SPE DISTINGUISHED LECTURER SERIES is funded principally SPE FOUNDATION

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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 Smart Completions, Smart Wells and Now Smart Fields; Challenges & Potential Solutions Shahab D. Mohaghegh, Ph.D. West Virginia University & Intelligent Solutions, Inc. 2

3 Smart Oil Field Technology Smart Completion: Remotely monitor & control downhole fluid production or injection. Downhole control to adjust flow distributions along the wellbore to correct undesirable fluid front movement. 3

4 Smart Oil Field Technology Smart Well: Using permanent downhole gauges for continuous monitoring of pressure, flow rates, and automatic flow controls. Capability of automatic interaction using extensive downhole communication. 4

5 Smart Oil Field The Missing link 5

6 Characteristics of Smart Fields Availability of high frequency data. The Missing link Making reservoir management decisions based on real time data from the field. Possibility of intervention, control and management from a distance. 6

7 Characteristics of Smart Fields Availability of high frequency data. The Missing link Making reservoir management decisions based on real time data from the field. Possibility of intervention, control and management from a distance. 7

8 Characteristics of Smart Fields Making reservoir management decisions based on real-time data from the field. Considerations: Reservoir management tools. Uncertainties associated with the geological model. Predicting the consequences of the decision. Real-time optimization. 8

9 Hardware / Software Intelligence requires a combination of hardware and software. We have made strong advances in hardware. Software development is lagging. Intelligent Systems will play a pivotal role: Artificial Neural Networks Fuzzy Set Theory Genetic Optimization 9

10 Hardware / Software Surrogate Reservoir Models (SRM) are developed to address the software need of smart fields. SRM are reservoir management tools for smart fields: Real-time full field reservoir simulation & modeling Predictive modeling Uncertainty analysis Real-time optimization i 10

11 Removing The Bottle-Neck Real-Time, High Frequency Data Stream Time Scale: Time Scale: Seconds, Minutes, Hours Full Field Flow Models for Reservoir Simulation & Modeling. One of the major tools for integrated Reservoir Management Days, Months,. 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 11

12 SURROGATE RESERVOIR MODEL Definition Surrogate Reservoir Models are replicas of the numerical simulation models (full field flow models) that run in real-time 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. 12

13 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. INPUT SYSTEM OUTPUT 13

14 Case Study Lets see an example of a Surrogate Reservoir Model in action. This case study demonstrates development of a surrogate reservoir model (SRM) that will run in real-time in order to accomplish the objectives of the project. 14

15 Background A giant oil field in the Middle East. Complex carbonate formation. 165 horizontal wells. Total field production capped at 250,000 BOPD. Each well is capped at 1,500 BOPD. Water injection for pressure maintenance. 15

16 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. 16

17 Objective Increase oil production from the field by identifying wells that: will not suffer from high water cut. will not leave bypassed oil behind. 17

18 Objective Accomplishing this objective requires: Exhaustive search of the solution space, examining all possible production scenarios, while considering uncertainties associated with the geological model. Hundreds of thousands of simulation runs; thus development of a Surrogate Reservoir Model (SRM) based on the Full Field Model (FFM) became a requirement. 18

19 Flow Model Characteristics Full Field Flow 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. 19

20 Very Complex Geology Naturally Fractured Carbonate Reservoir. Reservoirs represented in the Flow Model. 20

21 Steps Involved in SRM Development Identify Clear Objectives Design SRM s input and output Generate Data Build SRM Validate Analyze Results & Conclusions 21

22 SRM s Objective Accurately Reproduce the following for the next 25 to 40 years. Cumulative Oil Production Cumulative Water Production Instantaneous Water Cut 22

23 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 23

24 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. 24

25 Curse of Dimensionality Sources of dimensionality: STATIC: Representation of reservoir properties associated with each well. DYNAMIC: Simulation runs to demonstrate well productivity. 25

26 Well-Based Surrogate Reservoir Model Surrogate Model Elemental Volume The elemental volume includes 40 SRM blocks. 26

27 Curse of Dimensionality, Static Potential list of parameters that can be collected on a per-well basis. Parameters Used on a per well basis Latitude Deviation Horizontal Well Length Distance to Free Water Level Flowing Reference Point Cum. Oil Reference Point Distance to Nearest Producer Distance to Major Fault Longitude Azimuth Productivity Index Water Reference Point Oil Prod. Reference Point Cum. Water Reference Point Distance to Nearest Injector Distance to Minor Fault 16 Parameters 27

28 Curse of Dimensionality, Static Potential list of parameters that can be collected on a per-srm block basis. Parameters Used on a per segment basis Mid Depth Relative Rock Ttype Initial Water Saturations Horizontal Permeability Reference Point Capillary Pressure/Saturation Function Thickness Porosity Stylolite Intensity Vertical Permeability Reference Point Reference Point 12 Parameters 28

29 Curse of Dimensionality, Static Total number of parameters that need representation during the modeling process: 12 parameters x 40 grid block/well ll = parameter per well Total of 496 parameter per well Building a model with 496 parameters per well is not realistic, THE CURSE OF DIMENSIONALITY Dimensionality Reduction becomes a vital task. 29

