Abstract
Drilling in deep high-pressure high-temperature (HPHT) abrasive sandstone pose significant challenges: low rate of penetration (ROP), bit wear, differential sticking, and wellbore instability issues. These issues are magnified when attempting to drill long laterals in the direction of minimum stress.
This paper focuses on the use of Managed Pressure Drilling (MPD) and Artificial Intelligence (AI) analytics to improve ROP.
MPD is normally used to help drilling in formations with narrow mud weight window, it achieves this by controlling the surface backpressure to keep the annular pressure in the wellbore above the pore pressure and below the fracture gradient. One key benefit of using MPD is that high mud weight is no longer required, since the Equivalent Circulating Density (ECD) is going to be managed to maintain the overbalance. An example of a well that was drilled using MPD solely for ROP improvement is presented in this paper. This well achieved almost double the ROP of a control well, which was drilled in the same formation with no MPD.
Essentially most of the drilling parameters used, which include, pump rate, revolution per minute (RPM), weight on bit (WOB), and other drilling practices, are controlled by the people on the rig. Incorporating AI analytics in the equation, help minimizes human intervention and could achieve further improvement in ROP. After the ROP improvement observed while using MPD, both technologies were combined in a well drilling the same formation. An example is presented for the well drilled using both technologies.
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