Seismic Prediction Method of Shale Reservoir Brittleness Index Based on the BP Neural Network for Improving Shale Gas Extraction Efficiency

• Published in: Energies (Basel) 2024-09, Vol.17 (18), p.4751
• Main Authors: Zhang, Xuejuan, She, Haiyan, Zhang, Lei, Li, Ruolin, Feng, Jiayang, Liu, Ruhao, Wang, Xinrui
• Format: Article
• Language: English
• Subjects: Accuracy; BP neural network; Brittleness; brittleness index; Earthquakes; Environmental aspects; Gas extraction; Mechanical properties; Methods; Minerals; Neural networks; Optimization; Permeability; Quartz; seismic prediction; Seismic prospecting; Shale; Shale gas; shale reservoir
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17184751

The current seismic prediction methods of the shale brittleness index are all based on the pre-stack seismic inversion of elastic parameters, and the elastic parameters are transformed by Rickman and other simple linear mathematical relationship formulas. In order to address the low accuracy of the seismic prediction results for the brittleness index, this study proposes a method for predicting the brittleness index of shale reservoirs based on an error backpropagation neural network (BP neural network). The continuous static rock elastic parameters were calculated by fitting the triaxial test data with well logging data, and the static elastic parameters with good correlation with the brittleness index of shale minerals were selected as the sample data of the BP neural network model. A dataset of 1970 data points, characterized by Young’s modulus, Poisson’s ratio, shear modulus, and the mineral brittleness index, was constructed. A total of 367 sets of data points from well Z4 were randomly retained as model validation data, and 1603 sets of data points from the other three wells were divided into model training data and test data at a ratio of 7:3. The calculation accuracy of the model with different numbers of nodes was analyzed and the key parameters of the BP neural network structure such as the number of input layers, the number of output layers, the number of hidden layers, and the number of neurons were determined. The gradient descent method was used to determine the weight and bias of the model parameters with the smallest error, the BP neural network model was trained, and the stability of the brittleness index prediction model of the BP neural network was verified by posterior data. After obtaining Young’s modulus, Poisson’s ratio, and shear modulus through pre-stack seismic inversion, the BP neural network model established in this study was used to predict the brittleness index distribution of the target layer in the study area. Compared with the conventional Rickman method, the prediction coincidence rate is 69.16%, and the prediction coincidence rate between the prediction results and the real value is 95.79%, which is 26.63% higher. The BP neural network method proposed in this paper provides a reliable new method for seismic prediction of the shale reservoir brittleness index, which has important practical significance for clarifying the shale gas development scheme and improving shale gas exploitation efficiency.