Hybrid Machine Learning-Mathematical Programming Approach for Optimizing Gas Production and Water Management in Shale Gas Fields

López-Flores, Francisco Javier; Lira-Barragán, Luis Fernando; Rubio-Castro, Eusiel; El‐Halwagi, Mahmoud M.; Ponce-Ortega, José María

doi:10.1021/acssuschemeng.3c00569

Cited by 7 publications

(2 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, OMLT has been applied for the optimization of water management and shale gas production, energy storage system dispatch, Gibbs reactor chemical process flowsheet, integrated energy systems, and solar photovoltaic panel production …”

Section: Overview Of the Toolsmentioning

confidence: 99%

“…These tools can also be used to solve large-scale optimization problems represented by superstructures , which will enable the creation of an overall model that simultaneously integrates a part of the problem modeled with mathematical equations (such as material and energy balances) and other parts modeled by machine learning which might include the most complex processes or stages of the problem.…”

Section: Perspective and Conclusionmentioning

confidence: 99%

See 1 more Smart Citation

Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective

López-Flores,

Ramírez-Márquez,

Ponce-Ortega

2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

This article studies the relevance of innovative Process Systems Engineering (PSE) tools that can reformulate trained machine learning models that are driven by advances in computational technologies, showcasing a pivotal transformation in chemical engineering methodologies. The article also delves into how trained machine learning models are reformulated and optimized to refine engineering decisions as it provides a novel analysis of tools to develop machine learning models by reformulating them, and optimizing them in PSE, thus highlighting their significance and applications. It offers a comprehensive comparison of several cutting-edge tools, including JANOS, MeLOn, ENTMOOT, reluMIP, OptiCL, Gurobi Machine Learning, OMLT, and PySCIPOpt-ML, highlighting their distinct abilities for performance and decision-making. Furthermore, challenges related to the explicit formulation of the main machine learning models are discussed. Guidance is provided to select the appropriate tool according to users' requirements. Additionally, a comparative study of the tools is presented using a case study to analyze and compare the size and type of formulations, the optimal solution, and the computation times.

show abstract

Section: Overview Of the Toolsmentioning

confidence: 99%

Section: Perspective and Conclusionmentioning

confidence: 99%

Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective

López-Flores,

Ramírez-Márquez,

Ponce-Ortega

2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

Predicting the Gas Storage Capacity in Shale Formations Using the Extreme Gradient Boosting Decision Trees Method

Wang,

Li,

Huo

et al. 2024

Energy Tech

View full text Add to dashboard Cite

Accurate shale gas reserves estimation is essential for development. Existing machine learning (ML) models for predicting gas isothermal adsorption are limited by small datasets and lack verified generalization. We constructed an “original dataset” containing 2112 data points from 11 measurements on samples from 8 formations in 3 countries to develop ML‐based prediction models. Similar to previous ML models, total organic matter, pressure, and temperature are characterized as the three most significant features using the mean impurity method. In contrast to previous ML models, the study reveals that these three features are inadequate to be used to make reasonable predictions for the datasets from the measurements different from those used to train the models. Instead, the extreme gradient boosting decision trees (XGBoost) model with two more features (specific surface area and moisture) exhibits good robustness, generalization, and precision in the prediction of gas isothermal adsorption. Overall, An XGBoost model with optimal input features is developed in this work, which exhibits both good performance in gas adsorption prediction and good potential for the estimation of gas storage in shale gas development.

show abstract

A Comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions

Rahmanifard,

Gates

2024

Artif Intell Rev

View full text Add to dashboard Cite

Prediction of well production from unconventional reservoirs is a complex problem given an incomplete understanding of physics despite large amounts of data. Recently, Data Analytics Techniques (DAT) have emerged as an effective approach for production forecasting for unconventional reservoirs. In some of these approaches, DAT are combined with physics-based models to capture the essential physical mechanisms of fluid flow in porous media, while leveraging the power of data-driven methods to account for uncertainties and heterogeneities. Here, we provide an overview of the applications and performance of DAT for production forecasting of unconventional reservoirs examining and comparing predictive models using different algorithms, validation benchmarks, input data, number of wells, and formation types. We also discuss the strengths and limitations of each model, as well as the challenges and opportunities for future research in this field. Our analysis shows that machine learning (ML) based models can achieve satisfactory performance in forecasting production from unconventional reservoirs. We measure the performance of the models using two dimensionless metrics: mean absolute percentage error (MAPE) and coefficient of determination (R2). The predicted and actual production data show a high degree of agreement, as most of the models have a low error rate and a strong correlation. Specifically, ~ 65% of the models have MAPE less than 20%, and more than 80% of the models have R2 higher than 0.6. Therefore, we expect that DAT can improve the reliability and robustness of production forecasting for unconventional resources. However, we also identify some areas for future improvement, such as developing new ML algorithms, combining DAT with physics-based models, and establishing multi-perspective approaches for comparing model performance.

show abstract

Hybrid Machine Learning-Mathematical Programming Approach for Optimizing Gas Production and Water Management in Shale Gas Fields

Cited by 7 publications

References 66 publications

Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective

Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective

Predicting the Gas Storage Capacity in Shale Formations Using the Extreme Gradient Boosting Decision Trees Method

A Comprehensive review of data-driven approaches for forecasting production from unconventional reservoirs: best practices and future directions

Contact Info

Product

Resources

About