Determining Relative Permeability From Unsteady-State Radial Fluid 
Displacements

Tóth, János; Bodi, T; Szűcs, Péter; Civan, Faruk

doi:10.2523/94994-ms

Cited by 2 publications

(2 citation statements)

References 3 publications

(18 reference statements)

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“…The existing analytical inversion methods have ignored the influence of capillary pressure, which may result in low precision for the estimated water−oil relative permeability curve in some cases. 6 For this reason, it is essential to build a numerical inversion method for the water− oil relative permeability curve from radial displacement experiments. On this basis, the rules of relative permeability variation when the capillary pressure is neglected can be investigated.…”

Section: Introductionmentioning

confidence: 99%

“…The water–oil relative permeability curve can describe the flow characteristics of the water–oil two phase in the porous medium, and it has a great effect on the rules of water cut increase and production variation, which is one important data in oilfield development design and reservoir numerical simulation. − When obtaining the relative permeability curve from unsteady-state radial displacement experiments, the capillary pressure is the key parameter to affect its inversion accuracy. The existing analytical inversion methods have ignored the influence of capillary pressure, which may result in low precision for the estimated water–oil relative permeability curve in some cases . For this reason, it is essential to build a numerical inversion method for the water–oil relative permeability curve from radial displacement experiments.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Water–Oil Relative Permeability Curve from Radial Displacement Experiments. Part 2: Reasonable Experimental Parameters

Hou¹,

Luo²,

Wang³

et al. 2012

Energy Fuels

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The capillary pressure is the key parameter to affect the inversion accuracy of the water–oil relative permeability curve. The existing analytical inversion methods have neglected the influence of capillary pressure, which may cause low precision for the estimated relative permeability curve in some cases. On the basis of the numerical inversion method for the water–oil relative permeability curve established in part 1 (10.1021/ef300018w), taking the one-dimensional radial numerical experiment for example, the rules of relative permeability variation and influence of different displacement conditions on relative permeability deviation when neglecting the capillary pressure are investigated. With regard to water-wet cases whose oil–water viscosity ratio is greater than 1.5, it indicates that the estimated water-phase relative permeability curve is higher and the estimated oil-phase relative permeability curve is lower compared to the true relative permeability curve when the capillary pressure is neglected. The main displacement conditions influencing the inversion accuracy of the relative permeability curve include the injection rate, average permeability, and shape factor of the core sample. As the injection rate increases, the degree of relative permeability deviation caused by neglecting the capillary pressure becomes smaller. Moreover, the deviation trends of the water–oil relative permeability curve are the same as those of the increasing injection rate when average permeability decreases or the shape factor of the core sample increases. Finally, the orthogonal experimental design technique is used to establish the experimental conditions considering the combined effect of multiple factors, and then the water–oil relative permeability curve under every experimental condition is estimated implicitly. On this basis, the multivariate analysis is performed to obtain the threshold value charts of radial displacement experimental parameters, such as the injection rate, average permeability, and shape factor of the core sample, and their corresponding rational value domains are also achieved, which can be used to reduce the influence of neglecting capillary pressure data as much as possible and provide a calculation theory for estimation of the water–oil relative permeability curve accurately.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of the Water–Oil Relative Permeability Curve from Radial Displacement Experiments. Part 2: Reasonable Experimental Parameters

Hou¹,

Luo²,

Wang³

et al. 2012

Energy Fuels

View full text Add to dashboard Cite

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Exploring an Alternative Approach for Predicting Relative Permeability Curves from Production Data: A Comparative Analysis Employing Machine and Deep Learning Techniques

Gharieb,

Elshaafie,

Gabry

et al. 2024

Day 4 Thu, May 09, 2024

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This study aims to present a novel approach for estimating relative permeability curves using Machine Learning (ML) and Deep Learning (DL) techniques based on production data. This method aims to overcome the shortage in core data availability, which is much needed for reservoir simulation. The adopted approach involves devising an algorithm utilizing different methodologies employing synthetic simulation data. The water-oil relative permeability curves correlated with various parameters such as production data and reservoir parameters. Subsequently, the model was applied to real field data to predict relative permeability curves. The procedural framework involves data compilation, model training, application to real field data, and integration with reservoir simulation. In data compilation, synthetic simulation outcomes, including water-oil relative permeability curves, oil and water rates, flowing pressures, and diverse reservoir characteristics are gathered. Model training focuses on developing an ML model proficient in discerning complex relationships between production parameters and relative permeability curves. Following this, the model’s accuracy is rigorously assessed through validation, involving data not used during the training or the validation process. In the application to field data, the trained model is employed to estimate relative permeability curves using authentic production data. Finally, integration with reservoir simulation entails assimilating the estimated curves into simulations to enhance the process of initial history matching. The methodology encompasses the generation of synthetic data to train the Machine Learning model, validation of its performance, application to real field data, and the utilization of the estimated relative permeability curves in reservoir simulations. This process is particularly crucial in situations where core data is non-existent or only limited data points are available. The use of such data contributes significantly to the improvement of history-matching accuracy. The outcomes demonstrate the effectiveness of the employed methodology, highlighting the successful utilization of ML and DL to precisely predict relative permeability curves from production data. The integration of these curves into reservoir simulations yields a notable improvement in the accuracy of the history-matching process in a short time compared with the traditional approaches. The observations elucidate the adeptness of ML or DL in capturing intricate relationships between production parameters and relative permeability curves. The estimated curves serve as a pivotal initial step for history matching, playing a crucial role in substantially mitigating uncertainty within reservoir simulations. In conclusion, this study introduces a new approach for predicting relative permeability curves by leveraging ML and DL integrated with production data. This method contributes to addressing the uncertainties linked to traditional core-based measurements by furnishing a more accurate initial prediction for relative permeability curves. The successful integration of this approach into reservoir simulations holds the promise of streamlining and enhancing the accuracy of reservoir management practices. The emphasis on utilizing the available production data enhances our capability to mitigate the scarcity of Special Core Analysis (SCAL) data. Consequently, this methodology contributes to refined reservoir simulation outcomes and more efficient history-matching processes.

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Determining Relative Permeability From Unsteady-State Radial Fluid Displacements

Cited by 2 publications

References 3 publications

Estimation of the Water–Oil Relative Permeability Curve from Radial Displacement Experiments. Part 2: Reasonable Experimental Parameters

Estimation of the Water–Oil Relative Permeability Curve from Radial Displacement Experiments. Part 2: Reasonable Experimental Parameters

Exploring an Alternative Approach for Predicting Relative Permeability Curves from Production Data: A Comparative Analysis Employing Machine and Deep Learning Techniques

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