Drilling optimization is an engineering strategy to drill wells more productively and efficiently. Specific objectives of directional drilling are good hole quality, firm directional control, high angle-build capacity, maximal durability, optimal rate of penetration (ROP) and low non-productive time (NPT). This can be achieved through a perfect combination of well design, mathematical drilling models, drilling data analysis, and applications of new and high techniques.
High ROP in safe and stable drilling conditions is the most valuable optimization objective. Our study mainly focuses on ROP improvement in directional wells. In this paper, three directional control technologies: steerable mud motors, targeted bit speed systems (TBS) and rotary steerable systems (RSS) have been investigated. Average ROPs with implementation of these technologies have been compared by analyzing more than 30 wells drilled in Russia.
When drilling directional wells with a steerable mud motor, the number of sliding intervals has to be increased to achieve higher dogleg severity (DLS). It results in decreased total ROP, an averaged value in sliding and rotating operations. Conventionally, ROP in rotating intervals is roughly estimated to be two times higher than ROP in sliding intervals. From the analysis of several field cases, a model to describe the total ROP has been developed, where the DLS influence is taken into account. It provides useful knowledge for wellbore trajectory design in terms of achieving high ROP.
Nowadays, factors as bit types, tooth-wear and hydraulics, formation characteristics, drilling mud properties and operational conditions, etc. are considered to optimize rotary drilling. However, the influence of wellbore trajectory, inclination and azimuth in particular, is ignored in existing ROP models. In this study, a traditional multi-regression Bourgoyne and Young ROP model has been extended by dogleg severity factor (DLS) to link the wellbore trajectory design to ROP optimization. The proposed empirical model has been applied to several drilling data sets. Calculated ROP values were compared, and the increased accuracy was seen for all cases. This model can be used for wellbore trajectory planning, more precise ROP prediction and optimization, and post-analysis of drilled wells.