Impact Map for Assessment of New Delineation-Well Locations
- Yevgeniy Zagayevskiy (University of Alberta) | Clayton V. Deutsch (Univeristy of Alberta)
- Document ID
- Society of Petroleum Engineers
- Journal of Canadian Petroleum Technology
- Publication Date
- November 2013
- Document Type
- Journal Paper
- 441 - 462
- 2013. Society of Petroleum Engineers
- 5.1.5 Geologic Modeling
- 3 in the last 30 days
- 258 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
This paper describes a new technique for effective placement of delineationwells on the basis of the change of the uncertainty in the key global-reservesvariable. Uncertainty is summarized through the geostatistical framework. Theauthors develop a numerical and analytical methodology that is tested onsynthetic and real petroleum case studies. The implementation isstraightforward, and the results are promising. A methodology is developed toassist in delineation-well placement. Decisions for new-well locations areassisted with a quantitative measure of the expected reduction in globaluncertainty in the volume of original oil in place (OOIP). The availablerealizations are analyzed and processed to quantify the impact of wellplacement. Variograms and other required statistics are inferred from therealizations. As a result, a gridded map of impact values is produced, fromwhich locations with the highest impact are suggested for new-well locations.Numerical and analytical approaches for the impact-map calculation are proposedand compared. Pros and cons of each approach are summarized. The numericalapproach requires a large number of realizations for effective implementationof the impact map, which might not be practically achievable. On the otherhand, the analytical approach does not require many realizations and producesstable results. In most cases, only variogram models and current well locationsare needed for the analytical impact-map computation. Although computationaltime of this approach largely depends on the model size, some options aresuggested to reduce the cost. The analytical impact calculation is developedfor the OOIP model response, in which the petroleum reservoir is defined as acomplex geological architecture with multiple structural surface constraints.Several case studies, including a real-petroleum-reservoir example, demonstratethe use of the impact map for the assessment of new delineation-well locations.The developed tool is of significant help for well placement.
|File Size||3 MB||Number of Pages||22|
Bohrnstedt, G.W. and Goldberger, A.S. 1969. On the Exact Covariance ofProducts of Random Variables. J. Am. Stat. Assoc. 64 (328):1439-1442. http://dx.doi.org/10.2307/2286081.
Deutsch, C.V. and Journel, A.G. 1998. GSLIB: Geostatistical SoftwareLibrary and User's Guide, second edition. Oxford, UK: Oxford UniversityPress.
Ding, Y. 2008. Optimization of Well Placement Using Evolutionary Algorithms.Presented at the Europec/EAGE Conference and Exhibition, Rome, Italy, 9-12June. SPE-113525-MS. http://dx.doi.org/10.2118/113525-MS.
Goovaerts, P. 1997. Geostatistics for Natural Resources Evaluation.Oxford, New York: Oxford University Press.
Gradshteyn, I.S. and Ryzhik, I.M. 2007. Table of Integrals, Series, andProducts, seventh edition. London: Elsevier Science & Technology.
Guyaguler, B. and Horne, R.N. 2001. Uncertainty Assessment of Well PlacementOptimization. Presented at the SPE Annual Technical Conference and Exhibition,New Orleans, 30 September-3 October. SPE-71625-MS. http://dx.doi.org/10.2118/71625-MS.
Hazlett, R.D. and Babu, D.K. 2005. Optimal Well Placement in HeterogeneousReservoirs Through Semi-Analytic Modelling. SPE J. 10 (3):286-296. SPE-84281-PA. http://dx.doi.org/10.2118/84281-PA.
Johnson, R.A. and Wichern, D.W. 2007. Applied Multivariate StatisticalAnalysis, sixth edition. Englewood Cliffs, New Jersey: Prentice Hall.
Kumar, A. and Deutsch, C.V. 2012a. Optimal Vertical Placement of Well Pairsin SAGD. Technical Report No. 12, Centre for Computational Geostatistics,University of Alberta, Edmonton, Alberta, Canada (September 2010).
Kumar, A. and Deutsch, C.V. 2012b. Optimal Drainage Area and Surface PadPositioning for SAGD Development. Technical Report No. 12, Centre forComputational Geostatistics, University of Alberta, Edmonton, Alberta, Canada(September 2010).
