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

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.

Recognizing the best intervals for hydraulic fracturing using a new fracability index

“…On the other hand, having adequate oil in place, a proper amount of porosity, and low permeability necessitates coming up with a well-designed hydraulic fracturing treatment (Shadizadeh et al 2009). Fiho and Castro compared the performance of a neuro-fuzzy system and used a genetic fuzzy system for selecting wells for hydraulic fracturing based on the knowledge acquired from operational data (Filho and Castro 2014).…”