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.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.