As such, in addition to the data acquisition software agents,
other
agents will be created, configured, and instantiated that perform those
functions,
examples of which are trend agent A_T, display agent AD, and state agent A_S
at
rig client Ti (Figure 5). These and other software agents operable within the
system
of this embodiment of the invention preferably have specific agent properties
that
assist this inter-operation. As such, the software agents available and
operable within
this embodiment of the invention preferably constitute a network of
persistent,
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autonomous, goal-directed, sociable, reactive, non-prescriptive, adaptive,
heuristic,
distributed, mobile, and self-organizing agents. These properties and
attributes of
software agents will be familiar to those skilled in the art having reference
to this
specification.
Adaptive Data Access Tools (Data Grinder and Trendologist)
[0106] As noted above, multiple data acquisition software agents A_l will
typically be operating simultaneously to acquire measurement data and other
information (e.g., well parameters WP1, well properties WP2) regarding
drilling rig
W. The various measurement data provided via these multiple data acquisition
software agents will necessarily be of varying time frequency, of varying
"quality"
(i.e., precision or variability), and will have other varying attributes.
According to
this embodiment of the invention, data access tools 14a within information
integration
environment IIE provide the capability of intelligently and adaptively
acquiring these
data of varying quality and nature from the various data sources and data
acquisition
agents. As will now be described in connection with Figure 8, trendologist
function
28 and data grinders 44, through 44õ adaptively process the data acquired by
standardized data access tools 14a (Figure 4).
[0107] Figure 8 illustrates the flow of measurement data and other information
within the system of this embodiment of the invention. Data sources Si through
Sn
correspond to sensors S that provide measurement data to rig client T, or to
data
stored within rig client T itself (well parameters WP1, well properties WP2,
etc.), or
other information regarding drilling rig W. Data acquisition software agents
Al
through An are each instantiated and associated with corresponding data
sources S1
through Sn, and operate to acquire measurement data or other information from
its
corresponding data source Si through Sn, as described above. According to this
embodiment of the invention, each of data acquisition agents Al through An
forward
the acquired data to a corresponding data grinder 44t through 44,, as shown in
Figure
8. Data grinders 44 are computing resources located within rig client T, and
trendologist is a computing resource located within formulator ADA F (Figure
4).
[0108] Data grinders 44, through 44õ process the measurement data from
corresponding data sources Si through Sn via agents Al through An. According
to
this embodiment of the invention, the processing applied by data grinders 44
is
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determined by previously-determined formulations (rules, heuristics, and
calibrations)
stored in knowledge base KB and accessed by rules engine 26, such formulations
typically specific to the corresponding data source S, and in combination with
other
information regarding drilling rig W, including the current drilling state,
past
measurements, etc. An example of a process that is contemplated to be often
applied
by data grinders 44 is a low-pass filter, such that higher-frequency
variations in the
signals from data source S are smoothed over time.
[01091 According to this embodiment of the invention, this processing is
performed from one to N times on each block or group of measurement data, as
determined by repeat function 45. This number of repetitions applied by repeat
function 45 will vary among the various data sources S, as indicated by the
particular
rules being enforced by trendologist 28 upon data grinders 44. It is
contemplated that
the rules applicable to data grinders 44 and repeat function 45 will consider
the
quality of the data (i.e., the extent of noise in the data, the variance of
the data, the
presence and frequency of outliers, and the like), the frequency of the data
(i.e., the
frequency over time at which measurements are obtained), and the like. As
discussed
above, "reliability" ratings may be computed or otherwise derived from the
measurement data by software agents, and used to determine the extent of
processing
to be applied to the measurement data by data grinders 44, including the
number of
repetitions. For example, the rules may indicate that measurement data from
downhole sensors S, which are inherently noisier or otherwise less reliable
than
sensors at the surface, may require additional filtering and thus a higher
number of
repetitions through data grinder 44, as compared with the data from the
surface
sensors. By way of another example, it is contemplated that some sensors S may
provide measurement data at a much higher frequency than is relevant in the
use of
that data; additional repetitions of data grinder 44 may also be appropriate
for those
measurements. In addition, data grinders 44 for the various data sources may
also
apply some type of normalization or other processing so that the processed
data from
the various data sources S is consistently presented, in a recognizable form
relative to
the data from other data sources. It is contemplated that the data grinder
rules
formulated according to this embodiment of the invention will consider those
and
other factors in defining the data grinding sequence for each data source S.
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[0110] Trendologist 28 also includes combining function 46 that combines the
processed data from the multiple data sources, and formatting function 48 that
formats
the processed data, as combined, into a recognizable and processable format
for
various software agents. Combining function 46 refers to various rules,
heuristics,
and other previously determined formulations to select the various data
streams that
are to be combined for each of the various purposes and destinations, and
optionally
weighting the particular data sources in effecting such combination.
Formatting
function 48 arranges the result of the combined processed data into a form
that can be
immediately utilized by the various destinations, including not only a
physical fonnat
for the processed and combined data, but also its granularity, smoothness,
statistics
derived from the various datasets, and the like. The selecting and weighting
determined by combining function 46 within trendologist 28, and the formatting
determined by formatting function 48 within trendologist 28, are applied to
data
grinders 44 and to other software agents at rig clients T, so that the desired
combining
and formatting is accomplished.
[0111] As such, according to this embodiment of the invention, trendologist
28, working in concert with and through data grinders 44, effectively converts
the raw
input perceived by sensors S into data and information that can be immediately
utilized and comprehended by software agents and formulator ADA_F.
Trendologist
28 is automatically and manually scalable, for example by application of rules
selected by rules engine 26 for the current situation, to provide the
granularity of
information needed to provide recommendations to the driller at drilling rig W
for
adjusting the drilling process. As such, the combination of trendologist 28
and data
grinders 44 extend the simplistic concepts of WITSML into the creation of a
situationally-aware set of data access tools 14a (Figure 6), accepting any
number and
manner of drilling perception entities and delivering multiple sets of
industry
standard, common tool readable, datasets for use within or outside of
formulator
ADA F. The resulting processed and combined data can be rendered complete,
compensating for missing data types and incorrect or outlier data, for example
for
those data outside of previously "agreed" bounds of the downstream agents and
formulator processes.
