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A framework for combining physics-based and data-driven models to improve well construction is presented in this study. Additionally, the proposed approach provides a more robust and accurate model that mitigates the disadvantages of using purely physics-based or data-driven models. This approach can provide improved model-based control of drilling rig actuators (assocated with mud pumps, pipe handling systems, etc.). Traditionally, models based on physics including Hagen-Poiseuille flow, Hooke’s law, etc. are used during well construction. Physics-based models facilitate the design of the drilling plan and are vital to safely and successfully drilling wellbores. There are two major shortcomings, however, to using purely physicsbased models. First, the models can be inaccurate if the physical dynamics are not fully accounted for. Accurately capturing data to describe these processes can be involved, complex or prohibitively expensive. Second, these models must be maintained and calibrated during drilling, which requires a large amount of operator input and is liable to human error. On the other hand, pure datadriven approaches are unable to represent underlying mechanism dynamics and often struggle to properly capture causal relationships. It is shown in this work combining physics and data-driven modeling provides a more robust framework for well planning and execution. Machine learning techniques are combined with physics-based models via a rule-based stochastic hidden Markov model, using the modeling of frictional pressure losses during fluid circulation in the well as an example. Gaussian processes, neural networks and a deep learning model are trained and executed together with a physics model that is directly derived using first principles. The results show that combination modeling can accurately predict the pressure losses even outperforming the physics-based and purely data-driven modeling. The proposed approach has a good potential to allow safer, optimized well construction operations.

Combining Physics-Based and Data-Driven Modeling for Pressure Prediction in Well Construction