Abstract

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.

Highlights

  • Due to the exhaustion of conventional petroleum resources, much attention is being paid to some ultra-deep, unconventional and deep-water petroleum resources

  • On the basis of the reliability assessment theory, the uncertain Equivalent Mud Weight of Collapse Pressure (EMWCP) model was proposed by involving the tolerable breakout and the MG-C criterion, the uncertain equivalent mud weight of fracture pressure (EMWFP) and equivalent mud weight of collapse pressure (EMWPP)

  • Model was proposed, and the uncertain safe mud weight window (SMWW) model was determined by EMWCP, EMWPP and EMWFP

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Summary

Introduction

Due to the exhaustion of conventional petroleum resources, much attention is being paid to some ultra-deep, unconventional and deep-water petroleum resources. Moos et al [18] and Zoback [6] presented the use of QRA to formally account for the uncertainty in each input parameter to assess the probability of achieving a desired degree of wellbore stability at a given mud weight. The reliability assessment method had been integrated with wellbore stability models, but most of the above studies still have some shortcomings: (1) The required mud weight to prevent wellbore collapse is too high [28,29], due to the fact traditional wellbore stability models assume that the critical failure appears at the highest point of stress concentration, in other words, it’s performed to yield no shear failure along the borehole wall [2,28]. (2) Most of wellbore stability analysis that involved the reliability assessment method always uses the M-C criterion to determine the shear failure, while the M-C criterion ignored the influence of the intermediate principal stress, which makes the required mud weight to prevent wellbore collapse too conservative. The field observation of well SC-101X was reported and discussed

Reliability Assessment Theory
Stress Distribution on the Borehole Wall
Breakout Width Model
Collaspe Pressure Model for M-C Criterion where C and q are given as
Collapse Pressure
MG-C Criterion
Tensile Failure Criterion
Facture Pressure
SMWW Model
Results and Discussion
Uncertainty of Basic Parameters
EMWCP Probability Analysis for both M-C and MG-C Criteria
Probability
Influence
Influence of Variance
11. Influence
EMWFP Probability Analysis
14. Influence
Uncertainty
17. Probability
19. Influence
Uncertainty Analysis of SMWW
21. Cumulative
Field Observation Report
Conclusions
Full Text
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