Autonomous flow control device modelling and completion optimisation

Eltaher, Eltazy; Muradov, Khafiz; Davies, David Roland; Grassick, Peter

doi:10.1016/j.petrol.2018.07.042

Cited by 19 publications

(3 citation statements)

References 24 publications

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“…These devices have the ability to alter their performance based on fluid properties, allowing them to selectively control the flow of undesired fluids by inducing additional pressure drops after the breakthrough. There are two types of AICDs: (1) fluidic diode AICD (FD-AICD), a more density-sensitive device which operates without any mechanical components and utilizes a unique flow channel design to manage fluid flow, as shown in Figure 1c [9].…”

Section: Introductionmentioning

confidence: 99%

Completion Performance Evaluation in Multilateral Wells Incorporating Single and Multiple Types of Flow Control Devices Using Grey Wolf Optimizer

Ahdeema,

Sefat,

Muradov

et al. 2024

Processes

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There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integrating various types of flow control devices within multilateral wells, employing dynamic optimization process using numerical reservoir simulator while the Grey Wolf Optimizer (GWO) is used as optimization algorithm. The Egg benchmark reservoir model is utilized and developed with two dual-lateral wells. These wells serve as the foundation for implementing and testing 22 distinct completion cases considering single-type and multiple types of flow control devices under reactive and proactive management strategies. This comprehensive investigation aims to shed light on the advantages and limitations of these innovative completion methods in optimizing well and reservoir performance. Our findings revealed that the incorporation of multiple types of FCDs in multilateral well completions significantly enhance well performance and can surpass single-type completions including ICDs or AICDs. However, this enhancement depends on the type of the device implemented inside the lateral and the control strategy that is used to control the ICVs at the lateral junctions. The best performance of multiple-type FCD-based completion was achieved through combining AICDs with reactive ICVs which achieved around 75 million USD profit. This represents 42% and 22% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. The optimal settings for ICD and AICD in individual applications may significantly differ from the optimal settings when combined with ICVs. This highlights a strong correlation between the different devices (control variables), proving that using either a common, simplified analytical, or a standard sequential optimization approach that do not explore this inter-dependence between devices would result in sub-optimal solutions in such completion cases. Notably, the ICV-based completion, where only ICVs are installed with lateral completion, demonstrated superior performance, particularly when ICVs are reactively controlled, resulting in an impressive 80 million USD NPV which represents 53% and 30% increase in the objective function compared to single-type ICDs and AICDs installations, respectively.

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Section: Introductionmentioning

confidence: 99%

Completion Performance Evaluation in Multilateral Wells Incorporating Single and Multiple Types of Flow Control Devices Using Grey Wolf Optimizer

Ahdeema,

Sefat,

Muradov

et al. 2024

Processes

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“…ICDs are passive inflow control devices that regulate fluid flow by creating additional pressure drops through specialized flow paths such as labyrinth/helical channels, nozzles/slots, or hybrid [1]. AFCDs operate autonomously and can be classified into (1) autonomous interval control valves (AICVs), which almost completely shut off the flow of unwanted fluid by mechanically self-adjusting their flow area [2]; and (2) autonomous inflow control devices (AICDs), which selectively restrict the flow of unwanted fluid by creating an extra pressure drop after the breakthrough occurs. Two popular types of AICDs commonly used in the industry are the fluidic diode AICD (FD-AICD), which has no moving parts and relies on its special flow channel design to achieve fluid flow control, and the rate-controlled production AICD (RCP-AICD), which consists of a device body, a nozzle, and a free-floating disk that controls production from the nozzle.…”

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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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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“…Fluid flows from the oil reservoir into the wellbore through these completions, which result in additional pressure drop affects the production rate of horizontal wells. However, prediction models for horizontal well production rate based on slotted screen and ICD completions have been simplified, such that only the flow in the center tubing is considered while the flow in the annulus is disregarded (Alvarez, A. C. , et al, 2014;Javid, K. , 2018;Droppert, V. , 2020;Gurses, S. F., 2013;Goh, G. , 2016;Bol, L. , 2016;Shahkarami, A. , 2020;Eltazy, E., 2019;D Li, 2017). They all carried out the research on related models for ICD completion, but did not consider the effects of annulus flow.…”

Section: Introductionmentioning

confidence: 99%

Flow simulation for a horizontal well with slotted screen and ICD completions based on the wellbore–annulus–reservoir model

Luo

Liao²,

Wang³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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A flow is observed in the pipe for the horizontal section of slotted screen completion and inflow control device (ICD) completion, and an annulus flow exists between the pipe and the borehole wall. Based on the principles of mass conservation and momentum conservation, a model for variable mass flow in an oil reservoir to simulate wellbore–annulus–reservoir state in the horizontal section of slotted screen completion and ICD completion is proposed. Consequently, based on the newly established formation transient seepage model and wellbore and annulus variable mass flow model, an accurate prediction of the production rate of a horizontal well as well as optimized design parameters for slotted screen completion and inflow control device (ICD) completion are achieved. The predicted flow profiles of the established model are confirmed using the simulation software from Computer Modeling Group Ltd. When external casing packers are not considered, findings show that the predicted production rates of both slotted screen completion and ICD completion, which are obtained using the variable mass flow models without annulus flow modeling, are higher than the rates obtained using the variable mass flow models with annulus flow modeling. Both models show that the pressure profile and flow profile of the borehole wall are more uniform in wellbore–annulus–reservoir in horizontal wells. The flow model for horizontal wells with annulus flow modeling should be established to improve the accuracy of the predicted production.

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Autonomous flow control device modelling and completion optimisation

Cited by 19 publications

References 24 publications

Completion Performance Evaluation in Multilateral Wells Incorporating Single and Multiple Types of Flow Control Devices Using Grey Wolf Optimizer

Completion Performance Evaluation in Multilateral Wells Incorporating Single and Multiple Types of Flow Control Devices Using Grey Wolf Optimizer

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

Flow simulation for a horizontal well with slotted screen and ICD completions based on the wellbore–annulus–reservoir model

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