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



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