Research on the Declining Trend of Shale Gas Production Based on Transfer Learning Methods

Abstract

With the development of artificial intelligence technology, machine learning-based production forecasting models can achieve the rapid prediction and analysis of production. However, these models need to be built on a large dataset, and having only a small amount of data may result in a decrease in prediction accuracy. Therefore, this paper proposes a transfer learning prediction method based on the hierarchical interpolation model. It uses data from over 2000 shale gas wells in 22 blocks of the Marcellus Shale formation in Pennsylvania to train the transfer learning model. The knowledge obtained from blocks with sufficient sample data is transferred and applied to adjacent blocks with limited sample data. Compared to classical production decline models and mainstream time-series prediction models, the proposed method can achieve an accurate production decline trend prediction in blocks with limited sample data, providing new ideas and methods for studying the declining production trends in shale gas.