Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Malakooti, Reza; Muradov, Khafiz; Davies, David Roland; Kuznetsov, A. G.

doi:10.2118/173873-ms

Cited by 5 publications

(8 citation statements)

References 27 publications

(19 reference statements)

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“…Although only water breakthrough occurs in this synthetic case study, the formulations of fluid flow across the ICVs and wellbore can be modified to include the effect of gas flow rate caused by gas breakthrough. This adds the calculation of in situ gas mass fraction into the algorithm of estimation of zonal properties in addition to zonal reservoir pressure, zonal productivity index and zonal water-cut (Malakooti et al 2015). Malakooti et al (2015) also showed the applicability of MPFR workflow in gas reservoirs in which estimation of reliable values of reservoir parameters was difficult due to nonlinearity and extra unknowns in the inflow back-pressure equation.…”

Section: Resultsmentioning

confidence: 96%

“…This paper presents an ICM algorithm that designs the optimum number of flow tests required to simultaneously monitor the reservoir properties and (reactively) control the well's production. The MPFR, soft-sensing algorithm (Malakooti et al 2015), was our initial work in this area. The MFPR algorithm analyses use the measured data from an initial (n + 1) flow tests in an n-zone I-well to estimate the flow rate and phase fraction of the fluids being produced from each of the of the n zones.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The ICM concept extends the (Malakooti et al 2015) MPFR workflow which ensures that the loss in production during the initial (n + 1) flow tests is kept to a minimum by a two-stage optimisation workflow: • Step 1 estimates the zonal properties when the total mismatch of the objective function is minimised. • Step 2 simultaneously designs the future flow tests while minimising the individual mismatch function (equivalent to the reliability of the estimated zonal properties) or maximising the total oil production.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The soft-sensing estimation of zonal MPFRs may be performed in either passive or active mode. Fixed configuration of the completion is used in passive soft-sensing methods to obtain measurements and estimate the downhole flow rates (Gryzlov et al 2009;Lorentzen et al 2010Lorentzen et al , 2014, while active soft-sensing technique uses optimised sequence of ICV positions in a multi-rate flow tests to estimate zonal MPFRs (Malakooti et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…This work is the extension of application of active softsensing algorithm (Malakooti et al 2015) to monitor the zonal flow rates in I-wells. The active soft-sensing approach is different from previous passive soft-sensing studies.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Integrated production optimisation and monitoring of multi-zone intelligent wells

Malakooti

Ayop

Maulianda

et al. 2019

J Petrol Explor Prod Technol

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Multi-zone intelligent wells (I-wells) completed with interval control valves and downhole sensors divide the well completion into a number of production intervals that can be managed individually. The production rate from these wells is optimised using either reactive or proactive control strategy. Zonal inflow property values are often used to estimate the zonal multi-phase flow rates (MPFRs) to inform such control strategies. Real-time measurements of the zonal downhole pressure and temperature can be used to estimate the zonal MPFRs, which are considered the main input information to production optimisation algorithms. This paper presents an integrated control and monitoring (ICM) algorithm to maximise production from multi-zone I-wells. The algorithm includes two-level optimisation to design optimum number of required flow tests and optimise either reliability of estimated zonal production (monitoring) or oil production (control). An in-house optimiser has been developed to initiate the required flow tests to perform the ICM workflow, while active soft-sensing algorithm is used to design further flow tests required either to maximise the reliability of estimated zonal properties or maximise oil production. The algorithm was validated using a commercial transient wellbore simulator OLGA™ in which a five-zone intelligent well was modelled. The simulator results were used as inputs into the ICM algorithm to test the applicability of proposed workflow. Two different workflows of ICM and MPFR were compared in this synthetic case study, and both workflows achieve satisfactory estimates of the zonal properties. However, the ICM workflow attempts to achieve the maximum oil production with a reduced number of flow tests and results in higher cumulative oil production compared to the MPFR workflow. This confirmed that there is a potential to monitor and control zonal production simultaneously with less flow tests in comparison with applying a separate control and monitoring approach. The findings of this study showed it is not necessary to have the accurate estimation of zonal properties in order to maximise the oil production from a multi-zone I-well. The proposed ICM algorithm can also be applied in multi-well flow rate allocation of an interest production system network and optimisation of start-up of multi-zone I-wells.

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Section: Resultsmentioning

confidence: 96%

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Integrated production optimisation and monitoring of multi-zone intelligent wells

Malakooti

Ayop

Maulianda

et al. 2019

J Petrol Explor Prod Technol

Self Cite

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New generalized correlations for oil rate predictions through wellhead chokes for high GOR reservoirs

2022

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Surrogate-Based Prediction and Optimization of Multilateral ICV Flow Performance in a Real Field

Aljubran

Horne

2021

Day 1 Tue, January 05, 2021

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Smart completions enable physical measurements over space and time, which provides large volumes of information at unprecedented rates. However, optimizing inflow control valve (ICV) settings of smart multilateral wells is a challenging task. Traditionally, ICV field tests, evaluating well performance at different ICV settings, are conducted to observe flow behavior and configure ICV's, however this is often suboptimal. This study investigated a surrogate-based optimization algorithm that minimizes the number of ICV field tests required, predicts well performance of all unseen combination of ICV settings, and determines the optimal ICV setting and net present value (NPV). A numerical model of a real offshore field in Saudi Arabia was used to generate scenarios involving a two-phase (oil and water) reservoir with trilateral producers. Multiple scenarios were examined with variations in design parameters, mainly well count, placement and configuration. Eight discrete settings were assumed to match the commonly installed ICV technology, where all possible scenarios were simulated to establish ground truth. The investigation considered three major algorithmic components: sampling, machine learning, and optimization. The sampling strategy compared physics-based initialization, space-filling sampling, and triangulation-based adaptive sampling. A cross-validated neural network was used to fit a surrogate dynamically, while enumeration was adopted for optimization to avoid errors arising from using common optimizers. This study evaluated two sampling techniques: space-filling and adaptive sampling. The latter was found superior in capturing reservoir behavior with the smallest number of simulation runs, i.e. ICV field tests. Algorithm performance was evaluated based on the number of ICV field tests required to: 1) surpass an R2 threshold of 0.9 on all unseen scenarios, and 2) match the optimal ICV settings and NPV. Surface and downhole flow profile prediction and optimization were achieved successfully using this approach. To determine the diminishing value of additional ICV field tests, the triangulation sampling loss was used as a stoppage criterion. When running the algorithm on a single producer for both surface and downhole oil and water flow prediction, the algorithm required six and 11 ICV field tests only to achieve 80% and 90% R2 across the different cases of this real reservoir model. Fishbone wellbore configurations were found to pose a more challenging task as changes in any ICV pressure drop affects multiple laterals simultaneously, which increases the level of interdependence. The resultant surrogate was used to decide on the optimal settings of ICV devices and also predict the NPV effectively. Further improvement was accomplished through adaptively sampling and fitting surrogate to rather predict NPV explicitly where NPV predictions were generated with nearly 95% R2 given only ten ICV field tests. Using adaptive sampling and machine learning proved effective in the prediction of surface and downhole flow profiles, and optimization of smart wells. The method further allows for dynamically optimizing field strategy in a reinforcement learning setting where production data are used continuously to further improve the prediction performance.

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Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Cited by 5 publications

References 27 publications

Integrated production optimisation and monitoring of multi-zone intelligent wells

Integrated production optimisation and monitoring of multi-zone intelligent wells

New generalized correlations for oil rate predictions through wellhead chokes for high GOR reservoirs

Surrogate-Based Prediction and Optimization of Multilateral ICV Flow Performance in a Real Field

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