Note: Descriptions are shown in the official language in which they were submitted.
TITLE: METHODS AND SYSTEMS FOR IMPROVED
DRILLING OPERATIONS USING REAL-TIME AND
HISTORICAL DRILLING DATA
FIELD OF THE INVENTION
[0001] The present invention relates to drilling operations, and more
particularly, to
methods and systems for improved drilling operations through the use of real-
time and
historical drilling data.
BACKGROUND OF THE INVENTION
[0002] Historically, drilling operations rely on "after-the-fact"
analysis to determine
lithology, as well as other parameters. For example, in order to determine
lithology, drill
bit cuttings are physically analyzed at the surface by the well site
geologist, or to be
certain of a formations' lithology, physical parameters and geological facies,
30 feet core
sections or sidewall cores are taken and analyzed at the surface. In each of
the above
methods, samples must be physically removed from the formation and returned to
the
surface for evaluation.
[0003] In order to provide real-time operational data, in-situ tools
are used. The most
popular is spectral analysis of the formation being drilled. Spectral analysis
involves the
interpretation of spectra obtained from the formation drilled using logging
tools including
a passive gamma ray detector and a neutron induced gamma ray log. In the
latter tool, a
neutron source is placed alongside the formation and periodically emits bursts
of high
energy neutrons to excite the atoms in the formation. A detector records the
number of
counts of returning gamma rays and segregates them according to their
energies. The
major drawback of this method is the recorded neutron induced gamma ray
spectrums
which are contaminated by significant background noise due to Compton
scattering.
1
Date Recue/Date Received 2021-05-31
2
Hence, spectral analysis cannot determine lithology as precisely as coring-
based
techniques.
[0004] Measurement-while-drilling is a type of well logging that
incorporates
downhole tools providing real-time information to help with steering the bit.
These tools
typically include sensors for measuring downhole temperature and pressure,
azimuth and
inclination, drilling mechanics information (e.g. torque, weight-on-bit,
rotary speed, etc.)
and a resistivity to determine the presence of hydrocarbons and water.
[0005] As the hole drilling operation progresses, a drill bit
gradually degrades until it
breaks. Replacing a drill bit after it breaks can be costly because of debris
left in the hole
that will need to be cleaned out. At the same time, deciding to pull the bit
early results in
lower bit utilization, increased operating costs, and lower productivity due
to frequent bit
changes.
[0006] Accordingly, there remains a need for improvements in the art.
BRIEF SUMMARY OF THE INVENTION
[0007] The present application is directed generally to methods and
systems for
improved drilling operations using real-time and historical drilling data.
This may include
using measurements obtained using measurement-while-drilling technology to
make a
real-time bit wear prediction, lithology prediction, pore pressure estimation,
rotating
friction estimation, permeability estimation, and cost estimation. These
predictions may
be used to optimize weight on bit and bit rotation speed in an aim to obtain a
maximized
drilling rate and a minimized drilling cost. An expert decision engine may
also be
provided to guide the operator or user to avoid safety concerns while
drilling.
CA 3064241 2019-12-09
3
[0008] According to an aspect of the present invention, there is
provided a method of
drilling a formation, comprising: acquiring real-time drilling data; acquiring
a bit wear
equation; determining a real-time bit wear prediction by using the real-time
drilling data
to predict a bit efficiency factor and to detect changes in the bit efficiency
factor over
time; determining a real-time lithology prediction by processing the real-time
drilling
data through a multilayer neural network; and adjusting the drilling
parameters for the
drilling operation in real-time based on the real-time bit wear prediction and
the real-time
lithology prediction.
[0009] According to a further aspect of the present invention, there
is provided a
drilling system including a drilling fusion prediction engine for predicting
bit wear and
lithology in real-time for use in determining drilling parameters in a
drilling operation,
the drilling system comprising: one or more sensors for sensing data at a rig
during a
drilling operation; a computer system including a drilling fusion prediction
engine for
predicting bit wear and lithology in real-time, the drilling fusion prediction
engine
configured to receive the sensed data, the drilling fusion prediction engine
including a
software module for predicting lithology in real-time by processing the sensed
data
through a multilayer neural network, the drilling fusion prediction engine
including a
software module for determining a real-time bit wear prediction by using the
real-time
drilling data to predict a bit efficiency factor and to detect changes in the
bit efficiency
factor over time; and a communication network for transmitting the sensed data
to the
drilling fusion prediction engine.
[00010] According to a further aspect of the present invention, there is
provided a
computer program product, the computer program product comprising: a storage
medium
configured to store computer readable instructions; the computer readable
instructions
including instructions for, acquiring real-time drilling data; acquiring cost
data and a bit
CA 3064241 2019-12-09
4
wear equation; determining a real-time bit wear prediction by using the real-
time drilling
data to predict a bit efficiency factor and to detect changes in the bit
efficiency factor
over time; determining a real-time lithology prediction by processing the real-
time
drilling data through a multilayer neural network; and adjusting the drilling
parameters
for the drilling operation in real-time based on the real-time bit wear
prediction and the
real-time lithology prediction.
[00011] Other aspects and features according to the present application will
become
apparent to those ordinarily skilled in the art upon review of the following
description of
embodiments of the invention in conjunction with the accompanying figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[00012] Reference will now be made to the accompanying drawings which show, by
way of example, embodiments of the invention, and how they may be carried into
effect,
and in which:
[00013] FIG. 1 a shows the system architecture of a drilling system employing
the
drilling fusion software in a standalone arrangement according to an
embodiment of the
present invention;
[00014] FIG. lb shows the system architecture of a drilling system employing
the
drilling fusion software in a client-server arrangement according to an
embodiment of the
present invention;
CA 3064241 2019-12-09
5
[00015] FIG. 2a is a flow diagram of the overall architecture of the drilling
fusion
engine according to an embodiment of the present invention;
[00016] FIG. 2b shows the calculated values, estimated values, predicted
values and
raw inputs of the prediction engine shown in FIG. 2a according to an
embodiment the
present invention;
[00017] FIG. 3 is a flow chart of the prediction engine according to an
embodiment of
the present invention;
1000181 FIG. 4 is a schematic representation of mode selection used in the
prediction
engine according to an embodiment of the present invention;
[00019] FIG. 5 is a block diagram of bit wear prediction according to an
embodiment
of the present invention;
[00020] FIG. 6 is a graphical diagram depicting the bit wear prediction
results for bit
#7 from Well #2 according to an embodiment of the present invention;
[00021] FIG. 7 is a block diagram of a neuron according to an embodiment of
the
present invention;
CA 3064241 2019-12-09
6
[00022] FIG. 8 is a schematic diagram depicting a multilayer neural network
for
lithology determination according to an embodiment of the present invention;
[00023] FIG. 9 is a flow chart of a processing subsystem for real-time
lithology
prediction according to an embodiment of the present invention;
[00024] FIG. 10 is a screen shot of the user interface during neural network
training
for Well #2 according to an embodiment of the present invention;
[00025] FIG. 1 1 a shows an intermediate Sigmalog calculations for the
formation pore
pressure gradient (lbm/gal) estimation results for Well #1 within depth 2300m-
3200m
according to an embodiment of the present invention;
[00026] FIG. 1 lb shows an intermediate d-exponent calculations for the
formation
pore pressure gradient (lbm/gal) estimation results for Well #1 within depth
2300m-
3200m according to an embodiment of the present invention;
[00027] FIG. 11c shows a formation pore pressure gradient (lbm/gal) estimation
results using d-exponent and Sigmalog approaches for Well #1 within depth
2300m-
3200m according to an embodiment of the present invention;
[00028] FIG. 12a shows a D-exponent pore pressure estimation results for Well
#1
according to an embodiment of the present invention;
CA 3064241 2019-12-09
7
1000291 FIG. 12b shows a pore pressure results for Well #1 according to an
embodiment of the present invention;
[00030] FIG. 12c shows a pore pressure (PP) estimation by linear regression
for Well
#1 according to an embodiment of the present invention;
[00031] FIG. 13a shows a D-exponent pore pressure estimation results for Well
#2
according to an embodiment of the present invention;
[00032] FIG. 13b shows a pore pressure results for Well #2 according to an
embodiment of the present invention;
[00033] FIG. 13c shows a pore pressure (PP) estimation by linear regression
for Well
#2 according to an embodiment of the present invention;
[00034] FIG. 14a shows a D-exponent pore pressure estimation results for Well
#3
according to an embodiment of the present invention;
[00035] FIG. 14b shows pore pressure results for Well #3 according to an
embodiment
of the present invention;
[00036] FIG. 14c shows a pore pressure (PP) estimation by linear regression
for Well
#3 according to an embodiment of the present invention;
CA 3064241 2019-12-09
8
[00037] FIG. 15a shows a trend fusion results of pore pressure estimation from
Well
#1 according to an embodiment of the present invention;
[00038] FIG. 15b shows a trend fusion results of pore pressure estimation from
Well
#2 according to an embodiment of the present invention;
[00039] FIG. 15c shows a trend fusion results of pore pressure estimation from
Well
#3 according to an embodiment of the present invention;
[00040] FIG. 15d shows an optimized pore pressure estimation obtained from
integration of the trend fusion results of pore pressure estimation from Well
#1, Well #2,
and Well #3 according to an embodiment of the present invention;
[00041] FIG. 15e shows pressure losses in the circulating system according to
an
embodiment of the present invention;
[00042] FIG. 16a shows a calculated annular pressure loss (APL) for Well #1
according to an embodiment of the present invention;
[00043] FIG. 16b shows a calculated equivalent circulatory density (ECD) for
Well #1
according to an embodiment of the present invention;
CA 3064241 2019-12-09
9
[00044] FIG. 16c shows a bottom hole pressure (BHP) for Well #1 according to
an
embodiment of the present invention;
[00045] FIG. 16d shows a calculated trip margin for Well #1 according to an
embodiment of the present invention;
[00046] FIG. 17a shows a calculated annular pressure loss (APL) for Well #2
according to an embodiment of the present invention;
[00047] FIG. 17b shows a calculated equivalent circulatory density (ECD) for
Well #2
according to an embodiment of the present invention;
[00048] FIG. 17c shows a bottom hole pressure (BHP) for Well #2 according to
an
embodiment of the present invention;
[00049] FIG. 17d shows a calculated trip margin for Well #2 according to an
embodiment of the present invention;
[00050] FIG. 18a shows a calculated annular pressure loss (APL) for Well #3
according to an embodiment of the present invention;
[00051] FIG. 18b shows a calculated equivalent circulatory density (ECD) for
Well #3
according to an embodiment of the present invention;
CA 3064241 2019-12-09
10
[00052] FIG. 18c shows a bottom hole pressure (BHP) for Well #3 according to
an
embodiment of the present invention;
[00053] FIG. 18d shows a calculated trip margin for Well #3 according to an
embodiment of the present invention;
[00054] FIG. 19 is a diagram depicting a force balance on a drill string
element
according to an embodiment of the present invention;
[00055] FIG. 20 is a diagram depicting measured real-time drilling torque for
Well #2
according to an embodiment of the present invention;
[00056] FIG. 21 is a diagram depicting WOB data for Well #2 according to an
embodiment of the present invention;
[00057] FIG. 22 is a diagram depicting estimated rotating friction factor for
Well #2
according to an embodiment of the present invention;
[00058] FIG. 23 is a diagram depicting measured neutron porosity for an
interval of
Well #2 according to an embodiment of the present invention;
CA 3064241 2019-12-09
11
[00059] FIG. 24a is a diagram depicting estimated permeability for an interval
of Well
#2 according to an embodiment of the present invention;
[00060] FIG. 24b shows a front page summary of the tour sheet for Well #1
according
to an embodiment of the present invention;
[00061] FIG. 24c shows a portion of the tour sheet of Well #1 relating to the
drilling
assembly according to an embodiment of the present invention;
1000621 FIG. 24d shows a portion of the tour sheet of Well #1 relating to the
mud
record and circulation according to an embodiment of the present invention;
[00063] FIG. 24e shows a portion of the tour sheet of Well #1 relating to
reduced
pump speed according to an embodiment of the present invention;
[00064] FIG. 24f shows calculations displayed to the user for kill sheet #1
according to
an embodiment of the present invention;
[00065] FIG. 24g shows calculations displayed to the user for kill sheet #2
according
to an embodiment of the present invention;
[00066] FIG. 24h shows calculations displayed to the user for kill sheet #3
according
to an embodiment of the present invention;
CA 3064241 2019-12-09
12
[00067] FIG. 24i shows calculations displayed to the user for kill sheet #4
according
to an embodiment of the present invention;
[00068] FIG. 24j shows a copy of the bit record of Well #1 according to an
embodiment of the present invention;
[00069] FIG. 24k shows a copy of the bit record of Well #2 according to an
embodiment of the present invention;
[00070] FIG 241 shows a copy of the bit record of Well #3 according to an
embodiment of the present invention;
[00071] FIG. 25 shows a decision tree of the expert decision engine relating
to mud
volume according to an embodiment of the present invention;
[00072] FIG. 26 shows a decision tree of the expert decision engine relating
to
porosity according to an embodiment of the present invention;
[00073] FIG. 27 shows a decision tree of the expert decision engine relating
to
permeability according to an embodiment of the present invention;
CA 3064241 2019-12-09
13
[00074] FIG. 28 shows a decision tree of the expert decision engine relating
to the
concern of a stuck pipe according to an embodiment of the present invention;
[00075] FIG. 29 shows a decision tree of the expert decision engine relating
to bit
wear according to an embodiment of the present invention;
[00076] FIG. 30 shows a decision tree of the expert decision engine relating
to
environmental concerns according to an embodiment of the present invention;
[00077] FIG. 31 shows a graphical user interface (GUI) with three panels
according to
an embodiment of the present invention;
[00078] FIG. 32 shows a main display of the GUI according to an embodiment of
the
present invention;
[00079] FIG. 33 shows a MWD display of the GUI in metric units according to an
embodiment of the present invention;
[00080] FIG. 34 shows a MWD display of the GUI in imperial units according to
an
embodiment of the present invention;
[00081] FIG. 35 shows a hydrostatic pressure display of the GUI according to
an
embodiment of the present invention;
CA 3064241 2019-12-09
14
[00082] FIG. 36 shows a dynamic pressure display of the GUI according to an
embodiment of the present invention;
[00083] FIG. 37 shows a logging-while-drilling (LWD) display of the GUI
according
to an embodiment of the present invention;
[00084] FIG. 38 shows a kill sheet display of the GUI according to an
embodiment of
the present invention;
[00085] FIG. 39 shows an expert decision engine prompt for a mud volume issue
for
Well #1 according to an embodiment of the present invention;
[00086] FIG. 40 shows an expert decision engine prompt for potential stuck
pipe due
to mud density and mud volume for Well #1 according to an embodiment of the
present
invention;
[00087] FIG. 41 shows an expert decision engine prompt for a mud volume issue
for
Well #2 according to an embodiment of the present invention;
[00088] FIG. 42 shows an expert decision engine prompt for bit wear for Well
#2
according to an embodiment of the present invention; and
CA 3064241 2019-12-09
15
1000891 FIG. 43 shows an expert decision engine prompt for a porosity issue
for Well
#2 according to an embodiment of the present invention.
