Ensemble-Based Closed-Loop Field Development. Olwijn Leeuwenburgh (TNO / TU Delft)

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1 Ensemble-Based Closed-Loop Field Development Olwijn Leeuwenburgh (TNO / TU Delft)

2 OUTLINE E&P industry development cycles and barriers for IOR Integration as a solution - closed-loop reservoir management Examples of optimization and closed-loop field development Conclusions and outlook 2 5/22/2018

3 E&P INDUSTRY DEVELOPMENT CYCLES Phase 2 - execution 3 5/22/2018

4 CHARACTERISTICS OF CURRENT RM PRACTICE Cycle time (planning + execution) is typically 3-7 years New data are only taken into account after a long time Little coordination between different steps in development planning and operations Decoupling of long-term and short-term objectives Mostly manual workflows Very few data and optimization options can be incorporated (i.e. smart wells pose a challenge) Results may be subjective and non-repeatable Only very few reservoir model parameters are updated Uncertainty is hardly taken into account Reservoir engineers spend > 50% of their time on history matching Conventional model-assisted workflows to support the reservoir engineer are likely to require too many model simulations to be practical Conventional model-assisted workflows typically can not handle alternative data types 4 5/22/2018

5 MODEL-ASSISTED RESERVOIR MANAGEMENT Also known as closed-loop reservoir management, real-time reservoir management, smart fields, intelligent fields, integrated operations Hypothesis: Oil recovery can be significantly improved by changing reservoir management from a batch-type to a near-continuous model-based controlled activity. Why model-based? Capture and quantify information about the reservoir Test understanding of the reservoir (confront model predictions with data) Evaluate future development strategies (decision support) Allows for design of pro-active instead of reactive operational strategies Approach: Combine efficient methods for large-scale model parameter estimation and optimization with 5 concepts from systems and control theory 5/22/2018

6 SYSTEMS CONTROL VIEW OF RESERVOIR MANAGEMENT Aim: manipulate the input to produce the desired output 6 5/22/2018

7 SYSTEMS CONTROL VIEW OF RESERVOIR MANAGEMENT Aim: manipulate the input to produce the desired output Measure the output 7 5/22/2018

8 SYSTEMS CONTROL VIEW OF RESERVOIR MANAGEMENT Aim: manipulate the input to produce the desired output Measure the output and extract information to construct models 8 5/22/2018

9 SYSTEMS CONTROL VIEW OF RESERVOIR MANAGEMENT Aim: manipulate the input to produce the desired output Measure the output and extract information to construct models If needed: reduce models 9 5/22/2018

10 SYSTEMS CONTROL VIEW OF RESERVOIR MANAGEMENT Aim: manipulate the input to produce the desired output Measure the output and extract information to construct models If needed: reduce models Determine and implement an optimal development plan 10 5/22/2018

11 SYSTEMS CONTROL VIEW OF RESERVOIR MANAGEMENT Aim: manipulate the input to produce the desired output Measure the output and extract information to construct models If needed: reduce models Determine and implement an optimal development plan Use measured dynamic output to improve models Use models to find optimal (input) strategies to operate the system 11 5/22/2018

12 CLOSED-LOOP RESERVOIR MANAGEMENT (CLOREM) CLOREM is a systematic framework for model-assisted reservoir management (e.g. Jansen, 2009) connects to cyclic nature of industry development practice provides theoretical foundation and tools to improve its effectiveness CLOREM aims to make RM a near-continuous model-based activity. Requirements are: flexible HM methods for integration of all types of data into large-scale simulation models effective data collection and processing workflows (quality checking, reduction, ) efficient methods for optimization of field development plans and operating strategies methods to construct fast models for speed-up and computational cost reduction CLOREM has been the subject of several research programs over the past 10+ years Development of TNO tool for field-scale optimization (now continued with Statoil) Experience with application to real field cases 12 5/22/2018

13 2008 SPE CLOREM BENCHMARK LEARNINGS A good history match increases the probability of increasing ultimate value from the field Fully accounting for uncertainty (e.g. through ensemble methods) increases value year EnKF year Conventional tools cannot easily handle full uncertainty and exploit all control options Increasing the frequency of updating models and strategies increases value conventional 104 model realizations provided for history match 1800 controls to be optimized * Conventional tools are generally based on GA and EA

14 HISTORY MATCHING 14 5/22/2018

15 TNO HISTORY MATCHING ACTIVITIES IN NIOR Two-year postdoc project on 4D seismic history matching (Yanhui Zhang) Zhang, Y., O. Leeuwenburgh, Image-oriented distance parameterization for ensemble-based seismic history matching, Comput. Geosci., 21: , Research on time-lapse seismic data processing and inversion Collaboration with IRIS on ensemble-based optimization Stordal, A.S., S.P. Szklarz, O. Leeuwenburgh, A theoretical look at ensemble-based optimization in reservoir management, Math. Geosci., 48: , /22/2018

