Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models

DA Otchere, TOA Ganat, R Gholami, S Ridha - Journal of Petroleum …, 2021 - Elsevier
Abstract The advent of Artificial Intelligence (AI) in the petroleum industry has seen an
increase in its use in exploration, development, production, reservoir engineering and …

A systematic review of data science and machine learning applications to the oil and gas industry

Z Tariq, MS Aljawad, A Hasan, M Murtaza… - Journal of Petroleum …, 2021 - Springer
This study offered a detailed review of data sciences and machine learning (ML) roles in
different petroleum engineering and geosciences segments such as petroleum exploration …

Review of diagenetic facies in tight sandstones: Diagenesis, diagenetic minerals, and prediction via well logs

J Lai, G Wang, S Wang, J Cao, M Li, X Pang… - Earth-Science …, 2018 - Elsevier
The tight sandstones are characterized by low porosity, low permeability, complex pore
structure and strong heterogeneity due to the extensive diagenetic modifications they …

[图书][B] Quantitative seismic interpretation: Applying rock physics tools to reduce interpretation risk

P Avseth, T Mukerji, G Mavko - 2010 - books.google.com
Quantitative Seismic Interpretation demonstrates how rock physics can be applied to predict
reservoir parameters, such as lithologies and pore fluids, from seismically derived attributes …

Integrating well log interpretations for lithofacies classification and permeability modeling through advanced machine learning algorithms

WJ Al-Mudhafar - Journal of Petroleum Exploration and Production …, 2017 - Springer
In this paper, an integrated procedure was adopted to obtain accurate lithofacies
classification to be incorporated with well log interpretations for a precise core permeability …

Lithofacies classification and sequence stratigraphic description as a guide for the prediction and distribution of carbonate reservoir quality: a case study of the Upper …

MI Abdel-Fattah, AQ Mahdi, MA Theyab… - Journal of Petroleum …, 2022 - Elsevier
Abstract In the East Baghdad oilfield of central Iraq, the Upper Cretaceous Khasib Formation
is the largest producing carbonate reservoir. The basic architecture of the “Khasib …

Performance evaluation of machine learning-based classification with rock-physics analysis of geological lithofacies in Tarakan Basin, Indonesia

G Antariksa, R Muammar, J Lee - Journal of Petroleum Science and …, 2022 - Elsevier
This study aims to put a supervised learning method for automatically classifying lithofacies
in well-logging dataset, where several machine learning algorithms were compared in this …

Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs

WJ Al-Mudhafar - Journal of Petroleum Science and Engineering, 2020 - Elsevier
An integrated multidisciplinary workflow of machine learning and data analytics was
conducted for the multivariate geostatistical characterization of clastic reservoirs. This …

Lithology identification using graph neural network in continental shale oil reservoirs: A case study in Mahu Sag, Junggar Basin, Western China

G Lu, L Zeng, S Dong, L Huang, G Liu… - Marine and Petroleum …, 2023 - Elsevier
The continental shale oil reservoir of Fengcheng Formation in the northern slope area of
Mahu Sag, Junggar Basin, Western China is very heterogeneous in lithology. Thus, the …

Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): a comparative study of multilayer perceptron neural network …

O Ameur-Zaimeche, A Zeddouri, S Heddam… - Journal of African Earth …, 2020 - Elsevier
The purpose of this study is to investigate the possibility of applying multilayer perceptron
neural network (MLPNN) and cluster analysis approaches for rebuilding non-cored …