1000901 Like reference numerals indicate like or corresponding elements in the
drawings.
DETAILED DESCRIPTION OF THE EMBODIMENTS
1000911 Embodiments of the present invention are generally directed to methods
and
systems for improved drilling operations using real-time and historical
drilling data.
1000921 Embodiments of the invention may be used by any persons or entities
which
control or conduct drilling operations, such as oil and gas exploration and
production
companies and oil and gas service companies or mining exploration companies,
mining
coring companies and mining service companies.
1000931 According to embodiments of the present invention, the method, system
and
computer program product, when deployed, may identify and predict geological
and
drilling events ahead of the drill bit, such that the user's drilling system
may more
precisely steer the wellbore while rotating the drill string or when using the
mud motor to
increase rate of penetration (ROP) and reduce drilling cost. In this regard,
the present
invention may incorporate the steps detailed below for bit wear prediction,
lithology
prediction, pore pressure estimation, rotating friction coefficient
estimation, permeability
estimation, and cost estimation using the real-time data collected by a
variety of sensors,
including logging tools, deployed at the well site. These predictions provide
real-time
estimates of the geological formation ahead of the drill bit which allow
adjustment of
drilling parameters as well as the drill bit itself which may reduce time and
risk, and
CA 3064241 2019-12-09
16
therefore cost, in drilling operations. These real-time predictions fused with
trends
determined from the historical geological knowledge base of the region may
also permit
use of an expert system engine as described below to warn of safety hazards or
other
issues of concern to the operator while drilling. By recognizing unsafe
conditions in the
wellbore before they cause serious consequences, the present invention may
increase
safety for well site personnel and may reduce or eliminate adverse impacts on
the
environment.
[00094] According to an embodiment, there is a drilling fusion engine (DFE)
which
may integrate drilling data in real-time from a rig site to inform adjustments
to drilling
parameters to improve the results of drilling operations (also referred to as
"optimization"). This may include specific techniques that enable real-time
drilling
optimization: bit wear prediction, lithology prediction, pore pressure
estimation, rotating
friction coefficient estimation, permeability estimation, and cost estimation.
[00095] According to embodiments of the invention, software may be installed
and
used on site as a client-server application or a standalone computer program.
The client-
server application involves two subcomponents: 1) the client software with a
graphical
user interface to interact with the operator onsite; and 2) the server
software running the
engines described below, which may reside on a server computer onsite or
offsite, such as
in a remote data center or cloud infrastructure.
[00096] According to an embodiment as shown in FIG. la, which depicts the
system
architecture of a drilling system in the standalone arrangement, there is a
drilling system
comprising a well being drilled 320. Sensors 101, such as through an
electronic
drilling recorder, such as the Pason(tm) EDR or the NOV MID Totco(tm), or
other
logging tools, acquire data and the data is transmitted over a rig site
network 103 to the
DFA server software 106. According to an embodiment, historical database 309
may
CA 3064241 2019-12-09
17
provide historical data obtained from analogous wells 302.1 to 302.n to the
DFA server
software 106.
[00097] According to an embodiment as shown in FIG. lb, which depicts the
system
architecture of the drilling system in the client-server arrangement, sensors
101 acquire
data, such as through an electronic drilling recorder or other logging tools,
which is then
communicated over a rig site network 103 to the DFA client software 108 to an
operation
center network 104 via a satellite 105. The received data is then communicated
through
the operation center network 104 to the DFA server software 106. It will be
appreciated
that the data may be communicated from the sensors 101 to the DFA client
software 108
and the DFA server software 106 through various wired or wireless
communication
networks as appropriate according to other embodiments. According to an
embodiment,
historical database 309 may provide historical data obtained from analogous
wells 302.1
to 302.n to the DFA server software 106.
[00098] The overall drilling fusion engine architecture is shown according to
an
embodiment in FIGs. 2a and 2b. According to an embodiment, the drilling fusion
engine
301 comprises three main engines: a drilling fusion prediction engine 20, a
trend fusion
engine 308 and an expert decision engine 328. According to an embodiment, the
prediction engine 20 (which is shown in more detail in FIG. 3) may receive
real-time
drilling parameters 321 as data inputs 201 from sensors 101 at the well during
drilling
320. Using the data inputs 201, the prediction engine 20 may calculate the
following
values 324: hydrostatic pressure 3241, ECD 2074, annular circulation pressure
loss 3243,
BHP 3244, volume 3245, and water saturation 3246. Using the data inputs 201,
the
prediction engine 20 may estimate the following values 325: pore pressure
2053, porosity
2075, permeability 2076, and filter cake 3254. Using the data inputs 201, the
prediction
engine may predict the following values 326: lithology prediction 2057 and bit
wear
prediction 2056. According to an embodiment, certain data inputs 201 may be as
used as
raw inputs 327 such as: ROP (rate of penetration) 2032, torque 2036, WOB
(weight on
CA 3064241 2019-12-09
18
bit) 2033, RPM (revolutions per minute) 3273, pump pressure 3275, mud
parameters
3276 and differential pressure 3277. The calculated values 324, estimated
values 325,
predicted values 326 and raw inputs 327 may be used to determine adjusted real-
time
drilling parameters 329, which may employed in steering the drill bit during
drilling or
may be provided to an expert decision engine 328.
[00099] According to an embodiment as shown in FIG. 2a, the expert decision
engine
328 may receive the adjusted real-time drilling parameters 329 and as well as
analogous
well data from a historical database 309, and use this information to generate
flags and
control operations 330 for well control which may generate prompts to the
operator
through the graphical user interface. Well control refers to the management of
the
dangerous effects caused by an unexpected influx of formation fluids
(gas/oil/condensate)
into the wellbore and hence potentially causing damage to personnel and
surface
equipment and into the nearby environment and atmosphere. Part of well control
includes
preventing formation fluid, referred to as kick, from entering into the
wellbore during
drilling. According to an embodiment, the drilling fusion engine 301 comprises
monitoring a well for signs of impending influx in the formation being drilled
in real
time, and providing predictive capability for well control by combining real-
time drilling
data and trends in analogous well data into an expert knowledge system. The
expert
decision engine 328 may offer decision support towards potential safety issues
during
drilling and the associated operational suggestions to drilling engineers and
geologists on
the rig site or, according to an embodiment, be configured to interface with
the drilling
control system to automatically adjust drilling parameters to avoid well
control hazards.
[000100] According to an embodiment, the trend fusion engine 308 may generate
the
analogous well data to populate the historical database 309. The trend fusion
engine 308
may optimize the pore pressure estimation results by incorporating historical
well data
from multiple wells using trend analysis techniques, which may reduce
uncertainties due
to estimation errors in individual well data. Analogous wells 1 to n 302.1 to
302.n are
CA 3064241 2019-12-09
19
selected by a user and the data related to the historical drilling parameters
and operations
303.1 to 303.n from one or more past drilling jobs for each well are input or
imported. As
described further below, the trend fusion engine 308 may then generate
expected
analogous values for lithology 310, porosity 311, permeability 312, water
saturation 313,
bit wear 314, mud parameters 315, Bottom hole assembly (BHA) 331, pore
pressure 316,
Rate of Penetration (ROP) 332, Weight of Bit (WOB) 333, Revolutions per minute
(RPM) 334, volume (mud tanks and hole volumes) 335, pump 317 and filter cake
318,
and other parameters if the parameters are available in the historical data,
based on the
trends in the historical well data and, according to an embodiment, may also
insert the
current, real-time data into the historical database 309 for comparison.
10001011 FIG. 3 shows a flow chart of a prediction engine 20 according to an
embodiment of the invention. The data inputs 201 to the prediction engine 20
may be the
real-time data received from the sensors 101 (e.g. MWD or logged data) as
shown in FIG.
1. These data inputs are pre-processed and mapped by a data preparation and
mapping
component 201a. The data inputs 201 from sensors 101 may be pre-processed and
mapped to the real-time drilling data fields 203 and logged data fields 204 in
a database
202, such as a Microsoft SQL Server(tm) database or other object-based
database. Since
data sources may have different data formats, the data pre-processing and
mapping
includes converting the data to a unified format prior to importing the data
into the
database 202.
10001021 According to an embodiment, the prediction engine 20 may be
implemented
by a computer program which may receive various data inputs that may be
converted to
a unified format using a conversion program. Additionally, a unit conversion
table needs
to be established. For data mining and information processing tasks dealing
with a huge
amount of data, data processing may be carried out to prepare data for further
processing.
Such data processing tasks may include operations to replace missing data with
default
values, and to correct incorrect data inputs, for example, through time-based
interpolation
CA 3064241 2019-12-09
20
and to address non-registered multiple data sources, for example, through
aligning the
data with the same sampling rate or representing the same context. These data
processing
tasks may then output time or depth aligned data. According to an embodiment,
this data
may then also be fed into a data smoothing function to remove measurement
noise in
order to improve lithology prediction accuracy. One such data smoothing
function is to
use a moving average filtering technique. According to an embodiment, a depth-
weighted
smoother with a 1-meter window (e.g. the depth point has 0.2 m sampling
frequency, 1
meter window corresponds to 5 data points). The weight assigned to each data
point in
each window is inversely proportional to the depth interval between it and the
current
point. For example, where the input of the smoother is the raw MWD data, x,
the
smoother output y will be
y(0) = x(0)
.y(1) = (x(0) + x(1)) / 2
y(2) = (x(0) + x(1) + x(2)) / 3
y(3) = (x(0) + x(1) + x(2) + x(3)) / 4
y(4) = (x(0) + x(1) + x(2) + x(3) + x(4)) / 5
y(5) = (x(1) + x(2) + x(3) + x(4) + x(5)) / 5
where x(0) denotes the first data point available for smoothing operation. The
starting
point of averaging for the current output is found in the fifth data ahead in
this case.
10001031 Since the purpose of smoothing is to reduce the measurement noise
while still
preserving the underlying jumps or trends of the data, verification needs to
be done by
testing on the real drilling data. According to an embodiment, 1 meter (5
points) windows
were appropriate in that it did not erase the trends but reduced the overall
noise
significantly.
CA 3064241 2019-12-09
21
[000104] As discussed above, according to an embodiment, the database 202 may
include pre-processed and mapped real-time drilling data 203 and logged data
204. The
real-time drilling data 203 may include one or more data inputs relating to
depth 2031,
Rate of Penetration (ROP) 2032, Weight On Bit (WOB) 2033, rotary speed 2034,
flow
rate 2035, and torque 2036, as shown in Table 1, below, according to an
embodiment.
The logged data 204 may include data inputs from a bit record 2041, mud log
2042,
lithology core database 2043, sonic log 2044, gamma ray log 2045, neutron
density log
2046, resistivity log 2047, and micro log 2048. The bit record 2041 may
include a record
of every bit run during an analogous drilling operation including bit size,
IADC name and
type, depth in and out, time in the hole, WOB, RPM, bit gauge, bit tooth wear,
bearing
wear, and total time in the hole, as shown in Tables 3 and 4, below, according
to an
embodiment. The mud log 2042 may include mud weight, mud viscosity, filter
cake
thickness, chemical usage, mud properties, information found on a daily
drilling report,
and solids content, as shown in Table 2, below, according to an embodiment.
The
lithology core database 2043 may include data relating to the formation
geology such as
depth, lithology type, permeability, porosity, water saturation, core length
and core size,
gross and net returns, facies, laboratory generated permeability, and fluid
type associated
with the formations cored. The sonic log 2044 may include formation lithology
as
derived from calculations of sonic wave returns, sonic or interval transit
times, possible
fracture size and orientations. The gamma ray log 2045 may include a
measurement of
gamma radiation: gamma ray, as shown in Table 5, below, according to an
embodiment.
The neutron density log 2046 may include measurements of caliper, compensated
neutron
porosity, compensated neutron porosity dolomite/limestone/sandstone matrix,
photo
electric cross section, bulk density, temperature, density porosity, and total
gas, as shown
in Table 5, below, according to an embodiment. The resistivity log 2047 may
include
measurements of shallow induction, medium induction, deep induction, and
spontaneous
potential, as shown in Table 5, below, according to an embodiment. The micro
log 2048
may include measurements of micro normal and micro inverse, as shown in Table
5,
below, according to an embodiment. As discussed elsewhere in this description,
embodiments of the invention may operate in the absence of some or many of
these
parameters, if they are not available. Additional data, including other
captured or logged
CA 3064241 2019-12-09
22
data parameters, however, may improve the accuracy of the predictions made by
the
prediction engine 20 and the recommendations of the expert system engine 328.
10001051 The real-time drilling data 203 and the logged data 204 may be used
to
calculate the drilling bits hydraulics 2051, the drag 2052 and torque 2073,
the ECD 2074
& pore pressure 2053, the porosity 2075, permeability 2076 and skin factors
2054, rock
compressive strength 2055, bit wear prediction 2056 and lithology prediction
2057. For
example, the mud log 2042 and bit record 2041 may be used in porosity 2075 and
permeability 2076 estimation, and skin factor estimation 2054.
10001061 The real-time drilling data 203 and logged data 204 may be used by
the
drilling optimization engine 205 to generate optimized drilling parameters,
i.e. those
adjustments made to current drilling parameters intended to improve drilling
results.
According to an embodiment, supporting data 206, which may comprise bit dull
grade
2063, cost information 2061, i.e. the cost of the bit, and a bit wear equation
2062 may be
used to assist with optimizing drilling parameters with respect to bit wear
replacement.
Manufacturers of drilling software such as Wellman(tm) may provide supporting
data
206 such as bit dull grade for each bit run and cost information. Bit
manufacturer data
may include equations and parameters to determine the bit tooth wear for the
specific bit
used in drilling. According to an embodiment, the supporting data 206 from the
manufacturer may be imported into the computer program. The supporting data
may then
be used for the online optimization 207 of bit wear prediction and drilling
costs analysis
for generating the optimized drilling parameters 2072 and cost analysis 2071.