16 DEVELOPMENT STRATEGY OPTIMIZATION 16 5/22/2018

17 ENSEMBLE OPTIMIZATION Based on the Stochastic Simplex Approximate Gradient Combines advantages of gradient and gradient-free approaches Control updates are not restricted to tested samples Does not require a strictly differentiable objective function or continuous controls Flexible uses simulator as black box Computationally efficient for optimization under uncertainty based on multiple model realizations Can handle at least ~1000 controls Published applications to Multi-objective optimization (e.g. minimization of risk) Constrained optimization Well control (smart wells, WAG) and placement optimization Leeuwenburgh, O. et al., SPE , 2010 Fonseca, R.M. et al., J. Num. Meth. Eng., /22/2018

18 CO 2 -ENHANCED OIL RECOVERY 6 km x 6 km x 3 km part of the Chigwell Viking I Pool (Western Canadian Sedimentary Basin, Alberta) 9 injectors and 20 producers Current RF = 15% Production started in 1985 CO 2 -WAG since 2005 Initially immiscible, later miscible Operator strategy: 30 days water 90 days gas Optimization challenge: design improved WAG strategy over 1800 days Controls: slug lengths, injection rates, producer BHP (1140 in total) NPV model: Oil price = 60 $/bbl; Water production cost = -1.25$/bbl; Water injection cost = -1$/bbl CO 2 Injection Cost = $/mscf; CO 2 Recycle Cost = $/mscf Annual discount rate = 0.10

19 OPTIMIZATION RESULTS Increase of 35.4% in NPV relative to operator strategy 5% more oil recovered, 2.5% reduction in purchased CO 2 Increase of 17% in mean NPV over 20 realizations Cycles shortened / lengthened / eliminated 7-15 cycles remaining (depending on the injector) Simplified strategies lower cost and risk injector well 21 P90 P50 P10 19

20 RESULT ANALYSIS AND INTERPRETATION So 0.85 T = 0 days T = 365 days T= 1800 days 0.0 MMP is reached 400 days earlier Enhanced oil swelling Improved areal sweep Initial strategy Optimal strategy 20 Hewson, C. and O. Leeuwenburgh, O., SPE182597, 2017.

21 WELL SCHEDULING AND TYPE OPTIMIZATION: PEREGRINO 85 km from Rio de Janeiro Water depth ~100 m Reserve of 400 million bbl recoverable oil (160 million produced since 2011) Currently 2 platforms (39 production and 7 injector wells) and 1 FPSO in operation Heavy oil reservoir (14 o API; viscocity 360 cp) Two reservoir zones with shale barrier in-between Reservoir pinches out up-dip from the water contact Fairly strong aquifer support 21 5/22/2018

22 FIELD DEVELOPMENT Peregrino Phase II field development project will add 21 wells and a third well head platform Start production expected by end 2020 Expected to add 270 million bbl oil by end 2040 As part of the development phase, a drilling and completion strategy for 8 planned horizontal wells is needed Each well is drilled in either the upper or lower reservoir zone All 8 wells were initially planned to be producers and to be drilled in up-dip order 4-week testing period using a active-cell reservoir model injector OWC 22 5/22/2018

23 OPTIMIZATION CHALLENGE Objective: maximize average NPV over 14 geological model realizations Facies proportions (permeability and porosity), oil-water contact depth, fault properties, relative permeability curves, K v /K h ratio, initial water saturation distribution, pore volume multipliers for different regions Decision options: drilling sequence (order) and well type for 8 new wells with fixed trajectory Controls: drilling priority and well type controls w4, w2, w3, w1 Ordering complexity: 8 factorial (~40000) possible combinations of controls for 1 geological realization Solution approach: compute ensemble (approximate) gradients and apply a gradient -based optimization algorithm (OPT++, interior point algorithm) Each gradient evaluation requires 14 full-field model simulations (can be done in parallel) 23 5/22/2018

24 OPTIMIZATION RESULTS Summary main results: Optimizer suggests improved drilling order for 6 producers and 2 injectors 1% mean cumulative oil production increase Optimal strategy obtained in a few days (compared to 4 weeks for reference strategy) 24 Pressure support and well cost are the main drivers for the final result

