A novel method for predicting shallow hydrocarbon accumulation based on source-fault-sand (S-F-Sd) evaluation and ensemble neural network (ENN)-Reference-Cited by-同舟云学术

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A novel method for predicting shallow hydrocarbon accumulation based on source-fault-sand (S-F-Sd) evaluation and ensemble neural network (ENN)

Published:2024-04 Issue: Volume:359 Page:122684
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Wang Fuwei,Chen Dongxia,Li Meijun,Chen Zhangxin,Wang Qiaochu,Jiang Mengya,Rong Lanxi,Wang Yuqi,Li Sha,Iltaf Khawaja Hasnain,Wanma Renzeng,Liu Chen

Funder

American Association of Petroleum Geologists Foundation

National Natural Science Foundation of China

Science Foundation of China University of Petroleum Beijing

Publisher

Elsevier BV

Reference112 articles.

1. Methane hydrate—a major reservoir of carbon in the shallow geosphere?;Kvenvolden;Chem Geol,1988

2. Unconventional hydrocarbon resources: prospects and problems;Arthur;Elements,2014

3. Formation, distribution, potential and prediction of global conventional and unconventional hydrocarbon resources;Zou;Petrol Explor Dev,2015

4. Assessment of global unconventional oil and gas resources;Wang;Petrol Explor Dev,2016

5. China’s conventional and unconventional natural gas resources: potential and exploration targets;Zheng;J Nat Gas Geosci,2023

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1. Quantification of factors affecting fault sealing by using machine learning: Shuangtaizi fault anticline, southern margin of the Liaohe Western Depression, Northeastern China;Marine and Petroleum Geology;2024-10

2. Artificial Neural Network Model for Estimating the Pelton Turbine Shaft Power of a Micro-Hydropower Plant under Different Operating Conditions;Energies;2024-07-22

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