Recent Advances and Current Challenges in Geoscience Technology

Transcription

1 Recent Advances and Current Challenges in Geoscience Technology Fred Aminzadeh, AAGGP Bs As, Argentina November 12, 2004

2 Outline KEY PRACTICAL PROBLEMS MAJOR ADVANCES IN GEOPHYSICAL TECHNOLOGIS FUTURE TRENDS A FEW EXAMPLES

3 Major Challenges 1- Accurate positioning & detection of sub-salt /saltflank 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. nonsealing) 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.

4 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

5 Crossbedding two scales

6 SURE CHALLENGE Rock (core) Scale, Uncertainty Resolution Environment Well (Logs) Field (Seismic Attributes)

7 TRADITIONAL & UNCONVENTIONAL STATISTICS Wave Equations with Random Variables Multiple Realizations of Seismic Sections Fuzzy Logic, Neural Networks, Genetic Algorithms, DAI Chaos Theory, Complexity Theory As complexity increases, precise statements lose meaning and meaningful statements lose precision. L.A. Zadeh

8 Impact of balanced data set Unbalanced training set Balanced training set

9 Impact of Balancing on Prediction Unbalanced training set Balanced training set

10 Classification Confidence Seismic character classes for different lithologies % Class Seismic patterns Divided into 4 classes A B C D E F G A-Evaporates, B-Silt/Shales, C- Shore line, D- Wet Dunes, E- Dry Dunes, F- Fanglomerates, G- Volcanics

11 Inherent fuzziness in geology / seismic data From Aminzadeh and Wilkinson (2004)

12 Fuzzy Boundaries in Rock Type Classification From J. Gerard, 2001

13 THE CONCEPT OF PSEUDO-X n * Pseudo-number (non-precisiable granule) Pseudo-function Y n f * (non-precisiable) f if f is a function from reals to reals, f * is a function from reals to pseudo-numbers X

14 HAI: Combining Human Intelligence and AI HAI Attribute Energy O O O Input Attributes: Energy, Frequency, Cube Similarity Continuity, Dip Var., Azimuth Var., Absorption, Curvature,.. O O ANN O Interpreter s Knowledge

15 Can we model seismic patterns by DNA From Aminzadeh (2004)

16 Depth Imaging & Modeling Next-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-toend 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

17 CONSTRUCT MODEL HORIZON AND FAULTS

18 3D Reservoir Modeling

19 3-D Structural Model Building SEG/EAGE Salt and Overthrust Modeling Project 3 TB data, $25Million Courtesy of SEG

20 Highly Faulted Salt Area Curtesy of Seislink

21 Seismic Imaging Seismic imaging technology will continue to improve interpretation reliability in complex imaging environments, such as sub-salt and overthrust plays Interactive 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.

22 3D TIME/DEPTH MIGRATION TO CONSTRAIN SALT DOME Courtesy of Unocal

23 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

24 0 Time-lapse well log, seismic & production data Amplitude Geology & Core Dynamic Reservoir Characterization & Production Optimization 0 Temperature Increase ( F) Steam Thickness Temperature Saturation

25 Hybrid Method Data seismic, log, ) Statistical Methods (Regression, clusters, cross plot, ANN, ) Physical Methods (Rock physics, bright spot, S wave, ) Reservoir Properties Uncertainty

26 Dynamic Reservoir Model Building Data seismic, log, time laps data, production data Recursive Updating & Visualization Reservoir Properties Uncertainty

27 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

28 Model-Based Reservoir Characterization Data Physical Methods (Impedance contrast, bright spot, absorption, Bio-Gassmann, shear wave splitting,, etc.) Reservoir Properties seismic, log, core, geology Attribute magnitude λ/ 8 λ/ 4 λ/ 2 λ Target zone thickness Amp. Freq. Att3 Degree of Match

29 Geologic Framework Geologic Framework Data Segmentation: Real wells =>Pseudo wells => Synthetic

30 Fluid Factor Attributes Power Avg. Freq. Squared (AFS) Max. Spect. Amp. (MSA) Freq. Slope Fall (FSF) Absorption Qual. Factor (AQF) MSA*Dom. Freq. (MDA) Frequency Shift of dominant frequency

31 NEW ACQUISITION METHODS Ocean Bottom Surveys 4 Component Data Instrumented Oil Fields (4D Seismic) ROV s and Robots for Placing Phones Continuous Phones Very Large Offset Data Cross-Well Seismic+Cross Well EM

32 Time-lapse Inversion Input Inversion Reservoir simulator Gassmann Time equivalen t logs 9 9 Reflectivit 1 7 y 9 9 Acoustic 1 7 imp. φ Sw Shuey s equation AVO-modelled time-eq. logs 9 9 Elastic 1 7imp.

33 Water saturation volumes 0 Saturation 91 Saturation 97 1 Difference

34 NONLINEAR SIGNAL PROCESSING Anisotropy Chaotic Regimes Fractals Wavelets Our seismic signal processing methods are all based on the Convolution models. What if, mother earth refuses to convolve? Sven Treitel, 1995

35 A slice of fault cube Faults classified by supervised MLP neural network using 22 attributes

36 Fault Blocks Responsible for Hydrocarbon Migration Derived from Non-Linear Signal Processing 1 km

37 Non-Linear Generalized Median Filter Before After

38 DATA COMPRESSION / DATA MINING Data Explosion Remote/ Web-Based Processing Processing in the Compressed Domain Zeroing on the Information-Rich Part of the Data IT trends Where is the beef? Burger King Commercial, 1983

39 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, ondemand 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

40 Attributes Along Fault Plane Neural Network Energy Attribute Dip Variance Similarity (Coherence type)

41 Integrating Chimney Analysis & Basin Modeling Saturation > 3% Basin Model (Petromod) Accumulation of Gas Present Day Chimney Cube Data

42 Source 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

43 Outline of kitchen area Fault Chimney activity Fault Outline of modelled maturation area Other kitchen? Analyse observations in Basin model and update if required

44 SEISMIC WHILE DRILLING MWD and beyond Imaging and Image updating while drilling Oscillatory Drilling (Roto-Rooter) Use of Passive seismic data Use of production noise as seismic source

45 3D Model Updating while Drilling STRUCTURAL + THICKNESS + PETROPHYSICAL

46 INTEGRATION OF DATA, KNOWLEDGE AND DISCIPLINES True Integration: Disciplines not results Data Fusion Methods Knowledge Integration Geo-Engineer: The Wave of the Future

47 Let s Integrate Petro- Physics Geology Geostatistics Rock Physics Geophysics Least Wrong (Not Least Square) Solution? s

48 Integration of Disciplines vs Results Geology Geophysics Petrophysics Geochemistry Reservoir Engineering

49 INTEGRATED RESERVOIR PROPERTIES AND STRUCTURAL MODELING

50 VISUALIZATION AND EMMERSIVE TECHNOLOGY Virtual Reality: where are we? Next Generation VR in the oil industry Visualization as an data integration tool Reduce cost, improve efficiency A picture is worth a thousand words, Chinese expression

51 3D seismic and log attribute upscaling and girding Courtesy of T-surf

52 AVO on selected horizons with chimneys and wells AVO ( I*G) Seal in a key block Chimneys E Courtesy of Pemex

53 The Chimney Cube Sea floor Shallow Reservoir Sands Chimneys Salt Deep Reservoir Sands

54 Conclusions Many new opportunties Establishing the value of new technology applications Reducing cost Doing things faster Solving more complicated problems Risk reduction True integration Data Mining