Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
V Rodriguez-Galiano, M Sanchez-Castillo… - Ore Geology …, 2015 - Elsevier
Abstract Machine learning algorithms (MLAs) such us artificial neural networks (ANNs),
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
[HTML][HTML] Artificial intelligence: New age of transformation in petroleum upstream
P Solanki, D Baldaniya, D Jogani, B Chaudhary… - Petroleum …, 2022 - Elsevier
Abstract In the Oil and Gas industry, the implementation of artificial intelligence techniques
gives advantages of better use of existing infrastructure. It provides better future outcomes …
gives advantages of better use of existing infrastructure. It provides better future outcomes …
The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling
Direct measurements of formation properties such as the shale volume, porosity,
permeability, and fluid saturation are often accompanied by expensive cost and are time …
permeability, and fluid saturation are often accompanied by expensive cost and are time …
Support vector machine: principles, parameters, and applications
R Gholami, N Fakhari - Handbook of neural computation, 2017 - Elsevier
Abstract Support Vector Machine (SVM) has been introduced in the late 1990s and
successfully applied to many engineering related applications. In this chapter, attempts were …
successfully applied to many engineering related applications. In this chapter, attempts were …
Support vector machine for multi-classification of mineral prospectivity areas
In this paper on mineral prospectivity map**, a supervised classification method called
Support Vector Machine (SVM) is used to explore porphyry-Cu deposits. Different data …
Support Vector Machine (SVM) is used to explore porphyry-Cu deposits. Different data …
Permeability and porosity prediction using logging data in a heterogeneous dolomite reservoir: An integrated approach
Z Zhang, H Zhang, J Li, Z Cai - Journal of Natural Gas Science and …, 2021 - Elsevier
The accurate prediction of permeability and porosity is an important foundation for high-
quality reservoir identification and geological modelling. However, the strong heterogeneity …
quality reservoir identification and geological modelling. However, the strong heterogeneity …
Hierarchical automated machine learning (AutoML) for advanced unconventional reservoir characterization
Y Mubarak, A Koeshidayatullah - Scientific Reports, 2023 - nature.com
Recent advances in machine learning (ML) have transformed the landscape of energy
exploration, including hydrocarbon, CO2 storage, and hydrogen. However, building …
exploration, including hydrocarbon, CO2 storage, and hydrogen. However, building …
A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs
A Al-Anazi, ID Gates - Engineering Geology, 2010 - Elsevier
Porosity, permeability, and fluid saturation distributions are critical for reservoir
characterization, reserves estimation, and production forecasting. Classification of well-log …
characterization, reserves estimation, and production forecasting. Classification of well-log …
A Comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions
Prediction of well production from unconventional reservoirs is a complex problem given an
incomplete understanding of physics despite large amounts of data. Recently, Data …
incomplete understanding of physics despite large amounts of data. Recently, Data …