30 Curse of Dimensionality, Dynamic Well productivity it is identified d through h 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. 30

31 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. 31

32 Data Generation Total of 10 simulation runs were made to generate the required output t for the SRM development (training, calibration & validation) Using Fuzzy Pattern Recognition technology input to the SRM was compiled. 32

33 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.!!! 33

34 Fuzzy Pattern Recognition To address this issue, we use Fuzzy Pattern Recognition technology. 34

35 Fuzzy Pattern Recognition Parameter: Reference 35

36 Fuzzy Pattern Recognition 36

37 Key Performance Indicators 37

38 Validation of the SRM ate Model) t % (Surroga Water Cut Water Cut % (Reservoir Simulator) 38

39 Validation of the SRM Cumu odel) lative Oil Production (Surrogate Mo Cumulative Oil Production (Reservoir Simulator) 39

40 40

41 Validation of the SRM 41

42 Validation of the SRM 42

43 Validation of the SRM 43

44 Validation of the SRM 44

45 Using SRM for Analysis Identify wells that benefit from a rate increase and those that would not. Address the uncertainties associated with the simulation model. Generate Type curves for each well. Design production strategy. Use as assisted history matching tool. To perform the above analyses millions of simulation runs were required. Using the SRM all such analyses were performed quite quickly. 45

46 Optimal Production Strategy Well Ranked No. 1 IMPORTANT NOTE: This is NOT a Response Surface SRM was run hundreds of times to generate these figures. 46

47 Optimal Production Strategy Well Ranked No. 100 IMPORTANT NOTE: This is NOT a Response Surface SRM was run hundreds of times to generate these figures. 47

48 Optimal Production Strategy Wells were divided into 5 clusters. Production in wells in cluster 1 can be increased significantly without substantial increase in water production. Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 12 Wells 14 Wells 22 Wells 37 Wells 80 Wells Best Performance 48

49 Analysis of Uncertainty Objective: To address and analyze the uncertainties associated with the Full Field Model using Monte Carlo simulation method. 49

50 Analysis of Uncertainty Motivation: The Full Field Model is a reservoir simulator that is based on a geologic g model. The geologic model is developed based on a set of measurements (logs, core analysis, seismic, ) and corresponding geological and geophysical interpretations. 50

51 Analysis of Uncertainty Motivation: Therefore, like any other reservoir simulation and modeling effort, it includes certain obvious uncertainties. One of the outcomes of this project has been the identification of a small set of reservoir parameters that essentially control the production behavior in the horizontal wells in this field (KPIs). 51

52 Analysis of Uncertainty Following are the steps involved: 1. Identify a set of key performance indicators that are most vulnerable to uncertainty. 2. Define probability distribution function for each of the performance indicators. a. Uniform distribution b. Normal (Gaussian) distribution c. Triangular distribution d. Discrete distribution 52

53 Analysis of Uncertainty Following are steps involved: 3. Run the neural network model hundreds or thousands of times using the defined probability distribution functions for the identified reservoir parameters. Performing this analysis using the actual al Full Field Model is impractical. 4. Produce a probability distribution function for cumulative oil production and the water cut at different time and liquid rate cap. 53

54 Analysis of Uncertainty Following are steps involved: 5. Such results bounds to be much more reliable and therefore, more acceptable to the management or skeptics of the reservoir modeling studies. 54

55 Analysis of Uncertainty 55

56 Analysis of Uncertainty 56

57 Analysis of Uncertainty Average S Reference point in Top Layer II Value in the model = 8% Lets use a minimum of 4% and a maximum of 15% with a triangular distribution

58 Analysis of Uncertainty Average Capillary Reference point in Top Layer III Value in the model = 79 psi Lets use a minimum of 60 psi and a maximum of 100 psi with a triangular distribution

59 Analysis of Uncertainty PDF for HB001 Cumulative Oil and Cumulative Water production at the rate of 3,000 blpd cap after 20 years. Actual Models are available & can be demonstrated after the presentation. 59

60 Type Curves Type curves can be generated in seconds to address sensitivity of oil and water production to all involved parameters. Type curves can be generated for: Individual wells Each cluster of wells Entire field 60

61 Type Curves Cum. Oil Production as a function of Average Horizontal Permeability in one of the top layers. 61

62 Type Curves Water Cut as a function of Average Horizontal Permeability in the well layers. 62

63 Type Curves Water Cut as a function of Average Vertical Permeability in one of the top layers. 63

64 Type Curves Water Cut as a function of Average Vertical Permeability in the Well layers. 64

65 Results & Conclusions Upon completion of the project management allowed production increase in six cluster one wells. After 8 months of successful production rest of the cluster one wells were also put on higher production. It has been more than 15 months since the results were implemented with success. 65

66 Results & Conclusions A successful surrogate reservoir model was developed for a giant oil field in the Middle East. The surrogate model was able to accurately mimic the behavior of the actual full field flow model in real-time. 66

67 CONCLUSIONS Development of successful surrogate reservoir model is an important and essential step toward development of next generation of reservoir management tools that would address the needs of smart fields. 67