10001071 According to an embodiment, the lithology prediction 2057 may use
neural
networks to construct a non-linear map between the mechanical drilling data
and the
lithology types. The output of lithology types may be determined by the
training data.
Training data may be obtained from analogous wells. The input of the training
process
CA 3064241 2019-12-09
23
may be a vector of features extracted from the training data, and the output
may be a
vector of target lithology description. For example, if we have three
lithology types
(sandstone, siltstone and shale), the target output vector may be represented
by [1,0,0],
[0,1,0], [0,0,1] for the sandstone, siltstone and shale, respectively. The
prediction results
will be the similar 3-element vector. With a hard decision of which value of
the element
is highest, we can determine the predicted lithology type. For example, if the
neural
network output is [0.9, 0.04, 0.01], then we may make a conclusion that the
predicted
lithology is sandstone.
[000108] According to an embodiment, online optimization may provide drilling
parameters to be applied in drilling in real-time as well as associated
analysis for drilling
engineers. The parameters and analysis may include: the estimated cost for
drilling under
the current drilling conditions, and adjusted drilling parameters to maximize
drilling
efficiency while reducing cost, estimated depth or time to pull out the drill
bit.
[000109] Sample data analysis and input data specifications are described
according to
an embodiment of the present invention. The sample data comprises three sets
of well
data that were collected for three S-curve wells. The sample data for analysis
may be
categorized into the following seven categories: Measurement While Drilling
(MWD), an
oilfield instrumentation and data acquisition systems Electronic Drilling
Recorder (EDR),
tour sheet, bit report, drilling software data, geological report, sidewall
cores report, and
LWD (logging while drilling) (neutron density, gamma ray, and resistivity).
[000110] According to an embodiment, the sample data comprises data from three
sources: the oilfield instrumentation and data acquisition systems EDR,
geological data
and drilling software data. The sensors 101, such as those incorporated in the
EDR,
supplies well site data comprising real-time MWD data and logging data 204
such as, bit
record 2041, mud log 2042, gamma ray log 2045, neutron density log 2046,
resistivity
CA 3064241 2019-12-09
24
log 2047 and micro log 2048 as shown, according to an embodiment, in Tables 1,
2, 3,
and 5, below. As different EDRs and logging tools may provide different data,
the list of
parameters may be revised or expanded to match data capture capabilities. The
drilling
data may be downloaded as functions of either drill time or well depth. The
geological
data may comprise well path survey information, geological report data and
petrophysical
evaluation data. The drilling software data may comprise an integrated well
life cycle
report comprising geology, construction, drilling, and completion. Cost
information may
also be provided as set out in the Table 4. Other types and organizations of
data may be
provided as will be appreciated by those skilled in the art.
[000111] According to an embodiment as used with the sample data discussed
herein,
the following parameters may be used:
[000112] Table 1. Measurement-While-Drilling Data 203
Parameters Units
Depth 2031
Rate of penetration 2032 m/hr
On bottom ROP m/hr
Rotary speed 2034 Rpm
Weight on bit kDaN
Pump rate m3/min
Rotary torque Nm
Inclination Degree
Azimuth Degree
Differential pressure kPa
Trip speed m/min
Hook load kDaN
Standpipe pressure kPa
CA 3064241 2019-12-09
25
Total mud volume m3
Casing pressure kPa
Pump strokes Strokes/min
Circulating hours Hr
[000113] According to an embodiment, the MWD data may be acquired at a data
acquisition rate of up to 0.5 meters (depth-based) or 20 seconds (time-based)
for the
above parameters. Other data acquisition rates may be used beneficially in the
discretion
of the operator, subject to the capabilities of the particular EDR used at the
rig site.
[000114] Table 2. Tour sheet
Parameters Units
Mud density kg/m3
Mud type N/A
Depth
Funnel viscosity s/1
PVT m3
Drilling assembly components mm, mm, m, kg/m
(OD, ID, length, unit weight)
Depth in/out m/m
Pipe OD mm
Hole size mm
Number of jet nozzles integer
Jet size mm
Drill pipe (length, unit weight) m, kg/m
Torque at bottom Nm
Weight of Drill Collars kDaN
Weight of string kDaN
CA 3064241 2019-12-09
26
Deviation type N/A
Deviation survey depth m
Deviation degree
Direction degree
10001151 Table 3. Bit record 2041
Parameters Units
Bit diameter in
Bit manufacture N/A
Bit type (IADC code) N/A
Depth in/out m
Accumulated running hours hr
10001161 Table 4. Drilling Software Data 206
Parameters Units
Bit dull grade 2063 N/A
Cost 2061 Dollar/hr
10001171 Geological Report
10001181 The geological report may include the well summary, casing summary,
daily
drilling summary, bit record table, wireline logging summary,
deviation/direction survey
report, formation top summary, formation evaluations, and core sample
descriptions. This
report may be based on the information gathered from the geological
descriptions and the
wireline log analysis. This information may form part of the historical data
from
analogous wells.
CA 3064241 2019-12-09
27
[000119] Sidewall Cores Report
[000120] The sidewall cores report may comprise a parameter for irreducible
water
saturation for each formation (average value) (in fractional (cYo)).
[000121] Table 5. Logging-While-Drilling Data LWD (Neutron density log 2046,
resistivity log 2047, gamma ray log 2045 and micro log 2048) and other wire
line
electrical logging tools.
Parameters Units
Caliper mm
Compensated neutron porosity pu
Borehole size corrected pu
Compensated neutron porosity pu
dolomite/limestone/sandstone
matrix
Gamma ray gapi
Photo electric cross section B/E
Bulk density kgm3
Temperature degree
Density porosity Fractional
Total gas Units
Shallow induction Ohmm
Medium induction Ohmm
Deep induction Ohmm
Spontaneous potential my
Micro normal Ohmm
CA 3064241 2019-12-09
28
Micro inverse Ohmm
[000122] According to an embodiment, the neutron-density log 2046 and gamma
ray
log 2045 may provide data at a data acquisition rate of 0.1 meters. Other data
acquisition
rates may be used beneficially in the discretion of the operator, subject to
capabilities of
the particular logging tools employed at the drilling site.
[000123] Based on the data input specifications, the availability of the data
inputs 201
to be used in the prediction engine 20 may be checked and their status marked
as (1)
available, (2) available but needs to be calculated indirectly, and (3) not
available. For the
data inputs 201 available, the units and range of data used by each product
and reporting
standard may be recorded. The mud density information was not available from
the EDR
drilling data for the sample data provided herein, but it was available from
the Electronic
Tour Sheet (although the sampling frequency is much lower, i.e. up to 6 times
per day,
compared to other parameters, as low as 20 seconds per sample).
[000124] According to the embodiment used with the sample well data, the
different
formats of reports and data sheets were imported into a Matlab(tm) workspace.
The
Excel(tm) data was imported by a Matlab(tm) built-in method. The mud record
2200 was
extracted from a ETS 3.0 tour sheet and converted to text format using a
custom
conversion program which written using the Java(tm) programming language. The
.xs1
style-sheet file was reprogrammed for Java(tm) and the bit record extracted
and
converted to .csv format. The drilling software database was also linked to
Matlab(tm)
and the parameters imported and saved to workspace. A unit conversion table
was
identified and a unit conversion function established in the Matlab(tm)
program.
CA 3064241 2019-12-09
29
10001251 It was observed from the Tour Sheet that, for example, in the mud
record,
some data entries are empty and some are inputted incorrectly. Multiple data
sources (i.e.
Tour Sheet and other sources) have different sample rates, are either
uniformly or non-
uniformly sampled, are either time-stamped or depth-stamped, and these need to
be
aligned to form a unified data source. For example, a set of downhole
mechanics
measurements, e.g. ROP 2032, WOB 2033, torque 2036, rotary speed 2035, were
collected at a specific depth. If any of these measurements are negative or a
zero value,
the set of measurements is identified as invalid data by data validation, and
will be
removed from processing. The remaining validated data may then be fed into a
data
smoothing function to remove noise.
10001261 Unit Conversion
10001271 The data inputs 201 may employ metric units by default. The system
may also
enable unit conversion between Metric and English (i.e. imperial) unit
systems. Table 6
for the inputs and outputs of unit conversion between these two unit systems
is provided
below. The unit conversion may be carried out by establishing a data structure
whose
sub-fields are category, field, and conversion constant and loading the data
structure into
the software's graphical user interface.
[000128] Table 6. Inputs and outputs of unit conversion
Oilfield instrumentation and data acquisition systems EDR
Parameters Category Field
Metric(I.S.) Imperial Conversion
depth length METER2FEET m ft
3.281
rate of penetration length METER2FEET m/hr ft/hr
3.281
on bottom ROP length METER2FEET m/hr ft/hr
3.281
rotary speed N/A N/A rpm rpm N/A
weight on bit force KDAN2LBF kDaN lbf
2248.08943
pump rate volume CUBMETER2BBL m3/min bbl/min
6.28981
rotary torque force NM2LBFT Nm lb ft
0.7375621
CA 3064241 2019-12-09
30
Inclination N/A N/A degree degree
N/A
azimuth N/A N/A degree degree
N/A
differential pressure pressure KPA2PSI kPa psi
0.145
trip speed length METER2FEET m/min ft/min
3.281
hook load force KDAN2LBF kDaN lbf
2248.08943
standpipe pressure pressure KPA2PSI kPa psi
0.145
total mud volume volume CUBMETER2BBL m3 bbl
6.28981
casing pressure pressure KPA2PSI kPa psi
0.145
pump strokes N/A N/A strokes/min strokes/min
N/A
circulating hours N/A N/A hr hr
N/A
Tour sheet
Parameters Category Field Metric(I.S.) Imperial
Conversion
mud density density KGPM32PPG kg/m1 PPg 0.01
mud type N/A N/A N/A N/A N/A
depth length METER2FEET m ft
3.281
funnel viscosity SPL2SPQ secs/litre secs/quart
0.946
viscosity
PVT volume CUBMETER2BBL m3 bbl 6.28981
drilling length MILLIMETER2INCH mm,
mm, m, In, in, ft, 0.03937
assembly METER2FEET kg/m kg/ft
3.281
components
(OD, ID,
length, unit
weight)
depth in/out length METER2FEET m/m ft
3.281
pipe OD length MILLIMETER2INCH mm in 0.03937
hole size length MILLIMETER2INCH mm in 0.03937
number of jet N/A N/A N/A N/A N/A
nozzle
jet size length MILLIMETER2INCH mm in 0.03937
drill pipe length METER2FEET m, kg/m ft
3.281
(length, unit
weight)
torque at force NM2LBFT Nm lb ft
0.7375621
bottom
weight of DC force KDAN2LBF kDaN lbf
2248.08943
weight of force KDAN2LBF kDaN lbf
2248.08943
string
deviation type N/A N/A N/A N/A N/A
deviation length METER2FEET m ft
3.281
survey depth
deviation N/A N/A degree degree N/A
direction N/A N/A degree degree N/A
Bits
Parameters Category Field Metric(I.S.) Imperial
Conversion
bit diameter length MILLIMETER2INCH mm in
0.03937
bit manufacture N/A N/A N/A N/A N/A
bit type (IADC N/A N/A N/A N/A N/A
code)
CA 3064241 2019-12-09
31
depth in/out length METER2FEET m ft 3.281
accumulated N/A N/A hr hr N/A
running hours
[000129] Bit Wear Prediction
[000130] It is desirable to know the state of wear of a drill bit in use
without having to
remove it from the hole. Optimizing when to replace worn bits involves real-
time
estimation and prediction of the real-time state of bit wear which may assist
deciding
when to pull the drill bit and may lead to substantial cost savings. Observing
a decrease
in the ROP 2032 is, however, not sufficient information about the wear of a
drill bit
unless the strength of the rock penetrated is also known. In general, a
decrease in ROP
2032 may be because the bit is worn, or the bit is balling (i.e., the bit is
encased by an
accumulation of sticky cuttings), or the bit is still in good condition but is
simply
penetrating a more resistant formation (i.e. the wrong bit is being used for
the current
formation).
[000131] According to an embodiment, the applied bit wear prediction 2056 may
be
based on the bit-rock interaction model developed in T. Richard and E.
Detournay,
"Influence of bit rock interaction on stick slip vibrations of PDC bits"
Society of
Petroleum and Engineers, Texas, October 2002.
The model accounts for the cutting action of a single cutter and allows forces
between the WOB 2033 and torque 2036 to be expressed without involving rock
properties. When bit efficiency decreases, the coefficients which characterize
these
relationships will change. According to an embodiment, the present invention
may
monitor these coefficients and detect their variations on a real-time basis.
The cutting
action of each cutter is represented by two independent processes, namely, the
cutting
process and the friction process. The forces torque, T, and WOB, W, are
defined by:
Date Recue/Date Received 2021-05-31
32
T=T,
W=W+Wf
where
Tc=-1sa2d
2
W, = 4acl
T =-1,uayW1
2
d-21z-R
a : bit radius (m)
d : depth of cut (m)
= : bit shape factor (dimensionless)
: cutting force coefficient (dimensionless)
= : intrinsic specific energy (Mpa)
p : friction coefficient (fractional)
R : ROP (m/hr)
Q : bit angular velocity (rad/sec)
10001321 The subscripts c and f denote cutting and friction, respectively. The
response of the bit is obtained by combining the cutting and friction
processes:
CA 3064241 2019-12-09
33
2T õ\
=ki-p)e tur '
¨
ad2 ad
where fl = ,u
[000133] From the above description, the specific energy which corresponds to
the
necessary energy to grind a given volume of rock is defined by:
E = E0+ py.S
where E0 =(1- 13)s
E = ________ , a 2d specific energy (Mpa)
S =¨ drilling strength (Mpa)
ad'
[000134] The above relationship shows that the slope of E as a function of s
is only
dependent on the bit shape factor and the friction coefficient. The rock
properties are not
involved. When a change occurs on the bit wear, these two parameters will be
affected.
Consequently, the bit wear prediction 2056 may be based on the detection of a
change in
p and y, which leads to the change in the slope of E as a linear function of
S. If we
define the product of ,uy as a bit efficiency factor (BEF), the problem of bit
wear
prediction 2056 becomes a prediction of BEF using the measurements of the
specific
energy and the drilling strength.
[000135] A Rao-Blackwellised Particle Filter (RBPF) approach as described, for
example, in Arnaud, E. and Memin, E., "An efficient Rao-Blackwellized particle
filter for
object tracking," in IEEE International Conference on Image Processing, 2005.
