Version 1
: Received: 8 November 2023 / Approved: 8 November 2023 / Online: 8 November 2023 (10:20:02 CET)
How to cite:
Goucem, R. Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques. Preprints2023, 2023110540. https://doi.org/10.20944/preprints202311.0540.v1
Goucem, R. Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques. Preprints 2023, 2023110540. https://doi.org/10.20944/preprints202311.0540.v1
Goucem, R. Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques. Preprints2023, 2023110540. https://doi.org/10.20944/preprints202311.0540.v1
APA Style
Goucem, R. (2023). Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints202311.0540.v1
Chicago/Turabian Style
Goucem, R. 2023 "Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints202311.0540.v1
Abstract
Shear velocity logs are crucial in the oil and gas industry for assessing subsurface mechanical properties, including rock stiffness, shear strength, and seismic wave propagation, essential for optimizing hydrocarbon exploration and production strategies. However, obtaining shear velocity logs conventionally is expensive and time-consuming, especially when drilling additional wells solely for this purpose. With the recent boom in machine learning algorithms adoption across various scientific domains, it proved to be an extremely valuable tool for numerous applications in the oil and gas industry. It makes use of the readily available large datasets collected over decades and leverages this data to train powerful, data-driven models, reducing the reliance on empirical relationships that usually have poor generalization. This study follows this approach and presents the use and comparison of machine learning algorithms for predicting shear velocity logs from conventional and readily available logs in the Ahnet field, Algeria. Ultimately, this study aims to enhance reservoir assessment and optimize hydrocarbon recovery processes, potentially reducing exploration costs and improving oil and gas production decision-making in the region.
Keywords
Well logging; machine learning; shear velocity; petrophysics
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
