The Effects of Micro Tortuosity on Trajectory Accuracy and Torque and Drag
Research output: Research › Master's Thesis
Monitoring the trajectory of any hydrocarbon well being drilled is crucial for almost every single aspect of operation, starting with the approximation of the actual borehole to the planned one, creating a smooth wellbore and therefore reducing local dogleg severities which affect equipment lifetime, HSE considerations and well interceptions, and last but not least hitting a defined geological target of interest. While measurement while drilling (MWD) has been the industry standard in the past, a fairly new technology known as gyro while drilling (GWD) has been developed in the last decade. In comparison to MWD tools, which are based on magnetic working principles, gyro equipment works almost independently from external parameters and delivers results based on inertial effects. In this Thesis, the trajectory accuracy of conventional MWD surveys is compared to high resolution gyro data in terms of departure and vertical deviation throughout the length of a wellbore. Micro tortuosity, which can be defined as the borehole deviations on a scale smaller than the usual MWD spacing, highlights the importance of continuous MWD data, since varying the spacing of adjacent survey stations seems to be the main cause for error. The tortuous trajectory of the wellbore on a small scale reduces the effective hole diameter, and identifying where such reduced sections are located in the wellbore can be useful for the choice and installation of downhole equipment, as well as the productivity of a well. As there seems to be a correlation between the effective diameter, which can usually be calculated based on high resolution measurements, and dogleg severity/tortuosity from MWD data, an approximate formulation for the effective diameter was established. Another aspect is the link between micro tortuosity, effective diameter and torque and drag (T&D) modelling. The resulting effects on soft- as well as stiff-string T&D models conclude this Thesis.
|Award date||18 Dec 2015|
|State||Published - 2015|