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[01121 Some of these downstream destinations of data processed, combined,
and formatted, according to the formulations applied by trendologist 28, are
illustrated
in Figure 8. Inference engine 24 is one destination of these data. In response
to the
newly-acquired data from trendologist 28, inference engine 24 is capable of
modifying existing formulations, or of creating new formulations and rule
sets,
including new rule sets that control trendologist 28 itself. The new or
updated rules
are stored in knowledge base KB, and accessible to rules engine 26 for
selection
according to the current drilling situation. Inference engine 24 can also
apply new
inferences intelligently derived from the newly-received data into knowledge
base
KB. Agent engine 30 can also receive some or all of the processed combined
newly-
received data from trendologist 28, and can update or configure existing or
new
software agents accordingly; an agent instantiated by agent engine 30 at
formulator
ADA F can also manage the storage of the newly-received data into database DB.
Drilling state engine 32 can also receive the newly processed measurement data
and
other information, can identify a current or upcoming drilling state (Figure
7), and can
configure or update state agents A_S accordingly.
[01131 The processed data will also typically be forwarded to other software
agents, including agents that are being executed at rig client T for the
corresponding
drilling rig W (or, in some cases and depending on the rules, at rig clients T
associated
with other drilling rigs W beyond the source of the new data). Display agent A
-D can
receive the processed data from trendologist 28, formatted according to
previously
formulated rules for displaying useful information to a human driller via
display
TDISP, and consistent with the current drilling state and conditions at
drilling rig W.
In addition, according to this embodiment of the invention, display agent A -D
produces one or more recommendations regarding changes or adjustments to make
in
the drilling process, according to rules, heuristics, and calibrations
previously
formulated by formulator ADA F that are configured within display agent A -D;
the
resulting recommendation, if any, is displayed on touchscreen display TDISP,
as
described above, the driller or drilling engineer can provide an input
("ignore" or
"accept") via touchscreen display TDISP. Trend agent A -T also can receive the
processed data from trendologist 28, and derive various trends in the
measurements,
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again according to rules by which trend agent A_T was configured by
information
integration environment IIE.
[0114] In this manner, trendologist 28, working in concert with data grinders
44, process, filter, format, and otherwise condition the measurement data and
other
information, on a situationally aware basis, and present those processed data
to the
various destinations. According to this embodiment of the invention, estimates
of
measurement data are created, using certain data analyzer parameters selected
to
create those estimates, for example based upon the quality (e.g., noise
energy) of the
sensed data, knowledge regarding the model and manufacturer of the sensors
(and
past history of the quality of such data), other measurements concurrent with
the
sensed data, the current lithology into which the drilling is taking place at
the time of
the sensed measurements, and the like. Based on these estimates, such
parameters as
the current depth, and various current and cumulative parameter values, can be
iteratively derived, and used to determine the manner in which the
measurements are
displayed at the affected rig client T. For example, poor quality data from
sensors S
known to be noisy may be displayed over relatively long intervals, and heavily
filtered and processed, while "cleaner" data may be displayed closer to real-
time.
Furthermore, trendologist 28 may calculate data "reliability" values for each
measurement, in this manner, and use those reliability measurements in the
display of
data at rig client T, as well as in decisions and recommendations to be made
according
to the corresponding formulations.
[01151 Figure 9 illustrates an example of this operation of trendologist 28
and
the various software agents and functions within information integration
environment
IIE, in the form of a process flow diagram that creates estimates from various
measured parameters, for example the computation of estimates of a parameter
along
the length of the wellbore, from measurements taken at the surface. As
described
above, this operation is being carried out by the network of software agents,
as
instantiated by agent access server I6a (Figure 6), and operating in
combination with
tools 14 (Figure 4) of information integration environment IIE. In this
example, bus
management process 52 receives the data from the various sensors S and data
acquisition software agents concerned with drilling rig W, to provide
measurement
data and other information to the overall process. These data and information
are
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applied to estimate creation process 60 within data grinder 44, which
generates the
estimates that are desired to be created from these data and information, as
indicated
by trendologist 28 by way of data analyzer parameters 58 forwarded from
trendologist
28 to data grinders 44, incorporating the various rules and parameters for
processing
the received data and information, and for combining that processed data into
the
desired estimates. For example, in the process flow illustrated in Figure 9,
depth
computation process 56 computes the depth of the wellbore from the various
data and
information as processed by estimation process 60, and process 62 computes
downhole values from received data and information. Based on historical data
stored
in database DB and accessed by database management process 54, process 64
computes total values for the parameter based on the downhole values from
process
62, and from other processing of the data and information by process 60.
Process 60
then forwards the results to database management process 54 for storage in
database
DB, and for display at display TDISP of rig client T via display management
process
66. As part of process 60, trendologist 28 also determines a useful scale and
granularity for displaying the processed measurement data at touchscreen
display
TDISP of rig clients T; for example, measurement data that are changing slowly
but
for which precision is important may be displayed at a magnified scale so that
small
yet important shifts in the data are visible, while noisy or imprecise
measurement data
can be shown at a less-magnified scale, or over an expanded time scale.
Trendologist
28 also receives at least some of the results of the estimates from process 60
as
feedback for the data analyzer parameters 58 that it forwards to estimation
process 60.
Accordingly, the operation of data grinders 44 and trendologist 28 serves to
efficiently and intelligently create estimates of various drilling parameters,
conditions,
and estimated values.
Expert-based Drilling
[01161 According to another aspect of this invention, expert-based drilling is
implemented by way of formulator ADA F operating thorough software agents
instantiated by information integration environment IIE and executing at rig
clients T.
According to this aspect of the invention, this expert-based operation of the
system of
this embodiment of the invention effectively implements situationally aware
"experts", in the form of formulations (rules, heuristics, and calibrations)
that control
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the software agents operating in the system. The overall result of the expert-
based
system, as realized by way of the formulations according to which these agents
are
configured, is to provide the human driller or drilling engineer at the
drilling site with
recommendations regarding the drilling process, based on recently acquired and
processed measurement data and other measurements, and based on inferences and
previously generated formulations. The expert-based drilling aspect of this
invention
ensures that the formulations used in connection with the drilling operation
are up-to-
date, and accurate.
