Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C<sub>1</sub>–C<sub>5</sub>)/Bitumen Systems Using Genetic Programming

Al-Gawfi, Abdullah; Zirrahi, Mohsen; Abedi, Jalal

doi:10.1021/acsomega.9b00150

Cited by 19 publications

(7 citation statements)

References 32 publications

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“…Also, the deployment of machine learning tools for reservoir property estimations is gaining acceptance in the industry. Recently, genetic programing was deployed for the thermophysical property estimation of bitumen/light hydrocarbon system, with good correlation achieved …”

Section: Introductionsupporting

confidence: 91%

“…Recently, genetic programing was deployed for the thermophysical property estimation of bitumen/light hydrocarbon system, with good correlation achieved. 34 …”

Section: Introductionmentioning

confidence: 99%

“…Recently, genetic programing was deployed for the thermophysical property estimation of bitumen/light hydrocarbon system, with good correlation achieved. 34 To address the drawbacks of the existing techniques, for the first time, we present the implementation of machine learning algorithms for mineral surface charge prediction and feature ranking. This will aid the identification of surface charge controlling factors and rank factors according to their contribution to the mineral surface charge.…”

Section: Introductionmentioning

confidence: 99%

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Feature Ranking and Modeling of Mineral Effects on Reservoir Rock Surface Chemistry Using Smart Algorithms

et al. 2022

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Reservoir rock minerals and their surface charge development have been the subject of several studies with a consensus reached on their contribution to the control of reservoir rock surface interactions. However, the question of what factors control the surface charge of minerals and to what extent do these factors affect the surface charge remains unanswered. Also, with several factors identified in our earlier studies, the question of the order of effect on the mineral surface charge was unclear. To quantify the mineral surface charge, zeta potential measurements and Deryaguin–Landau–Verwey–Overbeek (DLVO) theories, as well as surface complexation models, are used. However, these methods can only predict a single mineral surface charge and cannot approximate the reservoir rock surface. This is because the reservoir rock is composed of many minerals in varying proportions. To address these drawbacks, for the first time, we present the implementation of machine learning models to predict reservoir minerals’ surface charge. Four different models namely the Adaptive Boosting Regressor, Random Forest Regressor, Support Vector Regressor, and the Gradient Boosting tree were implemented for this purpose with all the model predictions over 95% accuracy. Also, feature ranking of the factors that control the mineral surface charge was carried out with the most dominant factors being the mineral type, salt type, and pH of the environment. Findings reveal an opportunity for accurate prediction of reservoir rock surface charge given the enormous amount of data available.

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Section: Introductionsupporting

confidence: 91%

“…Recently, genetic programing was deployed for the thermophysical property estimation of bitumen/light hydrocarbon system, with good correlation achieved. 34 …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature Ranking and Modeling of Mineral Effects on Reservoir Rock Surface Chemistry Using Smart Algorithms

et al. 2022

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“…The developed model was able to address some issues in this area such as improving slow computational time, which is common in conventional physics-based simulators. Al-Gawfi et al 69 used genetic programming to develop three generalized correlations for the prediction of thermophysical properties of solvent/ bitumen mixtures such as viscosity, density, and solubility with implications in the design of thermal-and solvent-based bitumen recovery processes. The scientists used a comprehensive set of experimental phase behavior data to develop and validate the correlations.…”

Section: Introductionmentioning

confidence: 99%

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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“…Solvent/ Bitumen Viscosity. The following equation was used to correlate the viscosity data of the mixture53 …”

mentioning

confidence: 99%

Density and Viscosity of Multicomponent Solvent (n-Pentane + n-Hexane + n-Heptane + cyclo-Hexane + Toluene) and Bitumen Mixtures─Implications for In Situ Bitumen Recovery and Transportation of Diluted Bitumen

Khan,

Hassanzadeh

2023

Energy Fuels

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Conventional techniques for extracting and transporting bitumen, such as steam-assisted gravity drainage (SAGD), are associated with significant environmental drawbacks and contribute to greenhouse gas emissions. Embracing alternative processes could significantly mitigate these environmental impacts significantly. Furthermore, developing more energy-efficient extraction and transportation methods holds the potential to reduce energy consumption and associated carbon emissions, contributing to a more sustainable energy sector. This study introduces novel measurements pertaining to the thermophysical properties of a multicomponent synthetic solvent (n-pentane + n-hexane + n-heptane + cyclo-hexane + toluene) and bitumen mixtures within a pressure range extending up to 8.769 MPa and temperatures reaching up to 389 K. This information is of utmost significance in the design, optimization, and simulation of in situ recovery methods. Moreover, the measured properties are crucial in the design of surface processing and transport of diluted bitumen. Empirical relationships are established to determine the thermophysical properties of the studied multicomponent systems. These correlations, yielding average absolute relative deviations (AARD) of 0.36% for density and 8.81% for viscosity, offer simple tools for estimation of these properties in multicomponent systems composed of bitumen and synthetic diluent. Our results illustrate that the utilization of a synthetic diluent for viscosity reduction leads to a corresponding reduction in greenhouse gas emissions and results in significant energy savings. The results hold relevance across a spectrum of applications, spanning solvent-based bitumen recovery processes, surface treatments, and the efficient transportation of bitumen via pipelines.

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Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C₁–C₅)/Bitumen Systems Using Genetic Programming

Cited by 19 publications

References 32 publications

Feature Ranking and Modeling of Mineral Effects on Reservoir Rock Surface Chemistry Using Smart Algorithms

Feature Ranking and Modeling of Mineral Effects on Reservoir Rock Surface Chemistry Using Smart Algorithms

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

Density and Viscosity of Multicomponent Solvent (n-Pentane + n-Hexane + n-Heptane + cyclo-Hexane + Toluene) and Bitumen Mixtures─Implications for In Situ Bitumen Recovery and Transportation of Diluted Bitumen

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Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C1–C5)/Bitumen Systems Using Genetic Programming

Cited by 19 publications

References 32 publications

Feature Ranking and Modeling of Mineral Effects on Reservoir Rock Surface Chemistry Using Smart Algorithms

Feature Ranking and Modeling of Mineral Effects on Reservoir Rock Surface Chemistry Using Smart Algorithms

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

Density and Viscosity of Multicomponent Solvent (n-Pentane + n-Hexane + n-Heptane + cyclo-Hexane + Toluene) and Bitumen Mixtures─Implications for In Situ Bitumen Recovery and Transportation of Diluted Bitumen

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Development of Generalized Correlations for Thermophysical Properties of Light Hydrocarbon Solvents (C₁–C₅)/Bitumen Systems Using Genetic Programming