Wellbore Stability Analysis: A Stochastic Approach Applied to a Colombian Cretaceous Formation

Plazas, F.; Calderón, Zuly; Quintero, Yair Andres

doi:10.2118/177132-ms

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…Gholami et al (2015) [7] applied the Quantitative Risk Assessment (QRA) method to study the impact of input parameter uncertainty on the stability of vertical wellbores under different criteria, including Mohr-Coulomb, Modified Lade, and Hoek-Brown. Plazas et al (2015) [8] applied reliability methods to assess the risk of wellbore instability in the Colombian Cretaceous formation. Kinik (2016) [9] employed the Quantitative Risk Assessment (QRA) method to study the probability density distribution of reliability under the influence of input parameter uncertainty for fracture pressure in vertical wells.…”

Section: Introductionmentioning

confidence: 99%

Risk Analysis of Wellbore Instability of “Felt Layer” in Qiudong Sag Based on Monte Carlo Method

Jia,

Zhu,

Song

et al. 2024

Processes

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Both domestic and international scholars have conducted in-depth research on wellbore stability issues. They have established various empirical models, analytical models, and numerical simulation methods. However, there is relatively little research on the impact of the uncertainty of input parameters on wellbore stability, and the understanding of this aspect remains unclear. To address this, this paper introduces a probability distribution method. It is based on a wellbore stability mechanical analytical model and, using reliability theory, establishes a method for evaluating wellbore instability risks. By employing the Monte Carlo random simulation method, this study investigates the sensitivity of input parameters to wellbore stability, clarifying that ground stress is the main controlling factor affecting wellbore stability. Combining the analysis of the “felt layer” ground stress profile, this study utilizes two-dimensional simulation experiments to validate the accurate determination of ground stress magnitude in wellbore stability analysis. It also examines the impact of reducing its uncertainty. The results show that this approach significantly reduces the risk of wellbore instability, addressing the challenging issue of identifying wellbore instability in the Qiu Dong Depression’s “felt layer” within the TH Basin.

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Section: Introductionmentioning

confidence: 99%

Risk Analysis of Wellbore Instability of “Felt Layer” in Qiudong Sag Based on Monte Carlo Method

Jia,

Zhu,

Song

et al. 2024

Processes

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“…However, due to the uncertainty of formation lithology, the uncertainty of formation pressure, the uncertainty of mechanical properties of rocks, and the unstable wellbore pressure, the input parameters of wellbore stability analysis never can be known precisely. In other words, the input parameters are often uncertain, which might cause an incorrect result [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. In order to quantify the influence of uncertain parameters on wellbore stability and SMWW, it's necessary to utilize reliability assessment method.…”

Section: Introductionmentioning

confidence: 99%

“…Gholami et al [26] applied the QRA to consider the uncertainty of input parameters in determining of the mud weight window for different failure criteria, such as the Mohr-Coulomb (M-C), Hoek-Brown (H-B) and Mogi-Coulomb (MG-C) criterion. Plazas et al [27] investigated a stochastic approach along with the conventional wellbore stability analysis enables to take into account uncertainty from input data.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Evaluation of Safe Mud Weight Window Utilizing the Reliability Assessment Method

Tang

Chen

et al. 2019

Energies

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Due to the uncertainty of formation properties and improper wellbore stability analysis methods, the input parameters are often uncertain and the required mud weight to prevent wellbore collapse is too large, which might cause an incorrect result. However, the uncertainty evaluation of input parameters and their influence on safe mud weight window (SMWW) is seldom investigated. Therefore, the present paper aims to propose an uncertain evaluation method to evaluate the uncertainty of SMWW. The reliability assessment theory was introduced, and the uncertain SMWW model was proposed by involving the tolerable breakout, the Mogi-Coulomb (MG-C) criterion and the reliability assessment theory. The influence of uncertain parameters on wellbore collapse, wellbore fracture and SMWW were systematically simulated and investigated by utilizing Monte Carlo simulation. Finally, the field observation of well SC-101X was reported and discussed. The results indicated that the MG-C criterion and tolerable breakout is recommended for wellbore stability analysis. The higher the coefficient of variance is, the higher the level of uncertainty is, the larger the impact on SMWW will be, and the higher the risk of well kick, wellbore collapse and fracture will be. The uncertainty of basic parameters has a very significant impact on SMWW, and it cannot be ignored. For well SC-101X, the SMWW predicted by analytical solution is 0.9921–1.6020 g/cm3, compared to the SMWW estimated by the reliability assessment method, the reliability assessment method tends to give a very narrow SMWW of 1.0756–1.0935 g/cm3 and its probability is only 80%, and the field observation for well kick and wellbore fracture verified the analysis results. For narrow SMWW formation drilling, some kinds of advanced technology, such as the underbalanced drilling (UBD), managed pressure drilling (MPD), micro-flow drilling (MFD) and wider the SMWW, can be utilized to maintain drilling safety.

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Review of detection, prediction and treatment of fluid loss events

Amish,

Khodja

2024

Arab J Geosci

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Lost circulation has the potential to cause formation damage, wellbore instability and a blowout. Many methods have been introduced, but there is no industry-wide solution available to predict lost circulation due to some constraints in the field. It is essential to predict the onset of loss of circulation to mitigate its effects, reduce operational costs and prevent the risk to people and the environment. A wide range of methods, techniques and treatments, including environmentally friendly materials, are reviewed to mitigate the loss of circulation. Conventional and intelligent methods are presented for detecting and predicting lost circulation events. Using oil field data such as fluid parameters, drilling parameters and geological parameters, artificial intelligence can predict fluid losses using supervised machine learning (ML). Several ML models for predicting fluid loss are reviewed in this paper, and other possible applications are discussed. The sample size, field location, input and output features, performance and ML algorithms are extracted. The paper provides an inclusive presentation of the ML workflow for fluid loss prediction and is anticipated to help and support both drilling engineering practitioners and researchers in the resolution of drilling challenges, with recommendations for future development.

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Wellbore Stability Analysis: A Stochastic Approach Applied to a Colombian Cretaceous Formation

Cited by 3 publications

References 30 publications

Risk Analysis of Wellbore Instability of “Felt Layer” in Qiudong Sag Based on Monte Carlo Method

Risk Analysis of Wellbore Instability of “Felt Layer” in Qiudong Sag Based on Monte Carlo Method

Uncertainty Evaluation of Safe Mud Weight Window Utilizing the Reliability Assessment Method

Review of detection, prediction and treatment of fluid loss events

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