Prediction of Shale Gas Production by Hydraulic Fracturing in Changning Area Using Machine Learning Algorithms

Li, Dongshuang; You, Shuzhen; Liao, Qinzhuo; Sheng, Mao; Tian, Shouceng

doi:10.1007/s11242-023-01935-3

Cited by 13 publications

(1 citation statement)

References 29 publications

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“…Moreover, with the limitations of traditional numerical simulation methods in terms of computational efficiency [ 25 ], artificial intelligence algorithms [ 26 ], particularly deep learning algorithms, have emerged as promising tools for digital rock image analysis and the prediction of rock physical properties. Convolutional neural network (CNN) algorithms have been widely used in digital rock image processing [ 27 , 28 , 29 ], but they may lose valuable information and alter the original image data during the modeling process [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks

You

Liao

et al. 2023

Materials

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The permeability of porous materials determines the fluid flow rate and aids in the prediction of their mechanical properties. This study developed a novel approach that combines the discrete cosine transform (DCT) and artificial neural networks (ANN) for permeability analysis and prediction in digital rock images, focusing on nanoscale porous materials in shale formations. The DCT effectively captured the morphology and spatial distribution of material structure at the nanoscale and enhanced the computational efficiency, which was crucial for handling the complexity and high dimensionality of the digital rock images. The ANN model, trained using the Levenberg–Marquardt algorithm, preserved essential features and demonstrated exceptional accuracy for permeability prediction from the DCT-processed rock images. Our approach offers versatility and efficiency in handling diverse rock samples, from nanoscale shale to microscale sandstone. This work contributes to the comprehension and exploitation of unconventional resources, especially those preserved in nanoscale pore structures.

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Section: Introductionmentioning

confidence: 99%

Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks

You

Liao

et al. 2023

Materials

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

Rahmanifard,

Gates

2024

Artif Intell Rev

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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.

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Interpretable fracturing optimization of shale oil reservoir production based on causal inference

Yang,

Li,

Min

et al. 2024

Appl Intell

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Prediction of Shale Gas Production by Hydraulic Fracturing in Changning Area Using Machine Learning Algorithms

Cited by 13 publications

References 29 publications

Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks

Permeability Prediction of Nanoscale Porous Materials Using Discrete Cosine Transform-Based Artificial Neural Networks

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

Interpretable fracturing optimization of shale oil reservoir production based on causal inference

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