Application of artificial neural network in optimizing the drilling rate of penetration of western desert Egyptian wells

Azim, Reda Abdel

doi:10.1007/s42452-020-2993-8

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…Recent research has shown that a well-trained MLP-neural network can be transformed into an AI-based mathematical model by utilizing the optimized weights and biases of the network. This approach has been successfully applied to develop robust AI-based correlations for various petroleum applications, such as fluid properties, drilling operations, and production optimization. , In this study, we follow the same approach by integrating the in-house ANN code with the in-house poro-elastic finite element simulator to propose an AI-based explicit mathematical formula for the prediction of free and absorbed gas volumes in shale gas reservoirs. The workflow of such integration is as follows: Different realization of shale gas production is generated through a random combination of shale gas geo-mechanical parameters, as shown in Section . The randomly generated reservoir realizations are fed to our in-house poro-elastic simulator to create a data bank of free and absorbed gas volumes under different reservoir characteristics. The generated data bank is used to train the ANN and propose an explicit AI-based correlation for free and absorbed gas volumes in shale gas reservoirs taking into account the poro-elastic behavior, such as matrix shrinkage and stress sensitivity. …”

Section: Methodsmentioning

confidence: 99%

“…This approach has been successfully applied to develop robust AI-based correlations for various petroleum applications, such as fluid properties, drilling operations, and production optimization. 38 , 39 In this study, we follow the same approach by integrating the in-house ANN code with the in-house poro-elastic finite element simulator to propose an AI-based explicit mathematical formula for the prediction of free and absorbed gas volumes in shale gas reservoirs. The workflow of such integration is as follows: Different realization of shale gas production is generated through a random combination of shale gas geo-mechanical parameters, as shown in Section 3.2 .…”

Section: Methodsmentioning

confidence: 99%

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An Explicit Data-Driven Model for Estimation of Free and Absorbed Gas Volumes in Shale Gas Reservoirs

Abdel Azim,

Alatefi,

Alkouh

2024

ACS Omega

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Shale gas reservoir fluid flow is characterized by complex interactions between a number of variables, including stress sensitivity, matrix shrinkage, and critical desorption pressure. The behavior and productivity of shale gas reservoirs are significantly influenced by these variables. Therefore, in this study, an in-house finite element poro-elastic simulator was integrated with an in-house Fortran-coded artificial neural network model for accurate estimation of free and absorbed gas volumes in shale gas reservoirs, taking into account the combined effects of stress sensitivity, matrix shrinkage, and critical desorption pressure. The in-house finite element simulator was used to create a database of free and absorbed gas volumes under different reservoir, fluid, and geo-mechanical characteristics of shale gas, while the in-house neural network code was utilized using the outcomes of the poro-elastic simulator in order to propose an explicit data-driven mathematical formula of free and absorbed volumes of shale gas. The study findings indicate that the neural network model achieved high accuracy with an R-squared value exceeding 99% during both the training and blind-testing phases. The newly developed ANN-based correlations can be used as a quick and easy-to-use tool to forecast the volume of shale gas in place both free and adsorbed without any prior experience with utilizing or implementing machine learning models.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

An Explicit Data-Driven Model for Estimation of Free and Absorbed Gas Volumes in Shale Gas Reservoirs

Abdel Azim,

Alatefi,

Alkouh

2024

ACS Omega

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“…This is partly due to the fact that intelligent animals can solve problems which are impossible for even the most powerful modem computers and partly because of the desire by engineers and computer scientists to explore and exploit parallel hardware systems and apply them to solve practical problems. In petroleum engineering, successful applications include drill bit diagnosis, 10 seismic processing, 11 identification of well test interpretation model, 12 flow measurements, 13 identification of well productivity, 14 and wireline log analysis. 15 Also, McCormack and Day 16 and Fogelman-Soulie 17 provided some introductory articles on the use of neural networks in the petroleum industry.…”

