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Reeves, S.R., Bastian, P.A., Spivey, J.P., Flumerfelt, R.W., Mohaghegh, S. and Koperna, G.J. (2000) Benchmarking of Restimulation Candidate Selection Techniques in Layered, Tight Gas Sand Formations Using Reservoir Simulation. Proceedings of SPE Annual Technical Conference and Exhibition (Society of Petroleum Engineers), Dallas, 1-4 October 2000, 1-16.
http://dx.doi.org/10.2118/63096-MS

has been cited by the following article:

• TITLE: Selecting Oil Wells for Hydraulic Fracturing: A Comparison between Genetic-Fuzzy and Neuro Fuzzy Systems

  AUTHORS: Virgílio José Martins Ferreira Filho, Antônio Orestes de Salvo Castro

  KEYWORDS: Fuzzy Logic, Petroleum, Genetic Algorithms, Hydraulic Fracturing

  JOURNAL NAME: American Journal of Operations Research, Vol.4 No.4, July 9, 2014

  ABSTRACT: Hydraulic fracturing is widely used to increase oil well production and to reduce formation damage. Reservoir studies and engineering analyses are carried out to select the wells for this kind of operation. As the reservoir parameters have some diffuse characteristics, Fuzzy Inference Systems (FIS) have been tested for these selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for selecting wells for hydraulic fracturing, with knowledge acquired from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, despite the genetic fuzzy system being a newer process, it obtained better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variable linguistic values.