25 ANALYSIS OF RESULTS Pressure support is driving force for the optimization results: Two wells to be drilled as injectors to provide pressure support from above and below Positioning of wells (top or bottom layer) and proximity to surrounding injectors plays a role Also in deterministic case 2 injectors are drilled (in both scenarios well 8 is an injector), but strategy deliver lowers value for full ensemble Surrounding injectors have an impact on the results (may not be properly accounted for in a sector model) Final plan was changed based on these results Well Costs (mill USD) Well 7 (1.92) Well 4 (1.83) Well 8 (1.66) Well 3 (1.35) Well 5 (1.22) Well 2 (1.20) Well 6 (1.19) Well 1 (1.00) Reference order/type Well 1 Well 2 Well 3 Well 4 Well 5 Well 6 Well 7 Well 8 Optimal order/type Well 7 Well 4 Well 3 Well 6 Well 1 Well 8 Well 2 Well 5 Deterministic order/type Well 4 Well 1 Well 5 Well 2 Well 7 Well 3 Well 8 Well 6 Hanea. R.G., R.M. Fonseca, C. Pettan, M.O. Iwajomo, K. Skjerve, L. Hustoft, A.G. Chitu, F. Wilschut, Decision maturation using ensemble based robust optimization for field development planning, ECMOR XV, /22/2018

26 CLOSED-LOOP FIELD DEVELOPMENT Application of the CLOREM concept to field development ( Drill and Learn ): Re-optimize the drilling order for remaining wells after incorporating data from drilled wells One cycle length corresponds to the time required to drill and complete N wells, update models and perform an optimization. Concept was applied to a synthetic test case One truth model was defined to generate data Different values of N were tested Only dynamic data from drilled wells was used for model updating 26 Hanea, R.G. et al., SPE REE, submitted. 5/22/2018

27 DRILL AND LEARN Total of 8 wells (5 producers, 3 injectors) at fixed locations Only the drilling order is optimized Model permeability, porosity and fault properties of 45 model realizations are updated using rate and pressure data from drilled wells N = 4 N = 2 initial cycle 1 cycle 2 WI_1 OP_3 OP_3 WI_2 WI_1 WI_1 WI_3 WI_2 WI_2 OP_1 OP_1 OP_1 OP_2 OP_2 OP_2 OP_3 WI_3 OP_5 OP_4 OP_5 WI_3 OP_5 OP_4 OP_4 initial cycle 1 cycle 2 cycle 3 WI_1 OP_3 OP_3 OP_3 WI_2 WI_1 WI_1 WI_1 WI_3 WI_2 OP_2 OP_2 OP_1 OP_1 OP_1 OP_1 OP_2 OP_2 WI_2 WI_2 OP_3 WI_3 OP_5 OP_5 OP_4 OP_5 WI_3 WI_3 OP_5 OP_4 OP_4 OP_4 27 5/22/2018

28 PRODUCTION AND INJECTION PROFILES More frequent cycling leads to 4.5% more oil produced and 15% less water injected 28 5/22/2018

29 CONCLUSIONS The closed-loop concept provides a formal framework for integration of different reservoir management activities that has been proven in different benchmark studies to deliver improved oil recovery. Large IOCs are starting to apply model-assisted reservoir management workflows to both producing and new fields. The results are proving to be of real value. The systematic use of simulation models is increasingly applied to R&D on pilot and monitoring network design (building on VoI concepts). The required workflows are the same as those used in CLOREM. Topics that could use more attention: improved uncertainty quantification extracting useful/relevant information from data efficient gathering and processing of data to reduce the closed-loop cycle time connection to dai-to-day operations (production optimization) 29 5/22/2018

30 ACKNOWLEDGEMENTS The author acknowledges the Research Council of Norway and the industry partners, ConocoPhillips Skandinavia AS, Aker BP ASA, Eni Norge AS, Maersk Oil; a company by Total, Statoil Petroleum AS, Neptune Energy Norge AS, Lundin Norway AS, Halliburton AS, Schlumberger Norge AS, Wintershall Norge AS, and DEA Norge AS, of The National IOR Centre of Norway for support. 30 5/22/2018

31 FIELD DEVELOPMENT OPTIMIZATION BENCHMARK STUDY Initiative from industry partners in the ISAPP2 research consortium Proposal: apply optimization methods to field development planning Provide number of wells, well types, well paths, platform location(s), drilling schedule Provide operating strategies for all wells Realistic constraints on well paths and designs, drilling schedules etc. Costs for wells, platforms Uncertainty represented by multiple models 50 models have been released for use by participants Workshop organized by TNO with EAGE in September (connected to the ECMOR conference) 31 5/22/2018

32 THANK YOU FOR YOUR ATTENTION