Development and Evaluation of Deep Learning Models for Forecasting Gas Production and Flowback Water in Shale Gas Reservoirs-Reference-Cited by-同舟云学术

Development and Evaluation of Deep Learning Models for Forecasting Gas Production and Flowback Water in Shale Gas Reservoirs

Published:2023-03-29 Issue:16 Volume:62 Page:6434-6447
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

López-Flores Francisco Javier¹^ORCID,Lira-Barragán Luis Fernando¹^ORCID,Rubio-Castro Eusiel²^ORCID,El-Halwagi Mahmoud M.³^ORCID,Ponce-Ortega José María¹^ORCID

Affiliation:

1. Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Francisco J. Mujica S/N, Edificio V1, Ciudad Universitaria, 58060 Morelia, Mich., México

2. Chemical and Biological Sciences Department, Universidad Autónoma de Sinaloa, Av. de las Américas S/N, Culiacán, Sinaloa 80010, México

3. Chemical Engineering Department, Texas A&M University, 386 Spence St., Jack E. Brown Engineering Building, College Station, Texas 77843, United States

Funder

Coordinaci?n de la Investigaci?n Cient?fica

Consejo Nacional de Ciencia y Tecnolog?a

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.2c04239

Reference70 articles.

1. The pricing of shale gas: A review

2. EIA (Energy Information Administration). https://www.eia.gov/dnav/ng/ng_enr_shalegas_dcu_NUS_a.htm. 2022 (accessed 2022-08-01).

3. Shale gas monetization – A review of downstream processing to chemicals and fuels

4. Effects of green energy development on population growth and employment: Evidence from shale gas exploitation in Chongqing, China

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