Masoud Nikravesh

1. Intlligent Reservoir Characterization (IRESC) Masoud Nikravesh

2. Phase I-Release Date: Jan 2000.

3. Phase II-Release Date: March 2000.

4. Phase II-Release Date: Sept 2000.

5. Intelligent Reservoir Characterization (IRESC) Reservoir Engineering Data Geological Representation Log Data Seismic Data Mechanical Well Data Soft Data Hard Data Economic and Cost Data Risk Assessment Reservoir Model Inference Engine or Kernel User Interface Stratigraphic Model User

6. Basic Stratigraphic Model (BaSM) Exploration Phase Geological Model for Processing of Seismic Survey Geological Survey Seismic Survey Pre-processed Seismic Data based on Mathematical and Geological Model To IRESC Reduction of Dimension Via Feature Selection and Similarities Analysis Classification Process on Seismic and Geological Attributes Classification based on Neural Network, GA, Fuzzy Logic and Computation with word, and Geostatistic Inference Engine Stratigraphic Model Lithofacis, Similarities Cubes, ... Inference Engine or Kernel Simulate human decision making by performing User Interface, Interactive and Intelligent Visualization Tools (IV) approximate reasoning Prediction of the location of the exploration wells Knowledge Base (Expert or Knowledge of Engineer) IF … THEN Rules Linguistic Variables Knowledge Base (From Data) Intelligent and Predictive drilling based on data provided through Inference Engine, Expert Knowledge and Data Bases. User IF … THEN Rules Linguistic Variables

8. Surface Engineering and Production Data Real Time Data Acquisition and Interpretation Real Time Intelligent Control Strategy and Optimization Techniques Real Time Performance Prediction: Intelligent and Superfast Reservoir Simulator Real Time Visualization Decision Supervisor Reservoir Model Parameters from IRESC Model Reservoir Management Economic and Cost Data Risk Assessment

9. Training Phase Around the Well-Bore Similarities Cubes Seismic Attributes Inverse Model Based on Soft Computing Techniques WireLine Logs, LithoFacies Reflectivity, Impedance, Other Seismic Attributes Prediction Phase Away from the Well-Bore

10. What is Soft Computing? • “Soft computing is consortium of computing methodologies which collectively provide a foundation for the Conception, Design and Deployment ofIntelligent Systems.” L.A. Zadeh • Fuzzy Logic (GL) • Neuro-Computing (NC) • Genetic Computing (GC) • Probabilistic Reasoning (PR) • Genetic Algorithms (GA), Chaotic Systems (CS), Belief Networks (BN), Learning Theory (LT) • The role model for Soft Computing is theHuman Mind.

11. Cluster Analysis Task of Splitting Patterns Clustering Set of Pattern Suitable Similarity Measure Homogeneous Classes (Clusters)

12. Techniques to be Used for Calculating the Similarity Cubes Classical Pattern Recognition/ Statistical Techniques K-Mean Clustering, … Neural Network Self Organizing Map, Radial Basis Function Network, … Fuzzy Logic Fuzzy C-Mean (FCM), Fuzzy Kohonen Clustering Networks (FKCNs), …

13. Optimal Processing Window and Sub_Window Optimal Number of Attributes Attributes Raw seismic Amplitude envelope Instantaneous Frequency Instantaneous Phase Cosine instantaneous phase Integrated absolute amplitude etc., ... Optimal Number of Clusters Gas/No-Gas, Breccia/No Breccia DOL, LS, SH, CHAT, Others (SS, COAL) Cube Section Logs Seismic/ Logs Seismic Logs Seismic/ Logs Seismic This will be generated for around the well-bore. This part needs generation of Pseudo Logs from Seismic using Advanced Techniques. (See IRESC Model)

14. Well Path Seismic Traces Seismic Window Well Log Window

15. Statistical Technique Neural Network Technique Fuzzy Logic Technique

16. Cluster Analysis, Specific Section Well Fh Medium Production Well Bh High Production Well Ch Medium Production Well Dh Medium Production Well Eh 13182 MSCF/Mon Well Eh Low Production Well Ah High Production

18. Well Locations Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

19. Raw Seismic Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

20. Amplitude Envelope Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

21. Instantaneous Phase Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

22. Cosine Instantaneous Phase Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

23. Instantaneous Frequency Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

24. Instantaneous Frequency Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

25. Instantaneous Frequency Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

26. Integrated Absolute Amplitude Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

27. Cluster Well Fh Medium Production Well Dh Medium Production Well Bh High Production Well Eh Low Production Well Ch Medium Production Well Ah High Production

29. Typical k-means/neural network/fuzzy c-means distribution of clusters.

30. Well Ch Medium production Well Fh Medium Production Well Eh Low Production Well Ah High Production Well Dh Medium Production Well Bh High Production Cluster 11 that correlate with production, k-means/neural network/fuzzy c-means clusters. In the section passing through the wells as shown in Figure 2.

31. Potential for High Production Production Index Potential for Low Production Cluster Index Clusters and production, optimal well placement, 2-D time slice.

32. Well Dh Medium Production Well Bh High Production Well Fh Medium Production Production Index Well Ah High Production Well Eh Low Production Well Ch Medium Production Potential for High Production Cluster Index Potential for Low Production New Well Locations Clusters and production, optimal well placement, a 2-D time slice with 3-D representation.

33. Phase II-Release Date: Sept 2000.