Case Studies: Optimizing BHA Performance by Leveraging Data and Advanced Modeling

Abstract

Abstract Given the intensity of drilling operations in the North American unconventional reservoirs and the quality and amount of data gathered during a drilling operation, leveraging those data along with advanced modeling techniques for optimization purposes is becoming more feasible. In this study, historical data and advanced physical modeling are utilized to better understand and optimize the bottom-hole assembly (BHA) performance in drilling operations. A comprehensive data set is gathered for more than 300 BHA runs in the span of three years. This extensive data set enables thorough examination of the variation in the operational parameters and its effect on the drilling performance. Different indices are used to determine and evaluate drilling performance, such as rate of penetration (ROP). Excessive tortuosity in a well can have many detrimental effects while drilling such as excessive and erratic torque and drag, poor hole cleaning (cuttings removal), low ROP, along with problematic casing and/or liner runs and associated cementing procedures. In this paper, a tortuosity index (TI) is used to quantify the drilled well quality and correlate it to ultimate drilling performance. In the next step, patterns are extracted and used along with physical modeling for optimizing drilling performance before the well is drilled. The corresponding tortuosity index can be used as a proxy for the well path smoothness and may be used for quantifying parameters affecting drilling performance. According to historical drilling performance data, there appears to be a strong relationship between wellbore tortuosity and ROP. If drilling operating parameters (e.g., BHA configuration, directional company's performance, target formations, bit specification, mud types, etc.) can be related to the TI based on historical data, such parameters can be modified for optimizing the performance before the well is drilled. By investigating the historical data, different trends have been extracted. In addition, different models can be built to predict drilling performance (e.g., TI) prior to drilling and according to new well design specifications. Based on data from more than 300 BHA runs and using advanced physical modeling, the most strongly correlated parameters to drilling performance have been determined and shown using different case studies. Such a historical database along with modeling techniques are used to predict well quality and drilling performance during the design phase. Using this method, well design specifications can then be optimized to enhance drilling performance and reduce the cost.