Prediction of Wellbore Stability Using 3D Finite Element Model in a Shallow Unconsolidated Heavy-Oil Sand in a Kuwait Field

Khan, Khaleel Ur Rahman; Mohiuddin, Mohammad Ahmed

doi:10.2118/120219-ms

Cited by 13 publications

(2 citation statements)

References 14 publications

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“…Unconsolidated sandstone reservoirs are widely distributed in oil‐producing regions globally (Ahmed et al, 2009; Bennion et al, 2006; Bian et al, 2012; Chuanzhi et al, 2014; Huang et al, 2021; Lu et al, 2019; Toelsie & Goerdajal, 2013) and are mainly distributed in tertiary strata. Unconsolidated sandstone reservoirs are usually buried at shallow depths (<1800 m) and generally only experience poor compaction and weak cementation in the early diagenetic stage.…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning

et al. 2022

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Classification and evaluation of reservoirs conforming to geological characteristics are the proposed premises of the high‐quality development of unconsolidated sandstone heavy oil reservoirs. The P oilfield in Bohai Bay is one of the largest offshore oilfields in China, but the reservoirs are highly heterogeneous, have complex pore structures, and change fluid properties, which make the classification of reservoirs very difficult. At present, the most commonly used reservoir classification methods are based on the results of logging interpretation, which cannot eliminate the constraints of linear relationships such as empirical formulas. Machine learning has significant advantages for identifying reservoir classification problems that are complex and nonlinear with geological characteristics. First, this study fully considered the pore structure and fluid properties and selected four representative evaluation parameters. An improved K‐means method was used to establish a reservoir classification evaluation system. Characteristics, such as the sedimentary microfacies, pore structure and oilfield production performance, of different types of reservoirs were then analysed. Finally, six logging curves were selected as input results, reservoir classification was used as an output result, and XGBoost was used to carry out reservoir classification evaluation for the entire area. The accuracy of XGBoost was 85.35%. Furthermore, XGBoost was compared with three other machine learning methods to confirm its reliability. The results showed that the reservoirs in the study area can be divided into three types. Sandstone thickness and pore structure were the most important parameters that affected reservoir quality. Among various machine learning algorithms, XGBoost can accurately and quickly achieve a quantitative evaluation of reservoir quality.

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Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning

et al. 2022

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“…In general, wellbore instability occurs through changes in the original stress state due to rock removal, temperature change, changes of differential pressures as drawdown occurs, and many other factors. A significant number of available publications discuss wellbore stability (Frydman and da Fontoura 2000;Kårstad and Aadnøy 2005;Hawkes 2007;Pasic et al 2007;Ahmed et al 2009). However, these studies generally involve a single wellbore.…”

Section: Introductionmentioning

confidence: 99%

3D finite element modelling of multilateral junction wellbore stability

Mohamad-Hussein

Heiland

2018

Pet. Sci.

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Wellbore failure can occur at different stages of operations. For example, wellbore collapse might happen during drilling and/or during production. The drilling process results in the removal of an already stressed rock material. If the induced stresses near the wellbore exceed the strength of rock, wellbore failure occurs. The production process also changes the effective stresses around the wellbore. Such changes in stresses can be significant for high drawdown pressures and can trigger wellbore failure. In this paper, the Mohr-Coulomb failure criterion with a hyperbolic hardening is used. The model parameters are identified from triaxial compression tests. The numerical simulations of laboratory tests showed that the model can reproduce the mechanical behaviour of sandstone. In addition, the simulations of multilateral junction stability experiments showed that the model was able to reproduce yielding and failure at the multilateral junction for different levels of applied stresses. Finally, a numerical example examining multilateral junction stability in an open borehole during drilling and production is presented. The results illustrate the development of a localized failure zone proximate to the area where two wellbore tracks join, particularly on the side with a sharp approaching angle, which would significantly increase the risk of wellbore collapse at the junction.

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Analysis of Pore Pressure and Stress Distribution around a Wellbore Drilled in Chemically Active Elastoplastic Formations

Roshan

Rahman

2011

Rock Mech Rock Eng

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Prediction of Wellbore Stability Using 3D Finite Element Model in a Shallow Unconsolidated Heavy-Oil Sand in a Kuwait Field

Cited by 13 publications

References 14 publications

Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning

Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning

3D finite element modelling of multilateral junction wellbore stability

Analysis of Pore Pressure and Stress Distribution around a Wellbore Drilled in Chemically Active Elastoplastic Formations

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