The document discusses the application of IBM's cognitive computing technologies in the oil and gas sector, specifically focusing on reservoir management and decision-making processes. It outlines use cases such as reservoir analogues advising and seismic interpretation, emphasizing how these technologies can enhance productivity and support various business operations. The cognitive systems help petroleum engineers make informed decisions by providing insights and analytics derived from both structured and unstructured data.

1. Wake up with Watson
Cognitive ReservoirAnalogues
Renato Cerqueira
November 15, 2016

2. IBM Research Big Plays in Oil & Gas
Capital Projects,
Operations, Optimization
Resource Characterization
and Management
Nanotechnology Applied to
Enhanced Oil Recovery and
Mining
Cognitive Computing for Natural Resources (Oil & Gas)
HPC Industry Cloud
2

3. Selected Use Cases
3
UC2: Basin / Reservoir
Analogues Adviser
UC3: Seismic Interpretation
Adviser
UC1: Basin / Reservoir
Knowledge Base Builder
UC5: Machine-learnt Well Log
Analytics
UC4: Capital Project
Management Adviser
UC6: IoT-driven Reservoir
Digital Twin
Decision-
Making
Operation &
Monitoring
Data
Transformation
Living
Model
Insights
Actions,
strategies
Big Data IoT
News and
social media
Specs and
requirement
s
Rules and
Regulations
Contracts
Reports
Lessons
Learned
Capital Projects
Management
Adviser
Image /
video feeds
Projects
schedules
Weather
Risk factors
TEXTUAL/UNSTRUCTUREDDATA
Cognitive
Computing
Technologies
Optimization
Simulation
Forecasting
Look to the past to understand the
project’s current situation
Look to the future and identify main
points of attention
What can be done to avoid potential
impacts?
Define optimal course of action and
implement measures
Seismic
Coherence

4. Selected Use Cases
4
UC2: Basin / Reservoir
Analogues Adviser
UC3: Seismic Interpretation
Adviser
UC1: Basin / Reservoir
Knowledge Base Builder
UC5: Machine-learnt Well Log
Analytics
UC4: Capital Project
Management Adviser
UC6: IoT-driven Reservoir
Digital Twin
Decision-
Making
Operation &
Monitoring
Data
Transformation
Living
Model
Insights
Actions,
strategies
Big Data IoT
News and
social media
Specs and
requirement
s
Rules and
Regulations
Contracts
Reports
Lessons
Learned
Capital Projects
Management
Adviser
Image /
video feeds
Projects
schedules
Weather
Risk factors
TEXTUAL/UNSTRUCTUREDDATA
Cognitive
Computing
Technologies
Optimization
Simulation
Forecasting
Look to the past to understand the
project’s current situation
Look to the future and identify main
points of attention
What can be done to avoid potential
impacts?
Define optimal course of action and
implement measures
Seismic
Coherence

5. UC 2: Cognitive Reservoir Analogues Adviser
5

6. UC 2: Cognitive Reservoir Analogues Adviser
6
Basin/ Reservoir Similarity + DeepQA
Data Driven Analytics
(Machine learning
modeling/prediction)
Data Management
(Injection, Edition)
Unstructured Data Sources
(Technical Articles, Reports from OnePetro) RES.
ID
PERMEAB
ILITY
BASIN
CODE
… VISCOSIT
Y
POROSITY
TYPE CODE
R1 112.5 2-1-2 … ̶ 8
R2 63.0 2-3 … 6.5 24
R3 ̶ 1-2-4 … 12.0 18
… … … … … …
R1200 75.0 2-3 … 0.75 ̶
Structured Data Sources
(Known Basin/Reservoir Databases)
“What is the average
permeability in the F1
field?”
Answers both
directly based on
known facts from
data sources
as well as
extrapolated from
analogues
Questions about
reservoir
properties and
analogues in
natural language
“What is the reservoir most
similar to R7 in terms of
structural properties?”
“Average permeability
in the F1 field is 22mD
with 90% confidence.”
“R11 is the reservoir most
similar to R7 in terms of
structural properties with
similarity .78”
DeepQA (enhanced to
consider similarity reasoning)
Knowledge
Base
Domain Ontology
ponents
nents
Information
Extraction
Agents
Reasoning
Engine
Knowledge
Graph
Query
Interface

7. Cognitive Reservoir Analogues – Technical Details
7
Structural Code = 8
Area (Km2) = 20
Porosity (%) = 12
Oil Viscosity (MPa.s) = 1.1
Permeability (mD) = 22
Lithology Code = 410
ID
PERMEA
BILITY
BASIN
CODE
… VISCOSITY
CR1 112.5 2-1-2 … ̶
CR2 63.0 2-3 … 6.5
… … … … …
CR1200 75.0 2-3 … 0.75
New Target Reservoir
Known Reservoirs
Databases
Resulting Analogues
Rank ID Similarity
1 CR354 .82
2 CR180 .82
3 CR22 .81
… … …
Uncertainty Characterization
Target Reservoir with
predicted parameters
Reservoir Analogues Toolset
Data Management (injection/edition)
+
Data Driven Analytics (machine
learning modeling/prediction)
Structural Code = 8
Area (Km2) = 20
Porosity (%) = 12
Oil Viscosity (MPa.s) = ?
Permeability (mD) = ?
Lithology Code = ?

8. Flow Diagram of the RA method
8
Reservoir Analogues Toolset
Data Management (injection/edition)
+
Data Driven Analytics (machine
learning modeling/prediction)

9. Application Example
Computing ROI of a set of reservoir targets
9
Reservoir Visual Analytics (RVA)
ECONOMIC
EVALUATION
RESERVOIR SIMULATION
FACILITIES
DESIGN
RESERVOIR
MODELING
PRODUCTION
STRATEGY
Asset Uncertainty
Characterization
Business &
Market Information

10. Prototype Demo – Video (on site at event only)
10
Scenario
• How can a Cognitive Reservoir Analogues (CoRA) help Jonatan, a Petroleum Engineer,
make critical decision as to whether or not to go ahead with the well exploration in the
Sergipe-Alagoas Basin, Brazil?
You will see
• How CoRA helps Jonatan calculate the oil in place by identifying and estimating important
missing parameters
• How CoRA points out that Jonatan needs the value of Porosity of the rock to carry out the
task.
• How CoRA computes and shows evidences in support of its calculations.
As a result
• Jonatan was able to explore and inspect analogous reservoirs in his company’s DB as well
as external unstructured knowledge so as to estimate with high confidence unknown
parameters of the prospect reservoir.

11. Summing up
11
Natural
Language
Understanding
Analytics
Multimodal
Dialog
Machine
Learning
Visual
Analytics
Uncertainty
Quantification
Semantic-
based
Integration
Multimedia
Feature
Extraction
Automatic
Reasoning
Knowledge
Management
Predictive
Models
Parallel
Hypotheses
Generation
 Cognitive Computing is the use of computational
learning systems to augment human cognitive
capabilities and accelerate, enhance and scale
human expertise to solve real world problems.
 Cognitive technologies are ready to address the
challenges of the Oil & Gas industry and to transform
practices in the industry, in face of data overload,
new frontiers, workforce and skills shortage.
 Cognitive Systems can provide unprecedented gain
in productivity in knowledge-intensive processes,
such as exploration and production, giving geologists
to tap quickly into information and draw relationships
at a scale that is almost prohibitive today.
 CoRA can support several different business
processes, such ROI estimation of a target prospect,
seismic interpretation, geological risk assessment,
production planning and forecast, among others.

12. 12
Thank you.

13. 13
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