Method For Real-Time Bit Efficiency and Damage Monitoring; Leveraging Offset Data and Domain Knowledge
Authors: Chen Wei , Zhang Zhengxin , Shen Yuelin , Skoff Greg , Chatar Crispin , Converset Julien , Panayirci Huseyin Murat , Gjertsen Ole , Cazares Lucia 
Published: February 2025

Abstract
The ability to predict the useful life remaining for a drill bit or estimate the dull grading in real time while drilling with high accuracy is valuable for planning and real-time decisions within well construction. Domain experts and software engineers have developed a workflow that utilizes the company's global offset well database and bit knowledge to feed an intelligent decision-making system that can enhance the drilling process and prevent excessive bit damage by calculating the efficiency of the bit and predicting if the dull grading at the end of the run will be beyond a certain limit. The workflow was implemented on an existing software platform and validated on more than 100 wells, with insights gained from more than 44,000 offset wells’ detailed depth data from the company's global database. In real time, it is shown to enhance drilling efficiency and help make decisions to keep drilling or pull the bit out of the hole. The approach involves planning and execution stages. During the planning stage, experts analyzing offset data establish drill bit performance benchmarks. This step is powered by an automated offset well selection algorithm based on job similarity. These benchmarks integrate comprehensive metrics such as average rate of penetration (ROP), drilled footage, and bit damage conditions, as well as drilling mechanics and bit performance indices calculated along drilling depths of offset wells. In the execution stage, real-time data is used to compute drill bit performance indices, which are then compared against the established benchmarks. Accumulative bit damage is assessed through proprietary computations in the application. Inefficient drilling, hard stringers, and interbeds are detected in real time using machine learning methods. Advice is automatically generated to apply the best drilling practices based on detected drilling challenges. Alerts are generated based on predicted bit damage. The advice and alerts are reviewed by remote operation experts who provide actionable recommendations to the drillers.
Millions of bit performance summaries and tens of thousands of bit run data with detailed surface or downhole measurements have been utilized as the foundation of the workflow. A process, enriched with domain expertise, has been developed to efficiently construct offset benchmarks. The bit damage model has been validated across multiple fields, achieving an accuracy of more than 85%. Field tests confirm the workflow's effectiveness in providing real-time, relevant advisories to drillers, with remote operation experts in the loop. This approach has been proven to better separate bit performance variations due to bit damage rather than formation changes, which has been a challenge for many prior bit performance and damage monitoring methods. This paper describes how these computations can be used to determine the performance of the bit in real time. Included are overviews of the data used. Results and validation of all workflows will also be discussed. The workflow is the industry's first to utilize a large-scale global drill bit database for real-time bit performance benchmarking and prediction. It combines data-driven insights with domain expert inputs to help make decisions.