A data-driven shale gas production forecasting method based on the multi-objective random forest regression

L Xue, Y Liu, Y Xiong, Y Liu, X Cui, G Lei - Journal of Petroleum Science …, 2021 - Elsevier
Shale gas is an important unconventional natural gas resource existing in shale reservoir
with huge reserves. Due to the ultralow porosity and permeability, it requires the horizontal …

Reservoir characterization through comprehensive modeling of elastic logs prediction in heterogeneous rocks using unsupervised clustering and class-based …

M Ali, P Zhu, R Jiang, M Huolin, M Ehsan… - Applied Soft …, 2023 - Elsevier
Geophysical reservoir characterization is a significant task in the oil and gas industry and
elastic logs prediction of subsurface formations is a fundamental aspect of this process …

What is the probability of achieving the carbon dioxide emission targets of the Paris Agreement? Evidence from the top ten emitters

C Dong, X Dong, Q Jiang, K Dong, G Liu - Science of the Total Environment, 2018 - Elsevier
This study predicts the probabilities of achieving the carbon dioxide (CO 2) emission targets
set by the Paris Agreement and the Intended Nationally Determined Contribution (INDC) of …

Machine Learning in Oil and Gas Exploration-A Review

A Lawal, Y Yang, H He, NL Baisa - IEEE Access, 2024 - ieeexplore.ieee.org
A comprehensive assessment of machine learning applications is conducted to identify the
developing trends for Artificial Intelligence (AI) applications in the oil and gas sector …

Machine learning methods for estimating permeability of a reservoir

H Khan, A Srivastav, A Kumar Mishra… - International Journal of …, 2022 - Springer
The prediction of permeability from the information of a well log is a crucial and extensive
task that is observed in the earth sciences. The permeability of a reservoir is greatly …

Ensemble machine learning: An untapped modeling paradigm for petroleum reservoir characterization

FA Anifowose, J Labadin, A Abdulraheem - Journal of Petroleum Science …, 2017 - Elsevier
The successful applications of the conventional Computational Intelligence (CI) techniques
and Hybrid Intelligent Systems (HIS) in petroleum reservoir characterization have been …

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

JC Agbasi, JC Egbueri - Journal of sedimentary environments, 2023 - Springer
Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems
expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria …

Optimization of models for a rapid identification of lithology while drilling-A win-win strategy based on machine learning

J Sun, Q Li, M Chen, L Ren, G Huang, C Li… - Journal of Petroleum …, 2019 - Elsevier
The identification of lithology from well log data is an important task in petroleum exploration
and development. However, due to the complexity of the sedimentary environment and …

A multiple-input deep residual convolutional neural network for reservoir permeability prediction

M Masroor, ME Niri, MH Sharifinasab - Geoenergy Science and …, 2023 - Elsevier
Permeability plays an essential role in reservoir-related studies, including fluid flow
characterization, reservoir modeling/simulation, and management. However, operational …

Intelligent prediction for rock porosity while drilling complex lithology in real time

H Gamal, S Elkatatny, A Alsaihati… - Computational …, 2021 - Wiley Online Library
Rock porosity is an important parameter for the formation evaluation, reservoir modeling,
and petroleum reserve estimation. The conventional methods for determining the rock …