[0117] Figure 10 illustrates the overall operation of expert-based drilling
according to an embodiment of this invention, in connection with multiple
drilling
rigs W deployed in multiple fields F1 through F4. According to this aspect of
the
invention, inference engine 24 within formulator ADA F determines the
frequency
and timing at which software agents are to be instantiated and forwarded to
the
various rig clients T, for purposes of acquiring specific measurement data and
other
information that may be useful in the updating or creating of formulations
(rules,
heuristics, and calibrations). This determination may itself be based on rules
or
heuristics that are developed and updated in response to current conditions
and past
history. In response to the determination by inference engine 24, expert tools
server
I6b configures and instantiates the appropriate software agents for gathering
the data
and information desired by inference engine 24, according to the current
formulations
for acquiring such data and information. By way of such instantiation, expert
tools
server 16b causes the appropriate one or more of rig clients T to execute
those newly
instantiated agents, to acquire the measurement data and information required
by
inference engine 24. These data and information may also include any "ignore"
inputs issued by the driller or drilling engineer, and the recommendations and
situations giving rise to those "ignore" inputs. The data acquisition software
agents
instantiated for this purpose are preferably sufficiently autonomous as to be
self-
terminating, upon completion of their data acquisition tasks in this
instantiation.
[0118] Preferably, as indicated in Figure 10, the timing with which these
software agents are instantiated at rig clients T, for purposes of new or
updated
formulations, is based on a time sequence, rather than based on situational
awareness
as in the case described above relative to Figure 6. This is because the
creation or
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updating of rules, heuristics, and calibrations is not considered to be a real-
time
operation. Rather, the creation and updating of formulations can be performed
on a
periodic basis, with the gathering of data performed as a scheduled process
between
formulator ADA_F and rig clients T.
[01191 From the standpoint of the acquisition of the measurement data and
other information via the agents controlled by expert tools server 16b, the
sources of
that data and other information need not be the same sources as used in the
determination of current conditions and recommendations, as discussed above
relative
to Figures 6 through 9. However, the measurement data acquired in connection
with
the creation and updating of formulations, as shown in Figure 10, are
preferably
processed, conditioned, and formatted by data access tools 14a, including data
grinders 44 (Figure 8) and trendologist 28 as described above, to facilitate
the drawing
of inferences and accurate determination of new or updated formulations. The
processed and conditioned data provided by data access tools 14a in
information
integration environment IIE are then forwarded to agent engine 30, which in
turn
forwards the received data and any necessary software agents to inference
engine 24.
Inconsistencies in these rules by the inference engine can be detected by a
"consistency enforcer" function within inference engine 24; in the event of a
detected
inconsistency, inference engine 24 preferably uses its adaptive capability to
heuristically derive a potential new rule set. Inference engine 24 operates
upon the
newly received processed data, in combination with previous formulations and
other
information stored in knowledge base KB, to create new and updated
formulations,
including rules and heuristics. Another type of formulation that may be
created by
inference engine 24 in connection with this aspect of the invention is a
calibration, by
way of which measurement data from one or more sensors S or other data sources
may be calibrated with respect to other measurement data, or with respect to
inferences or conclusions or recommendations previously reached, thus
improving the
precision and fidelity of the operation of this system.
[01201 A generalized flow of the operation of inference engine 24 in updating
drilling rules, in its creation of new and updated formulations, is shown by
way of
example in Figure 11. Measurement data RAW data acquired from multiple data
sources at drilling rig W l, including sensors S via the data acquisition
system at rig
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client Ti, are applied to data conditioning process 70. Process 70, which is
performed
by trendologist 28 in concert with data grinders 44 in response to previously
formulated rules selected by rules engine 26 as applicable to the current
drilling state
and situation, filter, processes, and conditions measurement data RAW-data
according to the nature of the measurements, the signal and data quality, and
the like,
as discussed above. Figure 11 illustrates, in connection with process 70,
examples of
the conditioning applied in this example, including rejection of outlier
values, signal
conditioning through the application of low-pass filters and other signal
processing
functions, identification of stable values and trends, and the determination
of
confidence intervals. Of course, additional or fewer processes may be applied
to
measurement data from the various data sources represented within measurement
data
RAW data. The result of process 70 is conditioned dataset COND data. Dataset
CONDdata is then characterized by inference engine 24, in process 72, to
create
inferences from the conditioned data. As shown in Figure 11, examples of
inferences
that can be drawn from this characterization include increasing rate-of-
penetration,
stable weight-on-bit, changes in the slope of Mechanical Specific Energy
(MSE), and
whether the MSE value is determined or undetermined. These characterizations
of
dataset COND_data, in combination with other information including drilling
state
and measurement data history, lithology models, and the like are then used by
inference engine 24 to create or update drilling rules, and to store the
result in
knowledge base KB. These new and updated drilling rules are then used to
change
the parameters applied to newly-received measurement data and other
information (in
process 70), and also in the characterization of conditioned data in process
72, as
shown in Figure 11. This process continues over time for each drilling rig W
to
which the system of this embodiment of the invention is applied.
[01211 Expert-based drilling according to this embodiment of the invention
thus provides a mechanism by which situationally-aware expert software
functions
can control software agents that interact among multiple software servers and
hardware states to provide recommendations to human drillers in the drilling
of a
borehole into the earth at a safely maximized drilling rate. These expert
software
functions dispatch the agents, initiate transport of remote memory resources
and
provide transport of knowledge-base components including formulations (rules,
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heuristics, and calibrations), according to which a drilling state or drilling
recommendation is identified responsive to sensed drilling conditions, in
combination
with one or more parameters indicated by a lithology model of the location of
the
drilling rig, and in combination with operational limits on drilling equipment
sensor
parameters. These software experts develop formulations applicable to the
drilling
site derived from the knowledge base, and transmit those formulations, via an
agent,
to a drilling advisor client system located at the drilling site. That client
system
including the drilling advisor function is coupled to receive signals from
multiple
sensors at the drilling site, and is coupled to the servers within the
information
integration environment to configure and service the software agents.
