Deep hierarchical distillation proxy-oil modeling for heterogeneous carbonate reservoirs

Cirac, Gabriel; Farfan, Jeanfranco; Avansi, Guilherme Daniel; Schiozer, Denis José; Rocha, Anderson

doi:10.1016/j.engappai.2023.107076

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…The GS-GMDH model exhibited an RMSE of 1.88 psi and an R 2 of 0.9997, showcasing higher accuracy. Using geological data from 180 samples, Cirac et al [135] investigated a few models, including RF, Gradient Boosting Regressor, bagging, CNN, KNN, and Deep Hierarchical Decomposition, in their investigation of temporal reservoir analysis. They aimed to classify a variety of parameters, including porosity, fracture porosity, fracture permeability, rocky type, net gross, matrix permeability, water relative permeability, formation volume factor, rock compressibility, pressure dependence of water viscosity, gas density, water density, vertical continuity, relative permeability curves, oil-water contact, and fluid viscosity.…”

Section: Alternative ML Models Utilized For Predictive Analytics In T...mentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

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Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

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Section: Alternative ML Models Utilized For Predictive Analytics In T...mentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The GS-GMDH model exhibited an RMSE of 1.88 psi and an R 2 of 0.9997, showcasing higher accuracy. Using geological data from 180 samples, Cirac et al [ 137 ] investigated a few models, including RF, Gradient Boosting Regressor, Bagging, CNN, KNN, and Deep Hierarchical Decomposition models, in their investigation of temporal reservoir analysis. They aimed to classify a variety of parameters, including porosity, fracture porosity, fracture permeability, rock type, net gross, matrix permeability, water relative permeability, formation volume factor, rock compressibility, pressure dependence of water viscosity, gas density, water density, vertical continuity, relative permeability curves, oil–water contact, and fluid viscosity.…”

Section: Predicted Analytics Models For Oandgmentioning

confidence: 99%

A Review of Predictive Analytics Models in the Oil and Gas Industries

R Azmi,

Yusoff,

Mohd Sallehud-din

2024

Sensors

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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.

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