Improved prediction of heavy oil viscosity at various conditions utilizing various supervised machine learning regression

Aladwani, Faisal; Elsharkawy, Adel M.

doi:10.1080/10916466.2022.2060254

Cited by 5 publications

(1 citation statement)

References 47 publications

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“…The calculation was carried out using a special computer program developed in the language Visual Basic (VB) which calculates the area of the particles from the total area. This review analysis presents the increasing relevance and popularity of the introduction of intelligent technologies which have already proven themselves in various industries [34][35][36][37] into the oil industry; for example, the use of e-management tools for the intellectualization of the production [38], analysis, and prediction of crude oil prices based on machine learning methods [39][40][41] and the use of intelligent algorithms to evaluate the efficiency of equipment and processes in production [42][43][44] and in the assessment of various characteristics of oil products [45,46]. There are also studies describing the process of water segmentation in images using a convolutional neural network (CNN) [47].…”

Section: Introductionmentioning

confidence: 99%

Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms

Beskopylny,

Chepurnenko,

Meskhi

et al. 2023

Biomimetics

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Fluid particle detection technology is of great importance in the oil and gas industry for improving oil-refining techniques and in evaluating the quality of refining equipment. The article discusses the process of creating a computer vision algorithm that allows the user to detect water globules in oil samples and analyze their sizes. The process of developing an algorithm based on the convolutional neural network (CNN) YOLOv4 is presented. For this study, our own empirical base was proposed, which comprised microphotographs of samples of raw materials and water–oil emulsions taken at various points and in different operating modes of an oil refinery. The number of images for training the neural network algorithm was increased by applying the authors’ augmentation algorithm. The developed program makes it possible to detect particles in a fluid medium with the level of accuracy required by a researcher, which can be controlled at the stage of training the CNN. Based on the results of processing the output data from the algorithm, a dispersion analysis of localized water globules was carried out, supplemented with a frequency diagram describing the ratio of the size and number of particles found. The evaluation of the quality of the results of the work of the intelligent algorithm in comparison with the manual method on the verification microphotographs and the comparison of two empirical distributions allow us to conclude that the model based on the CNN can be verified and accepted for use in the search for particles in a fluid medium. The accuracy of the model was AP@50 = 89% and AP@75 = 78%.

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Section: Introductionmentioning

confidence: 99%

Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms

Beskopylny,

Chepurnenko,

Meskhi

et al. 2023

Biomimetics

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Characteristics of reservoir aggregation and main controlling factors in the P5-QS2 well area in the X basin

2024

J Therm Anal Calorim

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Enhanced intelligent approach for determination of crude oil viscosity at reservoir conditions

Langeroudy

Esfahani

Movaghar

2023

Sci Rep

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Oil viscosity plays a prominent role in all areas of petroleum engineering, such as simulating reservoirs, predicting production rate, evaluating oil well performance, and even planning for thermal enhanced oil recovery (EOR) that involves fluid flow calculations. Experimental methods of determining oil viscosity, such as the rotational viscometer, are more accurate than other methods. The compositional method can also properly estimate oil viscosity. However, the composition of oil should be determined experimentally, which is costly and time-consuming. Therefore, the occasional inaccessibility of experimental data may make it inevitable to look for convenient methods for fast and accurate prediction of oil viscosity. Hence, in this study, the error in viscosity prediction has been minimized by taking into account the amount of dissolved gas in oil (solution gas–oil ratio: Rs) as a representative of oil composition along with other conventional black oil features including temperature, pressure, and API gravity by employing recently developed machine learning methods based on the gradient boosting decision tree (GBDT): extreme gradient boosting (XGBoost), CatBoost, and GradientBoosting. Moreover, the advantage of the proposed method lies in its independence to input viscosity data in each pressure region/stage. The results were then compared with well-known correlations and machine-learning methods employing the black oil approach applying least square support vector machine (LSSVM) and compositional approach implementing decision trees (DTs). XGBoost is offered as the best method with its greater precision and lower error. It provides an overall average absolute relative deviation (AARD) of 1.968% which has reduced the error of the compositional method by half and the black oil method (saturated region) by five times. This shows the proper viscosity prediction and corroborates the applied method's performance.

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Improved prediction of heavy oil viscosity at various conditions utilizing various supervised machine learning regression

Cited by 5 publications

References 47 publications

Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms

Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms

Characteristics of reservoir aggregation and main controlling factors in the P5-QS2 well area in the X basin

Enhanced intelligent approach for determination of crude oil viscosity at reservoir conditions

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