In this work, we develop and apply an offset well data analysis framework to generate a digital twin that is representative of bit state. We also strive to produce performance maps for well planning. Our workflow involves three major elements: 1) offset well data analysis to generate detailed depth-based and time-based statistics 2) computation of wear on bit and efficient weight-on-bit (WOB) versus depth for all the runs 3) automated machine learning to generate an accurate predictive model for bit dull grade to deploy for real-time operations. We define and calculate the efficient WOB as the minimum WOB needed to fully engage all cutters on the bit and maximize depth of cut. The efficient WOB changes with both bit state and formation strength, so an indication of bit wear can be used to predict formation strength. Deployment of the bit dull grade predictor enables the drilling crew to monitor the bit state while drilling and decide whether a reduction in ROP is due to a worn bit or other factors. Application to field cases from US Land yielded promising results. After computing the bit wear, performance maps for different bit states (e.g., sharp vs worn bits) are created. These performance maps help identify the optimal drilling parameters in well planning.

With the ever-increasing pressure to drill wells efficiently at lower costs, the utilization of downhole sensors in the Bottom Hole Assembly (BHA) that reveal true downhole dynamics has become scarce. Surface sensors are notoriously inaccurate in translating readings to an accurate representation of downhole dynamics. The issue of 1 to 1 interpretation of surface to downhole dynamics is prevalent in all sensors and creates a paradigm of inefficient drilling practices and decision making. Intelligent mapping of downhole dynamics (IMoDD) is an analytical suite to address these inefficiencies and maximize the use of surface sensors, thus doing more with less. IMoDD features a new zeroing beyond the traditional workflows of zeroing the surface sensors related to weight and torque at the connection. A new method, Second-order Identifier of Maximum Stand-pipe-pressure: SIMS, is introduced. The method examines changes in stand-pipe pressure and identifies the point before bit-wellbore contact, using a set of conditions. The resulting calculations of weight and torque are verified with measured values of downhole weight and torque, for multiple stands of drilling in vertical, curve-lateral drilling. After the new zero, the deviation of torque-weight correlations is further examined to reveal the downhole weight changes confirmed also by the downhole sensor data. It is demonstrated that an intelligent mapping system that improves downhole characterizations would improve decision making to facilitate smoother energy transfer thus reducing Non-Productive Time (NPT) and increasing BHA life span.