Note: Descriptions are shown in the official language in which they were submitted.
PROSPECT ASSESSMENT AND PLAY CHANCE
MAPPING TOOLS
CROSS-REFERENCE TO RELATED APPLICATION
[0001]
BACKGROUND
[0002] A prospect includes an area of exploration in which
hydrocarbons have been predicted to exist in economic quantity. A
prospect may include an anomaly, such as a geologic structure or a
seismic amplitude anomaly that is recommended by explorationists for
drilling a well. Justification for drilling a prospect is made by assembling
evidence for an active petroleum system, or reasonable probability of
encountering reservoir-quality rock, a trap of sufficient size, adequate
sealing rock, and appropriate conditions for the generation and migration
of hydrocarbons to fill the trap. A single drilling location is also called a
prospect, but the term is generally used in the context of exploration:
exploration prospect assessment (EPA), hereinafter referred to as
Prospect Assessment (PA).
[0003] A group of prospects of a similar nature constitutes a play.
Thus, a play is a region in which hydrocarbon accumulations or prospects
of a given type may occur: a conceptual model for a style of hydrocarbon
1
CA 2779207 2018-10-23
i
CA 02779207 2012-06-08
,
IS10.0807-CA-NP
accumulation used by explorationists to develop prospects in a basin,
region, or trend and used by development personnel to continue
exploiting a given trend. A play (or a group of interrelated plays) may
occur in a single petroleum system.
[0004] Common Risk Segment Mapping (CRSM) is an exploration
method to define areas of low exploration risk. Certain companies
employ some method of play fairway mapping and common risk
mapping. These may be used to define play Chance of Success (play
COS) at the play level and local prospect Chance of Success (prospect
- COS) at the prospect level. "Traffic light" maps of red, yellow and
green
for high, moderate and low risk areas are examples of displays in the
industry. CRSM maps that combine the geological elements that
determine the Chance of Success of plays and prospects may be further
combined with maps that delineate other risk elements that affect the
overall prospectivity in an area, for example, distance from shore, water
depth, accessibility to acreage, and so forth.
[0005] Play-based exploration may have a different focus than
prospect-based exploration. Beyond the traffic light maps, there may be
maps that show shared/play-specific and local/prospect-specific
probabilities. A problem with these conventional probability and Chance
of Success maps, however, may be the relative complexity of arriving at
the map itself, such that if a geological condition changes, or when the
explorationist changes a hypothetical or a geological property
underpinning the map, the map has to be reconfigured and recalculated,
which may be a conventionally painstaking process.
[0006] Play fairway mapping, common risk mapping, and Chance of
Success mapping conventionally depend on numerous complex
processes. The shear amount of input data through which the user may
need to sort can make map creation difficult and sometimes non-intuitive.
2
,
CA 02779207 2012-06-08
8
IS10.0807-CA-NP
Additionally, there may be a lack of information on how to accomplish the
exploration workflows. Easy-to-use tools may be needed to give fast
results and simplify the clutter of inputting data for the process of creating
the Chance of Success maps and evaluating the results.
SUMMARY
[0007] Prospect
assessment and play chance mapping tools are
provided. For exploration prospect assessment of potential hydrocarbon
resources in a play or a prospect, an example system provides
dynamically linked, real time risk, chance of success, and chance of
failure maps ("chance maps"), transformed in real time from the
geological properties of one or more input geological maps, play fairway
maps, or other input data. The geological maps and data input to the
system are dynamically linked to the resulting output: chance maps, so
that a change to a geologic parameter of an input map or input datum
automatically updates the chance map(s) in real time or near real time.
In an example implementation, user-instigated changes in an example
user interface are also instantly reflected in the resulting chance map.
The example user interface allows the user to create and specify a
custom hierarchical matrix of risk maps, including specifying dynamically
linked input maps and data, and the dynamic links themselves. The user
can specify sub-maps and sub-matrices to construct the main risk matrix,
selecting and dropping maps directly into the matrix. A customizable
transform quickly converts geologic properties from the geologic domain
to the chance domain. The user interface also enables the user to
navigate geological maps, draw a polygon around areas of interest (A01)
or otherwise select areas on a geologic map. After selecting an area, the
user may drag-and-drop geologic properties within the polygon directly
into an uncertainty engine that maps risk by applying an equation or by
building a distribution to map uncertainty in a manner that is automatically
3
81632827
tied directly back to geologic reality. A merge tool can apply a customizable
formula
to perform a programmatic merge of multiple grids that are modeling multiple
different
geological interpretations of a prospect. The merge tool outputs a single
chance of
success value for multiple geologic property values at each grid node.
[0007a] According to one aspect of the present invention, there is
provided a
computer-executable method performed by a computer system, the method
comprising: receiving a plurality of sets of coefficients associated with
different
geologic scenarios from one or more data sources, wherein the plurality of
sets of
coefficients represent different sets of coefficients for a same geologic
property;
calculating first chance maps based on the plurality of sets of coefficients
using a
transform; merging the first chance maps into a second chance map, wherein the
first
chance maps and the second chance map are chance of success maps or chance of
failure maps; dynamically linking the plurality of sets of coefficients to the
second
chance map; updating the second chance map in real time when a change occurs
in
at least one of the plurality of sets of coefficients; and generating control
signals such
that petroleum production is adjusted correspondingly.
[0007b] According to another aspect of the present invention, there is
provided
a system, comprising: a computing device; a processor in the computing device;
a
storage medium; a transform residing in the storage medium to relate or
translate an
input geological property value to a chance value; and a mapping engine
residing in
the storage medium for performing a process, including: receiving a plurality
of sets
of coefficients associated with different geologic scenarios from one or more
data
sources, wherein the plurality of sets of coefficients represent different
sets of
coefficients for a same geologic property; calculating first chance maps based
on the
plurality of sets of coefficients using a transform; merging the first chance
maps into a
second chance map, wherein the first chance maps and the second chance map are
chance of success maps or chance of failure maps; dynamically linking the
plurality of
sets of coefficients to the second chance map; updating the second chance map
in
real time when a change occurs in at least one of the plurality of sets of
coefficients;
4
Date Recue/Date Received 2020-07-29
81632827
and generating control signals such that petroleum production is adjusted
correspondingly.
[0008]
This summary section is not intended to give a full description of
prospect assessment and play chance mapping tools, or to provide a
comprehensive
list of features and elements. A detailed description with example
implementations
follows.
4a
Date Recue/Date Received 2020-07-29
CA 02779207 2012-06-08
IS10.0807-CA-NP
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 is a block diagram of an example system and
environment for prospect assessment and play chance mapping tools.
[0010] FIG. 2 is a diagram of an example play chance matrix.
[0011] FIG. 3 is a diagram of an example transform table.
[0012] FIG. 4 is a diagram of an example property to chance of
success map conversion via transform.
[0013] FIG. 5 is a diagram of an example process of selecting an
area of a geological map to drag-and-drop property values into a
distribution for creating a live chance of success map.
[0014] FIG. 6 is a flow diagram of an example process setting up a
chance of failure map.
[0015] FIG. 7 is a diagram of an example histogram or distribution
builder for creating a chance of failure map.
[0016] FIG. 8 is a diagram of an example merge process for
generating a single chance of success value for a distribution of geologic
values at each grid node of a grid that is modeling a play or prospect.
[0017] FIG. 9 is a flow diagram of an example process for inputting
maps to generate a risk map.
