JPT

Vol. 59 No. 5

May 2007

Formation Evaluation

Core-Data Preprocessing To Improve Permeability Estimation From Logs

Two techniques of preprocessing data from core plugs were investigated to enhance the quality of synthetic permeability estimation from conventional logs by use of artificial neural networks (ANNs). A first technique consisted of "cleaning" the core-plug data set by removing measurements deemed as log-incompatible (i.e., data from plugs corresponding to log measurements having shoulder-bed effect) and those from layers with thickness less than the log vertical resolution. The second technique relies on building high-resolution digital models of cored intervals with a process-oriented-modeling (POM) approach in which the core model is populated with permeability values from core plugs and then scaled up to a log-equivalent support volume.

 

View a Synopsis of SPE 100748 published in JPT.

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 100748, "Core-Data Preprocessing To Improve Permeability Log Estimation," by M. Cozzi, L. Ruvo, SPE, P. Scaglioni, and A.M. Lyne, Eni E&P, prepared for the 2006 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24-27 September.

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