Machine Learning-Based Propped Fracture Conductivity Correlations of Several Shale Formations

Desouky, Mahmoud; Tariq, Zeeshan; Aljawad, Murtada Saleh; Alhoori, Hamed; Mahmoud, Mohamed; Abdulraheem, Abdulazeez

doi:10.1021/acsomega.1c01919

Cited by 13 publications

(8 citation statements)

References 25 publications

(41 reference statements)

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“…These ML techniques are mostly used for various tasks, such as classification, regression, and data analysis . Each algorithm has its unique characteristics and advantages, which make it suitable for different types of problems. − …”

Section: Methodsmentioning

confidence: 99%

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Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools

Al-Darweesh,

Aljawad,

Tariq

et al. 2024

ACS Omega

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Rheological models are usually used to predict foamed fluid viscosity; however, obtaining the model constants under various conditions is challenging. Hence, this paper investigated the effect of different variables on foam rheology, such as shear rate, temperature, pressure, surfactant types, gas phase, and salinity, using a high-pressure high-temperature foam rheometer. Power-law, Bingham plastic, and Casson fluid models fit the experimental data well. Therefore, the data were fed to different machine learning techniques to evaluate the rheological model constants with different features. In this study, seven different machine learning techniques have been applied to predict the rheological models' constants, including decision tree, random forest, XGBoost (XGB), adaptive gradient boosting, gradient boosting, support vector regression, and voting regression. We evaluated the performance of our machine learning models using the coefficient of determination (R 2 ), cross-plots, root-mean-square error, and average absolute percentage error. Based on the prediction outcomes, the XGB model outperformed the other ML models. The XGB model exhibited remarkably low error rates, achieving a prediction accuracy of 95% under ideal conditions. Furthermore, our prediction results demonstrated that the Casson model accurately captured the rheological behavior of the foam. Additionally, we used Pearson's correlation coefficients to assess the significance of various properties in relation to the constants within the rheological models. It is evident that the XGB model makes predictions with nearly all features contributing significantly, while other machine learning techniques rely more heavily on specific features over others. The proposed methodology can minimize the experimental cost of measuring rheological parameters and serves as a quick assessment tool.

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Section: Methodsmentioning

confidence: 99%

“… 34 Each algorithm has its unique characteristics and advantages, which make it suitable for different types of problems. 35 − 37 …”

Section: Methodsmentioning

confidence: 99%

Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools

Al-Darweesh,

Aljawad,

Tariq

et al. 2024

ACS Omega

Self Cite

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“…ML and artificial intelligence techniques have widely been implemented in the oil and gas industry for various applications such as reservoir characterization, well logging interpretations, reservoir engineering applications, production engineering, chemical analysis, and so forth. − …”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

et al. 2022

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Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and timeconsuming process. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. Rock mechanical properties such as Young's modulus (E), Poisson's ratio (ν), unconfined compressive strength, and indirect tensile strength (σ t ) were measured before conducting hydraulic fracturing tests. ML models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the ML model, we considered experimental conditions, including the injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's modulus (E), Poisson's ratio (ν), UCS, and indirect tensile strength (σ t ), porosity, permeability, and bulk density. ML models include artificial neural networks (ANNs), random forests, decision trees, and the K-nearest neighbor. During training of ML models, the model hyperparameters were optimized by the grid-search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation was predicted with an accuracy of 95%. The accuracy of all ML techniques was quite similar; however, an explicit empirical correlation from the ANN technique is proposed. The empirical correlation is the function of all input features and can be used as a standalone package in any software. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.

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“…It showed that the FLS is a powerful predictive tool for well stimulation, reservoir geomechanics, and permeability evaluation aspects. Furthermore, AI models, especially the ANN, assisted in foreseeing the capacity of propped hydraulic fractures to conduit fluids in gas shale zones. , In line with that, Artun utilized the ANN to recognize the connectivity between injector and producer wells using neural network weights into the features. In addition, the capability of ML methods in reservoir simulation was assessed by Ghassemzadeh et al through incorporating an extensive range of reservoir data and creating a deep net simulator (DNS).…”

Section: Introductionmentioning

confidence: 97%

Data-Driven Connectionist Models for Performance Prediction of Low Salinity Waterflooding in Sandstone Reservoirs

et al. 2021

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Low salinity waterflooding (LSWF) and its variants also known as smart water or ion tuned water injection have emerged as promising enhanced oil recovery (EOR) methods. LSWF is a complex process controlled by several mechanisms and parameters involving oil, brine, and rock composition. The major mechanisms and processes controlling LSWF are still being debated in the literature. Thus, the establishment of an approach that relates these parameters to the final recovery factor (RFf) is vital. The main objective of this research work was to use a number of artificial intelligence models to develop robust predictive models based on experimental data and main parameters controlling the LSWF determined through sensitivity analysis and feature selection. The parameters include properties of oil, rock, injected brine, and connate water. Different operational parameters were considered to increase the model accuracy as well. After collecting the relevant data from 99 experimental studies reported in the literature, the database underwent a comprehensive and rigorous data preprocessing stage, which included removal of duplicates and low-variance features, missing value imputation, collinearity assessment, data characteristic assessment, outlier removal, feature selection, data splitting (80–20 rule was applied), and data scaling. Then, a number of methods such as linear regression (LR), multilayer perceptron (MLP), support vector machine (SVM), and committee machine intelligent system (CMIS) were used to link 1316 data samples assembled in this research work. Based on the obtained results, the CMIS model was proven to produce superior results compared to its counterparts such that the root mean squared rrror (RMSE) values for both training and testing data are 4.622 and 7.757, respectively. Based on the feature importance results, the presence of Ca2+ in the connate water, Na+ in the injected brine, core porosity, and total acid number of the crude oil are detected as the parameters with the highest impact on the RFf. The CMIS model proposed here can be applied with a high degree of confidence to predict the performance of LSWF in sandstone reservoirs. The database assembled for the purpose of this research work is so far the largest and most comprehensive of its kind, and it can be used to further delineate mechanisms behind LSWF and optimization of this EOR process in sandstone reservoirs.

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Machine Learning-Based Propped Fracture Conductivity Correlations of Several Shale Formations

Cited by 13 publications

References 25 publications

Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools

Prediction of Foam Rheology Models Parameters Utilizing Machine Learning Tools

Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

Data-Driven Connectionist Models for Performance Prediction of Low Salinity Waterflooding in Sandstone Reservoirs

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