[01221 In this manner, the expert-based drilling aspect of this invention
enables the overall system to continue to improve the accuracy of its
inferences and
recommendations, as well as improve the confidence with which those inferences
and
recommendations are drawn, by periodic or otherwise repeated updating of its
rules
and heuristics. The expertise of the overall system therefore improves over
time, and
with additional information from each drilling rig W and from other drilling
rigs W
and production fields F. The instantiation or creation of software agents is
thus
improved with continued operation over time, as these newly instantiated
agents can
be configured with these updated formulations.
Knowledge-based Drilling
[0123] As described above, the system of this embodiment of the invention
operates to acquire measurement data and other information from one or more
drilling
rigs W in one or more production fields F, and to provide information back to
the
driller or drilling engineer based on that data and information, using
previously-
derived formulations and information from other wells. This information
includes
recommendations to the driller or drilling engineer regarding the way in which
the
drilling operation ought to be carried out, including suggested adjustments or
changes
in the operations carried out at drilling rig W. According to another aspect
of the
invention, the optimized manner in which the drilling operation is carried out
is
determined by the system itself, according to a notional "best well" model.
This best
well model is determined, according to this aspect of the invention, using
recently and
previously acquired measurement data, in combination with determinations of
the
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current drilling state and also in combination with the lithology model and
other
information acquired or derived extrinsically from the data acquired at
drilling rig W
itself. The creation and updating of formulations according to this notional
"best
well", and the forwarding of the software agents including formulations
corresponding to that best well model, will now be described with reference to
Figure
12.
[0124] As mentioned above, inference engine 24 creates the formulations
(rules, heuristics, and calibrations) that are applied by way of software
agents
instantiated by information integration environment IIE and executed at rig
clients T.
According to this embodiment of the invention, these formulations created by
inference engine 24 are directed toward an optimal notional "best well" model,
specific to each drilling rig W in each of the production fields F supported
by the
system.
[0125] A wide range of information sources are available to inference engine
24 in its creation of this "best well" model. For the example of the creation
of a best
well model for drilling rig W1 in field Fl, knowledge base KB includes "best
well"
models created for other drilling rigs W2 through W4 within the same
production
field FI, as well as models created for other drilling rigs W in the other
fields F2
through F4, and perhaps even prior wells in other fields not being currently
supported
by the system. The formulations in these previous well models are based on the
measurement data and lithology models for the wells at those locations, and
recommendations made by the system during the drilling of those models, but
also
preferably include responses from the driller or drilling engineer in response
to those
recommendations, as well as verification or adjustments made by off-line human
experts by way of remote administrators RA (Figure 5). In addition, the best
well
model for drilling rig WI will also be based on the measurement data
previously
acquired during the drilling performed so far at that location (stored in
database DB
and forwarded by agent engine 30), trends of those measurement data as created
by
trend agent A_T (Figure 8) based on trendologist function 28, the drilling
state history
as determined by drilling state engine 32, and well parameters WPI (Figure 3)
and
well properties WP2 stored at rig client Ti for drilling rig W1. An important
input
from well properties WP2 into inference engine 24 is, of course, the lithology
model
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to which well properties WP2 at rig client Ti link, as this model provides
insight into
upcoming layers to be encountered in the drilling. "Ignore" inputs issued by
the
driller or drilling engineer in response to recommendations suggested by the
system,
and the situations giving rise to those "ignore" inputs, constitute quite
useful
information in the deriving of a best well model.
[0126] The result of the operation of inference engine 24 in creating these
new
or updated formulations is a "package" of formulations pertaining to the
operation of
a drilling rig W. An exhaustive list of these formulations cannot possibly be
presented in this specification, given the wide variation of conditions and
situations
encountered in the drilling of even a single well, much less considering the
variations
of such conditions and situations over multiple wells in multiple different
locations
and production fields. As such, only examples of such formulations will be
provided
in this specification. One such example of such formulations is a set of
limits
(maximum, minimum) on various bottom-hole assembly (BHA) parameters, such
limits defined by inference engine as ranges of values within which drilling
will be
optimized (e.g., based on rate of penetration) within the current layer, or
more
specifically at the current depth within the current layer. Another example of
such
formulations may include BHA limits and also limits and recommendations on
other
parameters that are optimized to avoid a "washout" in an upcoming layer; these
recommendations can include not only an action, but a recommended time or
depth at
which the action is to be taken. In this manner, it is contemplated that the
"best well"
model includes those rules and heuristics, based for example on the lithology
model
and current trends in various parameter measurements, that define preventive
action to
be taken by the drilling rig in advance of unstable or dangerous situations
that are due
to arise in upcoming layers of the earth. These and other formulations are
contemplated to constitute the "package" of formulations corresponding to a
"best
well" model applied to a given drilling rig W.
[0127] It is contemplated that the creation of such a best well model, by
inference engine 24, can be performed according to any one of a wide variety
of
"artificial intelligence" techniques. For example, inference engine 24 may be
in the
form of a "neural net", in which a collection of inputs are applied to a
network of
weighted sum functions, to derive a set of outputs. As known in the art, such
neural
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nets are trained by the application of many training sets of inputs to an
initial net,
evaluation of the result of the net against a known or desired output value
set, and
back-propagation through the net to adjust the various weighting factors.
Alternatively, inference engine 24 may be arranged to implement a more
rigorous set
of logical rules to implement the best well model in optimizing one or more
measurement criteria, with additional information and instances used to modify
the
logical rules or to create additional rules and logical conditions. Still
further in the
alternative, inference engine 24 may realize its formulations by use of
heuristics,
according to which a "softer" result with confidence intervals and the like
can be
created from a simplification of the overall optimization problem. It is
contemplated
that those skilled in the art having reference to this specification will be
readily able to
realize inference engine 24 according to one or more of these or other known
artificial
intelligence techniques, without undue experimentation.
[0128] Upon creation of new or updated formulations, inference engine 24
stores the new or updated rules in knowledge base KB, for use by other
functions in
formulator ADA_F such as drilling state engine 32. In addition, these new or
updated
formulations are then forwarded to knowledge tools server 16c within
information
integration environment HE Knowledge tools server 16c then configures software
agents with the new or updated formulations corresponding to the "best well"
model
derived by inference engine 24, as discussed above. These software agents are
then
instantiated by information integration environment IIE (e.g., agent access
server
16a), and are "pushed" to one or more rig clients T in the supported
production fields
F to which the new formulation is applicable. In particular, it is
contemplated that
display agents A_D (Figure 8) will generally be the agents that are configured
with
the new formulations according to the "best well" model, as display agents A -
D are
the actors that present drilling recommendations to the driller or drilling
engineer.
