SPE DISTINGUISHED LECTURER SERIES is funded principally through a grant of the SPE FOUNDATION

1. SPE DISTINGUISHED LECTURER SERIES is funded principally through a grant of the SPE FOUNDATION The Society gratefully acknowledges those companies that support the program by allowing their professionals to participate as Lecturers. And special thanks to The American Institute of Mining, Metallurgical, and Petroleum Engineers (AIME) for their contribution to the program.

2. SPE Distinguished Lecture 2007-2008 Smart Completions, Smart Wells and Now Smart Fields; Challenges & Potential Solutions Shahab D. Mohaghegh, Ph.D. West Virginia University & Intelligent Solutions, Inc.

3. Smart Oil Field Technology • Smart Completion: • Downhole control to adjust flow distributions along the wellbore to correct undesirable fluid front movement. • Smart Well: • Using permanent gauges and automatic flow controls for continuous monitoring of events and automatic interaction using extensive downhole communication. • Smart Field: • Digital Oil Field, Field of Future, Intelligent Field, i Field, ….

4. Full Field Flow Models for Reservoir Simulation & Modeling. One of the major tools for integrated Reservoir Management Real-Time, High Frequency Data Stream Time Scale: Seconds, Minutes, Hours Time Scale: Days, Months, …. Characteristics of Smart Fields • Availability of high frequency data. • Possibility of intervention, control and management from a distance.

5. Full Field Flow Models for Reservoir Simulation & Modeling. One of the major tools for integrated Reservoir Management Real-Time, High Frequency Data Stream Time Scale: Seconds, Minutes, Hours Time Scale: Days, Months, …. The Bottle-Neck How can the bottle-neck be removed? Perform analysis at the same time scale as the High Frequency Data Streams; in seconds, or better yet, in REAL-TIME

6. Smart Fields – Automation & Intelligence • Are Automation and Intelligence synonyms? • Automation in the smart field is achieved though: • Placing permanent gauges downhole. • Making large volumes of data available in real-time. • Capability to control well and completion operations from a remote location (office).

7. Knowledge Information DATA Smart Fields – Automation & Intelligence • Are Automation and Intelligence synonyms? • Data has to be transformed into information, then knowledge, to be used as a tool for: • Analysis under uncertain conditions • Process optimization in real-time • Decision making & analysis in real-time

8. The Challenge • Make the most important reservoir management tools (Complex Numerical Solutions ) available at the same time scale as the (high frequency) data. • Surrogate Reservoir Models (SRM)

9. SURROGATERESERVOIRMODELDefinition • Surrogate Reservoir Models are replicas of the numerical simulation models (full field flow models) that run in real-time. • REPLICA. • A copy or reproduction of a work of art, especially one made by the original artist. • A copy or reproduction, especially one on a scale smaller than the original. • Something closely resembling another.

10. OUTPUT INPUT SYSTEM Characteristics of SRM • SRMs are not • response surfaces. • statistical representations of simulation models. • SRMs are • engineering tools • honor the physics of the problem in hand. • adhere to the definition of “System Theory”.

11. How Do You Build an SRM? • Define concrete objectives. • The objective determines the Type & Scale of SRM. • Generate required data. • Use the “ right tool ” to build the SRM. • Test and validate the accuracy of the SRM. Surrogate Reservoir Models are developed using State-Of-The-Art in Intelligent Systems (NN-FL-GA)

12. How Do You Build an SRM? • Clearly identify the objective of the project and how the SRM is going to be used. • SRMs are developed to address very specific issues such as: • Production/injection (rate & pressure) profiles of wells in reservoir/filed. • Changes in pressure and saturation throughout reservoir/field (Flood Fronts). • Interaction between wells.

13. How Do You Validate SRMs? • A considerable volume of data must be used as blind data for validation purposes. • SRMs must be validated in their accuracy before they are used for analysis. • This is possible since the data generation engine is accessible. In our case studies we have used 40-95% of the data as blind dataset for validation.

14. Types of SRM • SRMs are developed on different SCALES in order to address specific needs of a project. • The key to development of SRM is the recognition that numerical models are built based on discrete mathematics (small and manageable sub-models that are repeated over and over). • Our success is based on recognizing this fact and taking full advantage of its consequences.

15. Types of SRM • SRMs are classified based on the size of their elemental volume. • “Grid Block – Based” SRM • “Well – Based” SRM • “Domain – Based” SRM

16. DSw DSo DSg DP Types of SRM • “Grid Block – Based” SRM

17. DSw DSo DSg DP Types of SRM • “Grid Block – Based” SRM • Tracking changes in pressure and saturation at the grid block level. • Detect by-passed oil. • Has been used successfully to model DS and DP as a function of time-lapsed seismic attributes. • May be used for: • Flood front monitoring. • Optimization of rock-typing in flow models. • Populating geological models.

18. Types of SRM • “Well – Based” SRM

19. Types of SRM • “Well – Based” SRM • Monitoring pressure and rate at the injection and production wells. • Rate optimization. • New well placements in complex reservoirs. • Quantify uncertainties associated with geological model. • Selective injection and production into portions of reservoir.

