THERMAL SCIENCE

International Scientific Journal

MACHINE LEARNING-BASED EVALUATION OF ACIDIZING EFFECTIVENESS AND OPTIMIZATION OF ACIDIZING PARAMETERS FOR CARBONATE GAS RESERVOIR HORIZONTAL WELLS

ABSTRACT
The Dengsi formation in the Gaoshiti-Moxi block of the Sichuan Basin is characterized as having low porosity and low permeability. Typically, the development is carried out using horizontal wells and segmented acid fracturing techniques. In this study, based on a data-driven approach, geological, engineering, and well testing data were collected from 22 horizontal wells in the study area. Then, a high precision acid fracturing productivity model was established using Gaussian process regression. This model exhibited a high level of prediction accuracy, with an average relative error of only 8.77% for the test dataset. Furthermore, leveraging the established productivity model and employing a particle swarm optimization algorithm, research was conducted to optimize acid fracturing parameters and predict well productivity. The practical application of this approach in one well yielded favorable results, which hold promise for providing guidance on segmented acid fracturing design in the study area.
KEYWORDS
PAPER SUBMITTED: 2023-09-15
PAPER REVISED: 2023-10-14
PAPER ACCEPTED: 2023-12-12
PUBLISHED ONLINE: 2025-05-03
DOI REFERENCE: https://doi.org/10.2298/TSCI2502043J
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2025, VOLUME 29, ISSUE Issue 2, PAGES [1043 - 1048]
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2025 Society of Thermal Engineers of Serbia. Published by the VinĨa Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence