Combining Geostatistics With Bayesian Updating To Continually Optimize Drilling Strategy in Shale-Gas Plays
- Bart J.A. Willigers (BG Group) | Steve Begg (University of Adelaide) | Reidar B. Bratvold (University of Stavanger)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- November 2014
- Document Type
- Journal Paper
- 507 - 519
- 2014.Society of Petroleum Engineers
- 1.6 Drilling Operations, 5.1.5 Geologic Modeling, 5.8.2 Shale Gas
- Barnett shale, srilling strategy, Bayes rule, unconventional resources, geostatistics
- 1 in the last 30 days
- 560 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
We present a new methodology to evaluate subsurface uncertainty during the development of shale-gas plays. Even after many wells are drilled, the average well production and the variation of well performance (economics) remain highly uncertain. The ability to delineate a shale play with the fewest wells and to focus drilling in the most-productive areas is a major factor in commercial success. The importance of probabilistic modeling in managing uncertainty in shale-gas plays is emphasized in several studies. The objective of this study is to develop a practical methodology that addresses these complexities and is dynamic, in the sense that the optimal drilling strategy can be continually updated as we learn the outcome of each well drilled. Maximizing the returns from a shale-gas play is essentially a problem of choosing well locations and number of wells to optimize production volumes and rates. Drilling policies must take account of many already-drilled locations, possible new drilling locations, spatial dependencies between performance at those different (possible) well locations, and the extent of uncertainty concerning whether a well will be economical. These factors cause typical valuation methodologies to be impractical because of the “curse of dimensionality.” In this study, an unconventional play is divided into numerous segments. These segments are referred to as cells. In each cell, a fixed number of wells can be drilled. The chance of success (CoS) [of a well with a net present value (NPV) greater than zero] in any given cell is itself considered to be an uncertain variable. An initial probability distribution for the CoS of each cell is derived from analogous plays plus any available information about the specific play. The methodology proceeds as follows. First, as each new well (or group of new wells) is drilled, the outcome is used in combination with the prior probability distribution (with Bayes theorem) to create an updated probability distribution for the CoS of the relevant grid cell. Thus, our initial estimate can be continuously updated as more actual outcomes are realized. Second, the influence of the new CoS on the surrounding cells, because of spatial correlation, is updated by use of indicator Kriging (IK), a geostatistical technique. This combination of Bayesian update (BU) and IK is referred to as the BU-IK method. The methodology proposed in this study informs the development of drilling policies for shale-gas opportunities with a probabilistic model that accounts for the uncertainty in the CoS and its spatial dependency. The use of cells to represent a set of wells simplifies the analysis and greatly reduces the computing requirements. The methodology is applied to a well set from the Barnett shale in Texas.
|File Size||1 MB||Number of Pages||13|
Abbas, A.E. 2006. Entropy Methods for Joint Distributions in Decision Analysis. IEEE Trans. Eng. Manage. 53 (1): 146–159. http://dx.doi.org/10.1109/TEM.2005.861803.
Attanasi, E.D., Cobum, T.C., and Freeman, P.A. 2008. Economic Decision Making and the Application of Nonparametric Prediction Models. SPE Res Eval & Eng 11 (6): 1089–1096. SPE-107659-PA. http://dx.doi.org/10.2118/107659-PA.
Baihly, J., Altman, R., Malpani, R. et al. 2010. Shale Gas Production Decline Trend Comparison Over Time and Basins. Presented at the SPE Annual Technical Conference and Exhibition, 19–22 September, Florence, Italy. SPE-135555-MS. http://dx.doi.org/10.2118/135555-MS.
Bickel, J.E. and Smith, J.E. 2006. Optimal Sequential Exploration: A Binary Learning Model. Decision Analysis 3 (1): 16–32. http://dx.doi.org/10.1287/deca.1050.0052.
Bratvold, R.B. and Begg, S. 2010. Making Good Decisions. Richardson, Texas: Society of Petroleum Engineers.
Bratvold, R.B., Begg, S.H, and Rasheva, S. 2010. A New Approach to Uncertainty Quantification for Decision Making. Presented at the SPE Economics and Evaluation Symposium, Dallas, Texas, 8–9 March. SPE-130157-MS. http://dx.doi.org/10.2118/130157-MS.
Cui, H., Stein, A., and Myers, D.E. 1995. Extension of Spatial Information, Bayesian Kriging and Updating of Prior Variogram Parameters. Environmetrics 6 (4): 373–384. http://dx.doi.org/10.1002/env.3170060406.
Deutsch, C.V. 2002. Geostatistic Reservoir Modelling. New York, New York: Oxford University Press.
Ezisi, I.B., Hale, B.W., Watson, M.C. et al. 2012. Assessment of Probabilistic Parameters for Barnett Shale Recoverable Volumes. Presented at the SPE Hydrocarbon Economics and Evaluation Symposium, 24–25 September, Calgary, Alberta, Canada. SPE-162915-MS. http://dx.doi.org/10.2118/162915-MS.
Fan, L. Lindsay, G., Thompson, J. et al. 2011. The Bottom-line of Horizontal Well Production Decline in the Barnett Shale. Presented at the SPE Hydrocarbon Economics and Evaluation Symposium, 24–25 September, Calgary, Alberta, Canada. SPE-141263-MS. http://dx.doi.org/10.2118/141263-MS.