[0018] FIG. 10 is a flow diagram of the example process in Fig. 9
with an uncertainty option.
[0019] FIG. 11 is a flow diagram of the example process in Fig. 10,
with an auto update option.
[0020] FIG. 12 is a diagram of an example user interface for
creating a chance map.
[0021] FIG. 13 is a diagram of an example user interface showing
default templates.
CA 02779207 2012-06-08
IS10.0807-CA-NP
[0022] FIG. 14 is a diagram of an example user interface
showing
icons or buttons for creating and linking input maps and risk maps.
[0023] FIG. 15 is a diagram of an example user interface
showing
creation of submaps during matrix and map creation.
[0024] FIG. 16 is a diagram of an example user interface
showing
matrix handling.
[0025] FIG. 17 is a diagram of an example user interface
showing
matrix creation.
[0026] FIG. 18 is a diagram of an example user interface
showing
= value entering during matrix creation.
[0027] FIG. 19 is a diagram of an example user interface
showing
an option for loading a pre-made matrix.
[0028] FIG. 20 is a diagram of an example user interface
showing
how to input a play-fairway map 122.
[0029] FIG. 21 is a diagram of an example user interface
showing
input of a single value via typing or scaling on a visual slider.
[0030] FIG. 22 is a diagram of an example user interface
showing
how to link a pre-existing risk map and/or a play-fairway map 122.
[0031] FIG. 23 is a diagram of an example user interface
showing
how to create a link between an input map and a desired risk map.
[0032] FIG. 24 is a diagram of an example user interface
showing
how to specify a transform through a table format.
[0033] FIG. 25 is a diagram of an example user interface
showing
entry of matrix values.
[0034] FIG. 26 is a diagram of an example user interface
showing
entry of matrix values.
[0035] FIG. 27 is a diagram of an example user interface
showing a
linkage indicator to show when maps are dynamically linked.
6
CA 02779207 2012-06-08
IS10.0807-CA-NP
[00361 FIG. 28 is a diagram of an example user interface showing a
control for activating automatic updating between maps.
[0037] FIG. 29 is a diagram of an example user interface showing
an alternate method of linking maps for real time updating.
[0038] FIG. 30 is a diagram of an example user interface showing
output options.
[0039] FIG. 31 is a diagram of an example user interface showing
selection of uncertainty options for a single map.
[0040] FIG. 32 is a diagram of an example user interface showing
selection of uncertainty options for multiple maps.
[0041] FIG. 33 is a diagram of an example user interface showing a
map stack option, in which a user can enter a stack of maps within a
folder and select a weighting factor which to skew the distribution.
[0042] FIG. 34 is a diagram of an example user interface showing a
test button to check if there are missing data maps or value entries and if
there are current connections between the data maps and the risk maps.
[0043] FIG. 35 is a diagram of an example user interface showing
an example test result of the test in Fig. 34.
[0044] FIG. 36 is a flow diagram of an example method of creating a
live chance of success map.
[0045] FIG. 37 is a flow diagram of an example method of capturing
geological properties to generate a live chance of success map.
[0046] FIG. 38 is a flow diagram of an example method of merging
multiple geological grids into a single grid of chance of success values.
7
CA 02779207 2012-06-08
IS10.0807-CA-NP
DETAILED DESCRIPTION
Overview
[0047] This disclosure describes prospect assessment and play
chance mapping tools. An example system streamlines information
handling and provides a friendly and comprehensive user interface to
construct custom risk matrices and dynamically link geological property
maps and other input data to resulting chance maps and uncertainty
assessments. The terms "chance" and "risk" are used somewhat
interchangeable herein. Resulting chance (risk) maps may be live with
real time automatic updating when there is a change, for example, when
there is a change in a dynamically linked geological property map or a
user-initiated change in a hypothetical parameter.
[0048] Example systems may thus provide dynamically linked
chance maps, transformed in real time from geological properties and
other input data. Users can generate a custom risk matrix dynamically
linking geological maps with chance maps via comprehensive interface
tools, for example, by dragging-and-dropping maps directly into the
matrix. A customizable transform may programmatically convert the
geologic domain to the chance domain. The user can navigate input
maps, select areas of interest, and drag-and-drop geologic properties
directly into an uncertainty engine and distribution builder for uncertainty
assessment based directly on geologic reality. A merge tool can
programmatically unify multiple geological interpretations (multiple maps)
of the same prospect. The merge tool may output a single chance of
success value at each grid node for multiple geologic property values at
each corresponding grid node across the multiple grid maps.
8
CA 02779207 2012-06-08
IS10.0807-CA-NP
Example Environment
[0049] Fig. 1 shows an
example system, providing an environment
for prospect assessment and play chance mapping tools, such as
mapping tools 100. A computing
device 102 may implement
components, such as simulators 104 and an example, representative set
of the mapping tools 100. The simulators 104 may include seismic-to-
simulation programs and software suites, geological simulators, reservoir
simulators, oilfield modelers, and so forth. The example mapping tools
100 may include an example data capture tool 106, mapping engine 108,
transforms 110, matrix builder 112, user interface manager 114, merge
tool 116, distribution builder 118, uncertainty engine 120, and other
modules: for exploration and geological prospecting, risk mapping,
chance of success (or failure) studies and mapping, resource and site
assessment, etc. The mapping tools 100 are illustrated as software, but
can be implemented as hardware or as a combination of hardware, and
software instructions. The illustrated set of mapping tools 100 is provided
as an example for the sake of description, other mapping tools, or other
configurations of the mapping tools 100 can also be used.
[0050] In the
illustrated example, the computing device 102
receives geologic maps 122 and other data as input. One or more of the
geologic maps 122 may show at least one geological property 124 and
may be communicatively coupled via sensory and control devices with
real-world subsurface earth volumes 126, i.e., underground plays
including petroleum reservoirs, depositional basins, seabeds, oilfields,
wells, etc., as well as surface control networks, and so forth. A
subsurface earth volume 126 being modeled may be a candidate for
petroleum production, or for water resource management, carbon
services, or other uses.
9
t
CA 02779207 2012-06-08
,
IS10.0807-CA-NP
[0051] The computing device 102 hosting the mapping tools 100
may be a computer, computer network, or other device that has a
processor 128, memory 130, data storage 132, and other associated
hardware such as a network interface 134 and a media drive 136 for
reading and writing a removable storage medium 138. The removable
storage medium 138 can be, for example, a compact disk (CD); digital
versatile disk / digital video disk (DVD); flash drive, etc.
[0052] The removable storage medium 138 may include
instructions for implementing and executing the example mapping tools
- 100 and associated computer-executable methods (e.g., see Figures
36-
38 and associated descriptions). At least some parts of the mapping
tools 100 may be stored as instructions on a given instance of the
removable storage medium 138, removable device, or in local data
storage 132, to be loaded into memory 130 for execution by the
processor 128. Although the illustrated mapping tools 100 are depicted
as programs residing in memory 130, they may also be implemented as
hardware, such as application specific integrated circuits (ASICs) or as a
combination of hardware and software.
[0053] In an example implementation of this example system, the
computing device 102 may receive field data via the network interface
134, in the form of maps 122, derived from seismic data 140 and well
logs 142 from geophones, well measurement devices, and other sensors
at a potential petroleum field or other subsurface earth volume 126.
