Wake up with Watson

Cognitive Reservoir Analogues

Renato Cerqueira

November 15, 2016

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

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

ModelInsig

hts

Actio

ns,

stra

teg

ies

Big Data IoT

News and

social media

Specs and

requirement

s

Rules and

RegulationsContracts

ReportsLessons

Learned

Capital ProjectsManagement

Adviser

Image /

video feeds

Projects schedules

Weather

Risk factors

TE

XT

UA

L /

UN

ST

RU

CT

UR

ED

D

AT

A

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 SeismicCoherence

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

ModelInsig

hts

Actio

ns,

stra

teg

ies

Big Data IoT

News and

social media

Specs and

requirement

s

Rules and

RegulationsContracts

ReportsLessons

Learned

Capital ProjectsManagement

Adviser

Image /

video feeds

Projects schedules

Weather

Risk factors

TE

XT

UA

L /

UN

ST

RU

CT

UR

ED

D

AT

A

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 SeismicCoherence

UC 2: Cognitive Reservoir Analogues Adviser

5

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

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 = ?

Flow Diagram of the RA method

8

Reservoir Analogues Toolset

Data Management (injection/edition)

+

Data Driven Analytics (machine

learning modeling/prediction)

Application Example Computing ROI of a set of reservoir targets

9

Reservoir Visual Analytics (RVA)

EC

ON

OM

IC

EV

AL

UA

TIO

N

RESERVOIR SIMULATION

FA

CIL

ITIE

S

DE

SIG

N

RESERVOIR

MODELING

PRODUCTION

STRATEGY

Asset Uncertainty

Characterization

Business &

Market Information

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.

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

Thank you.

13

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