ICIP
CA 3064241 2019-12-09
34
2005, vol. 2, 2005, may
be applied to detect the
change in BEF. The RBPF approach is based on combining two functional modes,
one of
them corresponding to the levels of BEF and the other corresponding to the bit-
rock
interaction with respect to each level of BEF. Each of the defined functional
modes is
modeled by a state space system. The RBPF comes from the possibility of
combining the
features of Kalman filters (KF) and those provided by the particle filters
(PF). The role of
KF is to treat each of the defined state space representations and PF is used
to select the
one to be treated. Using RBPF, the underlying objective is achieved by two
parallel linear
regressions obtained by using RBPF and the prediction is made when the process
model
moves from the model which corresponds to the process when the BEF changes.
[000136] For example, if the mode levels are set from 0 to 8, there will be 9
state-space
models in total, and the RBPF algorithm may produce an estimate of all 9
models
simultaneously and select one winning model and its corresponding mode level.
If the
winning model at time T comes from the mode level 3, then the prediction at
time T for
the bit wear is 3. The bit wear may have a variation trend from mode level 0
to BEF level
8, so that any change of the winning model will reflect the worn bit.
[000137] When a system is in a mode 2, its behavior is given by the following
state-
space model:
{x, =f,(x,_,,t)+võfort .1
y, =,g2(xõt)+w1,fort 1
where x, E X is the unobserved n-dimensional state vector at time t, xo is the
initial
condition, 371 e Y is the m-dimensional observation vector at time t, ft e F
is the state
function vector in mode 2, g A c G is the observation function vector in mode
2, and v,
and w, are independent, identically distributed random noise sequence vectors
which can
be functions of mode 2. v, and w, are mutually independent and also
independent of the
Date Recue/Date Received 2021-05-31
35
initial state of the system. L is referred to as the system model and can be
non-linear and
non-Gaussian. According to an embodiment, for bit wear prediction, it is
linear. Then the
mode selection of RBPF from S. Tafazoli, et al., "Hybrid system state tracking
and fault
detection using particle filters", IEEE Transaction on Control Systems
Technology, vol.
14, no. 6, pp. 1078-1087, November 2006, may
be
applied as follows and its graphic illustration is shown in FIG. 4, which
shows a mode
selection used in the prediction engine 30:
[000138] Step 1 Initialization: The mode at time t = 0 is given as 20. For i
=1,= = = ,N,
sample x(0') from an initial distribution in mode .10 and set t =1.
[000139] Step 2 Prediction: For any mode A!, such that the transition
probability TA,_,
from mode 2 to is
not zero (j =1,- = =,K) where K is the number of such modes,
sample Zti)
(XtlX(ti)l)for =1,= = = , N ; For each mode A,' , evaluate the importance
weights ii),(1) = (y , for i ¨1,= = = , N
[000140] Step 3 Mode selection: Average the total particles weights in each
mode A,'
and multiply by the transition probability: 6)-7 T N;
Find the most likely
i=A,"
mode: 2, = arg max {c-te : for all , j =1,= = K1; Normalize the weights of
particles in mode
)4'
.1! .
Date Recue/Date Received 2021-05-31
36
[000141] Step 4 Resampling: Resample N new particles {x1), for i =1,- = =,N}
with
replacement from the particles in mode Aõ {i(:), for i =1,- = = , AT},
according to the
importance weights. Set t = t +1 and go to step 2.
[000142] According to an embodiment of the bit wear prediction method, the
level of
bit wear (i.e., the mode) may be assumed to vary from 1 to K. According to an
embodiment, there may be a specific measurement function corresponding to each
mode.
In other words, the measurement function is not only a function of BEF, but
also the
mode variable, which defines the measurement model. At the same time, it is
assumed
that BEF evolves with time. Based on the above assumptions, the bit wear
prediction
2056 may be developed according to the block diagram 40 shown in FIG. 5. At
the time
instant t, the inputs are the estimate of BEF at t ¨1 401, the covariance
estimate of BEF
at t ¨1 402, and the estimated probability density function (PDF) of the mode
variable at
the time instant t ¨1 403. These variables may be input into a RBPF particle
filter 404 to
compute the estimated PDF of the mode variable at the time t 410 by the mode
selection
scheme proposed above. Based on the estimated PDF of the mode variable 410,
computation of the mean of the mode variable at t 405 is performed. This mean
value
along with the estimate of BEF at t ¨1 401 and the covariance estimate of BEF
at t ¨1
402 is input into a Kalman filter (KF) 406. The prediction step of KF 406
yields the BEF
prediction 407 and the update step of KF 406 produces the estimate of BEF at 1
408 and
the covariance estimate of BEF at t 409. The mode variable is the value of
interest. The
mathematical description of this embodiment is as follows.
{w[000143] Given an importance distribution irk I v set of particles
(') m(') P(1) i N}
1-1, t-i, 1-1, 1-1, ¨ 5 9 and measurement yõ the set of
particles
=1,= = =,N} is processed as follows:
CA 3064241 2019-12-09
37
= Perform KF predictions for the means m21 and the covariance Pf'! of
particles, i = 1,= = = ,N conditioned on the previously drawn mode variable
.1.,(z1 as
= Al-1 (2,11n(L)õ
13;4')
= Draw new mode variables At for each particle in i =1,===,N from the
corresponding importance distributions 41) ¨ ITN _ i:ti =
= Calculate new weights as follows:
P(Y, AIC), Y1., )P(2,(i)
*(i) *(i) = = -' "
141, 14
71- ki)
where the likelihood term is the marginal measurement likelihood of the KF
,3 111,4 = N(Y I H, (41) )P,-(1)HT (e) ) R, (AP)),
such that the model parameters in KF are conditioned on the drawn mode
variable go .
= w.(i)
Normalize the weights to sum to unity as 1,1)i) = N =
E w:(i)
Perform KF updates for each of the particles conditioned on the drawn mode
variable 2,0)
y, ¨ H, (2(') )m;,
Ss =H, (e)P,-(I)H,T ())+ R, (A(')),
CA 3064241 2019-12-09
38
KI) = P,-(1)11,T ,
in(/) = +1(v(;),
pr(i) ¨Kr(')S,(')[1(')]T.
= If the effective number of particles is too low, perform resampling.
After the
set of particles have been acquired, the filtering distribution can be
approximated as:
wto .. ( - 2,0) )N(xt line ,P,('))
[000144] According to an embodiment, the results from the bit record 2041 for
bit #7
from Well #1 were input for prediction and FIG. 6 displays the bit wear
prediction results
50 for bit #7 from Well #1.
[000145] Complementary information may also be displayed to the user or
otherwise
provided from the historical bit data of the analogous wells showing what bit
was used
most often in other wells around the current well being drilled, the depth at
which the bit
was pulled, its dull coding when it was pulled, and the time the bit spent at
the bottom of
the well. An example of complementary information for analogous Well #2 and
Well #3
is shown in Table 7. This information may be part of the bit record 2041 and
used in bit
wear prediction 2056 to enhance the cost optimization of bit replacement.
[000146] Table 7. Complementary information for analogous Well #2 and Well #3
Well Make Type Dull code
-71
(1, g L
B GO2A RP
CA 3064241 2019-12-09
39
#2 REED MSF513M 3242 3271 8.25 29 2 2 CC M X 156 PN PR
#3 SHEAR 5H416 2300 3390 75.25 1090 1 3 WT H NO IN CT PR
[000147] Lithology Prediction
[000148] Neural networks may be used to establish complicated non-linear
mapping
between inputs and outputs. The relationship between formation lithology types
and real-
time drilling data is usually non-linear. In practice, it is difficult to
obtain a well-defined
relationship between drilling data and lithology type in an exact mathematical
format.
Using a neural network approach avoids an exact mathematical modelling of this
relationship, and instead non-linear mapping is established using a neural
network.
Training data may be applied to fine tune the network parameters in order to
obtain one
which is closest to the real world. For the underlying lithology prediction
problem, a
multilayer neural network may be established to represent the mapping between
the real-
time drilling data and the lithology class.
[000149] Features are calculated values based on data inputs. According to an
embodiment, the features may be rock compressive strength, formation
drillability
coefficient, dynamic drilling response, and formation shear strength. Feature
extraction
(the calculation process) may be a factor in the prediction performance of
neural
networks. Poorly selected features may deteriorate the network performance and
thus
may result in significant errors during prediction. The features should be
selected to best
represent the input of the non-linear mapping with less dimension, noise and
redundancy.
One may choose measurements which may better reflect the rock properties as
input
features, for instance, the rock strength. Rock properties may be inferred
from two
different sources: from elastic parameters via the seismic amplitude versus
offset (AVO)
data, and indirectly from mechanical drilling data by rock-bit interactive
formulations.
CA 3064241 2019-12-09
40
According to an embodiment, if seismic AVO data is not captured, mechanical
drilling
data may be used to form a set of features for the neural network. According
to a further
embodiment, if seismic AVO data is captured, additional information about the
geology
of the well to be drilled may be available, and features for seismic elastic
parameters,
including P-wave velocity, S-wave velocity, and density may also be included.
[000150] According to an embodiment, the mechanical measurements may include
ROP 2032, WOB 2033, torque 2036, and rotary speed 2034 such as shown in real-
time
drilling data 203 in FIG. 3. Other information may include mud density and bit
size. The
raw data may be pre-processed as discussed above. After pre-processing, four
features
may be calculated at each point of drilling depth.
[000151] Rock confined compressive strength (CCS) 2055
CCS = C, *In{ 3677(D, *W) } (psi),
a,*[a2*ln(M)+a,]
where T : rotary torque (ft-lbf)
DB : bit size (inches)
M : mud density (ppg)
W : WOB (lb)
[000152] CCS is normalized with respect to a mud density of 9.5 ppg. a1, a2,
a3 are
dimensionless correction factors determined by the laboratory experiments
which may be
used to estimate CCS at any mud density value.
CA 3064241 2019-12-09
41
Cb: a bit-related constant. For a PDC bit, C, = ¨0.125*106.
[000153] Formation drillability coefficient (FDC)
FDC = R = DB
W - N
R : ROP (ft/hr)
N : rotary speed (rpm)
[000154] Dynamic drilling response (DDR)
T - R
DDR =
w2 .N
[000155] Formation shear strength (FSS)
T . N
FSS =
R = DB2
[000156] The use of neural networks for lithology prediction 2057 may be
advantageous due to certain properties of neural networks, including learning
by
experience (neural network training), the ability to generalize (map similar
inputs to
similar outputs), and their robustness in the presence of noise and
multivariable
capabilities, which allows them to deal with the highly complex and uncertain
problem of
lithology determination. The basic processing element of neural networks is
called a
neuron.
CA 3064241 2019-12-09
42
[000157] FIG. 7 displays a block diagram of a neuron 60. Each neuron has
multiple
inputs põ p2, = = = 'Pk 600.1, 600.2, ... 600.k where k is limited only by
desired number of
features and the available computing processing power, and a single output a
620. Each
time a neuron is supplied with the input 600.1, 600.2, ... 600.k, it computes
the net input n
606 through the function E 605. The net input n 606 is passed through the
activation
function F(.) 630, which may be either linear or non-linear, producing the
neuron output
a 620.
w2,= = wk 610.1, 610.2, ... 610.k are neuron weights, and b 604 is a neuron
bias. The equation for the neuron output may be represented as follows: a =
F(wTp+ b)
[000158] According to an embodiment as shown in FIG. 8, the neural network 70
may
have multiple layers 640, 641, 642, and 643. Two or more of the neurons 60
described
above may be combined in a layer. The neural network 70 may be supplied with
inputs
p1 600, p2 601, p3 602, and p4 603. The number of inputs in a layer need not
equal the
number of neurons in a layer. Each layer has a weight matrix W 610, a bias
vector b
604, a function E 605, an activation function Fri 630 and an output vector a
620. The
output vector a 620 from each intermediate layer is the input to the following
layer. The
layers in a multilayer network 70 play different roles. A layer that produces
the network
outputs a 1 621, a2 622, a3 623 is an output layer 643. The other layers are
hidden layers
640, 641, 642. The neural network 70 shown in FIG. 7, for example, has one
output 643
layer and three hidden layers 640, 641, 642.
[000159] According to an embodiment, training procedures may be applied once
topology and activation functions are defined. The activation function may be
selected
from the pure linear, log-sigmoid, tangent sigmoid, etc. In supervised
learning, a set of
input data and correct output data (i.e. lithology type data) may be used to
train the
network. The network, using the set of training input, produces its own
output. This
CA 3064241 2019-12-09
43
output may be compared with the targeted output (the known lithology type) and
the
differences are used to modify the weights and biases. Methods of deriving the
changes
that might be made in a neural network or a procedure for modifying the
weights and
biases of a network are known as learning rules. A test set, i.e., a set of
inputs and
targeted outputs (i.e. correct output data) that were not used in training the
network, is
used to verify the quality of the obtained neural network. In other words, the
test set is
used to verify how well the neural network can generalize.
[000160] According to an embodiment, when the neural network 70 as shown in
FIG. 8
is used for lithology prediction, the inputs to the neural network 600, 601,
602, 603 are
the four features from feature extraction as discussed above: rock confined
compressive
strength (CCS), formation drillability coefficient (FDC), dynamic drilling
response
(DDR), and formation shear strength (FSS). According to an embodiment, the
learning
rule used here may be back propagation learning. Backward propagation learning
may be
composed of the following three steps:
[000161] Step 1: forward propagation: the first step is to propagate the input
forward
through the network
a tn+1 = T-71/1+1 (wm+lain bm+1)
for m = 0,1,- = = ,M ¨1
where M is the number of layers in the network. The neurons in the first layer
receive
external inputs: a =p, which provides the starting point for the above
equation. The
outputs of the neurons in the last layer are considered the network outputs: a
= am .
[000162] Step 2: backward propagation: the second step is to propagate the
sensitivities
backward through the network
CA 3064241 2019-12-09
44
sm ¨2Pm (nm )(t ¨a)
Sm =Em (nm )(Wm')sn' , for m = M ¨1,===,2,1
aP [aP aP aP
Where Sm. ¨ = ¨ ¨ , . . ¨ denotes the sensitivity;
ann., anlin anyl an'snm
P (x) = er (k)e(k)=(t(k)¨a(k))T (t(k)¨a(k)) is the approximate performance
index;
- Pm (nim ) 0 = = = 0
0 Pm (n2m 0
(nm)=
0 0 = = = Ern (nm. )
_
arn (n J)
and (n ¨ _______________
an"'
t is the target vector from the training set.
10001631 Step 3: weight and bias updates: finally, the weights and biases are
updated
using the approximate steepest descent rule:
W"' (k +1)=W" (k)¨ as"' (am )T
(k +1)= bm(k)¨ as"1
where a is the learning rate.
