Study on Evaluation Method for Wellbore Stability Based on Uncertainty Analysis

Zhi-Chuan  Guan; Ya-Nan  Sheng

doi:10.6180/jase.2017.20.4.06

Study on Evaluation Method for Wellbore Stability Based on Uncertainty Analysis

$Cumulative probability distribution of collapse and fracture pressure in 1750 m and 1250 m.$

Zhi-Chuan Guan¹ and Ya-Nan Sheng This email address is being protected from spambots. You need JavaScript enabled to view it.¹

¹Department of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, P.R. China

Received: March 27, 2017
Accepted: June 26, 2017
Publication Date: December 1, 2017

Download Citation: ||https://doi.org/10.6180/jase.2017.20.4.06

ABSTRACT

The wellbore instability is one of the main complex situations or accidents, which brings great difficulties and risks to the drilling engineering. The existing wellbore stability analysis models mostly ignored the uncertainty of geo-mechanical parameters. The result of geo-mechanical parameters were treated as the fixed values. So there are large errors between the predicted results and the actual underground pressure, which cannot meet the needs of drilling design under complex geological conditions. Therefore, this paper aimed to establish a new method for quantitative evaluation and analysis method for wellbore stability. Firstly, the uncertainty of geo-mechanical parameters were analyzed, the probability distribution of them were obtained. Then the quantitative uncertainty analysis method for fracture and collapse pressure were established based on Monte-Carlo simulation and normal information diffusion. Finally according to the collapse and fracture pressure with credibility, we realized the quantitative evaluation and analysis of wellbore stability. The method established in this paper can provide the scientific basis for evaluation of wellbore stability under complex geological conditions.

Keywords: Wellbore Stability, Uncertainty Analysis, Monte-Carlo Simulation, Normal Information Diffusion, Quantitative Risk Assessment (QRA)

REFERENCES

[1] Bratton, T., Edwards, S., Fuller, J., et al., “Avoiding DrillingProblems,”OilfieldReview,Vol.13, No. 2,pp. 150155 (2001).
[2] Wang, C. L., Gao, L. I., Yan, J. T., et al., “Experiment Study of Hard and Brittle Shale Wellbore Instability Mechanism in South of Sichuan,” Science Technology & Engineering, Vol. 8, No. 3, pp. 3035 (2012).
[3] Ottesen, S., Zheng, R. H. and McCann, R. C., “Borehole Stability Assessment Using Quantitative Risk Analysis,” SPE52864 (1999). doi: 10.2118/52864-MS
[4] Helio, S., Placido, J. C. R. and Claudio, W., “Consequences and Relevance of Drill-string Vibration on Wellbore Stability,” Society of Petroleum Engineers (1999). doi: 10.2523/52820-MS
[5] Liu, X. J. and Luo, P. Y., “Application and Development of Logging in Wellbore Stability Study,” Natural Gas Industry, Vol. 36, No. 2, pp. 7278 (1999).
[6] Payten, W. and Law, M., “Estimating the Plastic Collapse of Pressure Vessels Using Plasticity Contours,” International Journal of Pressure Vessels & Piping, Vol. 75, No. 7, pp. 529536 (1998). doi: 10.1016/S0308-0161(98)00036-2
[7] Bian, F., Lin, P., Wang, L., et al., “Establishing a New Model for Rock Fracture Pressure during Well Fracturing Stimulation,” Drilling & Production Technology, Vol. 18, No. 3, pp. 2933 (2004).
[8] Catuneanu, O., Principles of Sequence Stratigraphy, Wu Yinye translated. Beijing: Petroleum Industry Press (2009).
[9] Trangmar, B., Yost, R. S. and Uehara, G., “Application of Geostatistics to Spatial Studies of Soil Properties,” Advances in Agronomy, Vol. 38, No. 1, pp. 4594 (1986). doi: 10.1016/S0065-2113(08)60673-2
[10] Gong, F. Q., Hou, S. Q. and Yan, X. M., “Probability Model Deduction Method of Mohr-coulomb Criteria Parameters Based on Normal Information Diffusion Principle,” Chinese Journal of Rock Mechanics and Engineering, Vol. 11, pp. 22252234 (2013).
[11] Lukacs, E., Probability and Mathematical Statistics, Academic Press (1972).
[12] Jackson, J. E., Wiley Series in Probability and Mathematical Statistics, AUser’s Guide to Principal Components. John Wiley & Sons, Inc. pp. 611616 (2004). doi: 10.1002/0471725331.scard