Predicting a detailed permeability profile from minipermeameter measurements and well log data.

摘要

Permeability, along with the porosity, comprises one of the two most important properties in petroleum engineering with respect producing hydrocarbon fluids.;It is standard practice in the petroleum industry to determine permeability in one of two ways. These are pressure transient testing and core analysis. Both methods are expensive in their own ways. This research focuses on a way to minimize or limit the need for both of these testing procedures.;The purpose of this research was to utilize well log data, mainly gamma ray and density, Minipermeameter values, and basic information such as depth and spatial coordinates to predict permeability in the selected pilot area of the Stringtown field. This differed from previous research in that the known permeabilities of the cored wells were obtained using a Minipermeameter versus using only traditional core analysis.;This problem's solution may be in the utilization of Artificial Neural Networks. Recent studies have shown that permeability may be determined using ANNs and data obtained from wells logs, regardless of the heterogeneity of the reservoir. Log data, which has been shown to be prudent includes, gamma ray, density, and spontaneous potential.