10001641 As a result, the outputs are the weighting values associated with
each of
possible lithology types. The largest value of the output weight corresponds
to the
predicted type of lithology.
CA 3064241 2019-12-09
45
[000165] According to an embodiment, the overall processing system for real-
time
lithology prediction 2057 may be as shown in FIG. 9. As shown, the inputs to
the system
may include downhole mechanic measurements 801 (e.g., ROP 2032, WOB 2033,
torque
2036, rotary speed 2034), mud log 2042 (e.g., mud density), and bit record
2041 (e.g., bit
size, PDC cutter size). The downhole mechanics measurements 801 may undergo
pre-
processing 810 before being fed into the feature extraction unit 820. The pre-
processing
810 may include data validation 811 and data smoothing 812. The downhole
mechanics
measurements 801, for example, ROP 2032, WOB 2033, torque 2036, and rotary
speed
2034, are collected at a specific depth. If any of these measurements has a
negative or
zero value, the set of measurements may be designated as invalid data by data
validation
811, and may be removed from processing. Data smoothing techniques 812, such
as
moving average filtering, are then applied to the data to remove noise. The
pre-processed
downhole mechanics measurements 801 along with mud log 2042 and bit record
2041 are
inputs into feature extraction unit 820 to obtain, through further
computation, the four
features of interest, that is rock confined formation shear strength (FSS)
831, dynamic
drilling response (DDR) 832, formation drillability coefficient (FDC) 833, and
compressive strength (CCS) 834. The computation of the four features may be
according
to the equations discussed above. These four features may be used as inputs to
a
multilayer neural network 70 trained as discussed above. The output of the
multilayer
neural network is the predicted lithology type 840.
[000166] Sample prediction results are now provided using the embodiment
discussed
above. A three layer 12-20-12 (tansig-logsig-tansig) feed forward neural
network was
used with the four features for training and testing in Well #1. The predicted
lithology
1002 using the same network is shown in FIG. 10. In the sample prediction
results, the
lithology prediction 1002 had a rate of successful prediction of 82.33% when
compared
to the true lithology 1001.
CA 3064241 2019-12-09
46
10001671 Pore Pressure Estimation and the Trend Fusion Engine
10001681 Pore pressure estimation 2053 may be carried out in three stages:
before
drilling, while drilling and after drilling. Since the drilling fusion engine
uses real-time
drilling-while-processing for drilling optimization and decision support,
while-drilling
pore pressure estimation approaches are employed according to embodiments of
the
invention. According to an embodiment, two quantitative estimation techniques
may be
used: a D-exponent-based approach or a Sigmalog based approach.
10001691 D-exponent approach
10001701 Empirical models of the rotary drilling process have been proposed to
mathematically compensate for the effect of changes in the more important
variables
affecting penetration rate. One of the first empirical models is d-exponent,
which is
defined by:
log
1-
d= ___________________________________________
log( 12W
1000D,
R: Rate of penetration (ft/hr)
N: Rotary speed (rpm)
W: Weight on bit (k-lbf)
Db : Bit diameter (in)
10001711 Rehm and McClendon proposed a modified d-exponent to correct the
effect of
mud-density changes as well changes in weight on bit, bit diameter, and rotary
speed,
which is
CA 3064241 2019-12-09
47
,,, Põ
dmod = cs ¨
Pe
põ: Mud density equivalent to a normal formation pore pressure gradient
Pe: Equivalent circulating density (ECD) at the bit
The units for põ and Pe are the same.
10001721 To estimate formation pore pressure quantitatively, we may use linear
scales
for both drilling depth D and dmod values when constructing a graph. A
straight-line
normal pressure trend line may be generated having intercept (dm, )o and slope
m, thus
the points on this trend line are:
(d,õ, ) =(d_d)o + mD
D: Depth (ft)
10001731 Using the established trend line, there are two different equations
to obtain the
formation pore pressure gradient estimation:
( I ) gp = 7.65 log [(dmod ) ¨ (dmod )1 + 16.5 (lbm/gal)
(2) g = s _ (s _ g )x p ((dn.! )0 )12
[
" \ (d 1 (lbm/gal)
mod L
S: Overburden pore pressure gradient (lbm/gal)
gõ: Normal pore pressure gradient (lbm/gal)
CA 3064241 2019-12-09
48
[000174] The third approach is to use a linear scale for depth D but a
logarithmic scale
for dd . A straight-line normal pressure trend line may be obtained having
intercept
(d.d )0and exponent m:
(drõ,,d)o=(d ) '
modoe
[000175] Using this trend line, the empirical relation for the formation pore
pressure
gradient gp becomes:
(d,),,
gp = gn _________________________________ dmõõ
[000176] Since the dmod parameter considers only the effects of bit weight,
bit
diameter, rotary speed 2034, and mud log 2042, changes in other drilling
variables such
as bit type, bit wear, mud type, may still create problems in interpreting the
obtained
results. In addition, extreme changes in the variables included in the d
calculation
may create big uncertainties. Therefore any new trend may be established for
the
changed conditions. The utility of the d-exponent is diminished when the mud
density is
several pounds per gallon greater than the formation pore pressure gradient.
Because of
the overbalance, the penetration rates may no longer respond significantly to
changes in
formation pressure. Under these conditions, increases in drilling fluid
density may cause
an erroneous shift in the modified d-exponent plot, which may yield higher
pore pressure
readings.
[000177] Sigmalog approach (or formation capture cross section approach)
[000178] The Sigmalog was developed in the Po Valley in the mid-seventies as a
joint
venture between AGIP and Geoservices. The aim was to solve the shortcomings of
the d-
CA 3064241 2019-12-09
49
exponent while drilling overpressured sequences of carbonates, marls and silty
shales in
deep wells. Sigmalog model calculate the rock strength which is also derive
from drilling
parameters weight on bit 2033, rotary speed 2034, rate of penetration 2032,
and bit
diameter. The procedures are:
[000179] Step 1: Calculate total rock strength (i.e., Sigma raw)
0.5N .25
= w __________________________________________
DR0.25
b
R: Rate of penetration (m/hr)
N: Rotary speed (rpm)
W: Weight on bit (ton)
Db: Bit diameter (in)
\FY; = NETT + 0.028 x (7 D
1000 )
D: Depth (m)
10001801 Step 2: Calculate true Sigma õfro
(
1-40-En2)AP2 ..
Fx +
nAP
_{ 075
_________________ x 4_j if .jo7 > 1
640
t'
where n
3.25
if 1
640 x .1a-7
AP: Differential pressure of mud to formation fluid corresponding to the
normal
hydrostatic gradient (kg/cm2)
CA 3064241 2019-12-09
50
n: Factor expressing the time required for the internal pressure of cuttings
not yet
cleared from the bit face to reach mud pressure
[000181] Step 3: Establish a normal trend line Ng on plotted .107 values. At
any
point, AP is calculated as:
2 1¨ _____________________________________ _.)
AP = Nicr,, 1
1 ( 1 /07. )2
, F
10001821 Step 4: The formation pore pressure gradient is calculated using the
following
equation:
AP
gP = p --D
p: Mud weight (ppg)
[000183] Sigmalog may be used in place of d-exponent, but since the method is
not
easy to use it may be ill-suited to unexplored basins. The limitations of
Sigmalog are the
same as for d-exponent. According to an embodiment, its use might be entirely
restricted
to clays and shales.
[000184] Data input and processing
[000185] Table 8 shows a list of data inputs for pore pressure estimation.
CA 3064241 2019-12-09
51
[000186] Table 8. Data inputs for pore pressure estimation.
Para meter Unit Source
Depth m EDR
Rate of penetration 2032 m/hr EDR
Rotary speed 2034 rpm EDR
Weight on bit 2033 kDaN EDR
Pump rate m3/min EDR
Differential pressure kDaN EDR
Mud density kg/m3 Tour sheet
Depth in/out m Tour sheet
Pipe OD mm Tour sheet
Hole size mm Tour sheet
Bit diameter in Bit report
10001871 Calculations performed according to an embodiment of the invention
with the
three sets of sample well data and the results from Well #1 are shown below.
According
to the theory of d-exponent and Sigmalog based pore pressure estimation
approaches, the
trend lines may be established for the same bit and lithology types in normal
pressure
conditions to minimize the effects of other factors which may invalidate the
underlying
techniques. A sequence of samples from bit #6 was taken and the corresponding
lithology
types are listed in Table 9.
[000188] Table 9. Lithology samples from bit # 6 at various depths.
Depth in (m) Depth out (m) Lithology
2205 2375 shale
2375 2385 siltstone
2385 2445 shale
2445 2450 siltstone
2450 2460 shale
2460 2475 sandstone
2475 2500 siltstone
2500 2510 shale
2510 2515 siltstone
2515 2920 shale
2920 2930 sandstone
CA 3064241 2019-12-09
52
2930 2937 siltstone
2937 2946 shale
2946 3010 sandstone
3010 3040 shale
3040 3054 sandstone
3054 3070 shale
3070 3084 siltstone
3084 3100 shale
3100 3115 sandstone
3115 3125 siltstone
3125 3165 shale
3165 3175 siltstone
3175 3200 shale
3200 3210 sandstone
3210 3220 shale
3220 3231.5 siltstone
3231.5 3235.5 shale
[000189] FIGs. 1 1 a, 1 lb and 1 lc illustrate the formation pore estimation
results 2703
using d-exponent 2702 and Sigmalog 2701 methods. Intermediate d-exponent and
Sigmalog calculations are also shown. According to an embodiment, the normal
pore
pressure gradient used in the calculation is a constant 0.4650 psi/feet. Since
the D-
exponent based PP estimation equation is a function of normal pore pressure,
any
variation of the normal pore pressure gradient may change the real-time
estimation
results. According to an embodiment, the software has the capability of
programming the
mud weight to watch if it gets to a predefined value, say, 1 or 1.25 pp, over
the
programmed/estimated PP. In such cases, a flag may be raised thus averting a
potential
safety concern.
[000190] Trend Analysis and Fusion
CA 3064241 2019-12-09
53
[000191] According to an embodiment, the inputs to the trend fusion engine 308
according to an embodiment are grouped into two categories: historical data
and real-time
data.
[000192] Historical data
[000193] According to an embodiment, the historical data inputs as shown in
Table 10
may be estimates based on mining nearby historical drilling well sites (i.e.,
analogous
well data). This is based on the assumption that nearby analogous wells 302.1
to 302.n
may have similar geological properties. Therefore, this data may provide
useful
information to new wells and drilling operations thereof for potential safety
indications.
[000194] Table 10. Historical data input from analogous wells
Predicted lithology (from the NN lithology prediction tool) 310
Estimated pore pressure (gradient) 316
Porosity 311
Permeability 312
Water saturation 313
Predicted bit wear 314
[000195] According to an embodiment, trend fusion may be performed on
historical
data from the historical database 309 from multiple analogous wells 302.1 to
302.3n to
minimize the uncertainties and noise from individual wells in historical 303.1
to 303.3n
drilling data. According to an embodiment, FIGs. 12a, 12b, 12c, 13a, 13b, 13c,
14a, 14b,
14c show trend fusion performed on historical data for Well #1, Well #2, and
Well #3.
FIGs. 12a, 13a, and 14a show D-exponent pore pressure estimation results 2801,
2901,
CA 3064241 2019-12-09
54
and 3001 for Well #1, Well #2, and Well #3. FIGs. 12b, 13b, and 14b show pore
pressure
results 2802, 2902, and 3002 for Well #1, Well #2, and Well #3. FIGs. 12c,
13c, and 14c
show a pore pressure (PP) estimation by linear regression 2803, 2903, and 3003
for Well
#1, Well #2, and Well #3. FIGs. 12c, 13c, and 14c show the D-exponent based
pore
estimation results (dashed curve in the Pore Pressure (PP) estimation plot)
and their
associated trend analysis results (overlayed by a solid line in the PP
estimation plot) by
linear regression (with the associated statistical parameters tabulated in
Table 11) for
Well #1, Well #2, and Well #3, respectively. Since the D-exponent based PP
estimation
method may use a process of line fitting to get the intercept and slope of the
trend line of
D-exponent, the estimated intercept and slope feeds into the PP estimation
equation to
obtain the PP. However, the line fitting on D-exponent may not be carried out
over the
entire drilling depth. Instead, it may be applied only for the same drilling
bit. A line of
best fit is also shown in the D-exponent plot. FIG. 15a, FIG. 15b, FIG. 15c,
and FIG. 15d
illustrate the trend fusion engine 308 according to an embodiment in fusing
pore pressure
estimation results 3101, 3102 and 3103 from three different wells into
optimized pore
pressure estimate 3104. After performing the pore pressure estimation 316 for
each well
site, a centralized fusion algorithm may be carried out to integrate the
results into an
optimized pore pressure estimation with reduced uncertainties. For example,
pore
pressure estimated in Well #1 shows a spike around 2000 meters which is caused
by bit
transition, where the measured mechanical sensors provide certain erroneous
readings
and should not affect the pore pressure estimation. After trend fusion, this
effect may be
effectively smoothed out.
1000196] Table 11. Trend analysis parameters for three wells
Well #1 Well #2 Well #3
SSE 1.7631x109 1.0003 x109 2.097x109
R-Square 0.9463 0.9670 0.9426
RMSE 351.9952 267.2016 383.3287
SSE: the sum of squares due to error
CA 3064241 2019-12-09
55
R-Square: R-square is defined as the ratio of the sum of squares of the
regression (SSR)
and the total sum of squares (SST)
RMSE: root mean square error
[000197] Real-time data
[000198] According to an embodiment, real-time data inputs may be provided
from
ongoing drilling profiles to assist in identifying and addressing safety
concerns while
drilling. They may be conveyed to the trend fusion engine 308 as inputs to
find any
deviations from the static data inputs. As set out in Table 12, these inputs
may include:
[000199] Table 12 Real-time data inputs from the well being drilled
=
Mechanics Lithology Dynamic Pressure
= ROP 2032 = Pore pressure estimation = Mud
weight
= Torque 2036 2053 =
Differential pressure
= WOB 2033 = Lithofacies
estimation 3277
= RPM 3273 = Porosity 2075 =
Hydrostatic pressure
= Pump pressure 3275 =
Permeability 2076 3241
= Predicted bit wear =
Water Saturation 3246 = ECD 2074
2056 = Annular circulation
= Volume 3245
pressure loss 3243
= Bottom hole pressure
3244
[000200] Further real-time data inputs may be included at the discretion of
the operator,
as long as such real-time data may be obtained from the sensors 101 at the
well being
drilled.
