Prediction of Wax Appearance Temperature Using Artificial Intelligent Techniques

Benamara, Chahrazed; Gharbi, Kheira; Amar, Menad Nait; Hamada, Boudjema

doi:10.1007/s13369-019-04290-y

Cited by 25 publications

(4 citation statements)

References 37 publications

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“…Waxes, as opposed to asphaltenes which are a subclass of aromatics, are mixtures of heavy n-alkanes and i-alkanes (paraffins) [188]. Although mostly encountered in surface facilities, risers, pipelines and separators, and not in subsurface systems like the reservoir and the wellbore, it is worth mentioning several researchers who have focused on predicting wax deposition conditions and amount, like Amar et al [197] who developed an MLP optimized with LM and Bayesian Regularization algorithms to predict the deposited wax amount during production and Benamara et al [198] who used the same methodology to determine the Wax Appearance Temperature (WAT). In another study, Benamara et al [199] built RBFNN models coupled with two optimization algorithms, namely GA and Artificial Bee Colony (ABC) [200], to predict the WAT.…”

Section: Machine Learning Methods For Flow Assurance Problemsmentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Samnioti,

Gaganis

2023

Energies

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In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

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Section: Machine Learning Methods For Flow Assurance Problemsmentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Samnioti,

Gaganis

2023

Energies

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“…LMA is the commonly employed backpropagation-based approach for MLP weights and bias optimization since it has a high aptitude to obtain their final solutions regardless of the distance from the starting point. 96 LMA uses the principles of Newton's method with fair modifications in their conceptions, where in this algorithm the Hessian matrix is estimated instead of directly calculated, and an additional parameter known as the regularization parameter, which improves the stability during the calculation process, is proposed. The expressions of the gradient matrix as well as the approximated Hessian matrix evolved in LMA are shown below: 97…”

Section: Optimization Methodsmentioning

confidence: 99%

Modeling CO₂ Solubility in Water at High Pressure and Temperature Conditions

et al. 2020

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CO 2 dissolution in water at different temperature and pressure conditions is of essential interest for various environmental, geochemical, and thermodynamic related problems. The topic is of special interest in studies of CO 2 geological sequestration in brine-bearing aquifers. In this Article, four powerful machine learning (ML) techniquesRadial Basis Function Neural Network (RBFNN), Multilayer Perceptron (MLP), Least-Squares Support Vector Machine (LSSVM), and Gene Expression Programming (GEP)are implemented to develop economical, rapid, and reliable models to predict the solubility of CO 2 in water. To expand the prediction capability of the ML approaches, their control parameters are optimized by various techniques. To this end, four back-propagation algorithms are applied in the MLP learning phase, while Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), and Firefly Algorithm (FFA) are used to optimize the RBFNN and LSSVM control parameters. A wide-ranged database including temperature and pressure as inputs and CO 2 solubility in pure water as output is utilized to develop the models, which are then compared with each other and also against existing models. The results demonstrate that the prediction performance of the proposed models is quite satisfactory. In addition, the comparison results reveal that the LSSVM-FFA is the best paradigm to estimate the solubility of CO 2 in pure water, as it outperforms the other proposed ML techniques as well as prior models. The overall RMSE and R 2 values for LSSVM-FFA are 0.3261 and 0.9930, respectively.

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“…Bian et al 42 developed a metaheuristic algorithm (GWO) to optimize neural networks to predict the WAT of crude oil. Benamara et al 43 predicted WAT by using Levenberg−Marquardt algorithm and Bayesian regularization algorithm optimized gene expression programming and multilayer perceptron to predict density, viscosity, pour point, and wax content as input parameters, and the model results met engineering requirements.…”

Section: Crystallization Properties and Phase Behaviorsmentioning

confidence: 99%

Advances in and Perspectives on Strategies for Improving the Flowability of Waxy Oils

Yao

Sun

et al. 2022

Energy Fuels

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The research on the flowability of waxy oil has spanned more than a century and is still attracting much attention. How to improve the flowability of waxy oil to ensure safe and efficient pipeline transportation and storage is of great practical importance for the industry. In this work, we have systematically reviewed the research advances in improving the flowability of waxy oil. First, the crystallization properties and phase behavior of waxes are described based on their chemical structure, thermodynamic properties, and solution phase properties, with emphasis on the solventized layer properties of wax crystals. Then, the mechanisms, process, and research progress in flow improvement of waxy oil are discussed from three major aspects: process improvement, equipment improvement, and chemical treatment, with emphasis on the summary of waxy oil pour point depressants in recent years. The remaining challenges and perspectives for future research on flow improvement of waxy oil are presented.

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Prediction of Wax Appearance Temperature Using Artificial Intelligent Techniques

Cited by 25 publications

References 37 publications

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Modeling CO₂ Solubility in Water at High Pressure and Temperature Conditions

Advances in and Perspectives on Strategies for Improving the Flowability of Waxy Oils

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Prediction of Wax Appearance Temperature Using Artificial Intelligent Techniques

Cited by 25 publications

References 37 publications

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Modeling CO2 Solubility in Water at High Pressure and Temperature Conditions

Advances in and Perspectives on Strategies for Improving the Flowability of Waxy Oils

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Product

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Modeling CO₂ Solubility in Water at High Pressure and Temperature Conditions