Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods

Ebrahimi, Arash; Izadpanahi, Amin; Ebrahimi, Parirokh; Ranjbar, A.A.

doi:10.1016/j.petrol.2021.109841

Cited by 23 publications

(16 citation statements)

References 35 publications

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“…indicates how accurately the model or algorithm predicts the target value. Error values may include measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE) and RMSE (Ebrahimi et al 2022). The correlation coefficient, also known as the Pearson correlation coefficient is a statistical measure that quantifies the strength and direction of the linear relationship between correct and target variables (Cohen et al 2009).…”

Section: Performance Of ML Methodsmentioning

confidence: 99%

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Estimation of Drilling Rate of Penetration Using Hybrid Machine Learning Methods

Ranjbar,

Mohammadinia

2023

Preprint

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Rate of penetration(ROP) is one of the most important well drilling parameters, and its estimation and optimization is very important during well planning and reducing related costs. Meanwhile, the prediction of this parameter is challenging due to the complex interactions between the drill bit and the formation rock. In this study, different Machine Learning(ML) estimation techniques including Artificial Neural Networks(ANN), Random Forest(RF) and Least Squares Support Vector Machine (LSSVM) are hybridized with meta-heuristic algorithms, including Crow Search Algorithm(CSA), Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) has been used to estimate ROP more accurately. The aforementioned meta-heuristic algorithms have been used to intelligently adjust hyper-parameters of estimation ML methods based on data. The results show that it will significantly improve the estimation performance. Among the models, RF-GA, RF-CSA and LSSVM-GA were recognized as the top three models, respectively. The value of the correlation coefficients between the estimated and the actual values of ROP in these models was 0.98, 0.974, and 0.972, respectively. Also, the mean square error (RMSE) values for these models were obtained 2.89, 3.25 and 3.37, respectively. Depth, mud weight and rotation speed are identified as the most influential parameters in the response of estimation models. The findings emphasize the effectiveness of combining ML methods with meta-heuristic algorithms to accurately estimate drilling penetration rates. The results provide valuable insights to optimize drilling operations, reduce costs and increase drilling performance in oil fields. The results of this study in the field of drilling optimization can be useful in engineering-based drilling decisions.

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Section: Performance Of ML Methodsmentioning

confidence: 99%

“…In this study, two statistical parameters R 2 and RMSE have been used to analyze the performance of each of the ML methods in estimating ROP. The mathematical relationship for these parameters are as follows (Ebrahimi et al 2022):…”

Section: Performance Of ML Methodsmentioning

confidence: 99%

Estimation of Drilling Rate of Penetration Using Hybrid Machine Learning Methods

Ranjbar,

Mohammadinia

2023

Preprint

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“…The central objective of SVR pertains to the determination of a function denoted as ( , ) F X W , aimed at approximating output values with a level of precision deemed satisfactory. Further comprehensive insights concerning the SVR model are elaborated upon in the work of Ebrahimi et al [56].…”

Section:  mentioning

confidence: 99%

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Bayat,

Mousavi,

et al. 2023

Preprint

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Dynamic properties (i.e., shear modulus and damping ratio) of geomaterials play a vital role in civil engineering applications and are essential for reliable dynamic response analysis. This paper presents a novel approach for predicting the normalized shear modulus (G/Gmax) and damping ratio (D) of granular soils across a wide strain range using a Deep Neural Network (DNN) modeling strategy. Traditional methods for predicting these properties often rely on empirically derived relationships that may not capture the full complexity of granular soil behavior under varying strain conditions. A comprehensive dataset of shear modulus and damping ratio measurements from laboratory cyclic triaxial (CT) and resonant column (RC) tests conducted under various conditions is utilized. The dataset covers a wide range of strain levels, allowing for a more robust and versatile modeling approach. For predicting the G/Gmax and D of granular soils, a Deep Feed-Forward Neural Network (DFFNN) model was developed to learn the features from input data. The proposed model considers the influence of grading characteristics (Gravel Content, GC, median particle size, D50, Uniformity Coefficient, Cu, and Coefficient of Curvature, Cc), shear strain (\(\gamma\)), void ratio (e), mean effective confining pressure (\({\sigma ^{\prime}_m}\)), consolidation stress ratio (KC) and specimens’ preparation method (S-P) as input data. The empirical models (EMs) and three other intelligent techniques, namely Shallow Neural Network (SNN), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR) were used for comparison. The testing accuracy of the proposed DFFNN for predicting the G/Gmax and D was 0.9830 and 0.9396, respectively. The results demonstrate that the proposed DFFNN modeling strategy provides a highly accurate means of predicting G/Gmax and D for granular soils across a broad shear strain range. This method offers advantages over EMs by incorporating a data-driven approach that can adapt to the specific behavior of different granular soil types and loading conditions.

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“…Artificial neural network (ANN), extreme learning machine (ELM), support vector machine (SVM) and other algorithms based mainly on regression / correlation relationships have been successfully applied to progressively improve the prediction performance of variables relevant to the petroleum industry (Farsi et al 2021b). Some of the researchers work on the Vs based on the ML work (Weijun et al 2017;Azadpour et al 2020;Zhang et al 2020;Zhang et al 2021;Olayiwola et al 2021;Zhong et al 2021;Ebrahimi et al 2022).…”

Section: Machine-learning (Ml) Algorithmsmentioning

confidence: 99%

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

Rajabi

Hazbeh

Davoodi

et al. 2022

J Petrol Explor Prod Technol

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Shear wave velocity (VS) data from sedimentary rock sequences is a prerequisite for implementing most mathematical models of petroleum engineering geomechanics. Extracting such data by analyzing finite reservoir rock cores is very costly and limited. The high cost of sonic dipole advanced wellbore logging service and its implementation in a few wells of a field has placed many limitations on geomechanical modeling. On the other hand, shear wave velocity VS tends to be nonlinearly related to many of its influencing variables, making empirical correlations unreliable for its prediction. Hybrid machine learning (HML) algorithms are well suited to improving predictions of such variables. Recent advances in deep learning (DL) algorithms suggest that they too should be useful for predicting VS for large gas and oil field datasets but this has yet to be verified. In this study, 6622 data records from two wells in the giant Iranian Marun oil field (MN#163 and MN#225) are used to train HML and DL algorithms. 2072 independent data records from another well (MN#179) are used to verify the VS prediction performance based on eight well-log-derived influencing variables. Input variables are standard full-set recorded parameters in conventional oil and gas well logging data available in most older wells. DL predicts VS for the supervised validation subset with a root mean squared error (RMSE) of 0.055 km/s and coefficient of determination (R2) of 0.9729. It achieves similar prediction accuracy when applied to an unseen dataset. By comparing the VS prediction performance results, it is apparent that the DL convolutional neural network model slightly outperforms the HML algorithms tested. Both DL and HLM models substantially outperform five commonly used empirical relationships for calculating VS from Vp relationships when applied to the Marun Field dataset. Concerns regarding the model's integrity and reproducibility were also addressed by evaluating it on data from another well in the field. The findings of this study can lead to the development of knowledge of production patterns and sustainability of oil reservoirs and the prevention of enormous damage related to geomechanics through a better understanding of wellbore instability and casing collapse problems. Graphical abstract

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Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods

Cited by 23 publications

References 35 publications

Estimation of Drilling Rate of Penetration Using Hybrid Machine Learning Methods

Estimation of Drilling Rate of Penetration Using Hybrid Machine Learning Methods

Prediction of Normalized Shear Modulus and Damping Ratio for Granular Soils Over a Wide Strain Range Using Deep Neural Network Modelling

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

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