Drilling Performance Evaluation Using Advanced BHA Modeling and Field Validation

Abstract

Abstract A methodology to optimize bottomhole assembly (BHA) designs to deliver improvements in wellbore quality has been developed. BHA modeling accurately simulates the drilling process and is combined with measurements of the wellbore to establish selected drilling key performance indicators (KPIs) leading to the successful optimization of BHA designs. The benefit of improved hole quality is to drill further, faster, and increase downhole tool life. The methodology evaluates the drilling performance of mud motor runs using KPIs that evaluate the borehole quality and drilling performance. A BHA model evaluates the bending moment at critical housing locations at incremental measured depths. Changes in bending moment throughout the wellbore indicate variations in borehole tortuosity. Variation in borehole diameter during the slide and rotate modes identifies overall borehole rugosity. Multiple mud motor field runs in different basins in North America were analyzed to establish the KPIs. The tortuosity KPI results demonstrated the differences in borehole quality as a function of bending moment, where a tortuous borehole is attributable to increased bend ing moment. The fatigue life of the motor stator connections and the rate of life consumption with a larger accumulation of bending stress cycles was established. The borehole rugosity KPI demonstrated the increase in number of slide/rotate transitions, particularly in curve sections. In horizontal sections, the percentage of slide accounted for loss in rate of penetration (ROP) and consequently increased drilling time and borehole tortuosity. The combination of these factors was used to rank the different BHA designs in terms of their effectiveness to deliver a good quality borehole delivered in the least drilling time. Once the ranking was established, the effectiveness of each of the BHA design elements was established and this information used to improve the subsequent BHAs. The approach creates a new performance evaluation methodology by combining both BHA modeling and field data to optimize designs. This methodology can be implemented before drilling to select the proper BHA design and post-job to evaluate a BHA designs performance.