However, it is contemplated that other agents, including trend agents A T and
also
data acquisition agents A can also be modified according with the new or
updated
formulations. For example, different data sources may become of interest as a
result
of the updating of the drilling formulations, in which case new data
acquisition
software agents A_D are instantiated and forwarded to rig client T. The
software
agents configured according to these new or updated formulations are
preferably
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"pushed" to rig clients T in a planned manner, rather than on an immediate
basis.
This permits updated software agents to be instantiated at rig clients T at
safe times
from a computer update standpoint, rather than in the midst of critical
processing and
drilling operations, so that any issues or errors caused by such updating do
not cause a
failure of the drilling operation. Of course, once the new software agents
configured
according to the new rules, heuristics, and calibrations are instantiated at
rig clients T,
execution of these new agents and operation of the overall system proceeds,
according
to the approach described above.
[01291 According to this aspect of the invention, the knowledge-based drilling
feature in this embodiment organizes, validates and dispatches collection
agents for
all notional "best wells" for all fields based on all available information
for that field.
This available information includes historic, real time, and/or near-real-time
depth or
time based values in any format of drilling dynamics, earth properties,
drilling
processes and driller reactions. In effect, the inference engine operates
according to a
virtual, heuristic ontology that automatically extends based on the
environment
sampled, encapsulates rules about the "how" to drill the "best well", "when"
to push
toward the maximum operating parameters of the drilling rig, and "why" to
react
ahead of the driller's perception of an impending down hole vibration event.
As a
result, the drilling knowledge base KB suggests solutions to problems based on
feedback provided by human experts, learns from experience, represents
knowledge,
instantiates automated reasoning and argumentation for embodying best drilling
practices into the "best well".
10130] The ability of the system according to this embodiment of the
invention to create formulations that are directed to the drilling of an
optimal "best
well", responsive to a wide range of information, including measurement data,
lithology models, results obtained from other wells, and responses from human
drillers to previous recommendations, all subject to verification by off-line
experts via
remote clients, is contemplated to provide accurate and intelligent guidance,
in real-
time, to the drilling process. This guidance is provided by the system in real-
time, in
the sense that the mentoring and advising is provided by the system itself
based on its
knowledge base, rather than requiring remote analysis and consideration by a
human
user. Indeed, it is contemplated that this knowledge-based drilling approach,
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according to this embodiment of the invention, can enable less-experienced
drillers to
attain more success in drilling oil and gas wells, earlier in their careers,
by providing
an automated intelligent expert guide to assist the likelihood of success.
Examples of Operation
[0131] Various examples of the operation of the system according to the
described embodiments of the invention will now be described. These examples
are
intended to be illustrative of certain situations that can be encountered both
in drilling
a wellbore and also in managing the operation of the wellbore in the event of
lost
circulation of drilling mud. Those of ordinary skill in the art having
reference to this
specification will readily recognize, of course, that the system according to
these
embodiments of the invention is capable of similar operation in providing
recommendations and information to decision-makers at the drilling rig in
connection
with a wide range of other events and situations. As such, it is contemplated
these
examples described in this specification will be understood merely as
illustrative
examples, and will also be understood as not limiting of the full scope of
this
invention as claimed.
[0132] Figure 16 illustrates the overall operation of the system by way of
examples of processes carried out by software agents and other components of
the
system, operating within the multi-level system of rig clients T, information
integration environment IIE, and formulator ADA F. The processes and functions
illustrated in Figure 16 are those that are contemplated to be useful in
connection with
a wide range of situations and events in the drilling operation. However, in
the spirit
of the immediately previous paragraph, it will be understood that these
processes are
illustrative examples, and are not intended to limit or otherwise exclude
other
processes and functions carried out by this system.
[0133] Figure 16 illustrates the acquisition of current real-time sensor
information from sensors S at drilling rig Wi, in one of production fields F.
Rig
sensor system 100 at drilling rig W1 refers to an entire set of measurement
sensors S I
through Sn, which generate set 101 of isolated analog outputs corresponding to
various physical measurements and properties. Isolated analog outputs 101 are
digitized by analog/digital converter functions 35 in data acquisition systems
(not
shown) at or near drilling rig W1, and these digital data are read by data
acquisition
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software agents in process 102 at a desired frequency (e.g., 30 Hz, as shown
in Figure
16). The particular sensors S from which data are acquired, as well as the
frequency
of such acquisition etc., are determined in a situationally-aware manner, as
described
above, based on which particular data acquisition software agents are
instantiated and
the particular configuration of those agents.
[01341 As described above in connection with Figure 8, the sensor data
acquired by data acquisition software agents Al through An are forwarded to
data
grinders 44. The initiation of data grinders 44 is illustrated in Figure 16.
As
described above, data grinders 44 filter and otherwise process newly acquired
measurement data in concert with trendologist 28, in a situationally aware
manner
according to various rules, heuristics, and calibrations derived for the
current drilling
state of drilling rig W l and other parameters; in addition, as described
above, the
extent of filtering and other processing applied to these data is dependent on
which
sensor S is providing the measurements, as well as the nature and quality of
those
measurements. These processed measurement data are stored in a drilling data
file in
database DB. In addition, some or all of these processed measurement data are
obtained by drilling state agent A_S, which determines the particular drilling
state of
drilling rig W l as described above. If the current drilling state corresponds
to a state
in which active drilling is being carried out, then process 80 is performed by
another
instantiated software agent, to generate various drilling data according to
the rules,
heuristics, and calibrations for which that software agent has been
configured. These
drilling data are then forwarded to trendologist 28, as shown in Figure 8, for
combination with other measurement data and other information, and are
forwarded to
other software agents and other functions to carry out the processes
illustrated in
Figure 16. According to this embodiment of the invention, the various software
agents performing the processes shown in Figure 16 may be resident and
executing
within rig client Ti at the site of drilling rig W1, or some (or all) of these
software
agents may be distributed around the network, for example within information
integration environment IIE, depending on the computational capability of the
hardware realizing the overall system.
