Estimation of High-Frequency Vibration Loads in Deep Drilling Systems Using Augmented Kalman Filters

Abstract

Deep drilling operations are primarily used to produce oil, gas, and geothermal heat from reservoirs in the earth’s crust. A drill string built of thread-connected components is used to transfer mechanical energy from a drill rig on the surface to a drill bit at the bottom end. The lowest part of a drill string, which is called bottom-hole assembly (BHA), contains sophisticated sub-assemblies for process and trajectory control, formation evaluation, surface communication, power generation, and system diagnostics. The BHA can experience critical vibrations without indication further up to the string. These vibrations need to be closely monitored for process control, fatigue management, and design feedback. However, the number of sensors is too small to provide reliable indication of loads on all critical components of the drill string. Adding sensors to each component is currently neither economically nor technically viable. This paper presents an application of existing Kalman Filters, merging information from available sensors and dynamic models to obtain state estimates for all components of the BHA. The expected accuracy and limitations are discussed. The results of load extrapolation are confirmed by comparison with measurements proving the concept under inaccurately defined interaction with a downhole environment.