On Uncertainty Analysis of the Rate Controlled Production (RCP) Model

Taghavi, Soheila; Ghaderi, Ali

doi:10.3384/ecp21185271

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Most studies are restricted to the use of custom implementations of basic, quick-to-implement MCMC techniques, such as the random-walk Metropolis method (Ban et al, 2022;Ruiz Maraggi et al, 2022;Pan et al, 2021;Lødøen and Tjelmeland, 2007). There are only a handful of studies that apply HMC algorithms for parameter estimation, e.g., Taghavi and Ghaderi (2022) use HMC via Stan for a dimensionless model of a rate controlled production valve; Sandl et al (2021) use HMC via Stan for logistic regression models that predict the occurrence of gas migration in oil wells; and Moen et al (2022) use DynamicHMC (Papp, 2021) -an implementation of a variant of the NUTS algorithm proposed by Betancourt (2017) -via Turing for a model that estimates the inflow profiles in oil wells.…”

Section: Modeling Identification and Controlmentioning

confidence: 99%

HMC Techniques for Reducing the Uncertainty of Gas-Lifted Oil Field Model

Jayamanne¹,

Lie²

2023

MIC

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Parametric model uncertainties could have a high impact on the predictive capabilities of a model. When process measurements become available, these uncertainties may be reduced using parameter estimation techniques. Estimation techniques founded on the Bayesian framework in particular are powerful: they produce a probability density function (PDF) of the estimated parameter rather than a single point estimate.In this paper, we consider a gas lifted oil field model whose predictions are highly sensitive to uncertainty in its parameters. We apply Markov Chain Monte Carlo (MCMC) methods, which follow the Bayesian paradigm, to estimate these parameters, and thereby reduce the uncertainty in the model predictions; two different algorithms, Hamiltonian Monte Carlo (HMC) and No-U-Turn Sampler (NUTS), are used. The probabilistic programming language (PPL), Turing in Julia is used for implementation. Monte Carlo simulations and/or data retrodiction is performed prior to and post parameter estimation, to evaluate the uncertainty in model predictions; the outcomes are compared to determine the efficacy of parameter estimation. Results show that the computed posterior distributions yield model predictions that are in close agreement with the observations, and that model uncertainty is effectively reduced.

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Section: Modeling Identification and Controlmentioning

confidence: 99%

HMC Techniques for Reducing the Uncertainty of Gas-Lifted Oil Field Model

Jayamanne¹,

Lie²

2023

MIC

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“…where ∆PTot is the differential pressure across the AICV, ρcal and µcal are the calibration fluid density and viscosity, and ρmix and µmix are the mixture fluid density and viscosity respectively. The parameter aAICD is a valve characteristic given by the ICD strength, Q is the volumetric mixture flow rate, and x and y are constants (Taghavi & Ghaderi, 2021). It can be interpreted from equation ( 2) that the pressure drop through the AICV is much more viscosity dependent than density dependent.…”

Section: Aicvs In Advanced Wellsmentioning

confidence: 99%

The Impact of Autonomous Inflow Control Valve on Enhanced Oil Recovery in SAGD Application

Alam,

Tahami,

Furuvik

et al. 2023

Linköping Electronic Conference Proceedings

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The demand for non-conventional oil has increased globally. Non-conventional oil is categorized as extra heavy oil and bitumen. In reservoirs with extra heavy oil and bitumen, thermal methods are used to reduce the oil viscosity. Steam assisted gravity drainage (SAGD) is a thermal recovery method to enhance the bitumen recovery. In this method, steam is injected to bitumen and heavy oil to reduce the viscosity and make the oil mobile and extractable. To obtain an efficient SAGD process, the residence time for steam in the reservoir must be long enough for the steam to condense and release the latent energy to be transferred to the cold bitumen. Early breakthrough of steam in some parts of the well will eventually limit the oil production and must be avoided. Autonomous inflow control valve (AICV) can prevent the steam breakthrough and restrict the excessive production of steam. The objective of this paper is to investigate the performance of AICV and its impacts on increased oil production in a SAGD production well. This is achieved by focusing on the implementation, and performance evaluation of inflow control devices (ICDs) and AICVs compared with standard well perforations. CMG STARS, a multi-phase, multi-component thermal reservoir simulator, is used to perform numerical simulation studies. The simulation results demonstrate the significant benefit of AICV in steam reduction compared to ICD and well perforations. The simulation results demonstrate that utilizing AICV in a SAGD reservoir will lead to higher oil production, less steam production, and a more uniform temperature distribution, and steam chamber conformance. Reduction in steam production, will improve the overall SAGD operation performance. This will also result in more cost-effective oil production, as less steam is needed to be generated for production of each barrel of oil.

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“…The parameter 𝑎 𝐴𝐼𝐶𝐷 is a valve characteristic given by the ICD strength, 𝑄 is the volumetric mixture flow rate, and 𝑥 and 𝑦 are constants. [10] It can be interpreted from equation ( 2) that the pressure drop through the AICV is much more viscosity dependent than density dependent. The concept and principle of AICV is described in detail in earlier scientific works [11,12].…”

Section: Aicvmentioning

confidence: 99%

Application of Autonomous Inflow Control Valve for Enhanced Bitumen Recovery by Steam Assisted Gravity Drainage

Taghavi¹,

Madan²,

Timsina³

et al. 2022

Linköping Electronic Conference Proceedings

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Steam assisted gravity drainage is a thermal method for enhanced bitumen recovery. In this method, steam is injected to bitumen and heavy oil to reduce the viscosity and make the oil mobile. However, early breakthrough of steam in some parts of the well results in loss of the required amount of steam in contact with the cold bitumen, and poor distribution of the steam chamber. This limits the oil production and increases the SAGD operation cost. Autonomous inflow control valve (AICV) is able to prevent the steam breakthrough and restrict the production of steam. The objective of this paper is to investigate the performances of AICV and passive inflow control device (ICD) in a SAGD production well. This is achieved by developing a dynamic wellbore-reservoir model in the OLGA-ROCX simulator. Reservoir and fluid properties have been specified in ROCX, and the wellbore model has been developed in OLGA. Coupling OLGA and ROCX enable the user to simulate the fluid production from the reservoir into the well. The simulation results demonstrate the significant benefit of AICV in steam to oil ratio (SOR) reduction compared to ICD. Indeed, the simulation results show that utilizing AICV in the SAGD production wells will reduce the steam production by 88% after 300 days of production. From environmental aspect, reduction in the steam to oil ratio by utilizing AICV will reduce the energy demand for steam generation. This will eventually improve the economics of SAGD projects. Also, reduction in the steam and energy demand will consequently contribute to lower the intensity of greenhouse gas (GHG) emissions.

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On Uncertainty Analysis of the Rate Controlled Production (RCP) Model

Cited by 3 publications

References 3 publications

HMC Techniques for Reducing the Uncertainty of Gas-Lifted Oil Field Model

HMC Techniques for Reducing the Uncertainty of Gas-Lifted Oil Field Model

The Impact of Autonomous Inflow Control Valve on Enhanced Oil Recovery in SAGD Application

Application of Autonomous Inflow Control Valve for Enhanced Bitumen Recovery by Steam Assisted Gravity Drainage

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