Optimization of Proactive Control Valves of Producer and Injector Intelligent Wells under Economic Uncertainty

Sampaio, Misako U.; Barreto, Carlos Eduardo; Schiozer, Denis José

doi:10.2118/154511-ms

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…reactive or proactive approaches. The reactive strategy adjusts ICVs based on the current parameters of the well and reservoir, reducing the production of undesired fluids in real time [4,5]. While this approach is effective for an immediate response to undesired situations, it generally fails to achieve optimal long-term field management.…”

Section: Introductionmentioning

confidence: 99%

“…These methods involve coupling a reservoir simulator with an optimization algorithm. Following are some major publications classified based on the employed optimization algorithm: (a) metaheuristic and population-based algorithms [5,[7][8][9][10][11][12][13][14][15][16] (b) gradient-based or derivative-based algorithms [17][18][19][20][21], and (c) stochastically estimated gradient-based algorithms [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Framework for Enhanced Dynamic Optimization of Intelligent Completion Design in Multilateral Wells with Multiple Types of Flow Control Devices

Ahdeema,

Haghighat Sefat,

Muradov

2023

Energies

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Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Framework for Enhanced Dynamic Optimization of Intelligent Completion Design in Multilateral Wells with Multiple Types of Flow Control Devices

Ahdeema,

Haghighat Sefat,

Muradov

2023

Energies

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“…The core elements of intelligent fields, also referred to as I-fields, digital fields, and smart fields, are the I-wells (Robinson 2003). The core elements of intelligent fields, also referred to as I-fields, digital fields, and smart fields, are the I-wells (Robinson 2003).…”

Section: Introductionmentioning

confidence: 99%

Proactive Optimization of Intelligent-Well Production Using Stochastic Gradient-Based Algorithms

Sefat¹,

Muradov²,

Elsheikh³

et al. 2016

SPE Reservoir Evaluation &Amp; Engineering

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The popularity of intelligent wells (I-wells), which provide layerby-layer monitoring and control capability of production and injection, is growing. However, the number of available techniques for optimal control of I-wells is limited (Sarma et al. 2006;Alghareeb et al. 2009;Almeida et al. 2010;Grebenkin and Davies 2012). Currently, most of the I-wells that are equipped with interval control valves (ICVs) are operated to enhance the current production and to resolve problems associated with breakthrough of the unfavorable phase. This reactive strategy is unlikely to deliver the long-term optimum production. On the other side, the proactive-control strategy of I-wells, with its ambition to provide the optimum control for the entire well's production life, has the potential to maximize the cumulative oil production. This strategy, however, results in a high-dimensional, nonlinear, and constrained optimization problem.This study provides guidelines on selecting a suitable proactive optimization approach, by use of state-of-the-art stochastic gradient-approximation algorithms. A suitable optimization approach increases the practicality of proactive optimization for real field models under uncertain operational and subsurface conditions. We evaluate the simultaneous-perturbation stochastic approximation (SPSA) method (Spall 1992) and the ensemblebased optimization (EnOpt) method ). In addition, we present a new derivation of the EnOpt by use of the concept of directional derivatives.The numerical results show that both SPSA and EnOpt methods can provide a fast solution to a large-scale and multiple I-well proactive optimization problem. A criterion for tuning the algorithms is proposed and the performance of both methods is compared for several test cases. The used methodology for estimating the gradient is shown to affect the application area of each algorithm. SPSA provides a rough estimate of the gradient and performs better in search environments, characterized by several local optima, especially with a large ensemble size. EnOpt was found to provide a smoother estimation of the gradient, resulting in a more-robust algorithm to the choice of the tuning parameters, and a better performance with a small ensemble size. Moreover, the final optimum operation obtained by EnOpt is smoother. Finally, the obtained criteria are used to perform proactive optimization of ICVs in a real field.

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“…Genetic algorithms (GA) were used in the optimization framework in (Almeida et al, 2010;Sampaio et al, 2011;Sampaio et al, 2012), showing significant progress compared to classical optimization methods. In (Sampaio et al, 2015a), GA and model reduction by means of the POD-DEIM were coupled to yield even faster computations solutions.…”

Section: Introductionmentioning

confidence: 99%

“…This work employs a reservoir model well suited to represent a realistic oil field in the offshore of Brazil (in deterministic condition), therefore, without the need to realize the history matching of the model. For the optimization method, we employ a fast genetic algorithm (FGA), which is a robust and efficient method for sweeping the solution space with many variables and it has been used in some studies (Almeida et al, 2010;Sampaio et al, 2011;Sampaio et al, 2012), outperforming classical optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Short-Term and Long-Term Optimizations for Reservoir Management with Intelligent Wells

Sampaio

Gildin

Schiozer

2015

SPE Latin American and Caribbean Petroleum Engineering Conference

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Short-term optimization of an oil field has been used to increase economic value of oil recovery as compared to reactive control (shutting the well when water cut limit is reached, for instance), especially in the case of short-term production strategies. One way to improve the management of a field involves adjusting the production flow rates over a short time, maximizing the overall NPV during the life cycle of the field. Using intelligent wells (IW), the challenges include not only the optimization of well flow rates, but also the simultaneous adjustment of flow in each valve, controlling each aperture in a given production time. These optimal control strategies are often difficult to be realized in practice due to the large number of control variables involved in the optimization process, especially with larger number of wells and valves. To this end, this work proposes an efficient optimization framework employing a fast genetic algorithm (FGA) in order to adjust simultaneously the flow rates of wells and the valves aperture. We have used a commercial reservoir simulator whereby the flow rates of wells were optimized with an option available that calculates well rates when there is production constraint on the wells (platform capacity or other operational constraint) using production parameters in real time; and at the same time the flow in each valve was controlled through a keyword associated with the control of the aperture of valves by monitoring the pressure drop around of them. The FGA optimization algorithm employed is a global optimization method, which is robust and efficient for sweeping the solution space with many variables, and it is able to work with continuous and discrete variables simultaneously. We demonstrate the power of the FGA strategy by applying the methodology to a heterogeneous reservoir model based on Brazil's Namorado field, with four horizontal producers and four horizontal injector wells. Two producers were tested as intelligent, using two valves of continuous variation type. The rate of wells was determined using water cut values while there were constraints on the production of the platform. The valves were adjusted each 60 days, during the first four years of production, closing in the optimal time at the end of production. The results showed an improvement in reservoir management, increasing 3.7% of NPV, with additional gains around US$ 20 million (already discounted the costs of intelligent completion), increasing oil production and reducing water production. The combination of the tools available in a commercial simulator jointly with global optimization algorithm showed advantages of the operation of the wells and valves simultaneously.

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Optimization of Proactive Control Valves of Producer and Injector Intelligent Wells under Economic Uncertainty

Cited by 13 publications

References 20 publications

Hybrid Framework for Enhanced Dynamic Optimization of Intelligent Completion Design in Multilateral Wells with Multiple Types of Flow Control Devices

Hybrid Framework for Enhanced Dynamic Optimization of Intelligent Completion Design in Multilateral Wells with Multiple Types of Flow Control Devices

Proactive Optimization of Intelligent-Well Production Using Stochastic Gradient-Based Algorithms

Short-Term and Long-Term Optimizations for Reservoir Management with Intelligent Wells

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