[0054] A user interface manager 114 and display controller 144
may
extend an associated user interface 146 on a display 150 (and
input/output for mouse, pointing devices, keyboard, touch screen, etc.),
as well as geologic model images 148, such as a 2D or 3D visual
representation of layers or rock properties in a subsurface earth volume
126. The displayed geologic model images 148 may generated by the
,
CA 02779207 2012-06-08
IS10.0807-CA-NP
mapping tools 100. The mapping tools 100 may perform other modeling
operations and generate useful user interfaces 146 via the display
controller 144, including novel interactive graphics, for user control of
processes generating Chance of Success maps 152 or other maps.
[0055] In an example implementation, the chance maps 152,
representatively called Chance of Success maps 152 herein (also known
as and alternatively cast as risk maps or chance of failure maps), can
also be utilized to generate control signals to be used via control devices
in real world prospecting, modeling, exploration, prediction, and/or control
of resources, such as petroleum production, water resource
management, carbon services, etc., including direct control via hardware
control devices of such resources as drilling, injection and production
wells, reservoirs, fields, transport and delivery systems, and so forth.
Example General Operation
[0056] In an example implementation, the example system can
generate a living play chance map 152 from geological properties 124 or
attributes inherent in the input geologic maps 122 (for example, porosity).
When there is a change in the geological properties 124, the generated
play chance map 152 may adapt in real time to provide updated risk or
Chance of Success features, maps 152, and output. Thus, an example
system provides a dynamic play chance map 152 that can show, for
example, Chance of Success in real time, based on changing geological
properties 124 or user-initiated hypotheticals, e.g., as entered via the
example user interface 146.
[0057] The example user interface 146 can access the matrix
builder 112 for creating Chance of Success maps 152 (e.g., prospect
assessment) and enables the user to create and specify a custom
hierarchical matrix of risk maps, including the dynamically linked input
11
1
CA 02779207 2012-06-08
,
IS10.0807-CA-NP
maps and data, and the dynamic links themselves. The user can specify
sub-maps and sub-matrices for construction of the main risk matrix, and
can select and drop maps and other matrices directly into the main
matrix.
[0058] When
provided with a geologic property map 122, or with
selected representative geologic property values 124 from maps 122, the
system may apply one or more customizable transforms 110 to
programmatically generate the chance map 152, which in turn may then
be compiled into or used as a precursor for a larger, overall chance map
_ 152, e.g., for common risk segment mapping (CRSM).
[0059] For
uncertainty assessment, the uncertainty engine 120 may
provide visual and navigation tools via the user interface 146 for enabling
the user to harvest a geological property 124 of interest directly from the
geologic maps 122. The desired parameter values 124 can also be
entered manually, in a direct manner. The user can draw a polygon
around an area of interest (A01) on a geologic map 122 to collect
parameter values of the property 124 and then "drag-and-drop" the
selected visual region containing the desired property values 124 directly
into an uncertainty mapping capability of the uncertainty engine 120 or
distribution builder 118, which may apply a Monte Carlo simulation.
Specifically, the user interface manager 114 may enable the user to
obtain minimum, peak, and maximum petroleum-system parameter
values from a map 122 with user-friendly visual selection tools, which
then feed the distribution builder 118 to perform uncertainty and prospect
assessment. By obtaining geological data directly from the geological
map(s) 122, values in the distribution and thus the uncertainty
assessment are tied directly to geologic reality without conventional
guesswork.
12
,
CA 02779207 2012-06-08
IS10.0807-CA-NP
[0060] In an example implementation, an example system may
build a distribution for each grid node in multiple 2D or 3D models of a
resource. In a grid-node-to-grid-node manner, an example technique
and merge tool 116 converts multiple petroleum-system parameter
coefficients that result from multiple geologic interpretations, into a single
Chance of Success value for each grid-node. The merge tool 116
develops an integrated Chance of Success map 152, combining multiple
geologic scenarios (multiple maps of the same prospect) into a single
summary expression of Chance of Success for a parameter at each grid
node of a single resulting map 152.
Example Implementations
[0061] When provided with a geologic property map 122, or with
selected representative geologic property values 124 from maps 122, an
example mapping tool 100 applies one or more transforms 110 to
programmatically generate the chance map 152, which in turn may then
be compiled into, or used as, a precursor for an overall chance map 152,
for example, a common risk segment map 152.
[0062] A property-to-chance transform 110 for play chance mapping
can be viewed as a function converting a geologic property at each grid-
node in a model of a surface or in a model of a subsurface volume 126
into a chance of success value. Thus, a chance of success value at each
grid-node may be determined from a geologic property through the
property-to-chance transform 110. Chance of success (COS) is used
representatively herein, but chance of failure can also be used, where
COF = 1 - COS.
[0063] In order to estimate the chance of success for a given play to
be feasible, the matrix builder 112 may decompose the change into sub-
elements (COS for a reservoir, for a seal, for a trap, etc.). Each of these
13
i
CA 02779207 2012-06-08
.
IS10.0807-CA-NP
,
sub-elements can be split up still further into lower levels. For example,
COS for a reservoir may include a combination of COS for reservoir
presence and COS for reservoir quality, thereby building a matrix that has
a desired degree of complexity.
[0064] Fig. 2 shows an example (e.g., simplified) play chance
matrix 200 (i.e., risk matrix 200). The matrix 200 defines the nature and
characteristics of the final COS map 152. In order to populate the sub-
element chance maps 152 at the lowest level elements that branch
(toward the right side 202 of the matrix) the matrix builder 112 may utilize
some geological arguments. As an example, porosity can be used as a
geologic property 124 in order to define the reservoir quality (another
geologic property could just as easily be used as a representative
example). A geologist making an evaluation via the example mapping
tool 100 can qualify the porosity as "good" and therefore decide for a high
COS (or low COF) for reservoir quality and use this value in matrix
construction for further calculations of play chance.
[0065] In some simulators 104, (e.g., PETREL, which is developed
and distributed by Schlumberger, Ltd, Houston Texas and its affiliates)
the geologist can easily quantify porosity with a porosity map 122 for the
given reservoir. The porosity map 122 may have a certain range of
porosity values, varying with X and Y position, e.g., from approximately
5% to approximately 20%. The geologist may estimate, for example, that
below a porosity of approximately 8%, the reservoir quality may
considered "bad", and a porosity of more than approximately 15% may
be considered "good."
[0066] The geologist may also define "good" and "bad" via the
matrix builder 112. As an example, with perfect data quantity and quality
and a reasonably correct geological interpretation, "good" can mean COS
= 1 (COF = 0) and "bad" can mean COS = 0 (COF = 1). However, in
14
,
i
CA 02779207 2012-06-08
IS10.0807-CA-NP
certain cases, e.g., in frontier exploration, data and interpretation may be
highly uncertain, so a geologist's definition of "good" might not exclude
failure and "bad" might not exclude success. As an example, this can
mean that "good" may have a COS < 1 (COF > 0) and "bad" a COS > 0
(COF < 1). In an example, COS <= 0.7 (COF >= 0.3) may be used for
"good" and COS = 0.3 (COF = 0.7) may be used for "bad" (the foregoing
are merely example values, and other ranges are possible). This
limitation of the COS (COF) scale may prevent the geologist from
terminating prospect exploration of an area with an unduly "bad" result or
- giving an unduly high recommendation to another area with a "good"
result. The resolution of the uncertainty issue may be useful to the
interpretation.
[0067] In an example implementation, both the geological
arguments (e.g., porosity 302) and the chance (COS/COF) 304 may
constitute a transform 110 and may be entered into a table 300 by the
geologist, such as, for example, the property-to-chance transform table
300 for porosity shown in Fig. 3.