101351 For the case of the current state of drilling rig W 1 determined to be
one
of the drilling states, process 80 is performed by a corresponding software
agent to
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generate various data corresponding to the drilling operation, as determined
by the
various formulations according to which the software agent is configured. The
generated data are forwarded to trendologist 28 for combination with other
data and
information, as shown, and also to other processes and functions. For example,
data
grinder 44c can receive those drilling data that correspond to the circulation
of drilling
mud; data grinder 44c will filter and otherwise process those data according
to the
operative rules and heuristics, and forward those processed circulation to
trendologist
28 for combination with other data and information. The other functions
receiving
drilling data resulting from process 80 include software agents for computing
the rate
of penetration in process 82, for computing the current depth of drilling in
process 86,
for computing bit RPM in process 84, for computing the surface versus downhole
pressure in process 88, for computing bit torque in process 90, and for
computing
weight-on-bit in process 94. The software agents carrying out these processes
also
forward their respective results to trendologist 28, for combination with
other data and
information, and in some cases to software agents for computing other results
in other
processes in this overall system. The software agent performing process 84, in
which
bit RPM is calculated, as well as other agents computing other results, also
receive
information from database DB regarding the expected formations and layers to
be
encountered, historical drilling information, and rig characteristics and
configurations,
as linked to by the various well parameters WP acquired by corresponding data
acquisition software agents executing at drilling rig WI, as described above.
Many of
these processes also forward their results for storage in the drilling data
file within
database DB, as shown in Figure 16.
10136] Other software agents carry out downstream processes to compute
other information and data based on the results from the processes described
above
that receive the drilling data from process 80. These other processes can
include such
computations as process 92, in which a software agent executes the computation
of
various totals of other computed parameters (bit torque, RPM, etc.), and
process 96 in
which a software agent utilizes various parameters from other upstream
processes
(including process 92) to compute the mechanical specific energy (MSE) of the
drilling operation. These computed results and other data are forwarded to
trendologist 28 for combination with other data and information.
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[0137] A software agent is also instantiated to determine the current
"metalayer", in process 95; as described above, a "metalayer" refers to an
abstraction
of properties for a layer in the subsurface, such properties including
material
properties, the depth at the top and bottom interfaces of such a layer, as
well as
formulations (rules, heuristics, and calibrations) indicating the "best" way
to drill
through the layer. Process 95 utilizes both the depth data from process 86, as
well as
information regarding the metalayer from database DB, and forwards these
results to
a software agent that is performing process 98 to determine the expected
drilling
parameters for the current metalayer. These expected drilling parameters
include the
expected ranges for WOB, ROP, RPM, bit torque, delta pressure, and MSE, as
well as
the expected limits for WOB, RPM, ROP, and bit torque. These expected drilling
parameters are forwarded to driller display agent A -D.
[0138] As mentioned above, trendologist 28, residing in formulator ADA F,
combines data and results from various sources, including from data grinders
44, and
also from software agents that carry out various ones of the upstream
processes as
shown in Figure 16. Trendologist 28 combines these data and results, according
to
the currently-applicable rules, heuristics, and calibrations, to determine
current values
of parameters that it determines to be useful to the driller or drilling
engineer in the
current state of drilling rig W1, and also trends of those parameters, scaled
and
processed in a manner that it determines to also be useful to the driller or
drilling
engineer in the current state of drilling rig Wl. Those values current values
and
recent trends are forwarded to driller display agent A_D that is instantiated
and
executing at rig client TI. The trends of parameters derived by trendologist
28 are
also forwarded to rules engine 26.
[01391 Rules engine 26, as discussed above, is a computational resource in the
form of a software component executed by programmable processing circuitry
within
formulator ADA F, that selects formulations (rules, heuristics, and
calibrations) that
are appropriate for a certain situation, and forwards those formulations to
information
integration environment IIE for configuration into software agents that apply
those
rules at rig clients T. In this example of Figure 16, rules engine 26 receives
indications of recent trends from trendologist 28 regarding calculated and
measured
drilling parameters, as well as the expected drilling parameters from process
98
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regarding the current and upcoming metalayers that are being encountered. In
response, rules engine 26 selects the rules, heuristics, and calibrations that
are suitable
for configuring software agents, executable at rig client Ti, that determine
the
recommended drilling parameters and recommended actions, which are displayed
via
driller display agent A_D at touchscreen display TDISP at rig client Ti.
[0140] In response, as mentioned above, the driller or drilling engineer can
provide an input via touchscreen display TDISP. This input itself becomes
input data
that is considered and processed by the system in determining new or updated
recommendations or actions, or indeed in determining new or updated
formulations
for use in generating recommended actions not only at drilling rig WI, but for
other
drilling rigs W current operating or operating in the future. As shown in
Figure 16,
these inputs (especially an "ignore" input by way of which the driller or
drilling
engineer chooses to not follow the displayed recommendation produced by the
system) are forwarded from touchscreen display TDISP to process 80, by way of
which a corresponding data acquisition agent inserts that input into the
overall data
processed and considered by the system according to this embodiment of the
invention.
Examples of Operation in Drilling
[0141] Referring now to Figure 17, an example of the operation of an
embodiment of the invention in a more specific case of detecting and
responding to a
drilling dysfunction, specifically a downhole vibration event, will now be
described in
further detail. As shown in Figure 17, and as described above relative to
Figure 16,
measurement data is acquired from sensors S at drilling rig WI on a
substantially
continuous basis, for example on the order of thirty data measurements per
second
from each of the various sensors S, in process 110. As described above, these
data are
acquired by data acquisition software agents at rig client Ti, such agents
having been
instantiated and configured by information integration environment IIE. In
process
112, one or more data grinders 44 initiate filtering and other processing of
these
measurement data acquired in process 110. As described above, the extent of
filtering
and processing of these data applied by data grinders 44 is determined by
formulations from knowledge base KB, as applied by rules enforced by
trendologist
28, and are specific to the nature and quality of the measurement data for
each data
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source (e.g., each sensor S). In this example, the combination of trendologist
28 and
data grinders 44 configures and controls a baseline for characterization of
the
incoming measurement data. In process 114, data grinders 44 and trendologist
28
continue processing the incoming data relative to this characterization
baseline that
was set in process 112, with the baseline and other processing parameters
subject to
modification as indicated by rules engine 26 in a situationally-aware manner.
