Secondary Oil Recovery by Water Injection: A Numerical Study

Cunha, Acto de Lima; Neto, Severino Rodrigues de Farias; Lima, Antônio Gilson Barbosa de; Barbosa, Enivaldo Santos

doi:10.4028/www.scientific.net/ddf.334-335.83

Cited by 2 publications

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“…The different timescale data were selected to train the machine learning models. The numerical results confirmed the superiority of the SGB model in predicting the oil recovery factor. ,− ,− …”

Section: Introductionsupporting

confidence: 55%

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Integrated Hierarchy–Correlation Model for Evaluating Water-Driven Oil Reservoirs

et al. 2021

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With the increasing demands on energy and environmental domains, not only high oil production but also its accurate quantification has become one of the most important topics in academia and industry. This paper initially proposes a comprehensive workflow in which an integrated hierarchy–correlation model is used to thoroughly evaluate the influences of all relevant reservoir parameters on the ultimate oil recovery for water-flooding oil reservoirs. More specifically, the analytic hierarchy process, grey relation, and entropy weight are combined through the multiplicative weighting method to quantitatively describe the production parameters. Accordingly, novel multivariable linear and nonlinear correlations are developed to predict the production performance and validated through comparisons with numerical reservoir simulations. Seven factors, including five reservoir parameters, namely, permeability and its contrast, porosity, thickness, and saturation, and two production parameters, namely, the injection–production ratio and the operating pressure, have been identified as the most influential factors on recovery performances and thus are employed in the proposed correlations to predict the ultimate oil recovery factor. The results obtained by the proposed method are quite close to the real-time simulation data, while the accuracy is retained. The numerical results show that the recovery factors of water-flooding oil reservoirs are about 33.5–59.5%, and the corresponding linear and nonlinear correlation coefficients are 0.903 and 0.789, respectively. In comparison with the numerical simulation, the approximation error by the linear correlation is about 0.5%, which is lower than that of nonlinear correlation, for example, 12.3%. This study will be beneficial to analyze the reservoir-related parameters and provide a useful tool for rapid production performance evaluation of the water-flooding production scenario.

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Section: Introductionsupporting

confidence: 55%

“…The numerical results confirmed the superiority of the SGB model in predicting the oil recovery factor. 5 , 7 − 10 , 12 − 15 …”

Section: Introductionmentioning

confidence: 99%

Integrated Hierarchy–Correlation Model for Evaluating Water-Driven Oil Reservoirs

et al. 2021

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Supervision and Control System of the Operational Variables of a Cluster in a High-Pressure Gas Injection Plant

Morales

Medina

Vásquez³

et al. 2023

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The objective of this research was to develop a technological architecture proposal that allows for the supervision and control of the operational parameters of gas injection (flow, temperature, and pressure) in a cluster of a high-pressure gas injection plant. The proposal provides a supervision and control system for the HPGIP I high-pressure gas injection plant that includes instrumentation equipment (transmitters and actuators), a remote terminal unit (RTU) as a control device, and the creation of a control logic as the basis for the development of the SCADA GALBA®, through which the operational variables involved in the process of the gas injection plant can be visualized and controlled, allowing the automatic regulation of the flow of gas that enters the deposits. Automatization of the process allows for the elimination of the average error differential that increases from 2 to 5% when the control valve is opened manually. Currently, the MUC-67 and MUC-68 wells that make up cluster 5 require a control valve opening of 20% and 5%, respectively, and this percentage is directly affected by the average valve opening error when performed manually. In addition, there is a savings of around 40 min in the response time by the operators for the adjustment of the opening or closing parameters of the control valve manually. The proposal allows for the different control actions on the variables or parameters of gas injection present in the clump to be carried out from a control room.

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Secondary Oil Recovery by Water Injection: A Numerical Study

Cited by 2 publications

References 3 publications

Integrated Hierarchy–Correlation Model for Evaluating Water-Driven Oil Reservoirs

Integrated Hierarchy–Correlation Model for Evaluating Water-Driven Oil Reservoirs

Supervision and Control System of the Operational Variables of a Cluster in a High-Pressure Gas Injection Plant

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