Study and Classification of Porosity Stress Sensitivity in Shale Gas Reservoirs Based on Experiments and Optimized Support Vector Machine Algorithm for the Silurian Longmaxi Shale in the Southern Sichuan Basin, China

Zhikai Liang; Zhenxue Jiang; Wei Wu; Jie Guo; Meng Wang; Zhou Nie; Zhuo Li; Dongsheng Xu; Zixin Xue; Ruihua Chen; Yunhao Han

doi:10.1021/acsomega.2c03393

Study and Classification of Porosity Stress Sensitivity in Shale Gas Reservoirs Based on Experiments and Optimized Support Vector Machine Algorithm for the Silurian Longmaxi Shale in the Southern Sichuan Basin, China

ACS Omega. 2022 Sep 12;7(37):33167-33185. doi: 10.1021/acsomega.2c03393. eCollection 2022 Sep 20.

Authors

Zhikai Liang^{1

2}, Zhenxue Jiang^{1

2}, Wei Wu³, Jie Guo^{1

2}, Meng Wang^{1

2}, Zhou Nie⁴, Zhuo Li^{1

2}, Dongsheng Xu⁵, Zixin Xue^{1

2}, Ruihua Chen^{1

2}, Yunhao Han^{1

2}

Affiliations

¹ State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, P. R. China.
² Unconventional Oil and Gas Science and Technology Research Institute, China University of Petroleum, Beijing 102249, P. R. China.
³ Shale Gas Research Institute, PetroChina Southwest Oil & Gas Field Company, Chengdu 610056, P. R. China.
⁴ Exploration and Development Department, Sichuan Changning Natural Gas Development Co., Ltd., Chengdu 610056, P. R. China.
⁵ Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Karamay 834000, P. R. China.

Abstract

To understand the characteristics of variation in porosity and permeability, the physical properties of the shale reservoir under different stress conditions play an important role in guiding shale gas production. With the shale of the Wufeng-Longmaxi Formation in the south of the Sichuan Basin as the research object, stress-dependent porosity and permeability test, high-pressure mercury injection, and scanning electron microscope test were performed in this study to thoroughly analyze the variation in physical properties of different shale lithofacies with effective stress. Besides, the stress sensitivity of different lithofacies reservoirs was evaluated by using parameters such as pore compressibility coefficient (PCC) and porosity sensitivity exponent (PSE), while the optimized support vector machine (SVM) algorithm was adopted to predict the coefficient of reservoir porosity sensitivity. According to the research results, the porosity and permeability of shale reservoirs decline as a negative exponential function. When the effective stress falls below 15 MPa, the damage rate of permeability/porosity increases rapidly with the rise of effective stress. By contrast, the permeability curvature of the shale reservoirs plunges with the rise of effective stress. It was discovered that a higher siliceous content results in a higher permeability curvature of shale, indicating the greater stress sensitivity of the reservoir. The ratio of matrix porosity to microfracture porosity determines the PSE, which is relatively low, and low aspect ratio pores contribute to high porosity compressibility and stress sensitivity. Young's modulus shows a negative correlation with pore compressibility and a positive correlation with Poisson's ratio. High clay minerals have a large number of low aspect ratio pores and a low elastic modulus, which leads to both high PCC and low PSE. Based on the principal component analysis, a multiclassification SVM model was established to predict the PSE, revealing that the accuracy of the sigmoid, radial basis function (RBF), and linear kernel function is consistently above 70%. According to error analysis, the accuracy can exceed 80% with the RBF kernel function and appropriate penalty factor. The research results serve to advance the research on the parameters related to overburden pressure, porosity, and permeability. Moreover, the optimized SVM algorithm is applied to make a classification prediction, which provides a reference for shale reservoir exploration and development both in theory and practice.