[0068] At each cell of an input porosity map 122, the porosity
value
124 may be transformed into chance of success (COS value) using the
property-to-chance transform 110. As an example, the minimum porosity
value of the map 122 (approximately 5%) may be assigned a COS of 0.3,
which may be the same as a porosity of approximately 8% ("bad"). A
porosity of approximately 10% (between "good" and "bad") may be
assigned a COS of 0.5, and porosities >= approximately 12% ("good")
may be assigned a COS of 0.7.
[0069] Fig. 4 shows how a continuous porosity map 122 may be
transformed into a continuous chance of success (COS) map 152 (limited
to values between 0.3 and 0.7) using the example property (e.g.,
porosity-to-chance (COS) transform 110.
,
i
CA 02779207 2012-06-08
IS10.0807-CA-NP
[0070] In an example implementation, the mapping engine 108
applies the transform 110 to execute real time updating of the COS map
152.
Example Capture of Matrix and Map Data
[0071] An example data capture tool 106 implemented by the user
interface manager 114 may be used to gather geological data, such as
geologic maps 122 (e.g., play fairway maps) for construction of the risk
matrix 200 and geological property data 124 from the geologic maps 122
for uncertainty studies and also for matrix construction. In an example
- embodiment, the data capture tool 106 gathers real property values
from
a geologic map 122 and ties an expression of uncertainty in chance of
success mapping back to geological reality ¨ i.e., instead of basing the
uncertainty on guesswork or reliance on pure intuition as in conventional
techniques.
[0072] In a prospect assessment (PA) setting, the example data
capture tool 106 may target a workflow in exploration ¨ e.g., that of
prospect assessment and ranking utilizing the Monte Carlo process. The
result may include an estimate of a range of in-place and recoverable
hydrocarbon resources (e.g., oil, free gas, solution gas, condensate,
etc.).
[0073] Early in a prospect assessment process, it may be useful
to
determine whether or not a particular prospect is a practical investment
opportunity. At the early assessment stages, little information may be
available and there may exist uncertainty regarding petroleum-system
parameters (charge, timing, migration, reservoir, trap, seal, recovery,
etc.).
[0074] A stochastic process may allow an explorationist to
express,
without having to provide statistical input (variance, kurtosis, mean,
standard deviation, and so forth) the uncertainty regarding primary
16
,
i
CA 02779207 2012-06-08
IS10.0807-CA-NP
,
petroleum-system variables.
Stochastic processing may result in a
range of possible recoverable resources, an estimate of chance of
technical success, an estimate of chance of economic success, and
separate lists of parameters that may contribute to potential failure and to
uncertainty in the volume of recoverable hydrocarbons.
[0075] In an
example implementation, the uncertainty engine 120
may capture or assess uncertainty using a distribution of values 504 as
illustrated, for example, in Fig. 5. In an example distribution-building
scheme, such distributions may be built, in part, by the user supplying
. some combination of minimum, peak, and maximum values.
[0076] Human
explorationists may supply coefficients for minimum,
peak, and maximum values by applying their geologic intuition regarding
analogous situations they have experienced or by making estimates of
such coefficients by inspection of maps 122. The example data capture
tool 106 described herein may facilitate the automatic calculation of
minimum, peak, and maximum values for a petroleum-system parameter,
and seamless passage of these coefficients to the uncertainty engine 120
and prospect assessment distribution builder 118 so that uncertainty
about that parameter can be utilized in Monte Carlo resource volume
calculations. In an example implementation, the uncertainty engine 120
and distribution builder 118 may automatically derive minimum, peak, and
maximum values representing three geologic scenarios that describe the
range of possibilities for a property parameter value 124 in a particular
play.
[0077] A
feature of prospect assessment is its ability to utilize
diverse input: data derived from various maps 122 and map polygons
generated in play chance mapping, common risk segment mapping
(CRSM), and other functions of simulators 104, such as PETREL.
17
i
CA 02779207 2012-06-08
IS10.0807-CA-NP
[0078] The subsurface petroleum-system parameter "porosity" will
again be used to illustrate aspects of the example data capture tool 106.
The process described below, however, can apply to any petroleum-
system parameter 124 about which the explorationist is uncertain (for
which a data distribution would be uncertain) and which contributes to the
calculation of hydrocarbon resources.
[0079] In an example implementation, an example workflow
mediated and facilitated by the mapping tools 100 proceeds with the
explorationist creating, for example, three maps 122 representing three
different geologic scenarios. These scenarios, for example, may include
one or more of the following:
= Scenario #1 -- a situation in which poor quality and
poorly-sorted sand constitutes the prospect reservoir
rock. These rocks would, therefore, have relatively
low porosities.
= Scenario #2¨ a situation in which sands of "average"
quality constitute the reservoir rocks. This may be the
"most likely" scenario because the explorationist has
previously experienced this type of reservoir rock in
other similar prospects. Porosity values for this
scenario are "middle of the road" in magnitude.
= Scenario #3 a situation in which very high-quality
sands with corresponding high porosity values
characterize the reservoir.
[0080] Fig. 5 shows an example data capture process for assessing
uncertainty. In an example implementation of the example data capture
tool 106, a prospect polygon 502 is drawn on each of these three maps
(grids) 122, 122', and 122":
18
i
CA 02779207 2012-06-08
IS10.0807-CA-NP
1. T he corresponding mean porosity value is calculated from a
plurality of the grid-node values that fall within the prospect
polygon 502 (e.g., all grid-node values).
2. T he mean minimum, mean peak, and mean maximum values may
be "blue arrowed" (e.g., dragged-and-dropped) into the minimum,
peak, and maximum data-input slots of the distribution builder 118.
3. T he distribution builder 118 may use these three values (or some
combination of these values) to create a distribution 504 that will
be utilized in the Monte Carlo hydrocarbon-resource calculations.
[0081] If minimum, peak, or maximum values are not placed
(e.g.,
- blue arrowed, or dragged-and-dropped) into the data-input slots in
the
example distribution builder 118, then the user can simply type in values
that were not selected and dragged in from maps 122.
[0082] Fig. 6 shows a workflow 600 for interacting with the
example
user interface 146 associated with the example data capture tool 106.
[0083] At block 602, a prospect asset matrix is first set up.
[0084] At block 604, maps, polygons, and/or values are input
using
the data capture tool.
[0085] At block 606, a histogram resulting from a distribution
of the
input data and values can be edited.
[0086] At block 608, a chance of success (or chance of failure)
is
set.
[0087] Fig. 7 shows a screenshot of an example user interface
146
for histogram building associated with uncertainty assessment. Data
gathered by the data capture tool 106 for the distribution builder 118 may
be cast as a histogram 702. Chance, in this example, may be cast as
chance of failure (COF). The histogram 702 allows a number of inputs
and combinations of parameters to create a desired histogram shape 704
for assessing uncertainty. For example, input from a map 122 can be
used for all three of minimum, peak and maximum values, or for just one
19
,
i
CA 02779207 2012-06-08
IS10.0807-CA-NP
or two of these. Numeric input can be used as an alternative to maps
122 or in combination with maps 122. The minimum, peak and maximum
values can be toggled on and off, for example, "off' so that the histogram
702 does not use these numbers. Thereby, numerous possible
histogram types can be created. The example histogram 702 may
update automatically when an input item is changed by the user.
