Inversion-Based Auto Calibration for Resistivity Logging Tools
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Solution Overview
Problem
Conventional calibration methods for resistivity logging tools are costly and inaccurate, especially for multi-sub tools, due to repeated air-hang calibrations and varying formation environments, leading to errors in final answer products.
Innovation Solution
An inversion-based auto calibration system that calibrates both formation model parameters and calibration factors by using a consistent formation model for continuous logging points, fitting responses across multiple points, and producing calibration factors in real-time using inversion software, without requiring additional hardware or firmware changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional air-hang calibration is performed, then calibration cost is reduced, but calibration accuracy deteriorates because the calibration factor is not constant and varies with formation environment
Solution Approach 1:
The system performs self-calibration by using the measured signals themselves to determine calibration factors through inversion, eliminating the need for external calibration measurements in air or known formations. The tool calibrates itself automatically during the logging process by solving for calibration factors that minimize the misfit between measured and simulated signals.
Solution Approach 2:
The calibration factors are treated as variable parameters to be optimized through inversion rather than fixed constants determined by external calibration. The system allows calibration factors to change and adapt to different formation environments by solving for optimal values that minimize the cost function at each logging point or over a window of points.
2Measurement precision
If multiple calibration measurements are performed for multi-sub tools, then calibration accuracy may improve, but calibration cost and time increase significantly
Solution Approach 1:
The calibration process is merged with the formation evaluation inversion process. Instead of performing calibration as a separate preliminary step, the system combines calibration factor determination with the simultaneous inversion of formation model parameters, allowing both to be solved together from the measured signals during normal logging operations.
Solution Approach 2:
The calibration factors are continuously determined and updated as the tool moves through the formation, using a sliding window approach where inversion is performed over a window of continuous logging points. This provides continuous calibration rather than discrete calibration steps, maintaining accuracy throughout the logging process.
3Measurement precision
If in-situ calibration is performed, then calibration accuracy improves, but feasibility deteriorates because prior knowledge of known formation sections is required
Solution Approach 1:
The system performs self-calibration without requiring external calibration standards or prior knowledge of formation properties. The calibration factors are determined automatically from the measured signals themselves through inversion, making the method applicable to any formation type without needing pre-characterized sections for calibration.
Solution Approach 2:
The inversion-based calibration method is universally applicable to all logging situations and formation types. Unlike conventional methods that require specific calibration conditions (air or known formations), this method can determine calibration factors in any formation environment, making it adaptable to all well logging scenarios.
4Measurement precision
If calibration factor is treated as constant, then processing simplicity is maintained, but measurement precision deteriorates because calibration factor actually varies with formation environment
Solution Approach 1:
The calibration factor transitions from being treated as a fixed constant to being optimized as a variable parameter. The inversion process solves for calibration factors that minimize the misfit between measured and simulated signals, allowing the factors to adapt to varying formation conditions while maintaining a systematic processing approach through cost function minimization.
Data Source
AI summary
Systems and methods of the present disclosure relate to calibration of resistivity logging tool. A method to calibrate a resistivity logging tool comprises disposing the resistivity logging tool into a formation; acquiring a signal at each logging point with the resistivity logging tool; assuming a formation model for a first set of continuous logging points in the formation; inverting all of the signals for unknown model parameters of the formation model, wherein the formation model is the same for all of the continuous logging points in the first set; assigning at least one calibration coefficient to each type of signal, wherein the calibration coefficients are the same for the first set; and building an unknown vector that includes the unknown model parameters and the calibration coefficients, to calibrate the resistivity logging tool.