Last week we were pleased to be a part of the IADC DEC Tech Forum on Drilling Efficiency. We had the opportunity to share more about our RiMo Real Time Digital Twin for Drilling Dysfunctions. It was a great time presenting with good questions and, more in-depth analysis of how Drilling Digital Twin provides optimal drilling solutions and sustainability.

What engineered solutions do we need for optimal drilling efficiency? We answer the question. The digital twin combines real-time surface data with details of bottom hole assemblies to predict drilling dynamics. It is built with a workflow that automatically recognizes rig states and stops the predictive model after connection.

In case you missed it, here is a portion of our presentation:

Abstract:

A novel real time digital twin has been developed to predict drilling dysfunctions and improve operational efficiencies. Real time surface data is used as input to predict drilling dynamics and drilling dysfunctions in the bottom hole assembly at any depth. This provides a rare insight for drilling engineers to improve drilling performance and take predictive or corrective measures for reliability and operational efficiencies.
The digital twin uses real time surface as input along with the details of bottom hole assemblies and drillstring to predict downhole drilling dynamics responses. A fast-running time domain models based on mixed multi-body mechanics and finite element methods form the basis of the digital twin. The work flow is built to automatically recognize drilling rig states (rotary or slide drilling) and connection makeup to start and stop the predictive model.

The twin produces quantifiable drilling dynamics like shock and vibration, downhole MSE, rate of penetration, etc. in real time. The method allows seamless workflow for planning, real time operations and post job analytics. The digital twin does not require expensive hardware and can run at the edge. The framework allows real time models to be overridden for manual control, parameter optimization and virtual step test without ever leaving the workflow. This provides the power of predictive models for real time decision making.