[0088] Input from a map 122 may be averaged within the user-
selected polygon areas 502 of the prospect. Each polygon area 502 may
,
be selected by the user. In an example implementation, the example
. data capture tool 106 may take only values from within the selected
polygon area 502 for minimum, peak and maximum values.
Example Grid-to-Grid Merge Tool
[0089] Prospects may be investment opportunities for any energy
company or agency. Certain energy-business entities, at any given
moment, may have more opportunities in their portfolio than they can
reasonably pursue due to budget and other constraints. Therefore, it
may be useful that opportunities be ranked so that the most profitable
prospects are initially pursued. Evaluation of any prospect may include,
at least, the culmination of the combination of estimated recoverable
hydrocarbon volume and the prospect's chance of geologic success
(GCOS). The GCOS is the combination of the chance values for each of
the pertinent petroleum system parameters for a prospect. For each of
those parameters, again using porosity as a current example, multiple
geologic scenarios can exist. At any given location (position of the
prospect), multiple coefficients (percent porosity in this example) for a
parameter might exist.
[0090] The example prospect merge tool 116 can perform a grid-
to-
grid merge of multiple geological scenarios. A given exploration prospect
,
1
CA 02779207 2012-06-08
IS10.0807-CA-NP
or play (e.g., a subsurface volume 126) may have associated with it
several different geologic interpretations. The given subsurface volume
126 may be viewed in terms of different geologic properties, each
property resulting in a different set of petroleum-system parameter
coefficients within a 3D grid that models the subsurface volume 126. Or,
the subsurface volume 126 may be viewed in terms of one property 124,
but the explorationist may assign several different hypothetical values to
the property 124 in order to develop a chance map 152 that is based on
,
minimum, peak, and maximum values, for instance. The different values
- assigned to each grid-node (or cell) may give rise to different
theoretical
grids.
[0091] In an
example implementation, the example prospect merge
tool 116 may apply an equation to facilitate, at each map grid node, the
conversion of multiple petroleum-system parameter coefficients, e.g.,
resulting from multiple geologic interpretations, into a single Chance of
Success value at that grid node. The example grid-to-grid merge tool
116 can feed information to exploration applications, for example those
that perform Prospect Assessment and Play Chance Mapping.
[0092] The
example grid-to-grid merge tool 116 may support a
commonly-practiced workflow in exploration: that of Prospect Evaluation
and Ranking utilizing Monte Carlo simulations to determine recoverable
volumes and to estimate a Geologic Chance of Success (GCOS) for a
given investment opportunity. As
introduced above, the GCOS
represents the probability that elements of the petroleum system
(migration, trap timing, reservoir, charge, seal, and so forth) will
successfully combine to yield a viable prospect.
[0093] In
the beginning of the prospect-assessment process it may
be useful to estimate the volume of hydrocarbons (e.g., oil, free gas,
solution gas, condensate, etc.) that can be taken to market. It may be
21
,
1
CA 02779207 2012-06-08
,
IS10.0807-CA-NP
,
also useful to calculate the prospect's GCOS. The GCOS may result
from the combination of the chance of failure associated with each
pertinent petroleum system input parameter (porosity, net-to-gross, and
the like).
[0094] For investment purposes, a prospect's GCOS may need to
be reasonably determined. For example, if two prospects are both
estimated to potentially produce one-hundred MMBOE (100 Millions of
Barrels of Oil Equivalent) but one prospect has an approximately 5%
chance of being geologically successful while the other has an
- approximately 30% chance of geologic success, an investor may
decide
that capital should be spent on the opportunity with the higher GCOS.
Therefore, in certain situations, ranking of prospects by hydrocarbon-
volume potential alone may not be good business practice.
[0095] For play chance mapping, porosity is again selected as
an
example parameter 124 for the sake of description. But any pertinent
petroleum-system parameter 124 can serve as the example property.
The example prospect merge tool 116 may facilitate utilization of maps
122, such as PETREL-created maps 122, for hydrocarbon-related
parameters and for the transformation of the coefficients of those
parameters 124 (percent porosity in this example) into Chance of
Success (COS) or chance of failure (COF) coefficients. The example
prospect merge tool 116 addresses instances in which multiple possible
geologic scenarios exist for a location and correspondingly, multiple
corresponding values of a property 124 at that location.
[0096] For example, the explorationist might believe that a
physical
structure such as an anticline (as a prospect) exists in an offshore buried
river delta. The deltaic sedimentary rocks in which the prospect is
believed to exist may have been deposited millions of years ago. The
anticline may have formed long after the sedimentary rocks were
22
,
CA 02779207 2012-06-08
10.0807-CA-NP
deposited in the delta. Initial investigations, mainly through seismic
interpretation, might not make clear the extent to which satisfactory-
quality sands were deposited in the area of the anticline. Thus, referring
to Fig. 8 and using porosity as an example geologic property 124, the
following scenario describes an example use of the example merge tool
116. From experience with similar reservoirs and geologic settings, an
explorationist may know that if the porosity in a current prospect is below
approximately 5%, for example, then flow rates may be too low for the
prospect to be economically successful. So this approximately 5% value
may be adopted as the cutoff value (shown in Fig. 8).
[0097] The explorationist may create a porosity grid (map) 122 for
the geologic scenario in which sand that did reach the prospective area
might not be of a certain quality to create a viable prospect. This
situation may be represented by the minimum (top) porosity map 122
shown in Fig. 8. How to create such porosity maps 122 is already
common knowledge among explorationists.
= First Geologic Scenario 802: Sand that did reach the
prospective area might not be of a certain quality to create a
viable prospect. This situation is represented by the top grid
122 in Fig. 8.
[0098] The explorationist may create a porosity grid (map) 122 for
the geologic scenario in which sufficient desirable sand did reach the
area of the anticline (prospect). This situation may be represented by the
maximum (middle grid)) porosity map 122' shown in Fig. 8.
= Second Geologic Scenario 804: Sufficient desirable sand
did reach the area of the anticline (prospect). This situation
is represented by the middle grid 122' in Fig. 8.
23
i
CA 02779207 2012-06-08
,
IS10.0807-CA-NP
[0099] Referring to Fig. 8, the geologist might thus produce
two
maps 122 and 122' of percent porosity for the same area to reflect the
two aforementioned scenarios 802 & 804. The percent porosity at any
given location 806 (grid-node location on a map 122) might be markedly
different reflecting the reality of the disparate scenarios 802 & 804. The
example merge tool 116 converts these two maps 122 & 122' into a
single Chance of Success map 152 for porosity. A description of
Equation (1) below delineates an example merge process executed by
the merge tool 116.
[00100] On computer-generated maps 122, the example merge tool
-
116 combines multiple property values at each grid node 806 on the input
map(s) 122. The mean of the Chance of Success values from within a
prospect-outline polygon on a resulting COS map 152 generated by the
process can then be passed to an application, such as a Prospect
Assessment (PA) application.
[00101] In an example prospect assessment, corresponding
hydrocarbon volumes and chance of success values for a prospect can
be combined for the purpose of ranking the prospect against similarly-
evaluated prospects. This process may be used for determining how
capital may be distributed over a portfolio of potential investment
opportunities (i.e., the prospects).
[00102] The example merge tool 116 can enhance conventional
techniques by facilitating the combination of ever-present multiple-
geologic scenarios 802 & 804 into a single expression of COS (or COF)
for a parameter.