CA 3064241 2019-12-09
56
[000201] According to an embodiment, an expert decision engine 328 may be
employed
in the system to address the safety concerns, which may be based on feeding it
both
historical data and real-time data and determining potential safety issues
according to the
embedded expert decision rules.
[000202] Bit Hydraulics, Permeability Estimation, Volume and Capacity, Rotary
Friction Coefficient, Stuck Pipe Calculations and Cost Estimation
[000203] According to an embodiment, drilling bit hydraulics may be another
component in the drilling optimization software.
[000204] Accommodating for drilling bit hydraulics may contribute to the safe,
economic, and efficient drilling of wells and may be a particular concern for
deepwater,
high temperature-high pressure (HTHP), and highly directional wells.
Hydraulics may be
a cause or solution in many common drilling problems. Therefore, improvements
in
hydraulics simulation software may provide better insights into downhole
behaviour.
Moreover, hydraulic parameters may lend themselves to visualization.
[000205] Hydrostatic Pressure Estimation
10002061 Under a static condition, the bottom hole pressure (BHP) is equal to
hydrostatic pressure from the drilling fluid.
[000207] Hydrostatic pressure 3241
CA 3064241 2019-12-09
57
HP, psi = MW , ppg x 0.052 x TVD, ft
MW: mud weight, ppg
7'VD : true vertical depth, ft
[000208] Bottom hole pressure (static) 3244
BHP, psi = HP, psi
[000209] During many drilling operations, the well fluid column contains
several
sections of different fluid densities. The variation of pressure with depth in
this type of
complex fluid column must be determined by separating the effect of each fluid
segment.
In this case, the hydrostatic pressure 3241 at any vertical distance depth may
be
calculated by:
HP, psi = Po, psi + 0.052 x EMWõ ppg x (TVD, ¨TVD,_,), ft
µ=,
P 0 , initial pressure, psi
MW, : mud weight at the i -th segment, ppg
TVD, : true vertical depth at the i -th segment, ft
[000210] Dynamic Pressure Estimation
CA 3064241 2019-12-09
58
[000211] FIG. 15e shows a schematic representation of pressure losses in the
circulating
system 490 having a downhole 499, a drill string 496, a bottom hole assembly
497 and a
drill bit 498.
[000212] In a dynamic circulating system 490, the factors affecting
circulating pressure
may include:
= length of the drill string;
= fluid density or mud weight;
= yield point and plastic viscosity of the fluid;
= hydraulic diameters of the system components;
= available hydraulic horsepower; and
= circulating rate.
[000213] The various pressure losses shown in FIG. 15e, are reflected in the
following
equation:
Stand pipe pressure 495 = Pressure loss in drill string 492 + Pressure loss in
BHA
493 + Pressure loss across the bit 494 + Pressure loss in the annulus 491
[000214] The pressure loss acts opposite to the direction of fluid being
moved;
therefore, the bottom hole pressure on the annulus side at the dynamic
condition may be
determined according to the equation below:
BHP = Hydrostatic pressure + Pressure loss in the annulus
CA 3064241 2019-12-09
59
[000215] Under the dynamic condition, the only effect on the bottom hole
pressure may
be pressure loss in the annulus.
[000216] Downhole hydraulics parameters, including equivalent circulating
density
(ECD) 2074, annular velocity, annular pressure loss 3243, jet nozzle pressure
loss,
hydraulics horsepower, jet velocity, pore pressure gradient, and jet impact
force may be
calculated using the equations below:
Equivalent circulating density 2074
APL, psi
ECD, ppg = + MW , ppg
0.052 x TVD,fi
APL: annular pressure loss, psi
MW: mud weight, ppg
TV]): true vertical depth, ft
Annular velocity
24.5 xQ
AV , ft min =
Dh2 ¨ Dp2
2: circulation rate, gpm
Ph :hole diameter, in
Dp .
: pipe or collar 0.D, in
CA 3064241 2019-12-09
60
Annular pressure loss
APL, psi =1.4327 x10 7 = MW X Length x AV2
Dh2 ¨ Dp2
Jet nozzle pressure loss (Pb)
156.5 x Q2 x MW
Pb, psi =
[N,2 + +N]2
N1 , N2 N3 : jet nozzle sizes,32'd in
System hydraulic horsepower available (SysHHP)
SysHHP = SP, psi xQ
1714
SP: surface pressure, psi
Hydraulic horsepower at bit (HHPb)
HHPb=QxPb
1714
Hydraulic horsepower per square inch of bit diameter
HHPb I sq in.= HHPb x1.27
Db2
Db : bit diameter, in
CA 3064241 2019-12-09
61
Percent pressure loss at bit (%psib)
%psib =¨Pb x100
SP
Jet velocity (Vn)
417.2 xQ
Vn,ft / sec =2
+ A122 + Ar;
Impact force at bit (IF)
IF,1b=MW xVnxQ
1930
Impact force per square inch of bit area (IF/sq in.)
IF I sq in.= 1.27 x IF
Db2
10002171 Trip Margin
10002181 The pressure reduction through swabbing may be relevant when tripping
pipe
because: the balancing pressure is the static mud hydrostatic rather than the
higher
equivalent circulating density, there may be repeated swabbing as each stand
is pulled,
and the "piston" effect may affect every permeable formation in open hole.
CA 3064241 2019-12-09
62
[000219] According to an embodiment, the pressure reduction may be minimized
by
pulling the drill string at a slower speed and keeping mud viscosity as low as
possible,
bearing in mind that hole cleaning and cutting lift properties may have to be
maintained
while drilling.
[000220] A safety or trip margin may be calculated to ensure that the pressure
reduction
does not create an underbalance. In the static condition, the trip margin may
be calculated
by:
TripMargin (psi) = Hydrostatic pressure (psi) ¨ Formation pressure
(psi/ft) x TVD(ft)
[000221] In the dynamic condition, the trip margin may be calculated by:
TripMargin (psi) = APL(psi) + TripMargin(static)(psi)
[000222] Dynamic Pressure Estimation Results
[000223] Dynamic hydraulic parameters, including annular pressure loss,
equivalent
circulatory density and trip margin were calculated using an embodiment of the
invention
for Well #1, Well #2, and Well #3. FIGs. 16a, 17a, and 18a show calculated
annular
pressure loss (APL) 3201, 3301 and 3401 for Well #1, Well #2, and Well #3.
FIGs. 16b,
17b, and 18b show calculated equivalent circulatory density (ECD) 3202, 3302
and 3402
for Well #1, Well #2, and Well #3. FIGs. 16c, 17c, and 18c show a bottom hole
pressure
CA 3064241 2019-12-09
63
(BHP) 3203, 3303 and 3403 for Well #1, Well #2, and Well #3. FIGs. 16d, 17d,
and 18d
show calculated trip margin 3204, 3304, 3404 for Well #1, Well #2, and Well
#3.
[000224] Rotating Friction Coefficient Estimation
[000225] According to an embodiment, friction may also be considered in
drilling
operations as the drill string is tripped in or out, or rotated on or off
bottom. Friction may
affect the solid mechanics calculations, such as torque 2073 and drag 2052, as
well as the
hydraulics calculations, including surge, swab, and hook load estimation
during
cementing. Beyond the conventional torque and drag calculation model such as
described
in C.A. Johancsik et al., "Torque and drag in directional wells-prediction and
measurement", June 1984, SPE 11380 and M. Lesage et al., "Evaluating drilling
practice
in deviated wells with torque and weight data", 1988, SPE 16114, a further
model
incorporating both the sliding and rolling velocities for rotating friction
factor estimation
as described in R. Samuel, "Friction factors: what are they for torque, drag,
vibration,
bottom hole assembly and transient surgc/swarb analysis", Feb. 2010, SPE
128059, and
R.F. Mitchell et al., "How good is the torque/drag model?", 2009, SPE 105068,
may be employed as set out below:
The friction coefficient is defined as:
Coefficient of friction id (COF)
Ff
=
Ff.: frictional force acting at the point of contact
F,, : normal force acting at the point of contact
Date Recue/Date Received 2021-05-31
64
[000226] The drill string may be simultaneously rotated and tripped in or out
and it may
also be simultaneously rotated and reciprocated. The drag force can thus be
given as:
T = ,u, x F,, x r x cosy) x ¨1w1
1V.1
[000227] During rotation, the pipe tends to climb to the high side of the
wellbore, and
reaches an equilibrium angle of 0 = tan' p, where the friction force is
balanced.
Consequently, the force can be described as
Ic 1
T = ____________________ xFnxrx¨
F1-72; Irrs I
where Jç resultant velocity of a contact point on the drill string
pipe rotation velocity
I a)I angular velocity= diameter x 71- x
r : radius of the component
[000228] As a result, the rotary friction coefficients may be estimated from
the above
equation, which will lead to the calculation of P. The entire drill string may
be
segmented into small elements (e.g., 0.2 m per segment), and the calculation
starts at the
bottom of the drill string and proceeds stepwise upward. Each short element of
the drill
string contributes small increments of axial and torsional load. FIG. 19 shows
the forces
acting on a short, slightly-curved element. The net normal force F, is the
negative vector
sum of normal components from the weight, w, and from the two tension forces,
F, and
+ AP; . The magnitude of the normal force is
CA 3064241 2019-12-09
65
F,7 = [(F,Aasinn2 +(FAO+ Wsin)21Y2
where a : azimuth angle at lower end of drill string elements (degree)
Aa : increase in azimuth angle over length of element (degree)
0 : inclination angle at lower end of drill string element (degree)
AO : increase in inclination angle over length of element (degree)
-0- : average inclination angle of element (degree)
[000229] For drill collars and coiled tubing, the outside diameter of the
drill collar or
coiled tubing may be used to calculate the values in the above equation. For
drill pipe,
heavy weight and casing, the outside diameter of the tool joint may be used.
The
calculation of normal force may be done by estimation of Bouyed weight of each
element
together with the real-time inclination and azimuth information.
[000230] FIG. 19 shows a force balance 150 on a drill string element 1501.
Normal
force 1502 results from the combination of the two tension forces, F, 1504 and
Fr + AF;
1503. As shown, F, + AF, 1503, is comprised of component forces q
1505 and a +
Aa 1506 and Fi 1504 is comprised of component forces 0 1507 and a 1508.
[000231] Sample data from Well #1 was applied using an embodiment of the
invention
and resulted in the estimation results as shown in FIGs. 20, 21, and 22. FIGs.
20 and 21
display the measured real-time drilling data, torque 1601 (FIG. 20) and WOB
1602 (FIG.
21). FIG. 22 displays the resulting estimated rotary friction factor 1603 for
the sample
data.
CA 3064241 2019-12-09
66
10002321 Permeability Estimation
[000233] If neutron-density log 2046 data is available, the porosity 2075 may
be taken
from the compensated neutron porosity value. The permeability 2076 may be
calculated
based on empirical porosity-permeability correlation models as described in A.
Aziz et
al., "Permeability prediction: core vs log derived values", 1995 International
Conference
on Geology, Geotechnology and Mineral Resources of Indochina, B. Balan et al.,
"State-
of-the-art in the permeability determination from well log data part 1: a
comparative
study, model development", 1995, SPE 30978, S. Mohaghegh et al., "State-of-the-
art in
permeability determination from well log data part 2: verifiable, accurate
permeability
predictions, the touch-stone of all models", 1995, SPE 30979, U. Ahemd et al.,
"Permeability estimation: the various sources and their interrelationships",
1991, SPE
19604 and A. Timur, "An investigation of permeability, porosity and residual
water
saturation relationships for sandstone reservoirs", The Log Analyst 1968.
The model may include an irreducible water saturation
(S) value, as detailed below.
Kv2 =10002 0¨ s") (ref : Coates 1981)
K : permeability (md)
0 : porosity (fractional)
: irreducible water saturation (fractional)
10002341 According to an embodiment, FIG. 24 shows the calculated permeability
1702
based on the neutron porosity 1701 shown on FIG. 23 using an average Sw, value
of 21%
Date Recue/Date Received 2021-05-31
67
for Well #1. Further, real-time Sw, may be estimated if a neutron magnetic
resonance log
is available. That is, the water saturation may be either obtained from direct
measurement
data, or from the estimation using the neutron magnetic resonance log data.
10002351 Volumes, Capacities & Displacements
10002361 According to an embodiment, the volumes 3245, capacities and
displacements
calculations may provide fundamental indications leading to potential safety
issues.
These calculations, together with the permeability 2076 & porosity 2075,
lithology
prediction 2057, pore pressure estimation 2053, may serve as the combined
inputs to the
expert knowledge system to address a variety of safety issues during drilling.
10002371 According to an embodiment, the system may include pressure control
calculations, in which the kill sheets and its related calculations may be
carried out. These
may involve the following calculations:
= Drill string & annulus capacities
= Drill string & annulus volumes
= Total volumes
= Surface to bit strokes & time
= Bit to shoe strokes & time
= Maximum mud weight
= Maximum allowable annulus surface pressure
= Kill mud
= Pressure step down
CA 3064241 2019-12-09
68
10002381 Table 13 lists the calculations in the kill sheet for the pressure
control
according to an embodiment of the present invention. The calculation was
performed
with the three sets of sample well data and the results from Well #1 are
provided as an
example. The pre-recorded data for the calculation may be read from the
oilfield
instrumentation and data acquisition systems tour sheet and drilling software
database
software. The snapshots of the relevant data are shown in FIGs. 24b, 24c, 24d,
and 24e.
FIG. 24b shows the front page summary of a tour sheet for Well #1 3501, FIG.
24c shows
the drilling assembly portion of a tour sheet for Well #1 3601, FIG. 24d shows
the mud
record and circulation portions of a tour sheet for Well #1 3701, and FIG. 24e
shows the
reduced pump speed portion of a tour sheet for Well #1 3801. Table 14 shows
the casing
summaries read from the geology report. The calculation results are shown in
FIGs. 24f,
24g, 24h, and 24i. FIG. 24f shows a user interface display of calculations for
kill sheet #1
3901, FIG. 24g shows a user interface display of calculations for kill sheet
#2 4001, FIG.
24h shows a user interface display of calculations for kill sheet #3 4101 and
FIG. 24i
shows a user interface display of calculations for kill sheet #4 4201.
