A review of the application of data-driven technology in shale gas production evaluation

Abstract

Shale gas, as an important unconventional natural gas resource, is the main force to increase natural gas reserves and production in the future. For shale gas with huge resources, it is particularly important to accurately evaluate its development potential and realize scale benefit development. Given the importance of shale gas production evaluation in the context of digital transformation of the oil and gas industry, this paper presents a systematic review and examination of the application of data-driven technology in shale gas production evaluation to provide an overview of their current status. With the deepening of shale gas development theory and the maturity of data-driven technology, the existing data-driven technology has made great progress in shale gas production evaluation. It is worth noting that the application of these technologies in actual production needs to be further refined to assess shale gas production more accurately in various production demand scenarios, thereby effectively guiding production optimization and development plans. Therefore, in view of the limitations of the application of data-driven technology in shale gas production evaluation, this paper puts forward some possible priorities and trends in the future, aiming to optimize the application of data-driven technology in shale gas production evaluation in the future as much as possible.

Keywords

• Shale gas
• Production evaluation
• Data-driven technology
• Machine learning
• Model application