Also in
process 114, trendologist 28 combines the processed data from the various data
sources, and applies these data to software agents configured by rules engine
26 to
determine whether any one of a number of potential events, such as changes of
drilling state or indication of a drilling dysfunction are indicated by the
processed
data.
[01421 Decision 115 is executed by one or more software agents in the system
in applying the previously derived and stored formulations for detecting
dysfunction
events. In this specific example, a dysfunction event rule is triggered, more
specifically by detecting conditions (indicated by the processed measurement
data as
combined by trendologist 28) that a rule set or heuristic recognizes as being
indicative
of a downhole vibration event. An example of a formulation indicating a
downhole
vibration event is the combination of the following detected situations:
= MSE having a large value (exceeding a limit);
= ROP having a small value (below a minimum limit);
= ROP decreasing with respect to time;
= Bit torque fluctuating (deviation over time exceeding a limit); and
= RPM fluctuating.
In the example of Figure 17, the drilling vibration dysfunction event is
triggered by
this combination of detected measurement data conditions (decision 115 is
YES). As
discussed above, a software agent at rig client Ti has executed this
determination, and
communicates this condition to one or more other software agents, or to
information
integration environment IIE, which in turn communicates to display agent A -D
at rig
client Ti (or, alternatively, instantiates such a display agent A_D at rig
client Ti).
Display agent A -D generates an alert of the detected vibration event at
touchscreen
display TDISP. Another software agent generates a recommendation for the
driller or
drilling engineer, regarding action to be taken to alleviate the dysfunction
of
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downhole vibration that has been detected; display agent A -D presents this
recommendation at touchscreen display TDISP. As noted above, Figure 15
illustrates
an example of the display at touchscreen display TDISP for a drilling
dysfunction.
10143] In decision 121, the driller or drilling engineer provides the system
with an input corresponding to his or her response to the detected dysfunction
event,
preferably provided via touchscreen display TDISP. These inputs can be a
request for
more information ("need more info"), a decision to ignore the recommendation
("ignore"), or action taken on the recommendation ("act"). If the driller or
drilling
engineer requests more information, control returns to process 110 to acquire
more
information over time; in addition, the system can instantiate additional data
acquisition agents to acquire different sensor signals and measurement data,
if the
formulations indicate such a response to the request for more information.
[01441 If the driller or drilling engineer decides to ignore the event and
recommendation ("ignore"), process 124 can be optionally executed by a
software
agent at rig client TI to advance to a next recommendation, if the
formulations
provide such an option. If so, a new recommendation is displayed at
touchscreen
display TDISP for consideration by the driller or drilling engineer, as
before. In any
event, the "ignore" response to the recommendation is forwarded to the
knowledge
base KB in process 125, for example via inference engine 24 producing a new or
updated formulation based on the "ignore" response. In effect, inference
engine 24
defers to the decision of the driller or drilling engineer, and uses the
intelligence
provided by that human in updating formulations appropriate to the sensed
current
conditions.
[01451 If the driller or drilling engineer chooses to act on the
recommendation,
the current formulations are updated (or assigned a higher level of
confidence) in
process 122, comprehending the agreement or acquiescence of the driller or
drilling
engineer to the system recommendations. This action on the recommendation
generally results in a change to the drilling parameters applied to the drill
string. For
example, in the event of a detected actual or imminent downhole vibration
event, a
recommendation presented in process 120 is to increase the RPM by 10%, and
decrease WOB by 5%. Control then passes back to process 110, by way of which
new measurement data are acquired in light of the change of drilling
parameters, to
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determine whether the vibration has been resolved. The process then repeats
from
process 110, although the particular rule set evaluated in decision 115, or
the
recommendations to be presented in the event of continued dysfunction, may
differ
from those enforced previously.
[01461 In the example of Figure 17, in this second pass through the process
after the driller or drilling engineer has acted on the recommendation, if the
downhole
vibration is again detected in process 115 (YES), a new recommendation (e.g.,
set
RPM to 70 and decrease WOB by 15%) is presented in process 120. Again, the
driller or drilling engineer acts or ignores the recommendation, or requests
additional
information. The operation of the system continues in the manner of Figure 17,
with
trendologist 28 continuing to combine and monitor the measured and processed
data.
Upon the dysfunction being resolved, a recommendation is presented to the
driller or
drilling engineer to resume normal drilling operations, at normal drilling
parameters
for the current and upcoming metalayers.
[0147] As noted above, decision 115 may indicate that, based on the
processed and filtered measurement data and other information, no drilling
dysfunction is present or imminent. This situation is, of course, indicated by
decision
115 returning a NO result. This condition corresponds to the processed
measurement
data and other information indicating that the drilling parameters are within
their
normal or nominal ranges, or more generally that none of the rule sets or
formulations
detect the combination of measured parameters, trends, and other conditions
that,
according to the knowledge base KB, indicate actual or imminent dysfunction.
In this
situation, one or more software agents execute at rig client T1 to provide
recommendations toward optimizing the overall drilling process, based on the
"best
well" model derived according to the knowledge-based drilling approach
described
above. These recommendations are presented in process 116, for potential
action by
the driller or drilling engineer. In particular, it is contemplated that this
"best well"
approach in the absence of dysfunction will be especially useful in improving
the rate
of penetration attainable by a relatively inexperienced driller or drilling
engineer,
without requiring a human with more experience to be present at the drilling
rig Wl.
[01481 According to this embodiment of the invention, this drilling
optimization approach (decision 115 is NO) can recommend an increase in
certain
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drilling parameters, such as WOB and RPM, by an amount that intends to
incrementally increase the rate at which the wellbore is being drilled.
