Fault Diagnosis For Drilling using a Multitask Physics-Informed Neural Network

Mechanical faults, mud loss, and insufficient cuttings transport bear significant costs and can appear at unpredictable times during drilling operations. Early detection and diagnosis of such faults to support decisions is essential to avoid severe consequences and lengthy delays. We propose a novel system for Drilling Fault Diagnosis that combines principles of Physics-Informed Neural Networks (PINN) and Multitask Learning (MTL). Since measurements down-hole in the well are rarely available in real time, our proposed system uses measurements of flow and pressure at the drilling rig, only, from which type of fault and accompanying diagnostics such as depth in the well of the fault and its severity are predicted. State-of-the-art strategies of MTL with PINNs are deployed for Effective Neural Network (NN) training. Generalization performance is shown to be high as evaluated using randomly generated values for the diagnostic variables. Drilling data collected during normal drilling-ahead conditions may be utilized in the training phase to identify uncertain characteristics of the well, thereby increasing the quality of the physics prior available to the PINN, and in turn improving prediction accuracy of faults. The potential usefulness of the method is illustrated in a simulation, admittedly under quite ideal conditions.