Evaluation of Field Management Systems

1. Evaluation of Field Management Systems Chris L. Farmer, Schlumberger, Abingdon TC, and OCIAM Omer Gurpinar, Schlumberger, DCS, Denver farmer5@slb.com

2. Contents Overview of the Problems Objectives of Field Management Sources of data State of the art - building models ‘Ideal’ problem formulation(?) Specific questions for the study group Acknowledgement: I would like to thank the Royal Society for Research Support.

3. The Oilfield 3 Surface G & D Gas Injection GAS SALES Delivery Pipeline Wells SALES MANIFOLD GAS WELL GAS STORAGE GAS LIFTED WELL COMPRESSIONSTATION OIL SALES PROCESS GASOIL SEPARATOR EXPORT LINE FLOWLINE DEHYDRATION GATHERING TERMINAL FILTRATION TRUNK LINE MONITORING WATER DISPOSAL WATER INJECTION Reservoirs

4. General Statement of the Problem 4 • Knowledge about reservoir systems is always very limited • Additional data improves reservoir understanding. New data was usually infrequent / episodic. Thus assimilation into models was infrequent (~once a year) and manual • Technical advances now enable continuous collection of large amounts of many types of data The oil industry needs new approaches in reservoir management practices

5. 5 Field Management Objectives of Field Management 3 4 Optimization Target 5 Expected Performance 6 2 Cash Flow 7 1 Exploration Delineation Development Late Life We keep learning about the Reservoir 1 - Reduce time to first oil 2 - Accelerate plateau buildup 3 - Increase the capacity 4 - Extend life of plateau 5 - Reduce rate of decline 6 - EOR in late field life 7 - Postpone abandonment

6. 6 Sources of Data – Multi-physics / Multi-scale Flow related data

7. 7 State of the art - Building models • A common workflow: • Model faults (seismic, well logs) • Interpolate the horizons ( .. ) • Build a fine scale grid (well logs, geology) • Interpolate properties (cores, well logs, seismic, • outcrops, well tests) • Upgrid (= coarsen the grid) • Upscale the properties • History match (production data, seismic) Usually: Do a worst case, best case and optimistic case Sometimes: A few (~10) realisations all history matched

8. 8 ‘Ideal’ problem formulation - Multiple realisations We are uncertain about the properties use stochastic models Use Monte-Carlo methods to estimate the pdf’s of well rates and measures of recovery efficiency The problems are: (i) to solve a system of random pde’s (ii) to update the pdf using Bayes’ rule (iii) on what length scale should inversion be done?

9. Statement of the Problem 9 New challenges for the “data to decisions” methodology: • How should data be valued? • How should the level of certainty in the consequences of a decision be quantified and evaluated? • When used to make optimal decisions how frequently should models be updated?

10. Statement of the Problem 10 New challenges for the “data to decisions” methodology: • Is there a faster way to update reservoir models? • Is there an alternative way to use continuous data other than rational model building - eg ‘proxy’ models using response surfaces, or abstract statistical models?

11. Model Problem 11 injector producer

12. Where is the best place to drill the next well? 12 injector producer initial condition measured at wells elsewhere

13. Formal problem statement 13 Flow model Stochastic formulation pdf of production

14. Formal problem statement 14 Data assimilation Facilities cost model cost Field optimisation price of oil value of production find to maximise

15. Summary 15 • How little do we need to know to make an informed decision? • How do we express the level of confidence in a reservoir model? • What is the best way to tie reservoir model based knowledge to day-to-day field decisions?