10002391 Table 13. Volumes, capabilities & displacements calculation
Prerecorded data
Original mud weight (OMW) [ppg]
Measured depth (MD) [ft]
Kill rate pressure (KRP) [psi] @ rate [spm]
Drill string volume Ibbl]
Drilling pipe capacity [bbl/ft]x length [ft]
1-1WDP capacity [bbl/ft] x length [ft]
Drill collar capacity [bbl/ft] x length [ft]
Annular volume [bbl]
Drill collar/open hole capacity [bbl/ft] xlength [ft]
Drill pipe/HWDP open hole capacity [bbl/ft] x length [ft]
Drill pipe/casing capacity [bbl/ft]xlength [ft]
CA 3064241 2019-12-09
69
Pump data
Pump output [bbl/stk]g efficiency [%]
Surface to bit strokes [stk]=Drill string volume [bbl]/pump output [bbl/stk]
Bit to casing shoe strokes [stk]=Open hole volume[bbl]/pump output[bbl/stk]
Bit to surface strokes [stk]=Annulus volume[bbl]/pump output[bbl/stk]
Kick data
SIDPP [psi]
SICP [psi]
Pit gain [bbl]
True vertical depth [ft]
Other critical calculations
Kill weight mud (KWM) [ppg] = SIDPP [psi]/0.052/TVD [ft] + OMW [ppg]
Initial circulating pressure (ICP) [psi] = SIDPP [psi] + KRP [psi]
Final circulating pressure (FCP) [psi] = KWM [ppg] x KRP [psi]/OMW [ppg]
Psi/stroke [psi/stk] =( ICP [psi] ¨FCP [psi])/strokes to bit
[000240] Table 14. Casing summary
Casing type Casing size Landed depth Hole size
Surface 244.5 612.00 311.0
Intermediate 177.8 1992.00 222.0
Production 114.4 3314.00 156.0
[000241] Cost Estimation
[000242] According to an embodiment, one aspect of the software is to
recommend
drilling procedures which may result in the successful completion of the well
as safely
and inexpensively as possible. The drilling engineer may make recommendations
CA 3064241 2019-12-09
70
concerning routine rig operations such as drilling fluid treatment, pump
operation, bit
selection, and any problems encountered in the drilling operation. In many
cases, the use
of a drilling cost equation may be useful in making these recommendations. The
decision
concerning which bit to use often based on some performance criteria, such as
total
rotating hours, total footage, or maximum penetration rate. Other times, the
least
expensive bit is chosen. This approach may be satisfactory in areas where
practices and
costs are constants but it may not be satisfactory where drilling costs are
changing, and
drilling practices and bit selection vary. The usual procedure may be to break
the drilling
costs into (1) variable drilling costs and (2) fixed operating expenses that
are independent
of alternatives being evaluated. Selection of the bit best suited for a
specific use may be
further complicated by the variable performances and prices of the many types
of bits.
[000243] According to an embodiment, bit selection may be based on achieving
the
minimum cost-per-foot. In this way, it may be possible to achieve an optimum
relationship between penetration rate, bit footage, rig cost, trip time and
bit cost. A
common application of a drilling cost formula may be to evaluate the
efficiency of a bit
run. A large fraction of the time required to complete a well may be spent
either drilling
or making a trip to replace the bit. The total cost required to drill a given
depth AD may
be expressed as the sum of the total rotating time during the bit run, tb ,
the non-rotating
time during the bit run, t, and trip time, tt . The cost-per-foot as related
to these
variables may be determined by the equation:
CI =Cb _________________________________________ Cr(tb-Ft, + t,
AD
where
Cf is drilled cost per unit depth,
Cb is the cost of bit, and
Cr is the fixed operating cost of the rig per unit time independent of the
alternatives
being evaluated.
CA 3064241 2019-12-09
71
[000244] The following are sample calculations of cost-per-foot for bits used
in Well
#1, Well #2 and Well #3 according to an embodiment. FIGs. 24j, 24k, and 241
display
copies of the bit record of Well #1 4202, the bit record of Well #2 4203 and
the bit record
of Well #3 4204, respectively, which has the information for each bit run
including the
bit type, hour of running, average ROP, and depth. Tables 15, 17, and 19 list
the cost
information read from the drilling software. The resulting cost-per-foot
analysis results
are shown in Tables 16, 18, and 20.
[000245] Table 15. Cost information of Well #1
Bit # 1 2 3 4 5 6 7 8
Bit cost $ 2500 8500 14303 7920 8500 9500 14520
14520
Rig cost/hr $ 400 686 686 686 686 686 686 686
[000246] Table 16. Cost-per-foot calculation results for Well #1
Bit # 1 2 3 4 5 6 7 8
Cost-per-foot $ 49.375 45.00 55.017 40.57 104.00 78.49 731.73 1067.86
[000247] Table 17. Cost information of Well #2
Bit # 1 2 3 4 5 6 7 8
Bit cost $ 1450 8130 2450 15920 9500 9500 4000 9500
Rig cost/hr $ 645 646 646 646 646 646 646 646
[000248] Table 18. Cost-per-foot calculation results for Well #2
Bit # 1 2 3 4 5 6 7 8
Cost-per-foot $ 55.8 57.15 103.75 54.74 194.19
57.78 321.71 160.13
[000249] Table 19. Cost information of Well #3
CA 3064241 2019-12-09
72
B it # 1 2 3 4 5
Bit cost $ 850 11500 16870 10900 9500
Rig cost/hr $ 668 686 686 686 686
[000250] Table 20. Cost-per-foot calculation results for Well #3
Bit # 1 2 3 4 5
Cost-per-foot $ 5941.93 41.74 45.68 57.36 382.88
[000251] By using this cost-per-foot formula, a bit may be pulled when it no
longer
becomes economical to drill with it. When the drilling time-per-foot begins to
increase,
and the footage drilled-per-time decreases, the cost-per-foot will begin to
increase. When
that increase is first noticed, it may be economical to pull the bit. But
since this drilling
cost function ignores risk factors, the results of the cost analysis may also
be tempered
with engineering judgment. Reducing the cost of a bit run may not necessarily
result in
lower well costs if the risk of encountering drilling problems such as stuck
pipe, hole
deviation, hole washout is greatly increased.
[000252] Cost-per-foot calculations including downhole motors
[000253] When downhole motors are used and charged at an hourly rate, they may
be
included in the cost equation:
r, = Cb+Cr(tb+tc+tt)+Cm=tb
`-'1 AD
where Cm refers to the cost per hour of drilling
[000254] Target cost-per-foot and target ROP
CA 3064241 2019-12-09
73
[000255] According to an embodiment, the above cost-per-foot calculations may
also
be used in generating performance proposals. A target cost-per-foot may be
calculated
based on information from offset well performance. If the target is met or
exceeded, the
motor may be charged at normal list price; if it is not, then the hourly rate
may be
adjusted downwards (to an agreed minimum) until the target figure is reached.
Before a
target cost-per-foot is proposed to the operator, the user should be
reasonably sure that
the operator will be able to achieve a minimum ROP. This ROP may be the drill
rate
needed to meet both the proposed cost-per-foot figure and the pricing
structure. After
calculating this target ROP, it may be determined whether the target cost-per-
foot is
attainable.
[000256] From the cost-per-foot calculations including downhole motors, there
is
provided:
Cf = AD ¨C6 ¨ C, (t, + t, )
th ¨ _____________________________________________
Cr + Cõ,
ROP =AD
Id
[000257] It should also be noted that all terms in the preceding two formulas
are targets.
For example, Cf is the target cost-per-foot agreed upon with the user.
[000258] Expert Decision Engine
[000259] According to an embodiment, the decision making for expert system
recommendations for safety concerns while drilling may be based on inputting
both
historical data from the historical database 309 and adjusted real-time
drilling parameters
329 to the expert decision engine 328 which applies a set of expert decision
rules in
CA 3064241 2019-12-09
74
accordance with the decision trees set out below. According to an embodiment,
the expert
decision engine 328 may include six decision trees: mud volume, porosity,
permeability,
stuck pipe, bit wear and environmental. Further expert decision trees may be
added
according to the format described below in the discretion of the operator. The
expert
decision recommendations may appear as prompts to the user during operation of
the
client or standalone software through the graphical user interface. Moreover,
as discussed
by example below, the user may be warned through the graphical user interface
of a
danger level: green indicates normal, yellow indicates warning, and red
indicates
dangerous. At the yellow or red danger levels, the graphical user interface
display may
also flash to alert the user of the issue.
[000260] Mud Volume
[000261] According to an embodiment as shown in FIG. 25, the mud volume
decision
tree 4300 monitors for a change of the mud volume parameter in real time 4302
and if the
mud volume parameter 4301 is greater or equal to than a defined threshold
4303, the
pump is stopped 4304, a flow check is performed 4305, a visual check of the
tank for
input fluid, whole mud, water, oil, and for flow in the drill pipe and annulus
4306 may
occur, volume gain may be established 4307 and the reason therefor such as
potential
blowout or need to release pressure 4308 or potential loss in circulation
4310, and a
procedure in regards of potential blowout or need to release pressure 4309 or
a procedure
developed by the operator or contractor in regards of potential loss in
circulation 4311
may be presented to the user.
[000262] According to an embodiment, the defined threshold may be set
according to
the conditions as follows: 1) if losses are less than 20 barrels per hour, or
if company
guidelines permit losses of 20 barrels per hour while drilling with a water
base mud
continue drilling; if losses are greater than 20 barrels per hour, try to heal
or fix the loss
CA 3064241 2019-12-09
75
zone while drilling using 2 to 6 pounds per barrel loss circulation material
if there are
partial losses. For more extreme losses, but not full losses, stop, mix a tank
of whole mud
with lost circulation material of 6 plus pounds per barrel; if full losses
arise, stop, and
evaluate options to fix or stop loss circulation: a. set a plug. There are
various types (Di-
Seal, GUNK etc.), b. set cement plug across the loss zone, c. drill ahead
without
circulating (drill ahead blind).
10002631 Porosity
10002641 According to an embodiment as shown in FIG. 26, the porosity decision
tree
4400 monitors the porosity 2075 parameter for significant deviation from the
historical
reference value. If the deviation from the historical value is greater or
equal to 60% 4402,
expert decision engine 328 determines whether there has been a change in
lithology 4405
by comparing the predicted lithology type with a lithology sample collected at
a
particular depth. Once a sample is collected, the resulting variable may be
imported into
the program via a user interface for comparison. If there has been a change in
lithology
then the expert decision engine 328 panel may flash litho-red 4412, which
reflects both
porosity change and lithology change (whereas red reflects porosity variations
against the
historical porosity records only) and prompt the user to contact the drilling
engineer and
geologist to re-evaluate the drilling parameters for potential hole problems
4411. If the
deviation from the historical value is not greater or equal to 60%, but
greater or equal to
40% 4403, it is determined whether there has been a change in the lithology
4405. If
there has been a change in the lithology, the expert decision engine 328 panel
may flash
litho-red and prompt the user to contact the drilling engineer and geologist
4412 to re-
evaluate the drilling parameters for potential hole problems 4411. If the
deviation from
the historical value is not greater or equal to 40%, it is determined whether
it is greater or
equal to 20% 4404, and if it is, then it is determined whether there has been
a change in
the lithology 4405. If there has been a change in lithology, the expert
decision engine 328
panel may flash litho-red and yellow alternately and prompt the user to
contact the
CA 3064241 2019-12-09
76
drilling engineer and geologist 4414 to re-evaluate the drilling parameters
for potential
hole problems 4411. A person skilled in the art will appreciate that different
thresholds
than 60%, 40% and 20% may be used according to embodiments of the invention in
the
discretion of the operator or user.
[000265] Permeability
[000266] According to an embodiment as shown in FIG. 27, the permeability
decision
tree 4500 monitors the permeability 2076 parameter for significant deviation
from the
historical reference value 312. If the deviation from the historical value is
greater or equal
to 60% 4502, it is determined whether there has been a change in lithology
4506 as
described above, and if there has been a change in lithology then the expert
decision
engine 328 panel may flash litho-red and prompt the user to contact the
drilling engineer
and geologist 4512 to re-evaluate the drilling parameters for potential hole
problems
4511. If the deviation from the historical value is not greater or equal to
60%, but greater
or equal to 40% 4503, it is determined whether there has been a change in the
lithology
4506 in the same manner as described for porosity above. If there has been a
change in
the lithology, the expert decision engine 328 panel may flash litho-red and
prompt the
user to contact the drilling engineer and geologist 4512 to re-evaluate the
drilling
parameters for potential hole problems 4511. If the deviation from the
historical value is
not greater or equal to 40%, it is determined whether it is greater or equal
to 20% 4504
and if yes, it is determined whether there has been a change in the lithology
4506. If there
has been a change in lithology, the expert decision engine 328 panel may flash
litho-red
and yellow alternately and prompt the user to contact the drilling engineer
and geologist
4513 to re-evaluate the drilling parameters for potential hole problems 4511.
A person
skilled in the art will appreciate that different thresholds than 60%, 40% and
20% may be
used according to embodiments of the invention in the discretion of the
operator or user.
CA 3064241 2019-12-09
77
[000267] Stuck Pipe
[000268] According to an embodiment as shown in FIG. 28, the stuck pipe 4600
decision tree monitors the stuck pipe decision parameters 4601 such as hole
parameters
4602, directions 4603, volumes 3245, filter cake/mud cake 3254, mud density
3276, and
bottom hole assembly 4607 parameters for deviation from the historical
reference value
4608. If there is deviation, i.e. if you get stuck on the current well where
they did not get
stuck on the historical well, then a determination of the differences may be
made to aid in
decision making; the expert system engine 328 determines whether circulation
is possible
4609. If yes, then there may be stuck pipe due to differential sticking, hole
geometry
(keyseat, dogleg, etc.) or mechanical sticking (junk in the hole or casing
seat, etc.) 4611
and the expert decision engine 328 then reviews the difference in depth,
formation,
lithofacies, porosity, permeability, mud, filter cake, density, ROP, RPM, and
WOB 4613,
and the historical operations in the database 4615 and may analyze the
probability of
stuck, depth, free point, size and density of spotting pill 4617, and estimate
optimized
time and cost for the current operation 4619. If circulation is not possible,
there may be a
stuck pipe due to pack off 4610, and the expert decision engine 328 then notes
the
difference in depth, formation, lithofacies, porosity, permeability, mud,
filter cake,
density, ROP, RPM and WOB 4613, and the historical operations 4615 in the
database
and may analyze the probability of stuck pipe, depth, free point, size and
density of
spotting pill 4617, and estimate optimized time and cost for the current
operation 4619.
[000269] The analysis of the probability of getting stuck is made with regard
to whether
there is circulation or no circulation and the directionality during getting
stuck: drilling
ahead, tripping in the hole, tripping out of the hole, with drill string or
electric logging
tools. When drilling, the free point is analyzed via an equation (provided a
few
paragraphs below), which conveys the depth of stuck pipe. While tripping, it
is evident
(number of pipe, etc.) or may be determined from sensors 101. The size of the
spotting
pill may be done by rule of thumb. According to an embodiment, the spotting
pill may be
CA 3064241 2019-12-09
78
approximately 50 barrels. According to an embodiment, the density of spotting
pill may
be 1 to 1.5 pounds per gallon more than the drilling mud.
