Soft Computing Algorithms Accelerate and Improve The History Matching Process:
Elk Hills, 29R Reservoir
This paper presents the application of soft computing (virtual intelligence) techniques1 to a reservoir simulation history matching problem. The objective of this work was not to automate the history matching process as has been discussed by others2-7, but rather to provide the engineers with the necessary information to improve the speed and quality of their results.
Through the use of virtual intelligence techniques history match error (mismatch – the difference between calculated and observed flow and/or pressure) is correlated to variations in individual history match parameters like porosity, permeability, and so on. An objective function that describes the criteria to minimize the mismatch is defined.
This technology always finds the global minimum associated with the objective function. The technology provides multiple solutions that satisfy any error criteria, and produces related statistical information. The technology can handle continuous or discrete history match parameters.
In this paper we discuss the successful application of this technology to a simulation study of a complex, fractured, porcelanite oil reservoir (Elk Hills-29R, Bakersfield, California). This field has 28 years of history with 42 production wells. A dual porosity formulation was necessary to properly model the fractured nature of the reservoir. Successful history match results obtained in a short period of time for this field showed the accuracy and practicality of this unique history matching technology.