AAGGP Bs As, ArgentinaNovember 12, 2004

Recent Advances and Current Challenges in Geoscience Technology

Fred Aminzadeh,

Outline

KEY PRACTICAL PROBLEMS

MAJOR ADVANCES IN GEOPHYSICAL TECHNOLOGIS

FUTURE TRENDS

A FEW EXAMPLES

Major Challenges•• 1- Accurate positioning & detection of sub-salt /salt-flank plays• 2- Characterization of thinly laminated sand / shale sequence• 3- Deep exploration and accurate depth imaging

• 4- Distinguishing commercial gas from non-commercial gas• 5- Fault detection and their types (e.g. sealing vs. non-sealing)• 6- Environmental issues, geohazard and remediation

• 7- Exploration in difficult areas (gas clouds, mud volcano, basalt)• 8- Fluid and permeability prediction, detection and monitoring• 9- Fractures: type, orientation, frequency and connectivity• 10-Prediction of over-pressured reservoir zones.

New Technology Trends

1- Broader use of statistics / soft computing

2- Depth imaging and modeling

3- Dynamic reservoir characterization

4- Linking seismic patterns to rock properties

5- New acquisition methods / Instrumented oil fields

6- Integrating Seismic and Basin Modeling Information

7- Processing in the compressed domain/ data mining

8- Seismic while drilling and real time imaging

9- True integration data and knowledge

10-Immersive Visualization and Interpretation

Crossbedding – two scales

Well (Logs)

Scale, Uncertainty Resolution Environment

Rock (core)

Field (Seismic Attributes)

SURE CHALLENGE

TRADITIONAL & UNCONVENTIONAL STATISTICS

Wave Equations with Random VariablesMultiple Realizations of Seismic SectionsFuzzy Logic, Neural Networks, Genetic Algorithms, DAIChaos Theory, Complexity Theory

“As complexity increases, precise statements lose meaning and meaningful statements lose precision.”

L.A. Zadeh

Balanced training set

Impact of balanced data set

Unbalanced training set

Unbalanced training set Balanced training set

Impact of Balancing on Prediction

Seismic character classes for different lithologies

1

2

3

4

0

10

20

30

40

%

A B C D E F G

Class

Seismic patterns Divided into 4 classes

A-Evaporates, B-Silt/Shales, C- Shore line, D- Wet Dunes, E- Dry Dunes, F- Fanglomerates, G- Volcanics

Classification Confidence

Inherent fuzziness in geology / seismic data

From Aminzadeh and Wilkinson (2004)

Fuzzy Boundaries in Rock Type Classification

From J. Gerard, 2001

THE CONCEPT OF PSEUDO-Xn*

Pseudo-number

n

(non-precisiable granule)

Pseudo-function

X• if f is a function from reals to reals, f* is a function from reals to pseudo-numbers

(non-precisiable)

f*

Y

f

O

O

O

O

O

O

111

1

Energy

Input Attributes:Energy, Frequency, Cube Similarity Continuity, Dip Var., Azimuth Var.,

Absorption, Curvature, ..

Interpreter’sKnowledge

HAIAttribute

ANN

HAI: Combining Human Intelligence and AI

Can we model seismic patterns by DNA

From Aminzadeh (2004)

Depth Imaging & ModelingNext-generation earth modeling will incorporate quantitative representations of geological processes and stratigraphic / structural variability. Uncertainty will be quantified and built into the models The shared earth model will become the centerpiece for end-to-end technology integration from seismic imaging through reservoir and well performance simulation. As cycle time and costs for reservoir modeling and prediction fall, integration and feedback with real-time operational data become practical.

“So far as the law of mathematics refer to reality they are not certain; and so far as they are certain, they don’t refer to reality” Albert Einstein, 1951

CONSTRUCT MODEL HORIZON AND FAULTS

3D Reservoir Modeling

3-D Structural Model Building

SEG/EAGE Salt and OverthrustModeling Project 3 TB data, $25Million1994-1996

Courtesy of SEG

Highly Faulted Salt Area

Curtesy of Seislink

Seismic Imaging

Seismic imaging technology will continue to improve interpretation reliability in complex imaging environments, suchas sub-salt and overthrust playsInteractive 3D depth imaging and velocity modeling will become a standard procedure.Imaging while drilling will help fine tune subsurface images through new image updating techniques. Multi-component and time lapse measurements will grow in use for the reservoir management business, driving down unit costs, expanding application, and stimulating advances in imaging and interpretation technologies.

3D TIME/DEPTH MIGRATION TO CONSTRAIN SALT DOME

Courtesy of Unocal

DYNAMIC RESERVOIR CHARACTERIZATION

Isolating changes in reservoirs from acquisition foot prints

Sensitivity of rock properties to fluid/temperature

Permanent sensors

Incorporating time lapse seismic, log and production data

Efficient reservoir updating and visualization methods

Time: What a wonderful dimension, alas for now, we can only go in one direction: Carl Sagan, 1996

Temperature

0

0

1000

2000

3000

4000

5000

6000

7000

8000

9000 10000

11000

Am

plitu

de

‘96 ‘97

Tem

pera

ture

Incr

ease

( F

)

0

50

1100

1200

250

300

150

‘96 ‘97

Time-lapse welllog, seismic &production data

Steam Thickness Saturation

Dynamic ReservoirCharacterization

&Production OptimizationGeology & Core

Statistical Methods(Regression, clusters, cross plot, ANN, …)

Reservoir PropertiesData

Physical Methods(Rock physics, bright spot, S wave, …)

