Automatic Model Calibration for Drilling Automation

Abstract Physics-based hydraulic models are essential for proceeding to a high level of automation in drilling. Mathematical models can facilitate process understanding and problem detection, and determine appropriate actions in case of mismatch between model and data. Furthermore, calculations may replace measurements where and when the latter are not available, as normally occurs during connections or when instruments or signal transmissions fail. However, advanced hydraulic models rely on a large set of inputs, such as pipe and wellbore geometry, various tuning parameters and fluid properties. The models are therefore time-consuming and difficult to configure in the field, where third-party experts may be needed at each well, to properly initiate the automation system and adjust it during the drilling process. Although the methods described in this paper are relevant to any critical drilling operation, they are applied to Managed Pressure Drilling (MPD) as a widely deployed example of drilling automation. In MPD, hydraulic models predict downhole conditions and determine the requisite choke pressure for automatic adjustment. A new method for automatic configuration of key model parameters simplifies the tedious job of setting up the model and ensures that the automation system remains tuned to the well, even without onsite model tuning expertise. The proposed scheme is based on a simple method for separating inaccuracies due to co-linearity in frictional pressure losses and static mud weight. The search for optimal correction factors is based on a sequence of small oscillations of pump rate that can be applied during drilling without interrupting the operation. A massively parallel computing architecture improves the speed of the calibration algorithm proportional to the number of available CPU cores. A set of hydraulic model instances runs in parallel, allowing for efficient testing of changes in input signals within ranges of uncertainty. A method for selecting a subset of the best models that more accurately represent a given well is proposed. Computer simulations demonstrate how the novel calibration scheme allows automatic tuning of the friction factor and density correction factor, giving accurate prediction of the bottom hole pressure (BHP). The tuning scheme is run with a parallel architecture to demonstrate that correct values of unknown configuration parameters can be automatically determined sufficiently fast for real-time drilling control or as an advisory tool. The deployment of automation systems in drilling is hampered by the need for dedicated expert personnel to maintain systems that could have reduced the personnel needed on the rig. The proposed automated physics-based model tuning contributes to removing this roadblock, aiming at making automation systems a more cost-efficient option for drilling operations.