Intelligent Production Modeling Using Full Field Pattern Recognition
- Yasaman Khazaeni (West Virginia University) | Shahab D. Mohaghegh (West Virginia University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2011
- Document Type
- Journal Paper
- 735 - 749
- 2011. Society of Petroleum Engineers
- 5.4.1 Waterflooding, 5.6.1 Open hole/cased hole log analysis, 5.6.9 Production Forecasting, 6.1.5 Human Resources, Competence and Training, 7.6.6 Artificial Intelligence
- Production Data Analysis, Artificial Intelligence, Reservoir Simulation, Mature Fields
- 2 in the last 30 days
- 807 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Production-data analysis has been applied extensively to predicting future production performance and field recovery. These applications operate mostly on a single-well basis. This paper presents a new approach to production-data analysis using artificial-intelligence (AI) techniques in which production history is used to build a fieldwide performance-prediction model. In this work, AI and data-driven modeling are used to predict future production of both synthetic- (for validation purposes) and real-field cases.
In the approach presented in this article, production history is paired with field geological information to build data sets containing the spatio-temporal dependencies among different wells. These dependencies are addressed by compiling information from closest offset wells (COWs) that includes their geological and reservoir characteristics (spatial data) as well as their production history (temporal data).
Once the data set is assembled, a series of neural networks are trained using a back-propagation algorithm. These networks are then fused together to form the intelligent time-successive production-modeling (ITSPM) system. This technique uses only the widely available measured data such as well logs and production history of existing wells to predict future performance of the existing wells and production performance of the new (infill) wells. To demonstrate the applicability of this method, a synthetic oil reservoir is modeled using a commercial simulator. Production and well-log data are extracted into an all-inclusive data set. Several neural networks are trained and validated to predict different stages of the production. The ITSPM method is used to estimate the production profile for nine new wells in the reservoir. Furthermore, ITSPM is also applied to two giant oil fields in the Middle East. The first one has more than 200 wells and 40 years of production history. ITSPM?s production predictions of the four newest wells in this reservoir are compared with their actual production. The second real field has hundreds of wells producing from multiple layers. The field has undergone waterflooding for almost its whole life. This case also shows the capabilities of this technique in more-complex scenarios and especially multiphase systems.
|File Size||9 MB||Number of Pages||15|
Agarwal, R.G., Gardner, D.C., Kleinsteiber, S.W., and Fussell, D.D. 1999.Analyzing Well Production Data Using Combined-Type-Curve and Decline-CurveAnalysis Concepts. SPE Res Eval & Eng 2 (5): 478-486.SPE-57916-PA. http://dx.doi.org/10.2118/57916-PA.
Arps, J.J. 1945. Analysis of Decline Curves. In Petroleum Development andTechnology 1945, Vol. 160, SPE-945228-G, 228-247. New York: Transactions ofthe American Institute of Mining and Metallurgical Engineers, AIME.
Carter, R.D. 1981. Characteristic Behavior of Finite Radial and Linear GasFlow Systems--Constant Terminal Pressure Case. Paper SPE 9887 presented at theSPE/DOE Low Permeability Gas Reservoirs Symposium, Denver, 27-29 May. http://dx.doi.org/10.2118/9887-MS.
Chauvin, Y. and Rumelhart, D.E. ed. 1995. Backpropagation: Theory,Architectures, and Applications. London: Psychology Press/Taylor &Francis Group.
Fanchi, J.R. 2005. Principles of Applied Reservoir Simulation, thirdedition. Oxford, UK: Elsevier.
Fetkovich, M.J. 1980. Decline Curve Analysis Using Type Curves. J PetTechnol 32 (6): 1065-1077. SPE 4629-PA. http://dx.doi.org/10.2118/4629-PA.
Gomez, Y., Khazaeni, Y., Mohaghegh, S.D., and Gaskari, R. 2009. Top DownIntelligent Reservoir Modeling. Paper SPE 124204 presented at the SPE AnnualTechnical Conference and Exhibition, New Orleans, 4-7 October. http://dx.doi.org/10.2118/124204-MS.
Haykin, S. 1999. Neural Networks, a Comprehensive Foundation, secondedition. Upper Saddle River, New Jersey: Prentice-Hall Inc.
Maren, A.J., Harston, C.T., and Pap, R.M. 1990. Handbook of neuralcomputing applications. San Diego, California: Academic Press.
Mohaghegh, S.D. 2000. Virtual-Intelligence Applications in PetroleumEngineering: Part 1--Neural Networks. J Pet Technol 52 (9):64-73. SPE-58046-MS. http://dx.doi.org/10.2118/58046-MS.
Mohaghegh, S.D. 2009. Top-Down Intelligent Reservoir Modeling (TDIRM): A NewApproach in Reservoir Modeling by Integrating Classic Reservoir Engineeringwith Artificial Intelligence & Data Mining Techniques. Paper presented atthe 2009 AAPG Annual Convention, Denver, 7-10 June.
Mohaghegh, S.D., Gaskari, R., and Jalali, J. 2005. A New Method forProduction Data Analysis To Identify New Opportunities in Mature Fields:Methodology and Application. Paper presented at the SPE Eastern RegionalMeeting, Morgantown, Morgantown, West Virginia, USA, 14-16 September. http://dx.doi.org/10.2118/98010-MS.
Mohaghegh, S.D., Grujic, O.S., Zargari, S., and Dahaghi, A.K. 2011.Modeling, History Matching, Forecasting and Analysis of Shale Reservoirsperformance Using Artificial Intelligence. Paper SPE 143875 presented at theSPE Digital Energy Conference and Exhibition, The Woodlands, Texas, USA, 19-21April. http://dx.doi.org/10.2118/143875-MS.
Okabe, A., Boots, B., Sugihara, K., and Chiu, S.N. 2000. SpatialTessellations: Concepts and Applications of Voronoi Diagrams, secondedition. West Sussex, UK: Wiley Series in Probability and Statistics, JohnWiley & Sons.
Palacio, J.C. and Blasingame, T.A. 1993. Decline-Curve Analysis Using TypeCurves—Analysis of Gas Well Production Data. Paper Oral presentation presentedat the Rocky Mountain Regional/Low Permeability Reservoirs Symposium andExhibition, Denver, 26-28 April. http://dx.doi.org/10.2118/25909-MS.