--> ABSTRACT: Application of Quantitative Risk Analysis to Breakout and Mud Loss Limits Prediction: Multiwell Analysis from Offshore India, by Kumar, Rajeev R.; Rao, Dhiresh G.; #90155 (2012)

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Application of Quantitative Risk Analysis to Breakout and Mud Loss Limits Prediction: Multiwell Analysis from Offshore India

Kumar, Rajeev R.; Rao, Dhiresh G.
DCS, Schlumberger Asia Services Ltd, Mumbai, India.

The input variables for wellbore stability analysis have statistical ranges for different depth intervals. Data ranging from open hole logs (sonic, density, gamma, etc.) to calibration parameters (leak-off, formation pressure, well test etc.) have uncertainties associated with them. With the use of a deterministic approach in wellbore stability analysis, the stable mud weight window is oversimplified and does not take into account uncertainties. Quantitative assessment of the impact of each variable uncertainty for risk prediction is not considered in this process. Hence, the uncertainty of breakout and mud loss gradient cannot be quantified.

This study presents a Quantitative Risk Analysis (QRA) approach for stable mud weight prediction based on five-well dataset. The probability density function (PDF) for each input variable was developed with mean, standard deviation, and covariance. The combination of these variables with probabilistic properties in a deterministic methodology provided PDF for stable mud weight window. A Gaussian distribution was used for probabilistic distribution to account for randomness of the input dataset. The risk of equivalent mud weight being lower than the breakout limit and higher than mud loss limit was quantified.

Based on this study, wells can be categorized based on their associated risks and economic implications. The statistical distribution of pore pressure, breakout, and mud loss limits for different formation types was generated for the existing database. This can be utilized during drilling operations to reduce the risks of kick, breakout, or loss circulation. Casing depth selection can be optimized using QRA methodology taking into the consideration input uncertainties.

 

AAPG Search and Discovery Article #90155©2012 AAPG International Conference & Exhibition, Singapore, 16-19 September 2012