Compositional modeling of gas-condensate viscosity using ensemble approach

Rezaei, Farzaneh; Akbari, Mohammad; Rafiei, Yousef; Hemmati-Sarapardeh, Abdolhossein

doi:10.1038/s41598-023-36122-3

Cited by 3 publications

References 69 publications

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An ensemble-based machine learning solution for imbalanced multiclass dataset during lithology log generation

Jamshidi Gohari,

Emami Niri,

Sadeghnejad

et al. 2023

Sci Rep

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The lithology log, an integral component of the master log, graphically portrays the encountered lithological sequence during drilling operations. In addition to offering real-time cross-sectional insights, lithology logs greatly aid in correlating and evaluating multiple sections efficiently. This paper introduces a novel workflow reliant on an enhanced weighted average ensemble approach for producing high-resolution lithology logs. The research contends with a challenging multiclass imbalanced lithofacies distribution emerging from substantial heterogeneities within subsurface geological structures. Typically, methods to handle imbalanced data, e.g., cost-sensitive learning (CSL), are tailored for issues encountered in binary classification. Error correcting output code (ECOC) originates from decomposition strategies, effectively breaking down multiclass problems into numerous binary subproblems. The database comprises conventional well logs and lithology logs obtained from five proximate wells within a Middle Eastern oilfield. Utilizing well-known machine learning (ML) algorithms, such as support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression (LR), and extreme gradient boosting (XGBoost), as baseline classifiers, this study aims to enhance the accurate prediction of underground lithofacies. Upon recognizing a blind well, the data from the remaining four wells are utilized to train the ML algorithms. After integrating ECOC and CSL techniques with the baseline classifiers, they undergo evaluation. In the initial assessment, both RF and SVM demonstrated superior performance, prompting the development of an enhanced weighted average ensemble based on them. The comprehensive numerical and visual analysis corroborates the outstanding performance of the developed ensemble. The average Kappa statistic of 84.50%, signifying almost-perfect agreement, and mean F-measures of 91.04% emphasize the robustness of the designed ensemble-based workflow during the evaluation of blind well data.

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An ensemble-based machine learning solution for imbalanced multiclass dataset during lithology log generation

Jamshidi Gohari,

Emami Niri,

Sadeghnejad

et al. 2023

Sci Rep

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On the evaluation of surface tension of biodiesel

Rezaei,

Arab Juneghani,

Keshavarz Moraveji

et al. 2024

Sci Rep

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Over time, with the increase in population and the subsequent increase in energy consumption and also due to the non-renewability of fossil fuels, the study of alternative fuels has increased. One of these fuels is biodiesel, which is a suitable alternative to fossil fuels such as diesel and received much attention from researchers today. For this reason, measuring the physical properties of biodiesel is of great importance. Due to the high cost and time-consuming nature of laboratory methods, numerical methods are used to estimate material properties. The novelty of this research was the use of two white box models, including Group method of data handling (GMDH) and Gene expression programming (GEP), which work on the basis of artificial intelligence. By using these models, two simple mathematical equations with high accuracy were presented to predict the surface tension of biodiesel. These models can be used at different temperatures and molecular weights. To do modeling, 78 laboratory data available in the literature were gathered and the data were randomly divided into two groups, train and test, in a ratio of 80 and 20. The input parameters include mass fraction of fatty acid ethyl esters and temperature (T), and esters are divided into three groups according to their molecular weight: less than 200 (Mw 1 ), between 200 and 300 (Mw 2 ), and greater than 300 (Mw 3 ). The statistical error parameters were calculated for the two models developed in this research and after comparing the results, it was found that the GMDH model estimates the surface tension of biodiesel with a higher accuracy. The average absolute relative error for GMDH and GEP models was reported as 0.97 and 1.89, respectively. Also, other statistical error parameters of GMDH such as RMSE, SD, and R 2 for the GMDH model were obtained as 0.444, 0.000233, and 0.9233, respectively. Moreover, sensitivity analysis showed that temperature has the highest impact on the surface tension of biodiesel, which is also an inverse effect. Finally, suspicious laboratory and outlier data points were identified using the Leverage technique. According to this analysis, only five data points were identified as outliers and suspicious laboratory data.

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A Data-Based Continuous and Predictive Viscosity Model for the Oil-Surfactant-Brine Microemulsion Phase

Talapatra,

Nojabaei,

Khodaparast

2024

SPE Improved Oil Recovery Conference

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This study presents a computationally produced data-based model/correlation that can accurately estimate the magnitude and predict the peaks of microemulsion viscosity at dynamic reservoir conditions. Equilibrium molecular dynamics (MD) simulation is used on a decane-SDS-brine interfacial system to generate a dataset of viscosity values as a function of different temperatures, surfactant concentrations, and salinities. The viscosity testing and training data are computationally measured using the Einstein relation of the Green-Kubo formula. Several machine learning (ML) based regression algorithms, including K-nearest Neighbors (KNN), Support Vector regression (SVR), Multivariate Polynomial Regression (MLPR), Light Gradient Boosting Machine (LGBM), and Decision Tree (DT), are used to train the model. The SVR regression provides the best performputaance for our model compared to other methods with an R2 (0.978 and 0.963 for train and test data, respectively) and mean absolute error value (0.059 and 0.072 for train and test data, respectively). The chosen model is then used to predict microemulsion viscosity for different reservoir conditions. The proposed model aims to accurately estimate microemulsion viscosity at dynamic reservoir conditions with variable input parameters such as pressure, temperature, brine salinity, and surfactant concentration, enabling accurate estimation and prediction of the transport properties of reservoir fluids and present phases at reservoir conditions, which is key to achieving maximum recovery during chemical EOR.

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Compositional modeling of gas-condensate viscosity using ensemble approach

Cited by 3 publications

References 69 publications

An ensemble-based machine learning solution for imbalanced multiclass dataset during lithology log generation

An ensemble-based machine learning solution for imbalanced multiclass dataset during lithology log generation

On the evaluation of surface tension of biodiesel

A Data-Based Continuous and Predictive Viscosity Model for the Oil-Surfactant-Brine Microemulsion Phase

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