Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 22, No. 1 (February 2009) 89-106   

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S.D. Mohaghegh*and R. Gaskari

Department of Petroleum and Natural Gas Engineering, West Virginia University
P.O. Box 6070, Morgantown, U.S.A.
shahab.mohaghegh@mail.wvu.edu - razi.gaskari@mail.wvu.edu

* Corresponding Author
 
 
( Received: February 22, 2007 – Accepted in Revised Form: September 25, 2008 )
 
 

Abstract    State-of-the-art data analysis in production allows engineers to characterize reservoirs using production data. This saves companies large sums that should otherwise be spend on well testing and reservoir simulation and modeling. There are two shortcomings with today’s production data analysis: It needs bottom-hole or well-head pressure data in addition to data for rating reservoirs’ characterization. Analysis remains at the individual well level. It does not offer integration of results from individual wells to create a field-wide analysis. A new technique called Intelligent Production Data Analysis, IPDA, addresses both of these short-comings. Through an iterative technique, IPDA integrates Decline Curve Analysis, Type Curve Matching, and Numerical Reservoir Simulation (History Matching) in order to converge to a set of reservoir characteristics, compatible with all three techniques. Furthermore, once reservoir characteristics for individual wells in the field are identified through above process, and by using a unique Fuzzy Pattern Recognition technology the results are mapped on the entire field in order to evaluate reserve estimates, pin-point optimum infill drilling locations, track fluid flow and depletion, remaining reserves and finally identify under-performer wells.

 

Keywords    Production Data, Mature Fields, Brown Fields, Reservoir Characterization

 

References   

1. Matter, L. and Anderson, D. M. “A Systematic and Comprehensive Methodology for Advanced Analysis of Production Data”, Fekete Associates Inc., SPE 84472, (2003).

2. Intelligent Production Data Analysis-IPDATM, “Is Software Product Developed by Intelligent Solutions”, Inc. As Part of a Suite of Intelligent Software Applications for the Oil and Gas Industry”, Inc. Morgantown, West Virginia, U.S.A., http://www.intelligentsolutionsinc/. Com/IPDA.htm.

3. Arps, J. J., “Analysis of Decline Curves”, Trans., AIME, Vol. 160, (1945), 228.

4. Fetkovich, M. J., Fetkovich, E. J. and Fetkovich, M. D., “Useful Concepts for Decline-Curve Forecasting, Reserve Estimation, and Analysis”, Phillips Petroleum Co., SPE Reservoir Engineering, (February 1996).

5. Carter, R. D., “Type Curves for Finite Radial and Linear Gas-Flow Systems: Constant-Terminal-Pressure Case”, SPEJ, (October 1985), 719.

6. Agarwal, R., “Analyzing Well Production Data Using Combined-Type-Curve and Decline-Curve-Analysis Concepts”, Society of Petroleum Engineers Reservoir Evaluation and Engineering Journal (SPEREE), Richardson, Texas, U.S.A., Vol. 1, No. 5, (October 1999), 478-488.

7. Fraim, M. L. and Wattenbarger, R. A., “Gas Reservoir Decline-Curve Analysis using Type Curves With Real Gas Pseudopressure and Normalized Time”, SPEFEE, (December 1987), 671.

8. Palacio, J. C. and Blasingame, T. A., “Decline-Curve Analysis Using Type Curves-Analysis of Gas Well Production Data”, Paper SPE 25909 Presented at the 1993 SPE Rocky Mountain Regional Meeting/Low Permeability Reservoirs Symposium, Denver, Canada, (April 26-28, 1993).

9. Mohaghegh, S. D., “Virtual Intelligence Applications in Petroleum Engineering: Part 3; Fuzzy Logic”, Journal of Petroleum Technology, Distinguished Author Series, (November 2000), 82-87.

10. Mohaghegh, S. D., “Virtual Intelligence Applications in Petroleum Engineering: Part 2; Genetic Algorithms”, Journal of Petroleum Technology, Distinguished Author Series, (October 2000), 40-46.

11. Cox, D. O., Kuuskraa, V. A. and Hansen, J. T., “Advanced Type Curve Analysis for Low Permeability Gas Reservoirs”, SPE Gas Technology Symposium, SPE 35595, Calgary, Alberta, Canada, (April 28-May 1, 1995).

12. Mohaghegh, S. D., “Virtual Intelligence Applications in Petroleum Engineering: Part 1; Neural Networks”, Journal of Petroleum Technology, Distinguished Author Series, (September 2000), 64-73.

13. Gaskari, R., Mohaghegh, S. D. and Jalali, J., “An Integrated Technique for Production Data Analysis With Application to Mature Fields”, Society of Petroleum Engineers Production and Operations Journal (SPEPP), Richardson, Texas, U.S.A., Vol. 22, No. 4, (2008), 403-416.

14. Mata, D., Gaskari, R. and Mohaghegh, S. D., “Fieldwide Reservoir Characterization Based on a New Technique for Production Data Analysis (Single and Multilayer Formations)”, Proceedings, Society of Petroleum Engineer’s Eastern Regional Conference, SPE 11120, Lexington, Kentucky, U.S.A., (October 17-19, 2007).





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