An Analytical Method for Optimizing Fracture Spacing in Shale Oil Reservoirs

Mahmood, Md Nahin; Guo, Boyun

doi:10.2118/197083-ms

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…), and production dynamic parameters (cumulative oil production and water cut). Using these parameters, Pearson's correlation coefficient can be calculated, and the higher the correlation coefficient, the greater the potential of refracturing [24][25][26][27][28].…”

Section: Well and Layer Selection Methodsmentioning

confidence: 99%

Review of Shale Oil and Gas Refracturing: Techniques and Field Applications

Xu,

Wang,

Liu

et al. 2024

Processes

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Shale oil and gas wells usually experience a rapid decline in production due to their extremely low permeability and strong heterogeneity. As a crucial technique to harness potential and elevate extraction rates in aged wells (formations), refracturing is increasingly employed within oil and gas reservoirs globally. At present, the selection processes for refracturing, both of wells and layers, are somewhat subjective and necessitate considerable field data. However, the status of fracturing technology is difficult to control precisely, and the difference in construction effects is large. In this paper, well selection, formation selection, and the fracturing technology of shale oil and gas refracturing are deeply analyzed, and the technological status and main technical direction of refracturing technology at home and abroad are analyzed and summarized. The applicability, application potential, and main technical challenges of existing technology for different wells are discussed, combined with the field production dynamics. The results show that well and layer selection is the key to the successful application of refracturing technology, and the geological engineering parameters closely related to the remaining reservoir reserves and formation energy should be considered as the screening parameters. General temporary plugging refracturing technology has a low cost and a simple process, but it is difficult to accurately control the location of temporary plugging, and the construction effect is very different. Mechanical isolation refracturing technology permits the exact refurbishment of regions untouched by the initial fracturing. However, it is costly and complex in terms of construction. Consequently, cutting the costs of mechanical isolation refracturing technology stands as a pivotal research direction.

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Section: Well and Layer Selection Methodsmentioning

confidence: 99%

Review of Shale Oil and Gas Refracturing: Techniques and Field Applications

Xu,

Wang,

Liu

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…To address overfitting, we adopted a strategy of simplifying the model structure when it was observed. For instance, if the ANN with three hidden layers (10, 10, 10) was found to be overfitting, we tested configurations with fewer nodes, such as (8,8,8), or even reduced the number of hidden layers, e.g., (10,10). We found that in pure ANN, an optimal configuration consisted of two layers with 7 and 2 nodes, yielding relatively good results for both wells.…”

Section: Support Vector Machines-artificial Neural Network (Svm-ann)mentioning

confidence: 99%

“…Fracture porosity directly controls the transportation and storage of hydrocarbons in these reservoirs. Additionally, the fracture properties affect the flow direction and permeability of the reservoir rocks [8]. Cores extracted during drilling, and less often, side wall cores, can be used to calculate fracture porosity [9,10].…”

Section: Introductionmentioning

confidence: 99%

New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools

Ifrene,

Irofti,

et al. 2023

Fuels

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Fracture porosity is crucial for storage and production efficiency in fractured tight reservoirs. Geophysical image logs using resistivity measurements have traditionally been used for fracture characterization. This study aims to develop a novel, hybrid machine-learning method to predict fracture porosity using conventional well logs in the Ahnet field, Algeria. Initially, we explored an Artificial Neural Network (ANN) model for regression analysis. To overcome the limitations of ANN, we proposed a hybrid model combining Support Vector Machine (SVM) classification and ANN regression, resulting in improved fracture porosity predictions. The models were tested against logging data by combining the Machine Learning approach with advanced logging tools recorded in two wells. In this context, we used electrical image logs and the dipole acoustic tool, which allowed us to identify 404 open fractures and 231 closed fractures and, consequently, to assess the fracture porosity. The results were then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models were used to obtain comprehensive results, which were subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms into fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well log analysis made it possible to reliably estimate fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data are absent or expensive.

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New Insights in Fracture Porosity Estimation using Machine Learning and Advanced Logging Tools

Ifrene,

Irofti,

et al. 2023

Preprint

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The purpose of this work is to compare two fracture prediction models with real-world data. The pure Artificial Neural Network (ANN) model emphasizes regression analysis, while the hybrid model (SVM-ANN) focuses on the combination of regression and classification analysis or Support Vector Machine. The results were subsequently tested against logging data by combining the Machine Learning approach with advanced logging tools. In this context, we used electrical image logs and the dipole acoustic tool which together allowed the distinction of 404 open fractures and 231 closed fractures and, consequently the estimation of fracture porosity. The results are then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models are used to establish comprehensive results, which are subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms in fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well-log analysis do permit reliable estimation of fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data is absent and costly.

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An Analytical Method for Optimizing Fracture Spacing in Shale Oil Reservoirs

Cited by 4 publications

References 24 publications

Review of Shale Oil and Gas Refracturing: Techniques and Field Applications

Review of Shale Oil and Gas Refracturing: Techniques and Field Applications

New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools

New Insights in Fracture Porosity Estimation using Machine Learning and Advanced Logging Tools

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