Real-Time Prediction and Detection of Drilling Bit Issues Based on Along-String Measurements (ASM) Along Wired Pipes - A Case Study

When it comes to optimizing drilling, the focus is on running the bit into the well and performing the drilling efficiently. Included in this are methods for optimizing rate of penetration (ROP), determining the right time to change drilling bits, and managing bit run to reduce other drilling costs, such as tripping, hole conditioning, material consumption, and detecting drilling problems at the right time. The present study employs a new approach to drilling bit modeling that utilizes along-string measurement (ASM) data to continuously monitor the status of the drilling bit. A two-pronged approach is employed in the monitoring of drilling bit condition in addition to estimating rock drillability to keep track of change in lithology. First step involves developing a model for polycrystalline compact drilling (PDC) bits. It examines micro forces at the bit cutters and then upscales these forces to parameters applied to the drilling bits, such as weight and torque. Upscaling involves geometric remodeling of bits as equivalent cutters and equivalent blades. In the second part, a data-analytic approach is used to combine continuous measurement of downhole data with the developed experimental-based model. The real-time data is measured by using an along-string measurement system on the wired pipe. The results of this approach can be grouped into three categories. First, the drilling bit condition is estimated in real time in each equivalent cutter. A quantitative assessment could be undertaken based on model output, or a qualitative assessment could be carried out by analyzing specific energy. Having knowledge of the status of bit, the second conclusion is to monitor rock drillability according to variations in specific energy at the bit and publishing numerical value of rock drillability. In addition, the last corollary is to generate knowledge regarding drill string dynamics and the way to differentiate between vibration at the bit and at the drill string. In this paper, however, the first two outcomes are addressed. This approach is tested on a set of ASM data captured during drilling operations on the Norwegian continental shelf. The results are consistent with those reported from the field. Currently, the selection and evaluation of drilling bits requires knowledge of nearby well records. A drilling penetration rate model that requires calibration for a specific field may also be used to estimate bit condition in some cases. This research presents a new bit status simulator that overcomes the limitations of existing techniques by applying a delicate and intelligent application of ASM data to predict drilling events and mitigate them in real-time.