Enhancing Reamer Drilling Performance in Deepwater Gulf of Mexico Wells

Reamers are an integral part of deepwater Gulf of Mexico (GOM) drilling and their performance significantly impacts the economics of well construction. This paper presents a novel programmatic approach to model rate of penetration (ROP) for reamers and improve drilling efficiency. Three field implementations demonstrate value added by the reamer drilling optimization (RDO) methodology. Facilitated by user interface panels, the RDO workflow consists of surface and downhole drilling data filtering and visualization, detection of rock formation boundaries, frictional torque (FTRQ) and aggressiveness estimation, ROP modeling with analytical equations and machine learning (ML) algorithms [regression, random forests, support vector machines (SVMs), and neural networks], and optimization of drilling parameters. ROP model coefficients and bit and reamer aggressiveness are dependent on lithology and computed from offset well data. Subsequently, when planning a nearby well, bottomhole assembly (BHA) designs are evaluated on the basis of drilling performance and weight and torque distributions between cutting structures to avoid early reamer wear and dysfunctions. Geometric programming establishes optimal drilling parameter roadmaps according to operational limits, downhole tool ratings, rig equipment power constraints, and adequate hole cleaning. Separate ROP models are trained for reamer-controlled and bit-controlled ROP zones, defined by the proportion of surface weight on bit (WOB) applied at the reamer, in every rock formation. This novel concept enables ROP prediction with the appropriate model for each well segment depending on which cutting structure limits drilling speed. In the first of the three RDO applications with field data from deepwater GOM wells, optimal bit-reamer distances are determined by analyzing reamer weight load in uniform salt sections. Next, ROP modeling for the addition or removal of a reamer from the BHA is used in contrasting well designs to conceivably alleviate a USD 16 million casing inventory surplus. Finally, active optimization constraints are investigated to reveal drilling performance limiters, justifying equipment upgrades for a future deepwater GOM well. The proposed innovative workflow and methodology apply to any drilling optimization scenario. They benefit the practicing engineer interested in drilling performance optimization by providing insights on how different cutting structure sizes affect ROP behavior and ultimately aiding in the selection of appropriate bit and reamer diameters and optimal operational parameters.