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Compositional modeling is an effective and necessary technique for designing and optimizing complex oil and gas production processes, such as gas injection enhanced oil recovery (EOR). Generally, about 70% of computational time in compositional modeling is consumed by flash calculation. Replacing iterative flash calculations partially or completely in obtaining the satisfactorily accurate number of phases and compositionswith machine learning (ML) models is proven to be an efficient strategy to accelerate flash calculation. In this study, support vector machine (SVM), artificial neural network (ANN), and decision trees (DT) models were trained, validated, and tested based on 10,000 PVT data sets, which were artificially generated for a sour crude oil sample containing 13.32 mol% H2S and 2.9 mol% CO2. Comparative analysis showed that all three ML models could be used to make an accurate prediction. The obtained ANN model was compared with the vapor–liquid equilibrium (VLE) calculation and was used as a surrogate model for accelerating the flash calculation. The comparison revealed that the ANN model reduced the computational time of the VLE calculation by as high as 250 times. Moreover, the phase diagram of sour crude oil obtained by the ANN model was close to the phase diagram generated by VLE, which proves the accuracy and robustness of the ANN model. Overall, this work shows that ANN model is effective in accelerating flash calculation and reducing the computational time of compositional reservoir simulation.
Compositional modeling is an effective and necessary technique for designing and optimizing complex oil and gas production processes, such as gas injection enhanced oil recovery (EOR). Generally, about 70% of computational time in compositional modeling is consumed by flash calculation. Replacing iterative flash calculations partially or completely in obtaining the satisfactorily accurate number of phases and compositionswith machine learning (ML) models is proven to be an efficient strategy to accelerate flash calculation. In this study, support vector machine (SVM), artificial neural network (ANN), and decision trees (DT) models were trained, validated, and tested based on 10,000 PVT data sets, which were artificially generated for a sour crude oil sample containing 13.32 mol% H2S and 2.9 mol% CO2. Comparative analysis showed that all three ML models could be used to make an accurate prediction. The obtained ANN model was compared with the vapor–liquid equilibrium (VLE) calculation and was used as a surrogate model for accelerating the flash calculation. The comparison revealed that the ANN model reduced the computational time of the VLE calculation by as high as 250 times. Moreover, the phase diagram of sour crude oil obtained by the ANN model was close to the phase diagram generated by VLE, which proves the accuracy and robustness of the ANN model. Overall, this work shows that ANN model is effective in accelerating flash calculation and reducing the computational time of compositional reservoir simulation.
Relevance. Recent acculturation of foreign language education (FLE) and movement towards competency-based approach has put forward as a goal the emergence of professional-oriented competence. This paper presents a brief overview of research on professional-oriented competence through content and language integrated learning in the Republic of Kazakhstan. Purpose. This study aims to help assess future teachers’ strengths and weaknesses in professional-oriented competence development. Literature relevant to the topic was studied in order to understand how to improve linguistic and, in general, verbal communication between the teacher and students. Methodology. The methods used in this study are experimental in nature, implying tests and tasks for certain groups of subjects. The questionnaire was developed to obtain information about future teachers’ view who are going to work at the profile school on using content and language integrated technology and diagnose possible professional knowledge gap in this study. Results. The model of future foreign language teachers’ research competence development has been constructed; a system of tasks based on digital resources has been suggested and experimental work was provided to show the efficiency of the model designed. Conclusions. Conclusions of this study confirm the importance of the professionally oriented competence development model for achieving more effective communication in terms of information exchange and the learning process.
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