Pseudodensity Log Generation by Use of Artificial Neural Networks

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The challenges of reservoir characterization can be overcome accurately and efficiently by the use of computer-based intelligence methods such as neural networks, fuzzy logic, and genetic algorithms. This paper will describe how one integrates a comprehensive methodology of data-mining techniques and artificial neural networks (ANNs) in reservoir-petrophysical-properties prediction and regeneration.

Introduction

ANNs—machine-learning models that provide the potential to establish multi­dimensional, nonlinear, and complex models—can be powerful tools with which to analyze experimental, industrial, and field data.

It is crucial to find the optimal data from one well to build the model with ANNs for pseudowell-log generation of a target well. Manual stratigraphic interpretation, though labor-intensive, is regarded as one approach. Data-mining techniques are another applicable approach, involving the automatic processing of data associated with nonlinearity by use of a statistical method to discover data patterns. One application in petrophysics is facies (or electro­facies) classification, which is widely used to divide well-log data to obtain target information. Clustering analysis, an adjunct to artificial intelligence, can determine electrofacies and categorize lithological profiles quite efficiently.

Porosity, one of the more important petrophysical properties, can be obtained from density logs. In this study, a three-step approach was produced. First, the authors apply preprocessing of the log data by use of standardization and dimension reduction [­principal-component analysis (PCA)]. Second, they apply clustering [model-based clustering (MBC)] to recognize specific patterns and interpret stratigraphic information. Finally, a similar pattern is chosen as input to generate a target pseudodensity log by use of ANNs.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 180439, “Pseudodensity-Log Generation by Use of Artificial Neural Networks,” by Wennan Long, University of Southern California; Di Chai, University of Kansas; and Fred Aminzadeh, University of Southern California, prepared for the 2016 SPE Western Regional Meeting, Anchorage, 23–26 May. The paper has not been peer reviewed.
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Pseudodensity Log Generation by Use of Artificial Neural Networks

01 May 2017

Volume: 69 | Issue: 5

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