Machine Learning in Oil and Gas Exploration-A Review
A comprehensive assessment of machine learning applications is conducted to identify the
develo** trends for Artificial Intelligence (AI) applications in the oil and gas sector …
develo** trends for Artificial Intelligence (AI) applications in the oil and gas sector …
Utilizing integrated artificial intelligence for characterizing mineralogy and facies in a pre-salt carbonate reservoir, Santos Basin, Brazil, using cores, wireline logs, and …
In complex carbonate reservoirs, it is crucial to understand the connections between
reservoir compositions (minerals, facies, and properties). Conventionally, core samples …
reservoir compositions (minerals, facies, and properties). Conventionally, core samples …
Application of unsupervised learning and deep learning for rock type prediction and petrophysical characterization using multi-scale data
This study integrates well log data, routine core analyses, microcomputed X-ray tomography
(μ CT) images, and sedimentary petrography to accurately characterize and evaluate the …
(μ CT) images, and sedimentary petrography to accurately characterize and evaluate the …
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 …
Seismic driven reservoir classification using advanced machine learning algorithms: A case study from the lower Ranikot/Khadro sandstone gas reservoir, Kirthar fold …
Reservoir characterization of thin sand bed reservoirs has been a challenge for petroleum
explorers across the globe. In this study, we have studied the heterogeneous Paleocene …
explorers across the globe. In this study, we have studied the heterogeneous Paleocene …
Facies identification based on multikernel relevance vector machine
Facies identification is a powerful means to predict reservoirs. We achieve facies
identification using a relevance vector machine (RVM) and develop a facies discriminant …
identification using a relevance vector machine (RVM) and develop a facies discriminant …
Neural network application to petrophysical and lithofacies analysis based on multi-scale data: An integrated study using conventional well log, core and borehole …
AA Shehata, OA Osman, BS Nabawy - Journal of natural gas science and …, 2021 - Elsevier
Application of artificial neural network (ANN, eg, Multi-layer perceptron, MLP) became
widespread in the petroleum industry, especially in formation evaluation, reservoir …
widespread in the petroleum industry, especially in formation evaluation, reservoir …
Data quality considerations for petrophysical machine-learning models
A McDonald - Petrophysics, 2021 - onepetro.org
Decades of subsurface exploration and characterization have led to the collation and
storage of large volumes of well-related data. The amount of data gathered daily continues …
storage of large volumes of well-related data. The amount of data gathered daily continues …
Lithology identification from well-log curves via neural networks with additional geologic constraint
Lithology identification is of great importance in reservoir characterization. Recently, many
researchers have applied machine-learning techniques to solve lithology identification …
researchers have applied machine-learning techniques to solve lithology identification …
[HTML][HTML] Machine learning in petrophysics: Advantages and limitations
Abstract Machine learning provides a powerful alternative data-driven approach to
accomplish many petrophysical tasks from subsurface data. It can assimilate information …
accomplish many petrophysical tasks from subsurface data. It can assimilate information …