An improved optimization procedure for production and injection scheduling using a hybrid genetic algorithm

Azamipour, Vahid; Assareh, Mehdi; Mittermeir, Georg M.

doi:10.1016/j.cherd.2017.11.022

Cited by 15 publications

(4 citation statements)

References 22 publications

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“…A genetic algorithm (GA) optimization is used to calculate unknown parameters in M-CRM. GA is a common heuristic optimization method for engineering problems inspired by natural evolution (McCall 2005;Azamipour et al 2018;Bayat et al 2011;Chen et al 2014). There are five steps in GA including initial population, fitness function, selection, crossover and mutation (Emera and Sarma 2005;Ali Ahmadi et al 2013;Shafiee et al 2017;Dehghani et al 2008;Saemi et al 2007;Chen et al 2014).…”

Section: Optimizationmentioning

confidence: 99%

Prediction of immiscible gas flooding performance: a modified capacitance–resistance model and sensitivity analysis

et al. 2019

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Reservoir performance prediction is one of the main steps during a field development plan. Due to the complexity and time-consuming aspects of numerical simulators, it is helpful to develop analytical tools for a rapid primary analysis. The capacitance-resistance model (CRM) is a simple technique for reservoir management and optimization. This method is an advanced time-dependent material balance equation which is combined with a productivity equation. CRM uses production/ injection data and bottom-hole pressure as inputs to build a reliable model, which is then combined with the oil-cut model and converted to a predictive tool. CRM has been studied thoroughly for water flooding projects. In this study, a modified model for gas flooding systems based on gas density and average reservoir pressure is developed. A detailed procedure is described in a synthetic reservoir model using a genetic algorithm. Then, a streamline simulation is implemented for validation of the results. The results show that the proposed model is able to calculate interwell connectivity parameters and oil production rates. Moreover, a sensitivity analysis is carried out to investigate effects of drawdown pressure and gas PVT properties on the new model. Finally, acceptable ranges of input data and limitations of the model are comprehensively discussed.

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Section: Optimizationmentioning

confidence: 99%

Prediction of immiscible gas flooding performance: a modified capacitance–resistance model and sensitivity analysis

et al. 2019

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“…33 The authors improved their optimization approach in a subsequent article, using a hybrid genetic algorithm. 34 As part of their improvements, streamline simulation has also been adopted for the determination of good initial guesses for the water injection rates. Nevertheless, these contributions do not account for oil and gas surface production facilities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, an adaptive simulated annealing algorithm was employed by Azamipour et al, for the production optimization of a field undergoing water injection: therein, optimization time reduction was attained by sequentially implementing coarse and fine grids without discernible loss in model accuracy . The authors improved their optimization approach in a subsequent article, using a hybrid genetic algorithm . As part of their improvements, streamline simulation has also been adopted for the determination of good initial guesses for the water injection rates.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal rate allocation for production and injection wells in an oil and gas field for enhanced profitability

Epelle¹,

Gerogiorgis²

2019

AIChE Journal

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An oil and gas field requires careful operational planning and management via production optimisation for increased recovery and long-term project profitability. This paper addresses the challenge of production optimisation in a field undergoing secondary recovery by water flooding. The field operates with limited processing capacity at the surface separators, pipeline pressure constraints and water injection constraints; an economic indicator (Net Present Value -NPV) is used as the objective function. The formulated optimisation framework adequately integrates slow-paced subsurface dynamics using reservoir simulation and the fast-paced surface dynamics using sophisticated multiphase flow simulation in the upstream facilities. Optimisation of this holistic longterm model is made possible by developing accurate second order polynomial proxy models at each time step. The resulting formulation is solved as a Nonlinear Program (NLP) using commercially available solvers. A comparative analysis is performed using MATLAB's fmincon solver and the IPOPT solver for their robustness, speed and convergence stability in solving the proposed problem.By implementing 2 synthetic case studies, our mathematical programming approach determines the optimal production and injection rates of all wells and further demonstrates considerable improvement to the NPV obtained by simultaneously applying the tools of streamline, reservoir and surface facility simulation for well rate allocation.

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“…Moreover, some new methods, such as those using big data and artificial intelligence, have also been introduced into the optimization of water injection. For example, a combination of the hybrid genetic algorithm, particle swarm optimization (PSO), and streamline-based reservoir simulation (Naderi, 2021 [13]; Azamipour, 2018 [14], 2023 [15]) has been studied. Furthermore, the reservoir is also approximated as a multi-well injection-production system, and capacitance and resistant models (Yousef, 2006 [16]; Soroush, 2014 [17]; Yousefi, 2020 [18]; Huang, 2023 [19]; Guo, 2023 [20]), system analysis models (Liu, 2009 [21]), and INSIM-derived models (Zhao, 2014 [22], 2016 [23], 2019 [24], 2022 [25]) have been developed to predict and optimize inter-well connectivity and the water-injection rate.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Optimizing Water-Flooding Strategies in Multi-Layer Sandstone Reservoirs

Guo,

Yang,

Zhao

et al. 2024

Energies

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As one of the most important and economically enhanced oil-recovery technologies, water flooding has been applied in various oilfields worldwide for nearly a century. Stratified water injection is the key to improving water-flooding performance. In water flooding, the water-injection rate is normally optimized based on the reservoir permeability and thickness. However, this strategy is not applicable after oilfields enter the ultra-high-water-cut period. In this study, an original method for optimizing water-flooding parameters for developing multi-layer sandstone reservoirs in the entire flooding process and in a given period is proposed based on reservoir engineering theory and optimization technology. Meanwhile, optimization mathematical models that yield maximum oil recovery or net present value (NPV) are developed. The new method is verified by water-flooding experiments using Berea cores. The results show that using the method developed in this study can increase the total oil recovery by approximately 3 percent compared with the traditional method using the same water-injection amounts. The experimental results are consistent with the results from theoretical analysis. Moreover, this study shows that the geological reserves of each layer and the relative permeability curves have the greatest influence on the optimized water-injection rate, rather than the reservoir properties, which are the primary consideration in a traditional optimization method. The method developed in this study could not only be implemented in a newly developed oilfield but also could be used in a mature oilfield that has been developed for years. However, this study also shows that using the optimized water injection at an earlier stage will provide better EOR performance.

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An improved optimization procedure for production and injection scheduling using a hybrid genetic algorithm

Cited by 15 publications

References 22 publications

Prediction of immiscible gas flooding performance: a modified capacitance–resistance model and sensitivity analysis

Prediction of immiscible gas flooding performance: a modified capacitance–resistance model and sensitivity analysis

Optimal rate allocation for production and injection wells in an oil and gas field for enhanced profitability

A New Method for Optimizing Water-Flooding Strategies in Multi-Layer Sandstone Reservoirs

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