A data-driven workflow based on multisource transfer machine learning for gas-bearing probability distribution prediction: A case study

Abstract

Machine learning (ML) plays an important role in gas-bearing prediction based on multicomponent seismic data because it can reveal the complex relationship between reservoirs and their seismic attributes by establishing a nonlinear model. However, the degree of exploration in several areas remains limited, leading to insufficient sample data. Transfer learning (TL) can provide a feasible solution to this limitation by transferring learning information between data-rich sources and data-sparse target sources to accomplish a target task. Therefore, a data-driven workflow based on multisource TL is developed for predicting gas-bearing probability distribution to improve its accuracy in areas with limited sample data. First, the samples and labels of the synthetic data are established, and ML models (deep fully connected neural network and least-squares support vector machine) are applied to the synthetic data to predict the gas-bearing distribution. Subsequently, the samples and labels of the real data are established and completely mixed with the synthetic data to obtain the data set after TL. Finally, a supervised learning model is applied to predict the gas-bearing probability distribution in the study area. Adaptive mutation particle swarm optimization (AMPSO) is used to optimize the prediction performances of the two ML models. The prediction results of the AMPSO-ML are in good agreement with the actual drilling results. By comparing the prediction results after TL with those before TL, it is observed that the two models had higher efficiency and accuracy after TL, verifying the reliability of our scheme for predicting gas-bearing distribution. This method may be used for the effective prediction of gas-bearing probability distributions.