Estimation of Adsorption Gas in Shale Gas Reservoir by using Machine Learning Methods

Dennis Sabato Chinamo; Xiao-Qiang Bian; Zongyang Liu; Jing Cheng; Lan Huangc

doi:10.36948/ijfmr.2024.v06i05.27082

Estimation of Adsorption Gas in Shale Gas Reservoir by using Machine Learning Methods

Author(s)	Dennis Sabato Chinamo, Xiao-Qiang Bian, Zongyang Liu, Jing Cheng, Lan Huangc
Country	China
Abstract	Evaluation and development of shale gas deposits need exact calculation of adsorbed gas concentration. However, gas adsorption and desorption experiments are time-consuming and costly. Models based on physics and empirical correlations cannot anticipate these experiments. Based on geological factors, this study intends to construct a cost-effective and accurate machine-learning model to estimate adsorbed shale gas. In this study, 601 data points from shale gas reserves were utilized. These include reservoir temperature (T,°C), total organic carbon (TOC, wt%), vitrinite reflectance (Ro,%), Langmuir pressure (PL), and volume. Based on Support Vector regression (SVR), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Sparrow Search Algorithms (SSA) were created, trained, and evaluated. The accuracy of the different models is calculated by correlation coefficient (R2), root mean square error (RMSE), average absolute relative deviation (AARD), and the duration of time each specified model (PSO-SVR, GWO-SVR, and SSA-SVR) takes to evaluate the effectiveness of each prediction model. The results showed that three optimization models (PSO-SVR, GWO-SVR, and SSA-SVR) can make good predictions. However, the PSO-SVR model is the most accurate at predicting Langmuir pressure and volume and takes the least amount of time, with RMSE and R2 values of 0.09990 and 0.9605, respectively. The GWO-SVR has an estimated RMSE of 0.1092 and R2 of 0.9529, whereas the SSA-SVR has 0.1264 and 0.9368 for Langmuir volume. PSO-SVR had the lowest time requirement for Langmuir pressure data inputs, with RMSE and R2 of 0.5017 and 0.9306, respectively. The findings indicate that all models can accurately forecast adsorbed gas, with the PSO-SVR model performing somewhat better than the GWO- and SSA-SVR models
Keywords	Adsorption gas, Shale Gas, Machine Learning, Grey Wolf optimization (GWO), Particle swarm optimization (PSO), Sparrow search algorithm (SSA)
Field	Engineering
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-09-12
Cite This	Estimation of Adsorption Gas in Shale Gas Reservoir by using Machine Learning Methods - Dennis Sabato Chinamo, Xiao-Qiang Bian, Zongyang Liu, Jing Cheng, Lan Huangc - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.27082
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.27082
Short DOI	https://doi.org/gwfgtb

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Estimation of Adsorption Gas in Shale Gas Reservoir by using Machine Learning Methods

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