Real-time monitoring of borehole operations is utilized for a vast array of reasons, complementing the skills of a drilling team. The aim is to enable safe and cost-effective drilling and successful reaching of the well target. Recent proposals by the Unites Sates Bureau of Safety and Environmental Enforcement (BSEE) proposes that Real Time monitoring of down hole conditions is mandatory on many offshore wells. One particularly important aspect of this monitoring is pore pressure prediction.
Most analysis of real-time drilling data and indeed, geological, data are based on analysis by depth. These models are built with depth-based LWD and wireline data along with depth-based drilling parameters such as D-exponent etc. If calibrated correctly the depth-based model can be valuable and accurate, providing the drilling team with useful insight about present, potentially, future events. However, Real Time pore pressure models that are only produced with depth-based information are potentially flawed because most calibration points, such as swab gases, connection gases, tight hole, or pack off tendencies, occur while the bit is off bottom, which would not be captured/recorded in a depth-based model. Building a time-based model in parallel with the depth-based pore pressure model allows a more robust prognosis of downhole conditions to be ascertained.
By way of an example, depth-based gas analysis can be used as a qualitative indicator of pore pressure. But with time-based data analysis, the gas behavior can be a quantitative indicator by understanding the ECD, mud flow rates and block movement at time of production. Hole conditions, drilling parameter trends, and abnormalities are another primary calibrator for a pore pressure model. However changes in these parameters would all plot at the same depth unless software can display in both depth and time. Data may also be lost in a time to depth conversion.
Therefore this paper will highlight using case study material from the Gulf of Mexico the importance of constructing a time-based model. This paper will also demonstrate the discrepancy between the depth-based models and the time-based models. The result is a more robust, integrated solution to aid pressure prediction generally.
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