Real-time reservoir model updating in thermal recovery: Application of analytical proxies and Kalman filtering

Azad, Ali; Chalaturnyk, Rick; Movaghati, Sahar

doi:10.1016/j.petrol.2015.01.022

Cited by 3 publications

(2 citation statements)

References 37 publications

(31 reference statements)

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“…With respect to Eqs. (12) and (13), the changing in matrix is the significant role to alert the value of . Therefore, by increasing or decreasing the value of , a mismatch between and rn C can be improved.…”

Section: 2 Fuzzy Adaptive Estimation Of Matrixmentioning

confidence: 99%

“…This approach provides increased certainty in optimal well placement. Ali et al [12] optimized a physics based analytical proxy using the extended Kalman filter to describe reservoir rock properties including porosity and permeability under the thermal recovery process. The Kalman filter is always considered to estimate the system behavior for forecasting analyses and it is an optimal recursive data processing algorithm, which is linear, unbiased, and has a minimum error variance of the unknown state vector.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers

Soltani

Kordestani

Aghaee

2016

Journal of Petroleum Science and Engineering

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Section: 2 Fuzzy Adaptive Estimation Of Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers

Soltani

Kordestani

Aghaee

2016

Journal of Petroleum Science and Engineering

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Proxy models for caprock pressure and temperature dynamics during steam-assisted gravity drainage process

Ganesh

Xiong

Chalaturnyk

et al. 2019

Computers & Chemical Engineering

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Machine Learning Enhanced Upscaling of Anisotropic Shear Strength for Heterogeneous Oil Sands

Zhang

Zheng

et al. 2022

Day 2 Thu, March 17, 2022

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Weak shale beddings are widely distributed in the overburden and reservoir of oil sand deposits and lead to reduced anisotropic shear strength. Understanding the shear strength of the overburden and the reservoir is important in risk assessment of slope stability in open-pit mining and caprock integrity of in-situ thermal recovery of oil sands while optimizing the production of bitumen. Due to the restrictions of computational efficiency, cells used for simulation cannot be smaller enough to capture the details of heterogeneity in the reservoir. Therefore, a robust and efficient upscaling technique is important for modeling the impact of heterogeneity on the deformation and failure of oil sands during mining and in-situ recovery. However, current analytical and numerical upscaling techniques cannot provide computationally efficient geomechanical models that consider the impact of inclined shale beddings on shear strength. Therefore, we propose a machine learning enhanced upscaling (MLEU) technique that leverages the accuracy of local numerical upscaling and the efficiency of machine learning techniques. MLEU generates a fast and accurate machine learning-based proxy model using an artificial neural network (ANN) to predict the anisotropic shear strength of heterogeneous oil sands embedded with shale beddings. The trained model improves accuracy by 12%-76% compared to traditional methods such as response surface methodology (RSM). MLEU provides a reasonable estimate of anisotropic shear strength while considering uncertainties caused by different configurations of shale beddings. With the increasing demand for regional scale modeling of geotechnical problems, the proposed MLEU technique can be extended to other geological settings where weak beddings play a significant role and the impact of heterogeneity on shear strength is important.

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Real-time reservoir model updating in thermal recovery: Application of analytical proxies and Kalman filtering

Cited by 3 publications

References 37 publications

New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers

New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers

Proxy models for caprock pressure and temperature dynamics during steam-assisted gravity drainage process

Machine Learning Enhanced Upscaling of Anisotropic Shear Strength for Heterogeneous Oil Sands

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