KPN Hole Cleaning Monitoring in drilling with distributed sensors and hybrid methods

Effective hole cleaning to avoid the accumulation of cuttings during the drilling of wells is one of the prerequisites to reducing economic cost and environmental load. Current practice is mostly based on sophisticated physics-based calculations done before the operation starts, in some operations with real-time update during operations, and on human assessment of a limited number of measured parameters like for example trends in the net weight of the drill string as measured when lifting it up and lowering it down again. The introduction of high bandwidth data transmission from sensors at many positions along the drill string may, if teh measured values are interpreted accurately, give more accurate information on what goes on downhole. However, accurate and reliable interpretation of such measurements calls for mathematical methods that make full use of the many measured parameters downhole and topside. Accordingly, this project proposes to develop novel hybrid modelling approaches that will combine the interpretability, robust foundation and understanding of a physics-based modelling approach, with the accuracy, efficiency and automatic pattern-identification capabilities of advanced machine learning and artificial intelligence algorithms, for an efficient and improved monitoring of the hole cleaning process during drilling operations.

Effective hole cleaning during the drilling process is one of the prerequisites to reducing the incurred economic and environmental cost. Current practice is mostly based on sophisticated physics-based calculations done before the operation starts, in some operations with real-time update during operations, and on human assessment of a limited number of measured parameters like for example trends in hook load when picking up and slacking off the drill string when making connections. Introduction of high bandwidth data transmission from sensors at many positions along the string, calls for methods that make full use of the increasing number of measured parameters to determine hole cleaning status more accurately and reliably. Accordingly, this project proposes to develop novel hybrid modelling approaches that will combine the interpretability, robust foundation and understanding of a physics-based modelling approach with the accuracy, efficiency and automatic pattern-identification capabilities of advanced machine learning and artificial intelligence algorithms for more accurate and reliable interpretation of downhole and topside drilling data in real time. The methods will target efficient and improved monitoring of the hole cleaning process during drilling operations.