Process 110
and subsequent processes in the approach of Figure 17 are then repeated, and
the
measurement data again processed and analyzed relative to the applicable
formulations. For example, after a time interval at the new increased drilling
parameters, and if no dysfunction is then detected, a new "best well"
recommendation
can be presented in process 116, for example to again increase the WOB and RPM
in
an effort to incrementally increase the rate of drilling progress. The process
is then
again repeated, until a limit of some type is reached, or a new metalayer is
encountered.
Example of Operation in the Event of Lost Circulation
[0149] Similarly, the process illustrated in Figure 17 is applicable to other
operational events. A particularly important example of such events is "lost
circulation", which refers to an event in which drilling mud injected into the
wellbore
is not returning to the surface, at least in the quantity expected relative to
that injected.
In this event, the "best well" model includes formulations capable of
inferring a wide
range of downhole events, including lost circulation, for the sequence of
metalayers
defining the wellbore that is being drilled by drilling rig Wl. In this case,
data
grinders 44 include one or more "circulation" data grinder instances (data
grinder 44c
in Figure 16) that process and forward measurement data to trendologist 28,
for
application to rules engine 26 so that software agents can be instantiated and
configured to determine the state of drilling mud circulation within drilling
rig W1.
The particular formulations (rules, heuristics, and calibrations) contemplated
to be
applicable for evaluation of drilling mud circulation include situationally-
aware
formulations that are depth, time, and event based, for configuration into the
appropriate software agents. Preferably, these formulations are based on
models that
incorporate the lithology models for the metalayers along the wellbore to the
extent
previously drilled, and based on the depths and thicknesses of those
metalayers, as
well as upon other factors such as the makeup of the drilling mud, and the
like.
[0150] With reference to Figure 17, an imminent or actual lost circulation
event is detected by predictive rule sets or heuristics that are predicting an
amount of
lost drilling mud beyond a particular limit, at a particular depth (e.g_, >
200 barrels at
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a depth of 8600 feet from the surface). If the measurement data processed in
processes 112, 114 indicate drilling mud lost beyond this volume at that
depth, an
event rule is triggered (decision 115 indicates YES), causing one or more
drilling
agents and display agent A_D at rig client Ti to display a "lost circulation"
alert at
touchscreen display TDISP, and to derive and present a recommendation to the
driller
or drilling engineer, in process 120. For example, this recommendation may
include a
suggestion for a "pill" treatment to the drilling mud; preferably, the driller
action at
decision 121 provides an input to touchscreen display TDISP by way of which
the
driller or drilling engineer can provide such an input to the system. In the
event that
action is taken ("act" in Figure 17), the process is repeated based on the
predicted
result of the treatment applied, the time line of the treatment impact and
affected
metalayers, newly acquired measurement data, and other information, beginning
with
process 110, resulting in a new prediction if applicable. For example, such a
new
prediction can incorporate the previous prediction and a model of the effect
of the
treatment implemented, such as a prediction of the loss of another 100 barrels
of
drilling mud by the time that the wellbore reaches a depth of 9200 feet. If
decision
115 indicates that this rule set is triggered (e.g., more than 100 more
barrels of mud
lost), a new recommendation (e.g., increase drilling mud weight by including a
volume of walnuts) may be displayed in process 120, and a new input received
from
the driller or drilling engineer in decision 121 as before. The process
repeats until the
circulation loss event is no longer detected.
10151] In a more general sense, the drilling mud circulation monitoring and
evaluation implemented by the system according to these embodiments of the
invention utilizes the stored formulations in connection with the "best well"
model to
evaluate the likely causes of lost circulation, and the most effective
protocol of
corrective actions. The functions described above in connection with the
optimization
of the drilling operation are similarly applicable and useful in the
circulation function,
including use of the trendologist function to optimally process measurement
data from
each data source, and to display and analyze that data with a sensitivity most
appropriate for the current and upcoming situations. In addition, the "best
well"
model also preferably includes a model for deriving circulation parameters and
limits,
using the lithology model for the sequence of metalayers that have been
encountered
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and that will be encountered. It is contemplated that this operation will
derive a depth
and layer dependent model that accurately predicts expected drilling mud loss
at
various depths through the layers, so that true lost circulation events can be
accurately
detected, diagnosed, and resolved in an expeditious and effective manner. For
example, inference engine 24 of formulator ADA_F preferably accesses knowledge
base KB of formulator ADA F to acquire symbols that are based on the facts and
assertions applied in this optimization of the drilling operation, and
accesses data base
DB to acquire data that is or may be applicable to given drilling rig WI
(e.g.,
including its mud pump type, drilling mud system, drill bit being used,
formations,
etc.). Rules engine 26 then examines the existing set of rules to determine
which
rules may be relevant to the circulating state, so that the proper rule sets
or heuristics
can be applied to the monitoring of circulation.
[01521 It is further contemplated that the formulations (rules, heuristics,
and
calibrations) applied for lost circulation will also be adaptively generated
and updated
throughout the operation of the system, beginning with models for other
drilling rigs
and adaptively managed and updated for the current wellbore.
In Conclusion
[01531 As evident from this description, this invention provides important
benefits in the automation of intelligent assistance in the drilling of a
wellbore,
typically for the production of oil and gas from the earth, both in a land-
based and
offshore context. System-wide expertise regarding the drilling of the optimum
well in
a particular location, as well as the detection and resolution of drilling or
circulation
dysfunction, can also be intelligently and adaptively derived and applied.
Indeed, it is
contemplated that the system of this invention will have the capability of
identifying
relationships and dependencies not previously known, thus extending the
expertise of
drillers with long experience beyond specific wells and into more general
cases. As
such, it is contemplated that less-experienced drillers and drilling engineers
will
especially benefit, as they will have automated and user-friendly access to
the
recommendations of experts and expert systems. Experienced drillers will also
benefit, especially in drilling wellbores in new environments. Accordingly, it
is
contemplated that this invention enables the more efficient, effective, and
successful
drilling of hydrocarbon wells throughout the world.
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101541 While the present invention has been described according to its
preferred embodiments, it is of course contemplated that modifications of, and
alternatives to, these embodiments, such modifications and alternatives
obtaining the
advantages and benefits of this invention, will be apparent to those of
ordinary skill in
the art having reference to this specification and its drawings. It is
contemplated that
such modifications and alternatives are within the scope of this invention as
subsequently claimed herein.
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