Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models
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 …
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
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 …
different petroleum engineering and geosciences segments such as petroleum exploration …
Deep-learning inversion: A next-generation seismic velocity model building method
Seismic velocity is one of the most important parameters used in seismic exploration.
Accurate velocity models are the key prerequisites for reverse time migration and other high …
Accurate velocity models are the key prerequisites for reverse time migration and other high …
Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks
Frequency-domain wavefield solutions corresponding to the anisotropic acoustic wave
equation can be used to describe the anisotropic nature of the Earth. To solve a frequency …
equation can be used to describe the anisotropic nature of the Earth. To solve a frequency …
Seismic facies analysis using machine learning
Seismic interpretations are, by definition, subjective and often require significant time and
expertise from the interpreter. We are convinced that machine-learning techniques can help …
expertise from the interpreter. We are convinced that machine-learning techniques can help …
Applications of deep neural networks in exploration seismology: A technical survey
Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …
controlled (active) source into the ground, and recorded by an array of seismic sensors …
Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs
Lithofacies classification from well logs recorded through heterogeneous carbonate
reservoirs helps to improve reservoir discrimination with respect to fluid flow and storage …
reservoirs helps to improve reservoir discrimination with respect to fluid flow and storage …
Wavefield reconstruction inversion via physics-informed neural networks
C Song, TA Alkhalifah - IEEE Transactions on Geoscience and …, 2021 - ieeexplore.ieee.org
Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization
problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented …
problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented …
Prediction of shale-gas production at Duvernay formation using deep-learning algorithm
Decline–curve analysis (DCA) is an easy and fast empirical regression method for predicting
future well production. However, applying DCA to shale–gas wells is limited by long …
future well production. However, applying DCA to shale–gas wells is limited by long …
A machine-learning benchmark for facies classification
The recent interest in using deep learning for seismic interpretation tasks, such as facies
classification, has been facing a significant obstacle, namely, the absence of large publicly …
classification, has been facing a significant obstacle, namely, the absence of large publicly …