Autonomous Borehole Trajectory Estimation Using Sensor Fusion
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Solution Overview
Problem
Existing borehole drilling systems lack a standalone, autonomous system capable of real-time trajectory estimation with minimal positional uncertainty, especially for complex well trajectories, and are limited by the need for wireline communication and inadequate use of advanced Kalman Filter-based algorithms.
Innovation Solution
A system combining inertial and environmental sensor streams using multiple Kalman filters to differentiate between survey and continuous modes, incorporating three-axis MEMS gyroscopes, accelerometers, and magnetometers, along with drilling fluid pressure and temperature sensors, to estimate borehole trajectories and uncertainty ellipses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If wireline communication is used to provide measured depth and rate of penetration information, then basic Kalman Filtering can be performed, but the system cannot operate autonomously in real-time and requires surface processing only after the tool is run
Solution Approach 1:
The system performs autonomous self-calibration and self-processing using onboard processors and sensors. The Kalman filter algorithms execute independently downhole using locally acquired data from accelerometers, gyroscopes, and magnetometers, eliminating the need for wireline communication and surface processing. This self-service capability enables real-time trajectory estimation while drilling.
2Measurement precision
If only two gyro axes are used with synthesis from accelerometer or Earth rate, then device complexity is reduced, but measurement precision and reliability of the third axis deteriorates
Solution Approach 1:
The system merges data from multiple sensor types (three-axis accelerometers, three-axis gyroscopes, and three-axis magnetometers) to achieve complete three-dimensional trajectory estimation. By combining measurements from all three gyro axes directly rather than synthesizing, the system improves measurement precision while managing complexity through integrated sensor fusion algorithms.
Solution Approach 2:
The sensor package is designed as a multi-functional unit that performs multiple functions: accelerometers measure linear acceleration, gyroscopes measure angular velocity, and magnetometers measure magnetic field direction. This universal sensor suite enables comprehensive trajectory estimation without requiring separate specialized systems for each measurement type.
3Productivity
If Basic Kalman Filtering is performed only after the tool is run at the surface, then processing simplicity is maintained, but productivity and real-time navigation capability are reduced
Solution Approach 1:
The system performs preliminary calibration and data processing actions while the tool is stationary or during survey modes before continuous drilling begins. This preliminary action prepares the Kalman filter with accurate initial conditions and calibration parameters, enabling efficient real-time processing during drilling without requiring complex post-processing algorithms.
Solution Approach 2:
The Kalman filter implementation is dynamic, adapting its operation mode based on drilling conditions. During survey modes, the system performs more intensive calibration and filtering, while during continuous drilling, it operates in a streamlined real-time mode. This dynamic adaptation maintains productivity while managing algorithm complexity through conditional processing.
4Adaptability or versatility
If conventional vertical well systems are used, then system simplicity is maintained, but adaptability to complicated well trajectories deteriorates
Solution Approach 1:
The sensor package and processing system are designed as universal platforms that can handle any well trajectory type - vertical, directional, horizontal, or complex multi-planar paths. The three-axis sensors and full-state Kalman filter provide the mathematical foundation for calculating position and orientation in any configuration, making the system adaptable to complicated trajectories without requiring trajectory-specific hardware modifications.
Data Source
AI summary
Described is a system for estimating a trajectory of a borehole. The system processes signals of sensor streams obtained from an inertial sensor system. Using the set of processed signals, the system determines whether a drill is in a survey mode state or a continuous mode state, and a measured depth of the borehole is determined. A set of survey mode positioning algorithms is applied when the drill is stationary. A set of continuous mode navigation algorithms is applied when the drill is non-stationary. Using at least one Kalman filter, results of the set of survey mode positioning algorithms and the set of continuous mode navigation algorithms are combined. An estimate of a borehole trajectory and corresponding ellipse of uncertainty (EOU) is generated using the combined results.