Time-Dependent Hydro-Geomechanical Reservoir Simulation of Field Production

Bazyrov, Ildar; Glazyrina, A.; Lukin, Sergey; Alchibaev, D. V.; Salishchev, Mikhail Vsevolodovich; Ovcharenko, Yu. V.

doi:10.1016/j.prostr.2017.11.035

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Petroleum geomechanics forms a critical part of reservoir engineering and wellbore construction models (Rhett 1998;Bazyrov et al 2017;Akbarpour and Abdideh 2020;Mohamadian et al 2021). Interactions of stress fields with subsurface lithologies and the formed structures require a comprehensive understanding of the mechanical behavior of the lithologies associated with gas and oil fields.…”

Section: Introductionmentioning

confidence: 99%

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

Rajabi

Hazbeh

Davoodi

et al. 2022

J Petrol Explor Prod Technol

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Shear wave velocity (VS) data from sedimentary rock sequences is a prerequisite for implementing most mathematical models of petroleum engineering geomechanics. Extracting such data by analyzing finite reservoir rock cores is very costly and limited. The high cost of sonic dipole advanced wellbore logging service and its implementation in a few wells of a field has placed many limitations on geomechanical modeling. On the other hand, shear wave velocity VS tends to be nonlinearly related to many of its influencing variables, making empirical correlations unreliable for its prediction. Hybrid machine learning (HML) algorithms are well suited to improving predictions of such variables. Recent advances in deep learning (DL) algorithms suggest that they too should be useful for predicting VS for large gas and oil field datasets but this has yet to be verified. In this study, 6622 data records from two wells in the giant Iranian Marun oil field (MN#163 and MN#225) are used to train HML and DL algorithms. 2072 independent data records from another well (MN#179) are used to verify the VS prediction performance based on eight well-log-derived influencing variables. Input variables are standard full-set recorded parameters in conventional oil and gas well logging data available in most older wells. DL predicts VS for the supervised validation subset with a root mean squared error (RMSE) of 0.055 km/s and coefficient of determination (R2) of 0.9729. It achieves similar prediction accuracy when applied to an unseen dataset. By comparing the VS prediction performance results, it is apparent that the DL convolutional neural network model slightly outperforms the HML algorithms tested. Both DL and HLM models substantially outperform five commonly used empirical relationships for calculating VS from Vp relationships when applied to the Marun Field dataset. Concerns regarding the model's integrity and reproducibility were also addressed by evaluating it on data from another well in the field. The findings of this study can lead to the development of knowledge of production patterns and sustainability of oil reservoirs and the prevention of enormous damage related to geomechanics through a better understanding of wellbore instability and casing collapse problems. Graphical abstract

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Section: Introductionmentioning

confidence: 99%

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

Rajabi

Hazbeh

Davoodi

et al. 2022

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

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“…The focus of most current studies is the intrinsic properties, failure criteria, and failure mechanisms of rocks under temperature variation, and the hydraulic fracturing of the formation induced by the effects of thermal and water injection [32]. The research on the critical issue of wellbore integrity was also primarily around the collapse and necking of wellbores and stuck drills in conventional oil/gas drilling.…”

Section: Introductionmentioning

confidence: 99%

Study on Thermo-Hydro-Mechanical Coupling and the Stability of a Geothermal Wellbore Structure

Huan

Zhang

et al. 2021

Energies

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For processes such as water injection in deep geothermal production, heat transfer and fluid flow are coupled and affect one another, which leads to numerous challenges in wellbore structure safety. Due to complicated wellbore structures, consisting of casing, cement sheaths, and formations under high temperature, pressure, and in situ stress, the effects of thermo-hydro-mechanical (THM) coupling are crucial for the instability control of geothermal wellbores. A THM-coupled model was developed to describe the thermal, fluid, and mechanical behavior of the casing, cement sheath, and geological environment around the geothermal wellbore. The results show that a significant disturbance of effective stress occurred mainly due to the excess pore pressure and temperature changes during cold water injection. The effective stress gradually propagated to the far-field and disrupted the integrity of the wellbore structure. A serious thermal stress concentration occurred at the junction of the cased-hole and open-hole section. When the temperature difference between the injected water and the formation was up to 160 °C, the maximum hoop tensile stress in the granite formation reached up to 43.7 MPa, as high as twice the tensile strength, which may increase the risk of collapse or rupture of the wellbore structure. The tensile radial stress, with a maximum of 31.9 MPa concentrated at the interface between the casing and cement sheath, can cause the debonding of the cementing sheath. This study provides a reference for both the prediction of THM responses and the design of drilling fluid density in geothermal development.

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Combining natural gas recovery and CO2-based geothermal energy extraction for electric power generation

et al. 2020

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Time-Dependent Hydro-Geomechanical Reservoir Simulation of Field Production

Cited by 4 publications

References 5 publications

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

Study on Thermo-Hydro-Mechanical Coupling and the Stability of a Geothermal Wellbore Structure

Combining natural gas recovery and CO2-based geothermal energy extraction for electric power generation

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