Towards better shale gas production forecasting using transfer learning-Reference-Cited by-同舟云学术

Towards better shale gas production forecasting using transfer learning

Published:2022-09 Issue: Volume:9 Page:100072
ISSN:2666-2604
Container-title:Upstream Oil and Gas Technology
language:en
Short-container-title:Upstream Oil and Gas Technology

Author:

Alolayan Omar S.^ORCID,Raymond Samuel J.^ORCID,Montgomery Justin B.,Williams John R.

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Geophysics,Geotechnical Engineering and Engineering Geology,Fuel Technology,Chemical Engineering (miscellaneous)

Reference43 articles.

1. US Energy Information Administration. URL https://www.eia.gov/. Accessed: 2021-01-30.

2. A. Agosta, G. Boccara, B. Heringa, N. Browne, D. Dediu, Mckinsey & company global gas outlook to 2050, 2021, https://www.mckinsey.com/industries/oil-and-gas/our-insights/global-gas-outlook-to-2050. Accessed: 2021-05-30.

3. Gas reserves estimation in resource plays;Lee;Soc. Pet. Eng.,2010

4. History-matching and forecasting tight/shale gas condensate wells using combined analytical, semi-analytical, and empirical methods;Clarkson;J. Nat. Gas Sci. Eng.,2015

5. Prediction of shale-gas production at Duvernay formation using deep-learning algorithm;Lee;Soc. Pet. Eng.,2019

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A robust, resilience machine learning with risk approach: a case study of gas consumption;Annals of Operations Research;2024-05-07

2. Application of Neural Operator Technique for Rapid Forecast of CO2 Pressure and Saturation Distribution;Day 4 Thu, May 09, 2024;2024-04-29

3. Use of Transfer Learning in Shale Production Forecasting;Day 3 Wed, February 14, 2024;2024-02-12

4. Intraday residual transfer learning in minimally observed power distribution networks dynamic state estimation;Data-Centric Engineering;2024

5. Producing gas from coal reservoirs;Coal and Coalbed Gas;2024