Reservoir uncertainty tolerant, proactive control of intelligent wells

Sefat, Morteza Haghighat; Elsheikh, Ahmed H.; Muradov, Khafiz; Davies, David Roland

doi:10.1007/s10596-015-9513-8

Cited by 30 publications

(8 citation statements)

References 51 publications

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“…Figure 7 shows an example of 2D Euclidean space that is created by 21 × 21 distance matrix. In the 2D Euclidean space, the distance calculated between two points is considered as a similarity index in a way that: the closer the points, more similar the models will be (Haghighat Sefat et al 2016). 3.…”

Section: Distance-based Clustering With Simple Matching Coefficient (Dcsmc) Methodsmentioning

confidence: 99%

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

Mahjour

Santos

Correia

et al. 2021

J Petrol Explor Prod Technol

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The simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

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Section: Distance-based Clustering With Simple Matching Coefficient (Dcsmc) Methodsmentioning

confidence: 99%

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

Mahjour

Santos

Correia

et al. 2021

J Petrol Explor Prod Technol

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“…Application of SPSA for proactive optimization of ICVs under reservoir uncertainty is investigated in Haghighat Sefat et al (2015). R p is the reservoir-simulation operation; and x 2 R nx is the vector of n x control variables discretized over the field-production time (t).…”

Section: Formulation Of the Problem Of I-well Proactive Controlmentioning

confidence: 99%

“…These approaches can be classified as chasing either short-term objectives, known as reactive optimiza-tion (Grebenkin and Davies 2012), or long-term objectives, known as proactive optimization (Alghareeb et al 2009;Almeida et al 2010;Haghighat Sefat et al 2013). These scenarios must also respect the constraints defined by the equipment installed in the field or imposed by the wells, reservoir, or field-management policies.…”

Section: Introductionmentioning

confidence: 99%

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

Sefat¹,

Muradov²,

Elsheikh³

et al. 2016

SPE Reservoir Evaluation &Amp; Engineering

Self Cite

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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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“…They addressed considering intelligent well completions as a type of insurance policy against geological uncertainty (Yeten et al, 2004). (Haghighat Sefat et al, 2014) developed a robust approach that determines the ICVs setting in uncertain conditions. Birchenko et al examined the impact of ICDs and ICVs on the geological uncertainty.…”

Section: Introductionmentioning

confidence: 99%

The Improvement of Production Profile While Managing Reservoir Uncertainties with Inflow Control Devices Completions

MoradiDowlatabad¹,

Zarei²,

Akbari

2015

All Days

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The distributions of various parameters such as of the petro-physical properties, fluid contacts, relative permeabilities, aquifer strength etc. influence oil production profile. This requires implementing probabilistic approaches important for the field's performance prediction. Advanced Well Completions (AWCs) are capable to reactively and/or proactively mitigate the unpredictable production conditions of hydrocarbon reservoirs particularly in case of the heterogeneous reservoirs. However, determining the optimum design of the devices is still a big challenge since it should have been done respecting the long-term performance of the completion. This requires using reservoir models that include significant level of uncertainty due to ambiguity of the reservoir and geological parameters used.Latin Hypercube Sampling (LHS) approach was applied to evaluate various dynamic uncertain parameters with different distributions profiles. Numerous realisations of the reservoir models were also generated to provide a confident level of geological uncertainty by a sequential Gaussian simulation algorithm. Various completions such as Open-hole, Inflow Control Devices (ICDs) and Autonomous Inflow Control Devices (AICDs) were deployed in horizontal wells to investigate the uncertainty management by the well completions.The results showed AICDs completions are more appropriate to reduce the production of the unwanted fluids and increase oil recovery than ICDs. Moreover, overall as well as the individual impacts of the uncertain parameters on oil production profile were evaluated by statistical analysis. This helps determining the most influencing parameters on the production profile as well as the most influenced uncertain parameters when AWCs are deployed. The results showed that although both of the AICDs and ICDs completions significantly reduce the oil production variations comparing with Open-hole completion, AICDs completions perform much better than the ICDs to manage the effects of the reservoir uncertainties on oil recovery. The study aims at providing approaches quantifying the impacts of AWCs on the reservoir uncertainties and confirming the long-term benefits of AWCs while reducing the risk associated with the field's development plan.

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Reservoir uncertainty tolerant, proactive control of intelligent wells

Cited by 30 publications

References 51 publications

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

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

The Improvement of Production Profile While Managing Reservoir Uncertainties with Inflow Control Devices Completions

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