[00103] In the context of play chance mapping, the example merge
tool 116 facilitates the combination of multiple (e.g., two as in Fig. 8)
estimates of a parameter coefficient, two values for percent porosity for a
given geologic location, for example, to create a single estimate of
24
,
i
CA 02779207 2012-06-08
IS10.0807-CA-NP
Chance of Success (or COF) for that parameter at that location 806. This
process may be applied to each location or grid node on a computer-
generated map 122. The example merge tool 116 thus combines two
grids of parameter values, each grid representing a unique geologic
scenario, to generate a single chance map 152 of the same grid nodes.
[00104] An equation (algorithm, transform) such as Equation (1)
(Koller's Formula) may then be applied to the coincident grid node values
in the example top and middle grids, e.g., in Fig. 8, to create a Chance of
Success value for the porosity property at that grid node 806.
COS = 1 - ((Cutoff - Minimum) / Maximum) (1)
where "COS" represents the Chance of Success; "Cutoff' may represent
a property value below which an explorationist may determine that the
prospect might not be successful, "Minimum" may represent the property
value delineating a range of property values in which the prospect, e.g.,
might not be successful, and "Maximum" may represent a property value
delineating a range of property values in which the prospect will likely be
successful. In an example embodiment, if a COF (chance of failure)
value is required by the application to which success values are fed, then
COS is subtracted from 1.
[00105] For grid nodes 806 that fall within a prospect polygon 502
imposed on the maps 122, the mean Chance of Success values (or
mean COF values) can be calculated and used as the values input to
applications, e.g., for prospect assessment. Thus, in Fig. 8, the cutoff
value may be approximately 5%, the minimum porosity may be
approximately 2%, the maximum porosity may be approximately 15%,
and therefore the COS = 1 - ((5 - 2) / 15) = 1 - 3 / 15 = 1 - 0.2 = 0.8, or
approximately 80% Chance of Success (COF = 1 - 0.8 or 0.2 or
approximately 20%).
'
CA 02779207 2012-06-08
IS10.0807-CA-NP
[00106] Likewise, a Chance of Success value may be generated at
each grid node 806, yielding a map 152 of success (or failure) values. A
polygon of the geographic extent of the prospect can be imposed on the
map 152 (grid) of Chance of Success (or COF) and the mean of the grid-
node values within the prospect polygon can be calculated to be fed to
applications such as Prospect Assessment.
Example Real time Risk Mapping and User Interfaces
[00107] A dynamic connection to other types of mapping, e.g.,
CRSM (Common Risk Segment Mapping), in simulators 104 such as
PETREL is an option for the calculation of Chance of Success in
Prospect Assessment and Play Chance Mapping using the example
mapping tools 100.
[00108] In an example implementation, an example system facilitates
creation of risk maps 152 (e.g., common risk segment maps 152) and
Chance of Success maps 152 b y employing various hierarchies and
schemes. As introduced above, a resulting map 152 may be provided
live in real time, and may be updated automatically when input data
changes. The map 152 creation may use a user interface 146
administered by a user interface manager 114, to assist the user to sort
through project clutter to create final data inputs, in an integrated user-
interface 146. The example system makes it easy to link input maps 122
to risk maps 152, and to create a matrix 200 of risk maps 152. The
uncertainty engine 120 also makes it easy to incorporate uncertainty
assessment.
[00109] In an example implementation, the system has a user
interface 146 with tools to create the desired input matrix 200 and
hierarchies, via use of various selection / function icons or buttons. Input
may be entered using a "blue arrow" drop tool to drag-and-drop the maps
26
1
CA 02779207 2012-06-08
,
IS10.0807-CA-NP
122 or map values into the matrix 200. The link between these maps 122
and the risk maps 152 can be made by the user through a pop-up
window or menu where the user enters the relevant information to
complete the link.
[00110] Additional uncertainty analysis can be performed by the
uncertainty engine 120 on each user input. The user may enter a
number of inputs, and example equations may produce the risk map 152
between these, or else a distribution and Monte Carlo simulation may be
performed.
- [00111] Once saved, the mapping and linking process can be
opened and re-run at any time. Thereafter, the final map 152 may update
should the input map 122 be changed. In an example implementation,
there is an additional update facility selectable by the user, which scans
for updates in the user interface 146 and immediately updates the final
map 152 without the user actuating a specific control. This may be
applied on changes entered in the user interface 146 during the process,
not when there are changes in the input maps 122.
[00112] Fig. 9 shows an example workflow, which can be run
multiple times using different inputs 122, different transforms 110, or with
different risk matrices 200.
[00113] At block 902, a risk matrix 200 is set up. At block 904,
data
is received. The data may include geologic maps 122 and other maps.
[00114] At block 906, the received data is input, including
received
geologic maps 122, play fairway maps, risk maps 152, and values for
various parameters on the maps 122 & 152 and parameters related to
the prospect being assessed.
[00115] At block 908, a transform 110 is created to link the
input data
and a resulting risk map 152.
27
i
CA 02779207 2012-06-08
IS10.0807-CA-NP
[00116] At block 910, a live chance or risk map 152 is created that is
dynamically linked to (changes in) the data that was input. The matrix set
up at block 902 can also be output.
[00117] Fig. 10 shows another example workflow that includes
introduction of an uncertainty option. Once transforms 110 are complete,
the user can choose to run uncertainty assessments using, for example,
a Monte Carlo process on one map 122 or by inputting a number of maps
122.
[00118] At block 1002, a risk matrix 200 is set up.
[00119] At block 1004, data is received. The data may include
geologic maps 122 and other maps.
[00120] At block 1006, the received data is input, including received
geologic maps 122, play fairway maps, risk maps 152, and values for
various parameters on the maps 122 & 152 and parameters related to
the prospect being assessed.
[00121] At block 1008, a transform 110 is created to link the input
data and a resulting risk map 152.
[00122] At block 1010, one or more uncertainty options are run. An
uncertainty map may be created.
[00123] At block 1012, a live chance or risk map 152 and/or
uncertainty map is created that is dynamically linked to (changes in) the
data that was input. The matrix set up at block 902 can also be output.
[00124] Fig. 11 shows an example workflow that includes
introduction of an auto-update option for transforms 110. In an example
implementation, once the user has set up the process, there may be an
additional option to apply automatic updates on the transforms 110 so
that any changes to the transform 110 will update the final risk map 152
immediately. In an example implementation, the process is run once for
this to work. The automatic updating provides an advantage over
28
i
CA 02779207 2012-06-08
IS 10.0807-CA-NP
conventional programs that cannot perform such instant updates. The
user may save or store the links that are established between input maps
122 and the resulting output risks.
[00125] At block 1102, a risk matrix 200 is set up.
[00126] At block 1104, data is received. The data may include
geologic maps 122 and other maps.
[00127] At block 1106, the received data is input, including
received
geologic maps 122, play fairway maps, risk maps 152, and values for
various parameters on the maps 122 & 152 and parameters related to
- the prospect being assessed.
[00128] At block 1108, a transform 110 is created to link the
input
data and a resulting risk map 152.
[00129] At block 1110, an automatic update option is provided.
Although changes in parameters of the input data are already
dynamically linked to the resulting risk map 152 via the transform 110 for
real time updating, a selection of the automatic update option enables
changes to the transform itself 110 to update the final risk map 152
immediately.
[00130] At block 1118, one or more uncertainty options are run.
An
uncertainty map may be created.
[00131] At block 1116, a live chance or risk map 152 and/or
uncertainty map is created that is dynamically linked to (changes in) the
data that was input and to changes in the transform 110 when the auto-
update option at block 1110 is selected. The matrix set up at block 902
can also be output.
