Enhancing Petrophysical Studies with Machine Learning: A Field Case Study on Permeability Prediction in Heterogeneous Reservoirs

• URL: http://arxiv.org/abs/2305.07145v1
• Date: Thu, 11 May 2023 21:23:37 GMT
• Title: Enhancing Petrophysical Studies with Machine Learning: A Field Case Study on Permeability Prediction in Heterogeneous Reservoirs
• Authors: Fethi Ali Cheddad
• Abstract summary: The study employed three machine learning algorithms, namely Artificial Neural Network (ANN), Random Forest (RFC), and Support Vector Machine (SVM) The primary objective of this study was to compare the effectiveness of the three machine learning algorithms in predicting permeability and determine the optimal prediction method. The findings will be used to improve reservoir simulation and locate future wells more accurately.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This field case study aims to address the challenge of accurately predicting petrophysical properties in heterogeneous reservoir formations, which can significantly impact reservoir performance predictions. The study employed three machine learning algorithms, namely Artificial Neural Network (ANN), Random Forest Classifier (RFC), and Support Vector Machine (SVM), to predict permeability log from conventional logs and match it with core data. The primary objective of this study was to compare the effectiveness of the three machine learning algorithms in predicting permeability and determine the optimal prediction method. The study utilized the Flow Zone Indicator (FZI) rock typing technique to understand the factors influencing reservoir quality. The findings will be used to improve reservoir simulation and locate future wells more accurately. The study concluded that the FZI approach and machine learning algorithms are effective in predicting permeability log and improving reservoir performance predictions.

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