1887

Abstract

Summary

We present a framework that automatically generates an optimal well placement plan (WPP) based on a reservoir model of a shale gas field. The proposed WPP comprises wells, surface locations such as pads, well completion locations, and the drilling schedule. A suite of high-speed computational components allows generating this WPP in minutes. Different development strategies can be rapidly investigated.

The WPP is optimized using a constrained downhill-simplex approach. During a trial, WPPs proposed by the optimizer in previous trials were extrapolated to propose a new WPP. The proposed WPP must satisfy a wide range of geometric, operational, contractual, and legal constraints on the surface as well as in the overburden and reservoir. When a feasible WPP is discovered during a trial, the production forecast is computed using a high-speed semianalytic reservoir simulator. The framework supports a variety of objective functions, including recovery, net present value, return on investment, and profitability index. Optimization in the presence of subsurface uncertainty considers an ensemble of reservoir models. A proposed WPP will then have an uncertainty in the forecast value. For a specified aversion to risk, a conservative or aggressive WPP can then be optimized.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.20141890
2014-09-08
2024-04-25
Loading full text...

Full text loading...

References

  1. Bailey, W., Couet, B. and Wilkinson, D.
    [2005] Field Optimization Tool for Maximizing Asset Value. SPE Res Eval & Eng, 8(1), 7–21. SPE paper 87026.
    [Google Scholar]
  2. Beckner, B.L. and Song, X.
    [1995] Field Development Planning Using Simulated Annealing— Optimal Economic Well Scheduling and Placement. Paper SPE 30650 presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA, 22–25 October.
    [Google Scholar]
  3. Cinco-Lay, H., Samaneigo, V.F. and Dominguez, N.
    [1978] Transient Pressure Behavior for a Well with a Finite Conductivity Fracture. SPE Journal, 18(4), 253–264. SPE 6014.
    [Google Scholar]
  4. Cullick, A.S., Narayanan, K. and Gorell, S.
    [2005] Optimal Field Development Planning of Well Locations with Reservoir Uncertainty. Paper SPE 96986 presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA, 9–12 October.
    [Google Scholar]
  5. Djikpéssé, H., Couet, B., and Wilkinson, D.
    [2011] A Practical Sequential Lexicographic Approach for Derivative-free Black-box Constrained Optimization. Engineering Optimization, 43(7), 721–739.
    [Google Scholar]
  6. Ierapetritou, M.G., Floudas, C.A., Vasantharajan, S. and Cullick, A.S.
    [1999] Optimal Location of Vertical Wells: Decomposition Approach. AIChE Journal, 45(4), 844–859.
    [Google Scholar]
  7. Knabner, P., Korotov, S. and Summ, G.
    [2003] Conditions for the Invertibility of the Isoparametric Mapping for Hexahedral Finite Elements. Finite Elements in Analysis and Design, 40(2), 159–172.
    [Google Scholar]
  8. Medeiros, F.Jr.
    [2007] Semi-analytical Pressure-Transient Model for Complex Well- Reservoir systems. PhD dissertation, Colarado School of Mines, Golden Colarado, USA.
    [Google Scholar]
  9. Medeiros, F.Jr. and Ozkan, E.
    [2010] A Semi-analytical Approach to Model Pressure Transients in Hetergenous Reservoirs. SPE Journal, 13(2), 341–358. SPE 102834.
    [Google Scholar]
  10. Nelder, J. and Mead, R.
    [1965] A Simplex Method for Function Minimization. The Computer Journal, 7(4), 308–313.
    [Google Scholar]
  11. Rahman, N.M. A. and Ambastha, A.K.
    [2000] Generalized 3D Analytical Model for Transient Flow in Compartmentalized Reservoirs. SPE Journal, 5(3), 276–286. SPE 65106.
    [Google Scholar]
  12. Rahman, N.M.A. and Mattar, L.
    [2004] A New Method for Computing Pseudo-time for Real Gas flow using the Material Balance Equation. PETSOC-2004-182, presented at Canadian International Petroleum Conference, 8–10 June, Calgary, Alberta.
    [Google Scholar]
  13. Santellani, G., Hansen, B. and Herring, T.
    [1998] Survival of the Fittest” an Optimized Well Location Algorithm for Reservoir Simulation. Paper SPE 39754 presented at the SPE Asia Pacific Conference on Imtegrated Modeling for Asset Management, Kuala Lumpur, Malaysia, 23–24 March.
    [Google Scholar]
  14. Thambynayagam, R.K.M.
    [2011] The Diffusion Handbook: Applied Solutions for Engineers. New York, NY: McGraw-Hill Professional.
    [Google Scholar]
  15. Tilke, P.G., Banerjee, R., Halabe, V., Balci, B., Thambynayagam, R.K.M. and Spath, J.
    [2010] Automated Field Development Planning in the Presence of Subsurface Uncertainty and Operational Risk Tolerance. SPE paper 135168 presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy, 19–22 September.
    [Google Scholar]
  16. Tilke, P.G., Bogush, A., Bolanos, N., Corbett, C., Kolupaev, A., Grove, G.P., Spath, J. and Thambynayagam, R.K.M.
    [2013] High-Speed Field Development Planning in the Presence of Uncertainty and Risk Through the use of Constrained Numerical Optimization and Analytical Simulation. SPE paper 164882 presented at the European Petroleum Exhibition and Conference, London, UK, 10–13 June.
    [Google Scholar]
  17. Zhou, W., Samson, B., Krishnamurthy, S., Tilke, P. G., Banerjee, R., Spath, J., Thambynayagam, R. K.M.
    [2013] Analytical Reservoir Simulation and Its Applications to Conventional and Unconventional Resources. SPE paper 164793 presented at the European Petroleum Exhibition and Conference, London, UK, 10–13 June.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20141890
Loading
/content/papers/10.3997/2214-4609.20141890
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error