Predictive modeling of reservoir geomechanical parameters through computational intelligence approach, integrating core and well logging data

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Language: Английский

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A Review of AI Applications in Unconventional Oil and Gas Exploration and Development

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 391 - 391

Published: Jan. 17, 2025

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement industrial upgrading in this field. This paper systematically reviews the current applications trends AI exploration development, covering major research achievements geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced recovery; health, safety, environment management. how deep learning helps predict distribution classify rock types. It also explains machine improves simulation history matching. Additionally, we discuss use LSTM DNN models forecasting, showing has progressed from early experiments to fully integrated solutions. However, challenges such data quality, model generalization, interpretability remain significant. Based on existing work, proposes following future directions: establishing standardized sharing labeling systems; integrating domain knowledge engineering mechanisms; advancing interpretable modeling transfer techniques. With next-generation intelligent systems, will further improve efficiency sustainability development.

Language: Английский

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A Novel Hybrid Bayesian-Group-Based Machine Learning (HB-GML) Method for Predicting Uniaxial Compressive Strength (UCS) of Rock

Rock Mechanics and Rock Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

Language: Английский

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Regression models to estimate total organic carbon (TOC) from well-log data

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 43 - 77

Published: Jan. 1, 2025

Language: Английский

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Robust Machine Learning Predictive Models for Real-Time Determination of Confined Compressive Strength of Rock Using Mudlogging Data

Rock Mechanics and Rock Engineering, Journal Year: 2024, Volume and Issue: 57(9), P. 6881 - 6907

Published: April 11, 2024

Language: Английский

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Advanced machine learning approaches for uniaxial compressive strength prediction of Indian rocks using petrographic properties

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 7(6), P. 5265 - 5286

Published: July 3, 2024

Language: Английский

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Concatenating data-driven and reduced-physics models for smart production forecasting

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

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Comparison of machine learning models for rock UCS prediction using measurement while drilling data

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 11, 2025

Accurately determining the uniaxial compressive strength (UCS) of rocks is crucial for various rock engineering applications. However, traditional methods obtaining UCS are often time-consuming, labor-intensive, and unsuitable fractured sections. In recent years, using Measurement-while-drilling data to identify has gained traction as an alternative approach. To develop a method that can rapidly, efficiently, economically estimate across different types conditions based on while-drilling tests, this study compiles comprehensive dataset from existing literature. The includes drilling parameters their corresponding values, collected under varying lithologies, levels, drill bit types, conditions. Five machine learning models—multilayer perceptron (MLP), support vector regression (SVR), convolutional neural networks (CNN), random trees (RT), long short-term memory (LSTM)—were trained evaluated. Among these, RT demonstrated superior predictive performance, achieving root mean square error (RMSE) 15.851, absolute (MAE) 4.449, standard deviation residuals (SDR) 15.292, R² value 0.959 test set. SVR also performed well, with RMSE 21.905, MAE 17.962, SDR 21.144, 0.922. While CNN LSTM exhibited slightly higher errors, they showed better generalization capabilities validation datasets. Furthermore, models were validated unseen independent dataset, where achieved best results, followed by SVR, while other relatively poorly. This indicates demonstrate suitability prediction.

Language: Английский

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Extracting useful information from sparsely logged wellbores for improved rock typing of heterogeneous reservoir characterization using well-log attributes, feature influence and optimization

Petroleum Science, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Utilization of nano clay in the formulation of water based completion fluid for hydrocarbon containing reservoirs

Petroleum Science and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 27

Published: March 24, 2025

This study successfully developed a novel water-based completion fluid with enhanced properties. The system, incorporating nano clay (1.0%–3.0%) along additives like polyanionic cellulose, pre-gelatinized starch, polyethylene glycol 200, sodium formate, and chloride, was compared to conventional hydroxyethyl cellulose-based fluid. Viscosity filtration properties were analyzed using viscometer filter press. core flooding test Berea sandstone at an overburden pressure of 1000 psi conducted understand the formation damage potential optimized system. zeta analysis indicated better PAC-based colloidal system stability (−22.2 mV) HEC-based (−4.7 mV). HPHT 90 °C 500 demonstrated lower loss (9 ml), highlighting clay's effectiveness in control. higher surface area volume ratio & its capability fit between other particles, obstructing flow through them, assist return permeability value 97.3% for tests on revealed good control property Moreover, it also observed that enhances geomechanical rock samples, indicating borehole

Language: Английский

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