Uncertainty quantification of the factor of safety in a steam-assisted gravity drainage process through polynomial chaos expansion

Ganesh, Ajay; He, Wenhao; Chalaturnyk, Richard J.; Prasad, Vinay

doi:10.1016/j.compchemeng.2019.106663

Cited by 7 publications

(10 citation statements)

References 17 publications

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“…A schematic of the coupled reservoir-geomechanics simulation is shown in Figure 2. [24] A well pad of the MacKay River oil sand area is selected and utilized for the coupled simulations study. A total of six well pairs operate in the pad, with well spacing of 100 m. The grid sizes and numbers are nonuniform to retain heterogeneity due to sedimentation.…”

Section: Data Generation and Visualizationmentioning

confidence: 99%

“…In this work, we utilize the generated ensemble of 200 realizations of permeability through SGS utilizing the properties of the variogram. [24] Flow simulation is carried out using Computer Modelling Group (CMG)-STARS to obtain an ensemble of 200 realizations of temperature and pore pressure. Generating 200 realizations of temperature facilitates us in extracting the spatial parametric variations involved across the reservoir.…”

Section: Introductionmentioning

confidence: 99%

“…It is taken from the work done by Ganesh et al on the uncertainty quantification of the factor of safety in a SAGD process. [24]…”

Section: Introductionmentioning

confidence: 99%

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Modelling temperature dynamics of the SAGD process in an oil reservoir by the discovery of parametric partial differential equations

et al. 2022

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Many chemical and industrial processes are complex, and the dynamics of such processes cannot be explained using a partial differential equation (PDE) or a system of PDEs with constant coefficients. Parametric PDEs, that is, PDEs with their coefficients varying across time or space, are utilized for this purpose. The non‐availability of data at all spatial locations and partially available process knowledge add to the complexity of modelling such processes. This paper proposes a framework to discover parametric PDEs using data‐driven and hybrid modelling approaches with the temperature dynamics of steam‐assisted gravity drainage (SAGD) process in an oil reservoir as the system under study. We utilize an ensemble of 200 realizations of the temperature dynamics generated using the variogram for the PDE discovery. Permeability, which is one of the oil reservoir's petrophysical properties, is used to develop the hybrid models. We infer that utilizing partial process knowledge aids in improving the model's accuracy.

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Section: Data Generation and Visualizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling temperature dynamics of the SAGD process in an oil reservoir by the discovery of parametric partial differential equations

et al. 2022

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“…Uncertainty D r a f t quantification plays a vital role in assessing potential natural hazards (Chen et al 2017;Cui et al 2017;. The quantified uncertainty helps to provide a more sophisticated design of hydraulic fracturing, tunnelling, and caprock integrity in subsurface development considering the spatial variability of geotechnical properties, stress, and pore pressure distributions (Yang et al 2004;Ghassemi 2012;Ganesh et al 2019;Zhang et al 2020).…”

Section: R a F T 1 Introductionmentioning

confidence: 99%

Uncertainty quantification of in situ horizontal stress with pressuremeter using a statistical inverse analysis method

Zheng

Chalaturnyk

2022

Can. Geotech. J.

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Knowledge of in situ stress magnitude and orientation plays an important role in geological/geotechnical engineering and in the development of energy resources, such as caprock integrity, waste fluid disposal, geological storage of CO2, and geothermal energy extraction. The uncertainty of estimated parameters, especially horizontal stress, from in situ tests such as pressuremeter tests, is a long-existing challenge due to the existence of uncertainties from geomaterial spatial variability, measurement errors, limited information, and modelling method. Therefore, non-unique solutions are often encountered in the pressuremeter interpretation. In this research, a statistical inverse analysis method is proposed to solve this issue by combining the closed-form solution, finite-difference model, and selected optimization algorithms. The objective of the statistical inverse analysis is to determine the optimal parameters while providing the confidence intervals of derived parameters. Random variables generated in the optimization process reproduce the potential parameter uncertainties. The Jacobian matrix and the confidence intervals are derived from the optimization process to evaluate the variability of predicted horizontal stress and ground properties. A workflow that demonstrates a statistical inverse method for analyzing pressuremeter results is presented that helps to quantify the uncertainties of the ground properties and in-situ stress magnitudes and orientations.

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“…Polynomial chaos proxies of the linear poroelastic problem with hydromechanical coupling allows a variance-based SA on Lamé's constants, the Biot-Willis coefficient and the hydraulic mobility [16]. Another non-intrusive approach is used in sequentially coupled reservoir-geomechanical simulations to estimate the caprock safety factor in a probabilistic setting [17]. Global SA and parameter identification are carried out by means of Gaussian processes in a carbon capture and storage test case to model fault poromechanics and induced seismicity in a stochastic framework [18].…”

Section: Introductionmentioning

confidence: 99%

Generalized Polynomial Chaos Expansion for Fast and Accurate Uncertainty Quantification in Geomechanical Modelling

et al. 2020

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Geomechanical modelling of the processes associated to the exploitation of subsurface resources, such as land subsidence or triggered/induced seismicity, is a common practice of major interest. The prediction reliability depends on different sources of uncertainty, such as the parameterization of the constitutive model characterizing the deep rock behaviour. In this study, we focus on a Sobol’-based sensitivity analysis and uncertainty reduction via assimilation of land deformations. A synthetic test case application on a deep hydrocarbon reservoir is considered, where land settlements are predicted with the aid of a 3-D Finite Element (FE) model. Data assimilation is performed via the Ensemble Smoother (ES) technique and its variation in the form of Multiple Data Assimilation (ES-MDA). However, the ES convergence is guaranteed with a large number of Monte Carlo (MC) simulations, that may be computationally infeasible in large scale and complex systems. For this reason, a surrogate model based on the generalized Polynomial Chaos Expansion (gPCE) is proposed as an approximation of the forward problem. This approach allows to efficiently compute the Sobol’ indices for the sensitivity analysis and greatly reduce the computational cost of the original ES and MDA formulations, also enhancing the accuracy of the overall prediction process.

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Uncertainty quantification of the factor of safety in a steam-assisted gravity drainage process through polynomial chaos expansion

Cited by 7 publications

References 17 publications

Modelling temperature dynamics of the SAGD process in an oil reservoir by the discovery of parametric partial differential equations

Modelling temperature dynamics of the SAGD process in an oil reservoir by the discovery of parametric partial differential equations

Uncertainty quantification of in situ horizontal stress with pressuremeter using a statistical inverse analysis method

Generalized Polynomial Chaos Expansion for Fast and Accurate Uncertainty Quantification in Geomechanical Modelling

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