Advanced analytics of self-colmatation in terrigenous oil reservoirs

Random Forests

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Scikit-learn: Machine Learning in Python

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Support-vector networks

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Convex Optimization

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Support-Vector Networks

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Greedy function approximation: A gradient boosting machine

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Deep learning in neural networks: an overview

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Pearson Correlation Coefficient

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Noise Reduction in Speech Processing

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Entrainment and Deposition of Fine Particles in Porous Media

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Formation Fines and Factors Controlling Their Movement in Porous Media

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Reservoir Formation Damage

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WATER‐SENSITIVITY AND MIGRATION OF FINES IN THE HOPEMAN SANDSTONE

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Mathematical Model for Fines-Migration-Assisted Waterflooding With Induced Formation Damage

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Injectivity decline during low-salinity waterflooding due to fines migration

Chequer, L., JUNIOR, A. S. L. V., & Bedrikovetsky, P. (2018) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2018.01.012 · Cited by 70 · see citing works

Deep Bed Filtration Modelling of Formation Damage Due to Particulate Invasion from Drilling Fluids

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Coupled Simulation of Reservoir Flow, Geomechanics, and Formation Plugging With Application to High-Rate Produced Water Reinjection

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Estimation of minimum miscibility pressure of varied gas compositions and reservoir crude oil over a wide range of conditions using an artificial neural network model

Choubineh, A., Helalizadeh, A., & Wood, D. A. (2018) · ADVANCES IN GEO-ENERGY RESEARCH · doi:10.26804/ager.2019.01.04 · Cited by 40 · see citing works

A Genetic Algorithm for Predicting Pore Geometry Based on Air Permeability Measurements

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Multi-fluid model of suspension filtration in a porous medium

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Modelling of Injection Well Capacity with Account for Permeability Damage in the Near-Wellbore Zone for Oil Fields in Western Siberia

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Self-Colmatation in terrigenic oil reservoirs of Eastern Siberia

Orlov, D., Koroteev, D. A., & Sitnikov, A. (2018) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2017.12.087 · Cited by 5 · see citing works

Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future

Koroteev, D., & Tekic, Z. (2021) · Energy and AI · doi:10.1016/j.egyai.2020.100041 · Cited by 285 · see citing works

Gradient Boosting Machine: A SurveyPreprint

He, Z., Lin, D., Lau, T., & Wu, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.06951 · Cited by 32 · see citing works

On Application of Machine Learning Models for Prediction of Failures in Production Lines

Mokeev, V. V. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-71119-1_13 · Cited by 1 · see citing works

Proximal boosting: aggregating weak learners to minimize non-differentiable lossesPreprint

Fouillen, E., Boyer, C., & Sangnier, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09670

Sales Prediction of Big Mart based on Linear Regression, Random Forest, and Gradient Boosting

Kang, R. (2023) · Advances in Economics Management and Political Sciences · doi:10.54254/2754-1169/17/20231094 · Cited by 1 · see citing works

Accelerated proximal boostingPreprint

Fouillen, E., Boyer, C., & Sangnier, M. (2018) · arXiv (Cornell University)