Digital Twin has become pillar of Oil and Gas industry. Previously, there was no solution / tool available to detect early bit failure, therefore Real Time Operations Centre (RTOC) team decided to develop and implement Mechanical Specific Energy (MSE) Ratio in real time to detect drilling dysfunctions and consequently prevent Non-Productive Time (NPT). The paper aims to demonstrate how MSE ratio helps to enhance the performance efficiency in real time while drilling operation.

RTOC aggregates data from all the operational Rigs in real time and digital twin solution was developed to compute MSE Ratio in real time from downhole and surface MSE. Automated Machine Learning workflows compute downhole Weight on Bit and downhole Torque to compute downhole MSE. Surface MSE is automatically computed based on surface parameters. Output is filtered with Machine Learning Rig State workflow to avoid any false computation. The algorithmic outputs are calculated in time dataset and then converted to depth-based data in real time. Trend analysis of outputs will help to identify inefficiency and take decision on time.

The Dynamic Solution can be used as smart drilling decision tool to detect bit performance abnormality and to enhance the efficiency for drilling operation. Trend of MSE Ratio output has helped to identify the bit failure in real time which further paves the way to decide bit trip and optimize the performance of the well. Case Study will demonstrate where trend of MSE ratio reached below the defined baseline and provided alert for potential bit failure. Bit trip was performed and based on bit dull grading, it was decided to run with new bit. MSE ratio observed on the new bit reached back to normal trend as per defined baseline. New bit was able to drill and complete the section within the plan. This tool has been implemented successfully on all the operational Rigs to monitor performance in real time and can help to take decisions to safeguarding and optimize the performance of the sections and well. Trend analysis of MSE Ratio along with other parameters can help to detect inefficiency and optimize rate of penetration (ROP) in real time.

This innovative approach of using MSE Ratio can help to build new digital twin solutions and enhance utilization of MSE output. Machine Learning workflows leverages the objective of digital drilling transformation and to optimize drilling efficiency in real time. Output helps to improve performance and prevent unwanted events. Solution can be further enhanced to detect other drilling dysfunctions and define efficiency roadmap with the combination of Drilling Strength.