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 machine learning model for predicting the minimum miscibility pressure of CO2 and crude oil system based on a support vector machine algorithm approach

H Chen, C Zhang, N Jia, I Duncan, S Yang, YZ Yang - Fuel, 2021 - Elsevier
CO 2 enhanced oil recovery (EOR) is a potential way for carbon capture, utilization and
storage (CCUS). However, the effect of CO 2 injection is greatly influenced by the reservoir …

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

F Anifowose, J Labadin, A Abdulraheem - Applied Soft Computing, 2015 - Elsevier
The ensemble learning paradigm has proved to be relevant to solving most challenging
industrial problems. Despite its successful application especially in the Bioinformatics, the …

A parametric study of machine learning techniques in petroleum reservoir permeability prediction by integrating seismic attributes and wireline data

F Anifowose, A Abdulraheem, A Al-Shuhail - Journal of Petroleum Science …, 2019 - Elsevier
Highlights•Parametric study to investigate the comparative performance of ML
techniques.•Study is applied to the estimation of petroleum reservoir permeability.•Seismic …

Ensemble model of non-linear feature selection-based extreme learning machine for improved natural gas reservoir characterization

FA Anifowose, J Labadin, A Abdulraheem - Journal of Natural Gas Science …, 2015 - Elsevier
The deluge of multi-dimensional data acquired from advanced data acquisition tools
requires sophisticated algorithms to extract useful knowledge from such data. Traditionally …

Optimizing Minimum Miscibility Pressure Prediction Using Machine Learning: A Comprehensive Evaluation and Validation

O Olofinnika, A Selveindran, D Patel… - Energy & Fuels, 2024 - ACS Publications
This study provides the proof-of-concept for identifying the most suitable machine-learning
(ML) model that predicts minimum miscibility pressure (MMP) based on temperature, crude …

Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence …

F Anifowose, S Adeniye, A Abdulraheem… - Journal of Petroleum …, 2016 - Elsevier
Various petroleum reservoir properties have been estimated in literature using only one of
log, seismic or production data. The recent trend in data mining is integrating multi-modal …

A new model for estimation of bubble point pressure using a bayesian optimized least square gradient boosting ensemble

S Alatefi, AM Almeshal - Energies, 2021 - mdpi.com
Accurate estimation of crude oil Bubble Point Pressure (Pb) plays a vital rule in the
development cycle of an oil field. Bubble point pressure is required in many petroleum …

Ensemble learning model for petroleum reservoir characterization: a case of feed-forward back-propagation neural networks

F Anifowose, J Labadin, A Abdulraheem - Trends and Applications in …, 2013 - Springer
Conventional machine learning methods are incapable of handling several hypotheses.
This is the main strength of the ensemble learning paradigm. The petroleum industry is in …

A Data Driven PVT Model to Predict the Oil Formation Volume Factor, Solution GOR and Bubble Point Pressure for Characterizing an Oil Reservoir

S Kumar, S Gautam, NK Thakur, MA Khan… - SPE Reservoir …, 2023 - onepetro.org
Characterizing an oil reservoir requires one to understand the Pressure-Volume-
Temperature (PVT) properties of reservoir fluids, especially bubble point pressure, solution …