Hybrid Method

seismic, log, …)Uncertainty

Reservoir PropertiesData

Dynamic Reservoir Model Building

seismic, log, time laps data, production data

Uncertainty

RecursiveUpdating &

Visualization

FROM ATTRIBUTES TO ROCK PROPERTIES

Statistical Approach

Physics-Based Approach

Indirect Relationships (Formation Character)

Hybrid Methods

We can recognize a pattern far more easily than we can explain how we recognize it. We don’t even have reliable numerical confidence measures of patterns we think we can recognize. Bart Kosko, 1997

Reservoir PropertiesData

Physical Methods(Impedance contrast, bright spot, absorption, Bio-Gassmann, shear wave splitting,, etc.)

seismic, log, core, geology Degree of Match

λ/ λ/ λ24

Att

ribu

te m

agni

tude

Amp.Freq.Att3

Target zone thickness

λ/8

Model-Based Reservoir Characterization

Geologic Framework

Data Segmentation: Real wells =>Pseudo wells => Synthetic

Geologic Framework

Fluid Factor Attributes

Pow

er

Frequency

•Avg. Freq. Squared (AFS)•Max. Spect. Amp. (MSA)•Freq. Slope Fall (FSF)•Absorption Qual. Factor (AQF)•MSA*Dom. Freq. (MDA)

Shift of dominant frequency

NEW ACQUISITION METHODS

Ocean Bottom Surveys4 Component Data

Instrumented Oil Fields (4D Seismic)ROV’s and Robots for Placing PhonesContinuous PhonesVery Large Offset DataCross-Well Seismic+Cross Well EM

Time-lapse Inversion

φSw

Reflectivity

91

97

Acoustic imp.

Elastic imp.

InversionInput

Reservoir simulator

Gassmann

Shuey’sequation

91Timeequivalentlogs

AVO-modelled time-eq. logs

97 91

97

91

97

Water saturation volumes

0

1

Saturation 91

Saturation 97

Difference 97 -91

0.3

0

NONLINEAR SIGNAL PROCESSING

AnisotropyChaotic RegimesFractalsWavelets

Our seismic signal processing methods are all based on the Convolution models. What if, mother earth refuses to

convolve?Sven Treitel, 1995

A slice of fault cube

Faults classified bysupervised MLP neuralnetwork using 22 attributes

Fault Blocks Responsible for Hydrocarbon Migration Derived from Non-Linear Signal Processing

1 km

Non-Linear Generalized Median Filter

Before After

DATA COMPRESSION / DATA MINING

Data ExplosionRemote/ Web-Based ProcessingProcessing in the Compressed DomainZeroing on the Information-Rich Part of the DataIT trends

Where is the beef? Burger King Commercial, 1983

IT TRENDS

Seismic processing and interpretation technology will continue to ride the exponential growth curve in computational capability and connectivity. Virtually all work will be done via the Web, with interactive, on-demand access to applications, compute engines, and data. The Web will become the key enabler, reducing costs, providing worldwide access to technology, and supporting adaptation to local exploration and reservoir management requirements. More sophisticated human-machine-data interfaces will be developed beyond the capabilities of today’s visualization centers. Advanced measurement systems will create a new flood of data about reservoirs, wells, and operating facilities

Neural Network Energy Attribute

Dip Variance Similarity (Coherence type)

Attributes Along Fault Plane

Saturation > 3%

Chimney Cube Data

Integrating Chimney Analysis & Basin Modeling

Basin Model (Petromod) Accumulation of Gas Present Day

Source Rock ExpulsionSource Rock Expulsion

Fluid migration detection:

provides information on where hydrocarbons are being generated

Local fluid migration activity in source rocks

Assumed to be related to active hydrocarbon expulsion

Outline of ‘kitchen’ areaOutline of ‘kitchen’ area

Fault Fault Outline of modelled maturation area

Chimney activity

Other ‘kitchen’?

Analyse observations in Basin model and update if required

SEISMIC WHILE DRILLING

MWD and beyond Imaging and Image updating while drilling Oscillatory Drilling (Roto-Rooter)Use of Passive seismic dataUse of production noise as seismic source

3D Model Updating while Drilling

STRUCTURAL + THICKNESS

+ PETROPHYSICAL

INTEGRATION OF DATA, KNOWLEDGE AND DISCIPLINES

True Integration: Disciplines not results Data Fusion Methods Knowledge IntegrationGeo-Engineer: The Wave of the Future

Let’s Integrate

Least Wrong (Not Least Square)

Solution?

s

GeologyPetro-Physics

GeophysicsRock Physics

Geostatistics

Integration of Disciplines vs Results

Geology Geophysics Petrophysics

Geochemistry ReservoirEngineering

INTEGRATED RESERVOIR PROPERTIES AND STRUCTURAL MODELING

VISUALIZATION AND EMMERSIVE TECHNOLOGY

Virtual Reality: where are we? Next Generation VR in the oil industryVisualization as an data integration toolReduce cost, improve efficiency

A picture is worth a thousand words, Chinese expression

3D seismic and log attribute upscaling and girding

Courtesy of T-surf

E

AVO ( I*G)

Seal in a key block

Chimneys

Courtesy of Pemex

AVO on selected horizons with chimneys and wells

Sea floor

Deep Reservoir Sands

Salt

Chimneys

Shallow Reservoir Sands

The Chimney CubeThe Chimney Cube

Conclusions

Many new opportuntiesEstablishing the value of new technology applications

Reducing costDoing things fasterSolving more complicated problems

Risk reductionTrue integrationData Mining