Analysis of the Impact of an Intelligent Well Completion on the Oil Production Uncertainty (Russian)

Grebenkin, Ivan; Davies, David Roland

doi:10.2118/136335-ru

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…Nevertheless, economic analysis shows that latter development option for this subsea field in 100 m water depth located in a mature area had a greater NPV, mainly due to the reduced capital investment required. Moreover, as shown previously by (Grebenkin and Davies 2010), the operational flexibility of the intelligent completion can reduce the impact of unexpected events (e.g. fast water breakthrough) and reduce production uncertainty.…”

Section: Commercial Network Optimisation Software "Critical-water-cutmentioning

confidence: 66%

“…Passive Inflow Control Devices allow equalization of the production from different zones, reduce horizontal well's "heel-toe" effect and, as a result, increase the sweep efficiency (Birchenko et al 2009). By contrast, "active" devices -Inflow Control Valves (ICVs) -are controlled from the surface in order to reduce undesired fluid production, improve the recovery factor, avoid costly well interventions and reduce production uncertainty (Grebenkin and Davies, 2010). However, many oil companies today still do not feel confident in investing in the latter, more expensive, technology.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Optimisation Algorithm for Inflow Control Valve Management

Grebenkin¹,

Davies²

2012

SPE Europec/Eage Annual Conference

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Intelligent Wells are distinguished from conventional wells by being equipped with downhole sensors to monitor the Inflow Control Valves (ICVs) to control the (multiple) zonal flow rates. The data from the downhole sensors monitors the properties of the fluid flowing into the well from the reservoir at a zonal or a well level. The sensor data is analysed to provide the necessary information for the ICVs to be operated in the optimum manner i.e. to increase the hydrocarbon recovery and prevent unwanted fluid production. This objective is simply stated, but the optimisation calculations required to identify the optimum ICV settings necessitates the repetitive solution of a complex, non-linear problem. Several commercial software providers have made such optimisation algorithms available to the industry to perform this task. However, experience has shown that challenges still arise when they are applied to large, complex models even though these algorithms work well on many simple cases. This is especially true when the optimisation algorithm is combined with a large, multi-well simulation model of multiple reservoirs with a complex, surface production network that is typical of those used today by operators to study real-field cases prior to field development. Inclusion of the optimisation algorithm not only dramatically increases the calculation time (up to 50 times when compared with the equivalent run without such optimisation); but also stability and convergence problems give additional increases in the running time. More importantly, the combined software will sometimes simply stop, due to erroneous control parameters being provided by the optimisation algorithm. The optimisation algorithm may also return unrealistic results at random time intervals, a problem that can lead to unnecessary complications as it may not be immediately recognised. Such problems are particularly acute if the software is performing multiple realisations, for example when it is being applied to analyse the impact of a multiple field development scenarios or when studying how uncertainty in the reservoir's dynamic and static properties affect the field's production performance. This paper will present a novel method based on the direct search algorithm for implementing an ICV control strategy. This method was chosen since it is not affected by the convergence problems which have caused many of the difficulties associated with previous efforts to solve our non-linear optimisation problem. Our control strategy will use the current, zonal inflow rate and water cut data to identify the optimal ICV choke positions. The availability of this data reduces the number of possible choke positions that have to be evaluated at each time step by the simulator. Run times similar to the base case are potentially possible while, equally importantly, the optimal value identified is similar to the value returned by the other published optimisation methods referred to above. This paper outlines the assumptions made and, after exploring the method's use in...

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Section: Commercial Network Optimisation Software "Critical-water-cutmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

A Novel Optimisation Algorithm for Inflow Control Valve Management

Grebenkin¹,

Davies²

2012

SPE Europec/Eage Annual Conference

Self Cite

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“…They differ in the porosity and permeability distributions. The correlation length values and anisotropy in the orientation of each layer were randomly generated based on the parameters listed in Table 1 same as (Grebenkin and Davies, 2010).…”

Section: Variations Of Uncertain Geological Parametersmentioning

confidence: 99%

“…Manceau et al (Manceau et al, 2002) and Grebenkin (Grebenkin and Davies, 2010) suggested using an experimental design technique coupled with a response surface methodology (RSM) to quantify the impact of reservoir uncertainties on production forecasts. Although, the method reduces the time required for the analysis, the development of an appropriate RSM for cases with high degrees of complexity and uncertainty is not always an easy task.…”

Section: Variation Of the Dynamic Uncertaintiesmentioning

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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“…It has previously been shown by (Birchenko, Demyanov et al 2008;Grebenkin and Davies 2010) to be a suitable candidate for IW. It has previously been shown by (Birchenko, Demyanov et al 2008;Grebenkin and Davies 2010) to be a suitable candidate for IW.…”

Section: Iw Monitoring System Design Examplementioning

confidence: 99%

Review, Analysis and Comparison of Intelligent Well Monitoring Systems

Silva

Muradov²,

Davies³

2012

All Days

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Intelligent Well (IW) technology has built-up several years' production experience. Numerous publications have described how remote flow control and monitoring capabilities can lead to fewer interventions, a reduced well count and improved reservoir management. Despite the maturity of IW equipment, the concept of the integrated IW as a key element in the "Digital Oil Field" still not fully developed. Today's practice is to evaluate the IW value chain in a "fit-for-purpose" manner rather than by an integrated modeling workflow. Reservoir engineers still struggle to convince their management of the IW's value, while the well engineer is only consulted immediately before completion deployment. Hardware and software interface problems result because appropriate technology was not selected.An increasing variety of real-time, downhole, monitoring and measurement systems are now available for deployment or are in development. Sensors for high resolution pressure and temperature, high frequency pressure (acoustic), multiphase flow rate, phase-cut, electric potential (electro-kinetic), seismic (accelerometers) and casing condition monitoring (strain) are all now (at least semi-) commercial; not to mention the option of installing quasi-distributed and/or distributed rather than a single point measurement. The former sensors provide a wealth of information about flow performance and in-well and nearwellbore formation conditions. However, each extra sensor increases the well's installation complexity and operational risk. A well-founded understanding of what data is actually needed; the most suitable sensor types and interfaces together with avail liability of the necessary data reconciliation and validation methodology are key factors for the success of an integrated IW project.This paper will review intelligent well monitoring systems, their availability, applicability and limitations. It will discuss data acquisition issues in-depth; e.g. resolution, data processing and reliability. Examples of fit-for-purpose sensor/data sets applicable to different IW applications will be given. This paper will guide the completion engineer to identify the appropriate set of downhole sensors for a specific, integrated, IW application.

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Analysis of the Impact of an Intelligent Well Completion on the Oil Production Uncertainty (Russian)

Cited by 11 publications

References 10 publications

A Novel Optimisation Algorithm for Inflow Control Valve Management

A Novel Optimisation Algorithm for Inflow Control Valve Management

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

Review, Analysis and Comparison of Intelligent Well Monitoring Systems

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