Work

Machine learning-based evaluation of acidizing effectiveness and optimization of acidizing parameters for carbonate gas reservoir horizontal wells

Year: 2025

Type: article

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.

Source: Thermal Science

DOI: 10.2298/tsci2502043j

Authors: Shi-Wen Ji, You-Cheng Zheng, Yan Zhang, Xiao-Feng Lu, Si-Rong Zhu, Kai Ye

Institutions: PetroChina Southwest Oil and Gas Field Company (China), Research Institute of Natural Gas Technology, Southwest Petroleum University

Language: English

Topic: Hydraulic Fracturing and Reservoir Analysis

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $161

Funder: PetroChina Company Limited