Section: Application Of Ann To Predict Water Saturationmentioning

confidence: 99%

Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields

Azim¹,

Hamada²

2022

ACS Omega

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The accurate determination of water saturation in shaly sandstone reservoirs has a significant impact on hydrocarbons in place estimation and selection of possible hydrocarbon zones. The available numerical equations for water saturation estimation are unreliable and depend on laboratory core analysis. Therefore, this paper attempts to use artificial intelligence methods in developing an artificial neural network model (ANN) for water saturation (Sw) prediction. The ANN model is developed and validated by using 2700 core measured points from the fields located in the Gulf of Suez, Nile Delta, and Western Desert of Egypt, with inputs including the formation depth, the caliper size, the sonic time, gamma rays (GRs), shallow resistivity (Rxo), neutron porosity (NPHI), the photoelectric effect (PEF), bulk density, and deep resistivity (Rt). The study results show that the optimization process for the ANN model is achieved by distributing the collected data as follows: 80% for training and 20% for testing processes, with an R 2 of 0.973 and a mean square error (MSE) of 0.048. In addition, a mathematical equation is extracted out of the ANN model that is used to estimate the formation water saturation in a simple and direct approach. The developed equation can be used incorporating with the existing well logs commercial software to increase the accuracy of water saturation prediction. A comparison study is executed using published correlations (Waxman and Smits, dual water, and effective models) to show the robustness of the presented ANN model and the extracted equation. The results show that the proposed correlation and the ANN model achieved outstanding performance and better accuracy than the existing empirical models for calculating the formation water saturation with a high correlation coefficient ( R 2 ) of 0.973, lowest mean-square error (MSE) of 0.048, lowest average absolute percent relative error (AAPRE) of 0.042, and standard deviation (SD) of 0.24. To the best of our knowledge, the current study and the proposed ANN model establish a novel base in the estimation of formation water saturation.

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“…In the petroleum industry, among others, ANN algorithms have been applied for prediction such as ROP (Reda Abdel Azim (2020) [26], Ramin Aliyev (2019) [27]), ECD (Husam H. Alkinani (2020) [28], Amir et al, 2021 [24,25]), drilling speed (Ahmad Al-Abduljabbar et al (2020) [29]), and drilling-fluid-rheological-parameter real-time prediction (Khaled Al-Azani et al (2018) [30]). In addition, A. Alnmnr (2024) implemented machine learning to investigate Swell Mitigation [31].…”

Section: Introductionmentioning

confidence: 99%

Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

Mohammad,

Belayneh

2024

Applied Sciences

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This paper presents data-driven modeling and a results analysis. Group method data handling (GMDH), multivariable regression (MVR), artificial neuron network (ANN), and new proposed GMDH-featured ANN machine learning algorithms were implemented to model a field telemetry equivalent mud circulating density (ECD) dataset based on surface and subsurface drilling parameters. Unlike the standard GMDH-ANN model, the proposed GMDH-featured ANN utilizes a fully connected network. Based on the considered eighteen experimental modeling designs, all the GMDH regression results showed higher R-squared and minimum mean-square error values than the multivariable regression results. In addition, out of the considered eight experimental designs, the GMDH-ANN model predicts about 37.5% of the experiments correctly, while both algorithms have shown similar results for the remaining experiments. However, further testing with diverse datasets is necessary for better evaluation.

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Application of artificial neural network in optimizing the drilling rate of penetration of western desert Egyptian wells

Cited by 10 publications

References 26 publications

An Explicit Data-Driven Model for Estimation of Free and Absorbed Gas Volumes in Shale Gas Reservoirs

An Explicit Data-Driven Model for Estimation of Free and Absorbed Gas Volumes in Shale Gas Reservoirs

Novel Correlation for Calculating Water Saturation in Shaly Sandstone Reservoirs Using Artificial Intelligence: Case Study from Egyptian Oil Fields

Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

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