Monitoring Hole-Cleaning during Drilling Operations: Case Studies with a Real-Time Transient Model

Abstract

Steady-state hole-cleaning models used to monitor cuttings during well construction rely on static parameters that portrait specific drilling scenarios disconnected from each other. This paper presents the integration of transient hole-cleaning models validated in the field into a digital twin of the wellbore deployed while drilling. Thus, enabling the monitoring of the evolution of cuttings, which reduces uncertainty around the state of hole-cleaning procedures and minimizes the associated risk. A digital twin of the wellbore equipped with physics-based transient models is prepared in the planning phase, and later deployed to a real-time environment. While drilling, smart triggering algorithms constantly monitor drilling parameters at surface and downhole to automatically update the digital twin and refine simulation results. The physics-based transient model continuously estimates cuttings suspended in the drilling mud and cuttings deposited as stationary beds, which enables evaluation of cuttings distributions along the wellbore in real time. Automation systems consume the predicted results via an aggregation layer to refine fit-for-purpose hole-cleaning monitoring applications deployed at the rig. The transient hole-cleaning model has been integrated into digital twins used during pre-job planning as well as in real-time environments. The system deployed in real-time successfully tracks the state of cuttings concentration in the wellbore during all operations (drilling, tripping, off-bottom circulation, connections) considering the effects of high-temperature and high-pressure on the drilling fluid. Moreover, since the model uses previous results as starting point for the next estimation cycle, it creates a dynamic prediction of how the cuttings evolve while drilling. Fit-for-purpose automation and monitoring services predict drilling issues related to hole-cleaning, downhole pressure, among others. Drillers and drilling optimization personnel receive actionable information to mitigate hole-cleaning issues and avoid detrimental effects for operations. The user interface (UI) presents how the cuttings distribution change with evolution of input parameters (rate of penetration, string rotation, and flow rate). A set of case studies confirm the effectiveness of the approach and illustrate its benefits. One case study from the North Sea illustrates the reaction of the model to changing operational parameters, while another combines along-string-measurements of density with the cuttings predictions to confirm the trend established by the predicted cuttings concentration.