Li, L. and Jafarpour, B. 2012. A variable-control well placementoptimization for improved reservoir development. Comput. Geosci. 16 (4): 871-889. http://dx.doi.org/10.1007/s10596-012-9292-4.
Manchuk, J.G. and Deutsch, C.V. 2012a. Well Trajectory Optimization forSAGD. Technical Report No. 14, Centre for Computational Geostatistics,University of Alberta, Edmonton, Alberta, Canada (September 2010).
Manchuk, J.G. and Deutsch, C.V. 2012b. Optimization of Paved Drainage AreaConfigurations for SAGD. Technical Report No. 14, Centre for ComputationalGeostatistics, University of Alberta, Edmonton, Alberta, Canada (September2010).
Manchuk, J.G. and Deutsch, C.V. 2012c. Optimization of Non-Paved DrainageArea Configurations for SAGD. Technical Report No. 14, Centre for ComputationalGeostatistics, University of Alberta, Edmonton, Alberta, Canada (September2010).
Morales, A.N., Nasrabadi, H., and Zhu, D. 2011. A New Modified GeneticAlgorithm for Well Placement Optimization under Geological Uncertainties.Presented at the SPE EUROPEC/EAGE Annual Conference and Exhibition, Vienna,Austria, 23-26 May. SPE-143617-MS. http://dx.doi.org/10.2118/143617-MS.
Niven, E.B. and Deutsch, C.V. 2008. Updating Simulated Realizations with NewData. Technical Report No. 9, Centre for Computational Geostatistics,University of Alberta, Edmonton, Alberta, Canada (September 2008).
Norrena, K.P. and Deutsch, C.V. 2002. Automatic Determination of WellPlacement Subject to Geostatistical and Economic Constraints. Presented at theSPE International Thermal Operations and Heavy Oil Symposium and InternationalHorizontal Well Technology Conference, Calgary, 4-7 November. SPE-78996-MS. http://dx.doi.org/10.2118/78996-MS.
Özdogan, U. and Horne, R.N. 2004. Optimization of Well Placement with aHistory Matching Approach. Presented at the SPE Annual Technical Conference andExhibition, Houston, 26-29 September. SPE-90091-MS. http://dx.doi.org/10.2118/90091-MS.
Parker, M., Bradford, R.N., Corbett, L.W. et al. 2006. Using Real-TimePressure Data for Well Placement Planning. Presented at the SPE/DOE Symposiumon Improved Oil Recovery, Tulsa, 22-26 April. SPE-93444-MS. http://dx.doi.org/10.2118/93444-MS.
Pathak, R., Ogbe, D.O., and Jensen, J.L. 2000. Application of Geostatisticaland Fluid Flow Simulations to Evaluate Options for Well Placement. Presented atthe SPE/AAPG Western Regional Meeting, Long Beach, California, USA, 19-22 June.SPE-62554-MS. http://dx.doi.org/10.2118/62554-MS.
Stewart, J. 1999. Calculus, fourth edition. Salt Lake City, Utah:Brooks Cole Publishing Company.
Wang, C., Li, G., and Reynolds, A.C. 2007. Optimal Well Placement forProduction Optimization. Presented at the Eastern Regional Meeting, Lexington,Kentucky USA, 17-19 October. SPE-111154-MS. http://dx.doi.org/10.2118/111154-MS.
Yuen, B.B.W., Rashid, O.M., Al-Shammari, M. et al. 2011. OptimizingDevelopment Well Placements Within Geological Uncertainty Utilizing SectorModels. Presented at the SPE Reservoir Characterisation and SimulationConference and Exhibition, Abu Dhabi, UAE, 9-11 October. SPE-148017-MS. http://dx.doi.org/10.2118/148017-MS.
Zagayevskiy, Y. and Deutsch, C.V. 2013. AUTOLMC2D: A Program forAutomatic Variogram Modelling of the Linear Model of Coregionalization (LMC) of2D Realizations. Technical Report No. 15, Centre for ComputationalGeostatistics, University of Alberta, Edmonton, Alberta, Canada (September2013).
Zhou, Y., King, M., and Du, S. 2013. A Simulation-Free Approach for WellPlacement in Tight Gas Reservoirs. Presented at the 6th International PetroleumTechnology Conference, Beijing, 26-28 May. IPTC-16887-Abstract. http://dx.doi.org/10.2523/16887-Abstract.