Performance of nature inspired optimization algorithms for polymer Enhanced Oil Recovery process

Janiga, Damian; Czarnota, Robert; Stopa, Jerzy; Wojnarowski, Paweł; Kosowski, Piotr

doi:10.1016/j.petrol.2017.04.010

Cited by 44 publications

(11 citation statements)

References 45 publications

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“…The results are in line with previous investigation where the authors employed PSO and four different nature-inspired optimization algorithms to find optimal polymer injection strategy to enhance oil recovery. We indicated that of all the five implemented nature-inspired algorithms are comparable in magnitude and among them, PSO allowed to find the highest net present value (NPV) (Janiga et al 2017).…”

Section: Optimization Resultsmentioning

confidence: 97%

Utilization of nature-inspired algorithms for gas condensate reservoir optimization

et al. 2018

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Hydrocarbon production from a gas condensate reservoir has a major concern to the petroleum industry around the world. Due to pressure decline in a reservoir, liquid drops out of solution and cause a significant reduction of well productivity. As a result, huge amount of valuable oil stays underground. The proper production management for a gas condensate reservoir minimizes or eliminates the blocking problems. Optimization of condensate recovery can be achieved through appropriate selection of production and injection rates. However, determination of the optimal rates is a complex problem, involving many factors including geological uncertainty, rock petrophysical characteristics, fluid properties, economic costs, and technical ability. In this research, nature-inspired optimization algorithms are employed to evaluate the potential of production boost. Numerical testing and comparative study revealed that investigated natural-inspired algorithms outperform widely reported in the literature optimization methods and provide a higher quality solutions. The primary concern of this research is to develop an optimal gas condensate production strategy in terms of economic efficiency by utilization of nature-inspired optimization algorithm and providing their ability to generate better quality solution than engineering approach as well as gradient optimization.

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Section: Optimization Resultsmentioning

confidence: 97%

Utilization of nature-inspired algorithms for gas condensate reservoir optimization

et al. 2018

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“…The mathematical simulation of such a system has been already analyzed by several authors [14][15][16][17][18]. This has led to the development of commercial and academic simulators, namely: UTCHEM and IPARS (University of Texas at Austin) [19], MRST (open code for MATLAB) [20][21][22], CMG (Stars), Petrel and Eclipse (Schlumberger), and also others simulators found in the literature [23][24][25][26][27][28][29][30][31][32]. In addition to the physical phenomena, some problems of numerical source have been studied when the mass conservation equation was discretized.…”

Section: Previous Numerical Workmentioning

confidence: 99%

“…7. Finally, having all parameters calculated at iteration k + 1, evaluate two norms for the overall concentration of two components (i = p, c) with Equations (29) and (30).…”

Section: Solution Algorithmmentioning

confidence: 99%

Numerical Modeling and Validation of a Novel 2D Compositional Flooding Simulator Using a Second-Order TVD Scheme

Druetta¹,

Picchioni²

2018

Energies

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The aim of this paper is to present the latter and develop a numerical simulator aimed at solving a 2D domain porous medium, using the compositional approach to simulate chemical flooding processes. The simulator consists in a two-phase, multicomponent system solved by the IMplicit in Pressure, Explicit in Concentration (IMPEC) approach, which can be operated under an iterative/non-iterative condition on each time-step. The discretization of the differential equations is done using a fully second order of accuracy, along with a Total Variation Diminishing (TVD) scheme with a flux limiter function. This allowed reducing the artificial diffusion and dispersion on the transport equation, improving the chemical species front tracking, decreasing the numerical influence on the recovery results. The new model was validated against both commercial and academic simulators and moreover, the robustness and stability were also tested, showing that the iterative IMPEC is fully stable, behaving as an implicit numerical scheme. The non-iterative IMPEC is conditionally stable, with a critical time-step above which numerical spurious oscillations begin to appear until the system numerically crashes. The results showed a good correspondence in different grid sizes, being largely affected by the time-step, with caused a decrease in the recovery efficiency in the iterative scheme, and the occurrence of numerical oscillations in the non-iterative one. Numerically speaking, the second-order scheme using a flux splitting TVD discretization proved to be a good approach for compositional reservoir simulation, decreasing the influence of numerical truncation errors on the results when compared to traditional, first-order linear schemes. Along with these studies, secondary recoveries in constant and random permeability fields are simulated before employing them in tertiary recovery processes.

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“…During polymer flooding, floods presenting a mobility ratio <1 are considered as stable, thus leading to better recovery, while on the other hands floods with a mobility ratio >1 are considered as unstable [24]. The reservoir was then subjected to simulated polymer flooding.…”

Section: Reservoir Propertiesmentioning

confidence: 99%

Prediction of Polymer Flooding Performance with an Artificial Neural Network: A Two-Polymer-Slug Case

Ebaga-Ololo

Chon

2017

Energies

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Many previous contributions to methods of forecasting the performance of polymer flooding using artificial neural networks (ANNs) have been made by numerous researchers previously. In most of those forecasting cases, only a single polymer slug was employed to meet the objective of the study. The intent of this manuscript is to propose an efficient recovery factor prediction tool at different injection stages of two polymer slugs during polymer flooding using an ANN. In this regard, a back-propagation algorithm was coupled with six input parameters to predict three output parameters via a hidden layer composed of 10 neurons. Evaluation of the ANN model performance was made with multiple linear regression. With an acceptable correlation coefficient, the proposed ANN tool was able to predict the recovery factor with errors of <1%. In addition, to understand the influence of each parameter on the output parameters, a sensitivity analysis was applied to the input parameters. The results showed less impact from the second polymer concentration, owing to changes in permeability after the injection of the first polymer slug.

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Performance of nature inspired optimization algorithms for polymer Enhanced Oil Recovery process

Cited by 44 publications

References 45 publications

Utilization of nature-inspired algorithms for gas condensate reservoir optimization

Utilization of nature-inspired algorithms for gas condensate reservoir optimization

Numerical Modeling and Validation of a Novel 2D Compositional Flooding Simulator Using a Second-Order TVD Scheme

Prediction of Polymer Flooding Performance with an Artificial Neural Network: A Two-Polymer-Slug Case

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