Prediction of Equivalent Liquid Permeability from Gas Permeability Measurements: A Recurrent Neural Network Approach

Abstract

One of the most vital reservoir properties is permeability. It is usually measured using core samples with two major measurement methods; using gas or using liquid. The purpose of this work is to use a data-driven recurrent neural network model to estimate the equivalent liquid permeability based on gas permeability. By using this model, the equivalent liquid permeability can be predicted for the permeability of core samples with rich clay minerals measured using gas (or any core sample that is measured using gas). This will give an alternative way to the currently used method (Klinkenberg method). Core sample data measurements of more than 500 cores were obtained from limestone formations. The data went through a processing step to eliminate any measurement errors. Then, the data were clustered into training, validation, and testing. After many iterations, a decision was made to have a network with four hidden layer and twenty neurons in each hidden layer, and four delays in the input and the output. The findings showed that the network had stopped training after nine epochs with a validation mean squared error (MSE) of 5.3. The model exhibited excellent performance during training, validation, and testing with an overall R2 of 0.91 which is excellent. These findings prove that the model can closely track the actual equivalent liquid permeability measurements using the gas permeability measurements data within a reasonable margin of error. With the rise of machine learning and other artificial intelligence (AI) methods as well as the potential application in the petroleum industry, these methodologies can revolutionize the industry and save time and money.

Department(s)

Geosciences and Geological and Petroleum Engineering

Publication Status

Available Access

International Standard Book Number (ISBN)

978-161399717-8

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Society of Petroleum Engineers, All rights reserved.

Publication Date

01 Jan 2021

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