Example User Interface for Matrix Construction
[00132] Fig. 12 shows an example user interface 146 for
constructing a matrix 200. The shown layout may be displayed by the
29
,
i
CA 02779207 2012-06-08
,
IS10.0807-CA-NP
user interface manager 114 when the user first begins a chance mapping
process. The user may first select a name 1202 for the CRSM matrix,
risk matrix, or may choose to load and edit a pre-made matrix 200. The
system provides flexibility to build many types of play-fairway, risk, or
common risk matrices 200 without certain restrictions.
[00133] The user may leverage the matrix builder 112 to
construct a
matrix 200 that contains numerous risk maps 152. There can be
numerous inputs of various data maps 122, and there can be numerous
risk maps 152 under or within one top-level risk map 152. The
. hierarchical structure may continue with many levels and many
inputs.
The matrix 200 can be filled with numbers or maps 122.
[00134] The user interface 146 provides several functionalities,
including flexible ability to create levels of input, ability to add into the
matrix 200 using drag-and-drop or other selection input, ability to select
how data will be used to calculate the final chance or CRSM risk map
152, ability to set up how the play-fairway maps 122 are linked to the
individual risk maps 152, etc.
[00135] The user can set up most types of matrix 200. A user, or
company entity, may have unique chance, risk, or CRSM matrix
requirements, compared with other users or company entities. Matrix
use may also differ between projects. Thus, the structure of the matrix
200 can be flexible.
[00136] As shown in Fig. 13, the example user interface 146 may
offer options 1362 to choose to build either chance of success (COS)
maps or chance of failure (COF) maps using corresponding templates.
This option 1362 may include a warning message that appears if the user
tries to change from one to the other on an existing template. In an
example implementation, the user may select one or the other, COS or
COF, and the system may also have a default template. For example,
,
i
CA 02779207 2012-06-08
' IS10.0807-CA-NP
the user may load an existing CRSM template and desire to change it
from a COS to a COF. This may be allowed, but a warning message may
appear that may cause a problem if the input parameters are not also
changed.
[00137] In
another or the same implementation, an example user
interface 146 offers options to set up a CRSM matrix. For example, a
first row created in the CRSM matrix may be a pointer to a chance map
152 and may be added by default when a new CRSM matrix is created.
_
This map name may adopt a default under a "create new" option. In an
example implementation, by default the first row cannot be just one
-
number, but should be a map input.
[00138] In an
example embodiment, the user interface manager 114
may extend to the user capabilities to add other levels of a matrix 200.
For example, as shown by the example control icons in Fig. 14, there
may be option icons or buttons to add a row 1462, each row representing
a new risk map 152; an option to delete a row 1464 (delete a risk map
152); an option to link 1466 a play-fairway input map 122 to a risk map
152; an option to load 1468 an existing (e.g., CRSM) matrix (may not be
required if a drag-and-drop or other selection operation 1410 to choose
an area of interest (A01) is used to drop in a domain-specific data).
[00139] To
add a risk map 152 to a matrix 200, e.g., using add
control icon 1462, the place in the matrix 200 where it is to be added
(e.g., see Fig. 2 or Fig. 12) can be selected when that place is active. To
delete a risk map 152, the risk map 152 may be selected to make it
active and then a delete icon 1464 may be actuated. To link 1466 a play-
fairway map 122 to an output risk map 152, the risk map 152 may be
active and then the relevant option selected. To load 1468 a CRSM
matrix, the risk map 152 may be selected and active for the place in the
matrix 200 to which the user wishes to load the matrix 200 (or the
31
,
1
CA 02779207 2012-06-08
.
IS10.0807-CA-NP
selection process 1410 or other navigation control accepts a domain-
specific matrix icon).
[00140] Figs.
15-20 show the example user interface 146 in various
states of interaction with a user during creation of an example matrix 200.
Possible inputs for individual risk maps 152 may include play-fairway
maps 122, a value, or another risk matrix. The matrix builder 112
enables the user to add in a number of other play-fairway maps 122 as
well as values or possibly another pre-made matrix. In Fig. 15,
instruction 1502 may show the user how to create submaps for an active
map. In Fig. 16, example instructions 1602 may guide the user in
-
renaming a map or deleting an active map. In Fig. 17, example
instruction 1702 may guide the user in activating a particular map 122 in
a part of the matrix 200 and adding submaps to the activated map 122.
In Fig. 18, example instructions 1802 and 1804 may guide the user in
selecting views and operators and may offer options for renaming maps
122. In Fig. 19, example instructions 1902 may guide a user in loading a
pre-existing matrix 200 or map 122. Fig. 20 shows controls 1466 & 2002
for inputting (linking 1466) a play-fairway map 122, for example.
[00141] Fig.
21 shows instructions 2102 for entering (e.g., via load
icon 1 468) a single value number by typing each entry, or by entry
through other user input components, such as scaling on a scale bar or
slider 2104.
[00142] Fig.
22 shows instructions 2202 and controls 2204 & 2206
for entering another risk matrix: for example, by loading a pre-existing
matrix, e.g., a risk map and/or a play-fairway map 122.
[00143] In an
example implementation, the user can load to a
simulator program, such as a PETREL project, a pre-existing CRSM
matrix. This can be loaded to another CRSM matrix to be edited or used
within that matrix. For example, a geologist may have performed a
32
,
CA 02779207 2012-06-08
IS10.0807-CA-NP
CRSM matrix on some geological parameters, and a petroleum systems
expert may have produced a CRSM matrix based on the petroleum
systems elements. Another geoscientist may now wishes to combine
these two together into one risk map 152 which includes both risk maps
152.
[00144] When loading 1468 a matrix 200 the user may have the
choice to include the matrix 200 or also the play-fairway maps 122 which
created that matrix and the links between them.
[00145] There can be clear visual definition between risk maps 152
and play-fairway maps 122, using either a color scheme in the matrix
200, or other identifiers.
Example User Interface for Creatino_Dynamic Links
[00146] Fig. 23 shows instructions 2302 and controls 1466 & 2304
for creating a dynamic link between input data, such as an input map
122, and a desired chance map 152. First, geologic or play-fairway maps
122 are entered into the risk matrix 200. The user enters a data map
122, and then actuates the "link" icon 1466 or button. The dynamic link
between input geologic data and the resulting chance map 152 is also
referred to as a data-to-chance or a "data-to-risk" link that is
accomplished by a transform 110, also called a data-to-risk transform
110.
[00147] As shown in Fig. 24, to create a risk map 152, the user may
specify how the values in the play-fairway map 122 are used to create
the risk map 152. That is, a transform 110 may be entered, for example
through a table format, by actuating a transform link icon or button. This
can be done in a separate window 2402 which opens when the link
button 1466 is selected. Here, in the newly opened window 2402, the
user can specify the play-fairway values and the risk map 152 values for
33
CA 02779207 2012-06-08
IS10.0807-CA-NP
the upper and lower limits of risk. The units for the risk map 152 may be
between 0 and 1 as example range values 2404. The user can reduce
these range boundaries (for example, to between 0.3 and 0.7 if little is
known about the area). Values between these end points may then be
scaled.
[00148] As shown in Figs.
25-26, the user may enter a cutoff value
for creating a transform 110 and select whether the input value is the
upper or lower cutoff. The user may then select the output risk range
which, in an example implementation, by default may be between 0 and
1. The default option may include a manually entered transform 2502
which shows the table of data 2504 entered (risk value and cutoff value).