[000270] The expert decision engine 328 may therefore determine stuck pipe
situations
while drilling. The free point constant for the stuck pipe may be determined
from the
depth at which the pipe is stuck and the number of feet of free pipe may be
estimated by
the drill pipe stretch table (see Table 21).
[000271] Table 21. Drill pipe stretch table
ID(in.) Nominal ID Wall Area Stretch constant
Free point
Weight (lb/ft) (in) (sq in.) (in/klb/kft) constant
2-3/8 4.85 1.995 1.304 0.30675 3260.0
6.65 1.815 1.843 0.21704 4607.7
2-7/8 6.85 2.241 1.812 0.22075 4530.0
10.40 2.151 2.858 0.13996 7145.0
3-1/2 9.50 2.992 2.590 0.15444 6475.0
13.3 2.764 3.621 0.11047 9052.5
15.5 2.602 4.304 0.09294
10760.0
4.0 11.85 3.476 3.077 0.13000 7692.5
14.00 3.340 3.805 0.10512 9512.5
4-1/2 13.75 3.958 3.600 0.11111 9000.0
16.60 3.826 4.407 0.09076
11017.5
18.10 3.754 4.836 0.08271
12090.0
20.00 3.640 5.498 0.07275
13745.0
5.0 16.25 4.408 4.347 0.09145 10935.0
19.50 4.276 5.275 0.07583
13187.5
5-1/2 21.90 4.778 5.828 0.06863
14570.0
24.70 4.670 6.630 0.06033
16575.0
6-5/8 25.20 5.95 6.526 0.06129
16315.0
[000272] The length of free pipe may be calculated as follows:
CA 3064241 2019-12-09
79
stretch (in.) x free point constant
Feet of free pipe ¨ _________________________________________
pull force (10001b)
[000273] The free point constant (FCF) may be determined for any type of steel
drill
pipe if the outside diameter and inside diameter are known:
FPC = As x2500
where A, denotes the pipe wall cross sectional area (sq. in)
[000274] Bit wear
[000275] According to an embodiment as shown in FIG. 29, the bit wear
prediction
decision tree 4700 monitors the current bit wear prediction 2056 and
historical data for
the same bit 314. The bit manufacturer 4702, IADC (International Association
of Drilling
Contractors) code for bit type 4703, bit size 4704, recommended bit hours
4705, depth in
4706, time in 4707, are checked against historical data for same bit run 4709,
checked
against historical data for run time 4710, and checked against historical data
for bit total
depth 4711 for deviation from the historical data 4713. If there is a
deviation from
historical data, i.e. when all or some of the parameters are different from
the historical
database for the same bit, and there is a rate of penetration (ROP) decrease
4712, then it
is determined whether the formation changed 4714, and if no, then it is
determined
whether there has been a change in the drilling parameters 4719. If yes, the
parameters
are reviewed and changed as required to maintain rate of penetration, if not
the expert
system engine prompts to trip for bit 4720. If the formation has changed, i.e.
which is
determined in the same way as a lithology change as discussed above, if the
formation is
harder 4718, then the bit is checked as to whether it is a hard formation bit
4721, if it is
then is it checked to determine whether there has been a change in drilling
parameters
4722 and if not, the expert system engine prompts to trip for bit 4723 to
change the bit. If
yes, the parameters are reviewed and changed as required to maintain the rate
of
CA 3064241 2019-12-09
80
penetration 4720. If there has been a rate of penetration decrease, the expert
decision
engine 328 also determines if RPM (revolutions per minute, which is a measure
of bit
rotary speed) has changed 4715. If RPM has not changed, then the expert
decision engine
328 prompts to trip the bit if it is close to the bit manufacturer's
recommended hours
4728. If the RPM has changed, then the reason is reviewed 4730 and the expert
decision
engine 328 determines if any changes will make a difference 4727. If changes
to the
drilling parameters do not make a difference then the expert decision engine
328 prompts
to trip the bit 4723. If the changes make a difference, then the expert
decision engine 328
continues to monitor that bit hours are under manufacturer's recommended hours
4725. If
there has been a rate of penetration decrease, the expert decision engine 328
also
determines if WOB has changed 4716. If WOB has not changed, then the expert
decision
engine 328 trips the bit if close to the bit manufacturer's recommended hours
4728. If the
WOB has changed, then the reason is reviewed 4730 and the parameters may be
optimized to increase ROP. If an increase is not possible, the system trips
for bit 4729. If
there has been a rate of penetration decrease, the expert decision engine 328
also
determines if the drill string is vibrating 4717. If the drill string is
vibrating, the geology
is reviewed 4734 and the system optimizes the parameters to minimize vibration
and
increase the ROP, and if an increase is not possible, the system trips for bit
4731. If the
expert decision engine 328 determines that the drill string is not vibrating,
the system
continues drilling 4733. The geology review 4734 may be carried out by
checking the
current lithology sample to see if there is any evidence of a different
geology than that
indicated by the predicted lithology. For example, is there any chert or other
hard or soft
material at this depth that would indicated this formation is such that
vibration would
occur. Drilling parameters may be optimized by changing pump rates (stokes per
minute,
SPM), RPM, and WOB to see if any minor variation causes an improvement.
10002761 Environmental
CA 3064241 2019-12-09
81
[000277] According to an embodiment as shown in FIG. 30, the environmental
factors
decision tree 4800 may review the environmental factors 4801 to determine if
there is
deviation from historical values 4802. The environmental factors 4801 reviewed
may
include what type of fluid that was spilled (e.g. water based mud, oil based
mud,
formation fluid, engine fluid, gas or diesel?), what was the volume of spilled
fluid, how
sensitive are the surrounding areas to the fluid (e.g. dry land or water,
lake, pond, swamp,
river, or creek, or underground water table?), is the well in an
environmentally sensitive
area, such as a 'wet lands', a 'national park', etc., and other environmental
factors
conceived of by the drilling contractor and operator. If there is a deviation,
the expert
decision engine 328 determines how these differences differ from the
historical values
4803, and then the expert decision engine 328 may compare the operations at
the time of
the hazard with historical wells in the area or other wells in the drilling
area that have had
similar incidents and the potential outcomes are analyzed 4804. The potential
outcomes
are analyzed by comparing them to the historical well problems in the same
area, if any,
or to outcomes given the contingency plans and other wells in the drilling
area. The
expert decision engine 328 then provides probabilities of the potential
outcomes in
different operations for planning 4805 and the drilling operations are then re-
evaluated to
make changes 4806. The probabilities of outcomes are generated by having more
than
one of these incidents, problems, concerns or issues occurring in the area on
or by other
rigs or in the historic well data. When the contingency plan is developed by
the operator
or contractor, they may research this data.
[000278] Software Implementation
[000279] According to an embodiment as shown in FIG. 31, a graphical user
interface
(GUI) 5001 may be provided for the software. According to an embodiment, the
functions in the prediction engine 20, the trend fusion engine 308 and the
expert decision
engine 328 may be integrated into the software so that the full set of
relevant drilling
parameters, prediction tools, optimization tools, analytic tools and the
expert decision
CA 3064241 2019-12-09
82
system are available to the user through a common interface. According to an
embodiment, the GUI 5001 may be made up of three subpanels which govern
different
aspects of the software's functionality. According to the embodiment as shown
in FIG.
31, those subpanels are a Historical Reference Data Panel 5002, a While-
Drilling Data
Panel 5003 and Expert Decision Panel 5004.
10002801 As shown in FIG. 31, the Historical Reference Data panel 5002 may
display
the historical drilling data obtained from the nearby analogous wells as
reference
information. The Expert Decision panel 5004 may allow comparison between the
real-
time drilling parameters and operations with the historical drilling data in
the Historical
Reference Data panel 5002. Since the reference information in the Historical
Reference
Panel 5002 is time/depth dependent, there may be a slider and text box for
users to
provide the depth and the information may be retrieved accordingly. The
reference data
may comprise information relating to the drill bits including bit type, depth
in/out,
diameter, manufacturer, manufacturer recommended hours, historical hours of
running
and bit wear status when pulled out; information relating to lithology
including lithology
type, estimated port pressure gradients, normal pore pressure for the region,
porosity
2075, permeability 2076 and water saturation 3246; information relating to mud
records
including mud weight, mud type, viscosity, and PH; and information relating to
the
bottom hole assembly (BHA) including assembly components, outer diameter,
inner
diameter, pipe length, and weight.
10002811 The While-Drilling Data panel 5003 may show the real-time drilling
data
either from the real-time data acquisition system or from the data simulator.
The while-
drilling data may be loaded from the drop-down list on the top of the While-
Drilling Data
panel 5003 if it is from the real-time data acquisition system. In this
scenario, the start
button may initiate the data acquisition result in the plotting of the real-
time drilling data
as well as generation of the associated engine outputs. Alternatively, the
simulator may
be enabled when no real-time data is loaded. In this case, the start button
may initiate the
CA 3064241 2019-12-09
83
simulator to plot the simulated data in an animated fashion. The simulator may
simulate
various cases of potential problems in the drilling process which may not
appear in the
real-time data. The drilling fusion prediction engine 20 and the expert
decision engine
328 may be triggered immediately after the simulation or loading starts. The
views of the
historical data panel 5002 and the expert decision panel 5004 may be enabled
or disabled
by clicking the check boxes on top of the while-drilling data panel 5003.
[000282] Also on top of the while-drilling data panel 5003, a drop-down list
may be
provided for the user to switch between five different categories, which may
represent
five different views of current drilling parameters (Table 22). All plots for
different
categories may be synchronized. After clicking the start button, plotting may
be executed
in real-time so that switching between different categories does not interfere
with the
plotting processing.
[000283] Table 22. Categories in while-drilling information display
Category Parameters
Lithology (from logging), Lithology (from prediction), Pore
Main Display pressure estimate (from Trend Fusion Engine), mud
volume,
mud density, predicted bit wear
M W D ROP, WOB, RPM, torque, flow
Hydrostatic Pressure Hydrostatic pressure, BHP (static)
Dynamic Pressure APL, ECD, BHP (circulation), Trip margin
L WD Gammy ray, tension, neutron porosity, water
saturation, filter
cake
Kill Sheet Capacity and volume calculations
[000284] FIGs. 32, 33, 34, 35, 36, 37, and 38 show screen shots for these five
categories: GUI-main display 5100, GUI-MWD (metric units) 5200, GUI-MWD
(imperial units) 5300, GUI-Hydrostatic pressure 5400, GUI-Dynamic pressure
5500,
GUI-LWD 5600, GUI-Kill sheet 5700. FIGs. 33 and 34 belong to the same category
(i.e.,
MWD) but with a different unit system. The unit conversion may be enabled by
clicking
CA 3064241 2019-12-09
84
the switch buttons on the bottom of the GUI. According to an embodiment, the
default
unit system may be the metric unit system. Real-time data may be plotted in
red and the
referencing historical data, if applicable, may be plotted in blue or green.
The category
kill sheet may retrieve the current drilling assembly information to calculate
the
capacities and volumes along the drill string and pipe, which may be displayed
in the first
GUI window. The GUI may also allow users to continue calculation by inputting
more
information in the second, third and fourth GUI windows.
10002851 The Expert Decision panel 5004 (right panel in FIG. 31) may list the
status
reports from the six expert decision engines. According to an embodiment,
different
drilling statuses may be indicated by using different colours on the top of
the panel, for
example, Normal (green), Warning (yellow), and Dangerous (red). The status may
be
continuously updated to indicate an ongoing problem while drilling as well as
the
suggested actions to take. Similar to the Historical Reference Data panel
5002, the Expert
Decision panel 5004 may also enable users to examine the drilling parameters
in detail at
a specific depth.
10002861 According to an embodiment, the warning and dangerous statuses may
trigger
the system to be stopped immediately so that the drilling engineers or
operator may
examine the problem. Normal status would not interrupt the drilling as all
parameters
would have been determined to be within the safety requirement. In the cases
of a
warning status or dangerous status, the current potential issues may be shown
in the
status window for review by the user. The associated problematic parameters
may also be
flashed in their corresponding displays at the current drilling depth. All
potential
problems as determined by the expert decision engine may also be recorded in a
log for
subsequent viewing.
CA 3064241 2019-12-09
85
[000287] FIGs. 39, 40, 41, 42, and 43 show the expert decision engine prompts
for mud
volume issues for Well #1 5800 and Well #2 6000, a porosity issue for Well # 2
6200, a
bit wear issue for Well #2 6100 and a potential stuck pipe 5900 for Well #1
according to
an embodiment. As the potential issues are determined, further suggestions
towards the
underlying drilling parameters may be displayed in a pop-up window.
[000288] According to an embodiment, optimized drilling parameters 2072, as
shown in
FIG. 3, from the drilling optimization engine 205 may be used by the control
software of
the drill string to adjust drilling operations in view of the real-time
predictions or expert
system engine recommendations. Drilling operations may be adjusted manually,
by a
human operator viewing the optimized drilling parameters 2072, according to an
embodiment, on a standard computer display, and acting on them to control
drilling
operations; alternatively, the optimized drilling parameters 2072 may be
automatically
fed into the control software for operating a drilling rig, subject to a
manual override by a
human operator. According to an embodiment, the optimized drilling parameters
2072
may be used to update the geological model and trajectory database to ensure
their
contents reflect the real-time observations and measurements, or to replace
planned
parameter values with actual measurements in the drilling model. The
prediction-
optimization control may form a closed feedback loop for drilling operation,
which may
reduce the overall drilling cost and risk.
[000289] Embodiments of the invention, or aspects thereof, may be provided in
a
computer program comprising computer readable instructions for execution on a
computer. The computer program is storable on any suitable computer storage
medium so
as to comprise a computer program product. Such a computer program may provide
a
graphical user interface which may allow the drilling operator to view and
override
adjusted drilling parameters as desired.
CA 3064241 2019-12-09
86
10002901 The present invention may be embodied in other specific forms without
departing from the spirit or essential characteristics thereof. Certain
adaptations and
modifications of the invention will be obvious to those skilled in the art.
Therefore, the
presently discussed embodiments are considered to be illustrative and not
restrictive, the
scope of the invention being indicated by the appended claims rather than the
foregoing
description, and all changes which come within the meaning and range of
equivalency of
the claims are therefore intended to be embraced therein.
CA 3064241 2019-12-09
