An Empirical Correlation to Predict the SAGD Recovery Performance

Dong, Xiaohu; Liu, Huiqing; Hou, Jirui; Chen, Zhangxin; Zhang, Tianlin

doi:10.2118/176410-ms

Cited by 7 publications

(4 citation statements)

References 31 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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“…Through literature review and field experience, a series of typical attributes which could be considered in the numerical simulation model are chosen as input parameters. 30 − 34 Input parameters attached to initial conditions include initial reservoir pressure, initial oil saturation, and thermal conductivity of rocks. Input parameters attached to reservoir characteristics include effective thickness, porosity, horizontal permeability, and ratio of vertical permeability to horizontal permeability.…”

Section: Methodsmentioning

confidence: 99%

Data-Driven Proxy Model for Forecasting of Cumulative Oil Production during the Steam-Assisted Gravity Drainage Process

Liu

et al. 2021

ACS Omega

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The purpose of this study is to develop a data-driven proxy model for forecasting of cumulative oil (Cum-oil) production during the steam-assisted gravity drainage process. During the model building process, an artificial neural network (ANN) is used to offer a complementary and computationally efficient tool for the physics-driven model, and the von Bertalanffy performance indicator is used to bridge the physics-driven model with the ANN. After that, the accuracy of the model is validated by blind-testing cases. Average absolute percentage error of related parameters of the performance indicator in the testing data set is 0.77%, and the error of Cum-oil production after 20 years is 0.52%. The results illustrate that the integration of performance indicator and ANN makes it possible to solve time series problems in an efficient way. Besides, the data-driven proxy model could be applied to fast parametric studies, quick uncertainty analysis with the Monte Carlo method, and average daily oil production prediction. The findings of this study could help for better understanding of combination of physics-driven model and data-driven model and illustrate the potential for application of the data-driven proxy model to help reservoir engineers, making better use of this significant thermal recovery technology for oil sands or heavy oil reservoirs.

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“…Siavashi et al used a numerical simulation to analyze the influence of steam injection temperature, well rates, and different distances between injection-production wells (9,14,20, and 27 m) on oil recovery in SAGD process; the result showed that the steam temperature and well distance remarkably had an effect on SAGD performance; the oil production increased with well distance widening [29]. Dong et al developed sensitivity analysis model to comprehensively evaluate the influences of reservoir/fluid parameters and operation parameters on SAGD recovery performance, which confirmed that reservoir thickness, permeability, and pressure have great influence on SAGD production [30]. Zhang et al used a 2D numerical model to predict the lateral spreading and confinement stage of steam chamber, and they also found that a higher steam pressure results in a higher oil rate [31].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Well Placement Optimization for Steam-Assisted Gravity Drainage to Enhance Recovery of Thin Layer Oil Sand Reservoirs

et al. 2021

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SAGD (steam-assisted gravity drainage) technique is one of the most efficient thermal recovery technologies for exploiting Mackay River thin layer oil sand reservoirs. However, when making use of the traditional SAGD technique (the production and injection well are located on the same axis with the horizontal well spacing of 0 m), the steam chamber development is usually insufficient because of the high longitudinal sweep rate of steam, which seriously influences the SAGD performance for developing thin layer oil sand reservoirs especially. It is extremely important to find an economical and practicable method to promote the steam chamber development in thin oil sand reservoirs in the process of SAGD production; optimizing well placement is a reliable method. In this paper, an improved well placement method is proposed to enhance production performance of traditional SAGD, which is changing the horizontal well spacing to place the production well below of the side of the injection well (two wells are not located on the same axis). Three groups of 2D visualization experiments with different horizontal distances between two wells (0 cm, 10 cm, and 20 cm) were carried out, respectively, to observe the development and change of the steam chamber development, and to explore the EOR mechanisms. On the 2D experiment basis, optimal horizontal distance was selected to perform 3D physical simulation experiment to study and verity the production mechanism systematically. The results of 2D visualization experiment showed that the final oil recoveries of experimental groups with 10 cm and 20 cm horizontal distances were 7.6% and 2.3% higher than those of traditional SGAD (horizontal distance was 0 cm), respectively. Combined with 3D experimental results, the change in the horizontal relative position of two wells makes the steam first spread laterally between injecting and producing wells; thus, the lateral development of the steam chamber was promoted, and changes of temperature field also display that the lateral sweep area of steam was increased obviously and the form of steam chamber is changed. Meanwhile, the generation of appropriate horizontal well spacing can combine the effect of steam displacement and gravity drainage better and improve the sweep efficiency of steam. Nevertheless, the overlarge horizontal well spacing will also hinder the steam chamber development because the strength of steam overlap is weakened. Furthermore, the findings of this study help for better understanding that changing the horizontal well spacing can promote the lateral development of steam chamber, which can be used to enhance the oil recovery of thin layer oil sand reservoirs especially.

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An Empirical Correlation to Predict the SAGD Recovery Performance

Cited by 7 publications

References 31 publications

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

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

Data-Driven Proxy Model for Forecasting of Cumulative Oil Production during the Steam-Assisted Gravity Drainage Process

Experimental Study on Well Placement Optimization for Steam-Assisted Gravity Drainage to Enhance Recovery of Thin Layer Oil Sand Reservoirs

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