Goodchild, M. 1992. Geographical Information Science. International J. Geographic Information Systems 6 (1): 31–45. http://dx.doi.org/10.1080/02693799208901893.
Goovaerts, P. 1997. Geostatistics for Natural Resources Modelling. New York, New York: Oxford University Press.
Gray, W.M., Hoefer, T.A., Chiappe, A. et al. 2007. A Probabilistic Approach to Shale Gas Economics. Presented at the Hydrocarbon Economics and Evaluation Symposium, 1–3 April, Dallas, Texas. SPE-108053-MS. http://dx.doi.org/10.2118/108053-MS.
Hale, B.H. 2010. Barnett Shale: A Resource Play—Locally Random and Regionally Complex. Presented at the SPE Eastern Regional Meeting, 12–14 October, Morgantown, West Virginia. SPE-138987-MS. http://dx.doi.org/10.2118/138987-MS.
Harding, N.R. 2008. Application of Stochastic Analysis for Shale Gas Reservoirs. Presented at the SPE Russian Oil and Gas Technical Conference and Exhibition, 28–30 October, Moscow, Russia. SPE-114855-MS. http://dx.doi.org/10.2118/114855-MS.
Haskett, W.J. and Brown, P.J. 2005. Evaluation of Unconventional Resources Plays. Presented at the SPE Annual Technical Conference and Exhibition, 9–12 October, Dallas, Texas. SPE-96879-MS. http://dx.doi.org/10.2118/96879-MS.
Jaynes, E.T. 1982. On the Rationale of Maximum-Entropy Methods. Proc. IEEE 70 (9): 939–952. http://dx.doi.org/10.1109/PROC.1982.12425.
Kaiser, M.J. 2012. Haynesville Shale Play Economic Analysis. J. Petrol. Sci. Eng. 82–83, 75–89.
LaFolette, R.F. and Holcomb, W.D. 2011. Practical Data Mining: Lessons Learned From the Barnett Shale of North Texas. Presented at the SPE Hydraulic Fracturing Technology Conference, 24–26 January, The Woodlands, Texas. SPE-140524-MS. http://dx.doi.org/10.2118/140524-MS.
Martin, A.N. 2012. The Potential Pitfalls of Using North American Tight and Shale Gas Development Techniques in the North African and Middle Eastern Environment. SPE Econ & Mgmt 4 (3): 147–157. SPE-141104-PA. http://dx.doi.org/10.2118/141104-PA.
Marzban, C. and Sandgathe, S. 2009. Verification With Variograms. Weather and Forecasting 24 (4): 1102–1120. http://dx.doi.org/10.1175/2009WAF2222122.1.
Matheron, G. 1971. The Theory of Regionalized Variables and Its Applications. Les Cahiers du Centre de Morphologie Mathématic, No. 5, Écoles des Mines de Paris, Paris.
Maxwell, S.C., Cho, D., Pope, T. et al. 2011. Enhanced Reservoir Characterization Using Hydraulic Fracture Microseismicity. Presented at the SPE Hydraulic Fracturing Technology Conference, 24–26 January, The Woodlands, Texas. SPE-140449-MS. http://dx.doi.org/10.2118/140449-MS.
Schmoker, J.W. 2002. Resource Assessment Perspectives for Unconventional Gas Systems. AAPG Bull. 86: 1993–1999. http://dx.doi.org/10.1306/61EEDDDC-173E-11D7-8645000102C1865D.
Shannon, C.E. 1948. A Mathematical Theory of Communication. Bell System Technical J. 37: 379–423, 623–656.
Stabell, C., Gonzales, R., and Langlie, E. 2007. Stochastic Modelling of Shale Gas Resource Play Economics. Presented at the Hydrocarbon Economics and Evaluation Symposium, 1–3 April, Dallas, Texas. SPE-108081-MS. http://dx.doi.org/10.2118/108081-MS.
Strickland, R., Purvis, D., and Blasingame, T. 2011. Practical Aspects of Reserve Determination for Shale Gas. Presented at the North American Unconventional Gas Conference and Exhibition, 14–16 June, The Woodlands, Texas. SPE-144357-MS. http://dx.doi.org/10.2118/144357-MS.
Sutton, R.P., Cox, S.A., and Barree, R.D. 2010. Shale Gas Plays: A Performance Perspective. Presented at the Tight Gas Completions Conference, 2–3 November, San Antonio, Texas. SPE-138447-MS. http://dx.doi.org/10.2118/138447-MS.
Valko, P.P and Lee, W.J. 2010. A Better Way To Forecast Production From Unconventional Gas Wells. Presented at the Annual Technical Conference and Exhibition, 19–22 September, Florence, Italy. SPE-134231-MS. http://dx.doi.org/10.2118/134231-MS.
Weijermars, R. 2012. Jumps in Proven Unconventional Gas Reserves Present Challenges to Reserves Auditing. SPE Econ & Mgmt 4 (3): 131–146. SPE-160927-PA. http://dx.doi.org/10.2118/160927-PA.
Wright, J.D. 2008. Economic Evaluation of Shale Gas Reservoirs. Presented at the SPE Shale Gas Production Conference, 16–18 November, Fort Worth, Texas. SPE-119899-MS. http://dx.doi.org/10.2118/119899-MS.