20. Types of SRM • “Domain – Based” SRM

21. Types of SRM • “Domain – Based” SRM • Monitoring the interaction of wells with one another. • Monitoring flood front during water flood operations. • Optimizing injection rates for maximum sweep efficiency. • New well placement to optimize enhance recovery. • Developing new strategies for field development.

22. Intelligent System; the Foundation of SRM • Intelligent Systems • Artificial Neural Networks • Genetic Algorithms • Fuzzy Logic

23. Case Study • Lets see an example of a Surrogate Reservoir Model in action.

24. Background • A giant oil field in the Middle East. • Complex carbonate formation. • 168 horizontal wells. • Total field production capped at 250,000 BOPD. • Each well is capped at 1,500 BOPD. • Water injection for pressure maintenance.

25. Background • Management Concerns: • Water production is becoming a problem. • Cap well production to avoid bypass oil. • Uncertainties associated with models. • Technical Team’s Concerns: • May be able to produce more oil from some wells (which ones? How much increase?) without significant increase in water cut. • Increasing well rate may actually help recovery.

26. Objective • Increase oil production from a giant oil field in the Middle East by identifying wells that by increasing the oil rate: • will not suffer from high water cut. • will not leave bypassed oil behind. • Accomplishing this objective required hundreds of thousands of simulation runs; thus development of a Surrogate Reservoir Model (SRM) based on the Full Field Model (FFM) became a requirement.

27. FFM Characteristics • Full Field Model Characteristics: • Underlying Complex Geological Model. • Industry Standard Commercial Reservoir Simulator • 165 Horizontal Wells. • Approximately 1,000,000 grid blocks. • Single Run = 10 Hours on 12 CPUs. • Water Injection for Pressure Maintenance.

28. Very Complex Geology Naturally Fractured Carbonate Reservoir. Reservoirs represented in the FFM.

29. Steps Involved in SRM Development • Identify Clear Objectives • Design SRM’s input and output • Generate Data • Build SRM • Validate • Analyze • Results & Conclusions

30. SRM’s Objective • Accurately Reproduce the following for the next 25 to 40 years. • Cumulative Oil Production • Cumulative Water Production • Instantaneous Water Cut

31. SRM’s Input & Output • OUTPUT was identified by the Objective • Cumulative Oil Production • Cumulative Water Production • Instantaneous Water Cut • INPUT must be designed in a way to capture the complexity of the reservoir. • Well-based SRM • Well-based SRM grid • Curse of dimensionality

32. Curse of Dimensionality • Complexity of a system increases with its dimensionality. • Tracking system behavior becomes increasingly difficult as the number of dimensions increases. • Systems do not behave in the same manner in all dimensions. • Some are more detrimental than others.

33. Curse of Dimensionality • Sources of dimensionality: • STATIC: Representation of reservoir properties associated with each well. • DYNAMIC: Simulation runs to demonstrate well productivity.

34. Curse of Dimensionality • Representing reservoir properties for horizontal wells.

35. Curse of Dimensionality, Static • Potential list of parameters that can be collected on a “per-well” basis. 16 Parameters

36. Curse of Dimensionality, Static • Potential list of parameters that can be collected on a “per-grid block” basis. 12 Parameters

37. 12 parameters x 40 grid block/well = 480 16 parameter per well Total of 496 parameter per well Curse of Dimensionality, Static • Total number of parameters that need representation during the modeling process: Building a model with 496 parameters per well is not realistic,THE CURSE OF DIMENSIONALITY Dimensionality Reduction becomes a vital task.

38. Curse of Dimensionality, Dynamic • Well productivity is identified through following simulation runs: • All wells producing at 1500, 2500, 3500, & 4500 bpd (nominal rates) • No cap on field productivity (4 simulation runs) • Cap the field productivity (4 simulation runs) Need to understand reservoir’s response to changes in imposed constraints.

39. Curse of Dimensionality, Dynamic • Well productivity through following simulation runs: • Step up the rates for all wells • No cap on field productivity (1 simulation runs) • Cap the field productivity (1 simulation runs) Need to understand reservoir’s response to changes in imposed constraints.

40. Data Generation • Total of 10 simulation runs were made to generate the required output for the SRM development (training, calibration & validation) • Using Fuzzy Pattern Recognition technology input to the SRM was compiled.

41. Fuzzy Pattern Recognition • In order to address the “Curse of Dimensionality” one must understand the behavior and contribution of each of the parameters to the process being modeled. • Not a simple and straight forward task. !!!

42. Fuzzy Pattern Recognition • To address this issue, we use Fuzzy Pattern Recognition technology.

43. Fuzzy Pattern Recognition Parameter: Pressure @ Reference

44. Fuzzy Pattern Recognition

45. Please Note: The lower the bar, the higher the influence. Key Performance Indicators

46. Validation of the SRM

47. Validation of the SRM

48. Validation of the SRM

49. Validation of the SRM

50. Validation of the SRM