Empirical Modeling of Gravity Drainage in Fractured Porous Media

Zendehboudi, Sohrab; Chatzis, Ioannis; Shafiei, Ali; Dusseault, Maurice B.

doi:10.1021/ef1015507

Cited by 49 publications

(54 citation statements)

References 29 publications

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“…One of the robust sensitivity analysis strategies is through the use of the analysis of variance (i.e. ANOVA) method . Using this analysis, the dependency of the dependent variable (i.e.…”

Section: Review Of the Methodology Results And Discussionmentioning

confidence: 99%

“…ANOVA) method. [58][59][60][61][62][63][64][65] Using this analysis, the dependency of the dependent variable (i.e. damaged permeability due to scale deposition) on each of the independent variables including initial permeability, temperature, injection rate, calcium concentration, barium concentration, strontium concentration, and sulphate concentration was determined.…”

Section: Hybrid Evolutionary Algorithms and Ann Output Resultsmentioning

confidence: 99%

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A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

2017

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One of the detrimental issues in upstream oil industry is permeability impairment in oil reservoirs. The permeability may be reduced because of scale deposition originated from injected water incompatibility with the in‐situ water. To tackle this issue and monitor permeability impairment owing to scale deposition, an inventive evolutionary approach of Artificial Neural Network (ANN) is utilized which is based on the bio‐inspired science. This approach is optimized by various optimization algorithms which provide a high‐precision decision‐making process with low uncertainty associated with the interconnected weights of the developed neural network model. The constructed intelligent approaches are evaluated using extensive experimental data from the open literature. Moreover, two regression models are developed to highlight the robustness and precision of the addressed techniques. For model validation, the predictions obtained from the smart technique as well as the regression method are compared with the formation damage experimental data. Based on various performance criteria, it was obtained that the predictions from the optimized genetic algorithm have infinitesimal average relative and absolute deviation from the experimental data (< 0.1 %). The results obtained in the current research prove that implication of HGAPSO‐ANN in monitoring of formation damage associated with scale deposition in porous media results in reliable estimation of permeability impairment. This can result in designing a more reliable reservoir simulation model that captures the permeability impairment due to scale deposition, hence providing thorough action plans for developing EOR technologies that may cause scale deposition‐related formation damage.

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“…One of the robust sensitivity analysis strategies is through the use of the analysis of variance (i.e. ANOVA) method . Using this analysis, the dependency of the dependent variable (i.e.…”

Section: Review Of the Methodology Results And Discussionmentioning

confidence: 99%

Section: Hybrid Evolutionary Algorithms and Ann Output Resultsmentioning

confidence: 99%

A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

2017

Self Cite

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“…Previously, considering process variables and design parameters have been studied in the case of flow through porous media . It was proved that this method can be entirely informative in the case of modelling fluid flow thorough porous media . Eventually, the product of this stage is a polynomial for each of the responses.…”

Section: Methodsmentioning

confidence: 99%

On the evaluation of Alkaline‐Surfactant‐Polymer flooding in a field scale: Screening, modelling, and optimization

et al. 2017

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Chemical enhanced oil recovery methods, including alkaline surfactant polymer (ASP) flooding, have found special significance for oil reservoirs. ASP flooding provides different situations based on ratio of injection of three ASP components. This study identifies the behaviour of injection components and their interaction, investigating the synergy between them. To this end, an Iranian oil reservoir is studied. The factors influencing the injection process are identified and then a combination of these factors and the concentration of ASP components are modelled. All modelling results are fitted with R‐squared and adjusted R‐squared values above 0.96. Finally, the optimization is conducted to determine the best injection scenario. The most important aspect of this study is to investigate simultaneously the effect of different parameters of ASP in a real case. The screening results show that the effect of polymer on viscosity is the most influential factor in ASP flooding. The modelling results show that water cut in the case of simultaneous injection of three components is less than other cases of injection. This represents the synergy of components in the injection process during ASP flooding. The miscibility between water and polymer solution also leads to less produced water and more produced oil. In the final stage, the optimization results show that the optimal scenario is injection of surfactant and polymer with the greatest amount of miscibility between water and polymer solution. It can be concluded that with this approach, ASP injection can efficiently be analyzed and optimized from a technical point of view.

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“…A considerable amount of the world's oil production comes from the naturally fractured reserves located mostly in the Middle East and USA . This type of reservoirs is usually characterized by fractures with high permeability and low porosity .…”

Section: Theorymentioning

confidence: 99%

Estimation of breakthrough time for water coning in fractured systems: Experimental study and connectionist modeling

et al. 2014

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in Wiley Online Library (wileyonlinelibrary.com) Water coning in petroleum reservoirs leads to lower well productivity and higher operational costs. Adequate knowledge of coning phenomena and breakthrough time is essential to overcome this issue. A series of experiments using fractured porous media models were conducted to investigate the effects of production process and pore structure characteristics on water coning. In addition, a hybrid artificial neural network (ANN) with particle swarm optimization (PSO) algorithm was applied to predict breakthrough time of water coning as a function of production rate and physical model properties. Data from the literature combined with experimental data generated in this study were used to develop and verify the ANN-PSO model. A good correlation was found between the predicted and real data sets having an absolute maximum error percentage less than 9%. The developed ANN-PSO model is able to estimate breakthrough time and critical production rate with higher accuracy compared to the conventional or back propagation (BP) ANN (ANN-BP) and common correlations. The presence of vertical fractures was found to accelerate considerably the water coning phenomena during oil production. Results of this study using combined data suggest the potential application of ANN-PSO in predicting the water breakthrough time and critical production rate that are critical in designing and evaluating production strategies for naturally fractured reservoirs.

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Empirical Modeling of Gravity Drainage in Fractured Porous Media

Cited by 49 publications

References 29 publications

A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

A cutting edge solution to monitor formation damage due to scale deposition: Application to oil recovery

On the evaluation of Alkaline‐Surfactant‐Polymer flooding in a field scale: Screening, modelling, and optimization

Estimation of breakthrough time for water coning in fractured systems: Experimental study and connectionist modeling

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