Then, in Fig. 26, the user can enter the final cutoff value 2602 in the table
2504 and can edit the table 2504 further if desired.
[00149] Fig. 27 shows an
example visual indicator 2702 showing the
existence or nonexistence of a current (successful) link between risk map
152 and play-fairway maps 122 via the created transform 110. Before
the link has been created between the play-fairway maps 122 and risk
map 152, the link may be evident in the indicator with a dashed or broken
line or other representative graphic. In an example embodiment, once
the link has been established between the play-fairway maps 122 and the
risk map 122, the presence of the successful link may be made evident
by a solid line or other representative graphic, and colors may also
change when the maps (122 & 152) are linked or not linked.
[00150] Fig. 28 shows an
example options tab segment of the user
interface 146 for actuating a dynamic, real time option 2802. This allows
the user to specify if changes to the input will be dynamically updated,
with associated warning messages. In an example implementation, the
user can change any of the values in the transform link window and may
not have to actuate the "apply" or "OK" icons 2802 for consequent
34
i
CA 02779207 2012-06-08
.
IS10.0807-CA-NP
changes to take effect and to be saved into the CRSM matrix. In an
example implementation, the changes may be applied automatically and
saved, as though the user had pressed an "OK" button for the transform
tab and the "OK" button for the entire process. In an example
implementation, this option is available after the CRSM process has been
run once.
[00151] Fig.
29 shows an alternative example of an auto-apply or
"make dynamic" option 2902 on a user interface 146. After checking an
option selector 2902, value changes to play-fairway maps 122 are
applied directly to the final risk map 152. In an example embodiment, the
-
process window has to be open for this alternative to apply. An
advantage of such a dynamic linking process may be that the input maps
122 and resultant risk maps 152 stay linked and can be updated if input
data changes.
Outputting a Chance Map and Risk Matrix
[00152] Fig.
30 shows example output options selection. For output
options, such as matrix 200 output for use as input elsewhere, e.g., at an
input tab of the user interface 146, users can transfer matrices 200 to
other projects, add them into new matrices 200, or edit and re-run them.
For output of risk maps 152 to the input tab of the user interface 146,
these can be output as attributes on the risk map 152 or as maps 152
within a folder structure mirroring the matrix 200: the user can choose
the level of the output maps 152. In an example embodiment, a JPEG of
the matrix 200 can be output (from the domain-specific matrix icon). The
user can add a date into the output. A user-input area of interest (A01)
polygon object used to sample the average from the maps 122 can also
be output (e.g., from the settings dialog of an output risk map 152).
i
i
CA 02779207 2012-06-08
= IS10.0807-CA-NP
[00153] In an example implementation, the JPEG output may be by
the first tab (create matrix), with an image of the input names and maps
122 and calculations between them. Additionally the numbers used to
create the link between the input map 122 and the output data map 152
can be included.
Example User Interface for Uncertainty Assessments
[00154] Referring to Fig. 31, in an example user interface 146 for
controlling a data-to-risk transform 3102, uncertainty assessment can be
accessed, e.g., by selecting a row for specifying risk parameters and a
relevant link. In an example implementation, a first tab may be provided
with a link that defaults to a "no uncertainty assessment" setting.
Otherwise, the user may enter the value for a cutoff 3104 for a transform
110 and select whether the entered value is a high or a low cutoff 3106.
The user may then select the risk range 3108, which may default to a
range of 0-1. In an example implementation, manual risk assignment
3110 is the default, in which the user enters the values for the input play
fairway map 122 cutoffs.
[00155] In an example implementation, if the user then selects
another method for risk assignment, uncertainty assessment can be
included in the process. For example, uncertainty assessment can be
included with generation of a single chance map 152 by selecting a
customized distribution 3112.
[00156] Fig. 7 above shows a screenshot of an example user
interface 146 for histogram building associated with uncertainty
assessment.
[00157] As shown in Fig. 32, uncertainty assessment can be
included with multiple maps 3202. Options may be provided for
36
,
CA 02779207 2012-06-08
= IS10.0807-CA-NP
minimum, peak, and maximum or for P10, P50 and P90 defaults 3204.
When a distribution is selected 3206, the user can also enter the
distribution. Equation (1), described above (Koller's Formula 3208), may
also be selected for a risk transform.
[00158] Fig. 33 shows a user interface 146 for a map stack option
3302, in which the user can enter a stack of maps 122 within a folder.
The user can also select a weighting factor 3304 which can favorably
skew the distribution.
Test Option and Check For Errors
[00159] In an example implementation, shown in Fig. 34, the system
includes a test option 3402 for the linking process to check whether there
are any missing data maps 122 or missing value entries and whether
there are current connections between the data maps 122 and the risk
maps 152. If there are any detected problems then one or more fail
indicators 3404 may be displayed and the problem line highlighted, or a
text message in a text dialog box 3406 & 3408 & 3410 may appear
showing the nature of the problem and at which line the error occurs.
Fig. 35 shows a "test okay" result for the check described above for Fig.
34.
Example Methods
[00160] Fig. 36 shows an example method 3600 of creating a live
chance of success map 152. In the flow diagram, the operations are
summarized in individual blocks. The example method 3600 may be
performed by hardware or combinations of hardware and software, for
example, by the example system or the example mapping tools 100.
[00161] At block 3602, a property of a geologic map 122 is
dynamically linked to a real time rendering of a chance of success map
152.
37
i
CA 02779207 2012-06-08
= IS10.0807-CA-NP
[00162] At block 3604, the chance of success map 152 is updated
in
real time when a value of the property changes in the geologic map 122.
[00163] Fig. 37 shows an example method 3700 of capturing
geological properties to generate a live chance of success map 152. In
the flow diagram, the operations are summarized in individual blocks.
The example method 3700 may be performed by hardware or
combinations of hardware and software, for example, by the example
system or the example mapping tools 100.
[00164] At block 3702, a geologic map 122 is displayed.
[00165] At block 3704, navigation and selection of an area of
the
-
geologic map 122 is enabled.
[00166] At block 3706, geologic property values from a user-
selected
area of the geologic map 122 are entered into a distribution for
generating a live chance map 152.
[00167] Fig. 38 shows an example method 3800 of merging multiple
geological grids into a single grid of chance of success values. In the
flow diagram, the operations are summarized in individual blocks. The
example method 3800 may be performed by hardware or combinations
of hardware and software, for example, by the example system or the
example mapping tools 100.
[00168] At block 3802, multiple grids are received, each grid
modeling a different set of coefficients for a geologic property of the same
geological prospect. The multiple grids that are received may model
different geological properties, instead of different coefficients of the
same geological property.
[00169] At block 3804, the multiple coefficients (or multiple
geological properties) associated with corresponding grid nodes of the
received multiple grids are transformed into a single chance of success
value for each individual grid node. The single chance of success value
38
,
i
CA 02779207 2012-06-08
IS 10.0807-CA-NP
for each grid note provides a single chance map from the multiple
received geological maps. Chance of failure may be used instead of
chance of success.
Conclusion
[00170]
Although example systems and methods have been
described in language specific to structural features and/or
methodological acts, it is to be understood that the subject matter defined
_
in the appended claims is not necessarily limited to the specific features
or acts described. Rather, the specific features and acts are disclosed as
,
example forms of implementing the claimed systems, methods, and
structures.
39
,
