Multi-Array Laterolog Invasion Depth Estimation
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Solution Overview
Problem
Existing multi-array laterolog tools face challenges in accurately determining true formation resistivity and invasion zone characteristics due to the ill-posed problem of calculating these parameters from single measured values, which requires significant processing resources and is sensitive to initial guess values.
Innovation Solution
A method and system that utilize a one-dimensional optimization procedure to estimate invasion depth from multi-array laterolog measurement data, followed by a three-dimensional or two-dimensional optimization to calculate formation and invasion resistivity, reducing reliance on accurate initial values for these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sets of guard electrodes at different spacings are used to enable multiple depths of investigation, then the depth of investigation is improved, but the device complexity increases
Solution Approach 1:
The tool is divided into multiple independent electrode arrays, each with different guard electrode spacings. Each array can be independently activated to measure at specific depth intervals, allowing the system to achieve multiple depths of investigation without requiring all electrodes to be active simultaneously, thus managing complexity through modular segmentation.
Solution Approach 2:
The multi-array laterolog tool is designed to perform multiple measurement functions using a single integrated tool body. By incorporating multiple sets of guard electrodes at different spacings within one tool, the system achieves universal capability to measure formation resistivity at various depths of investigation, eliminating the need for multiple separate tools.
2Measurement precision
If iterative optimization procedures are used to calculate formation and invasion resistivity, then the measurement precision is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary measurements using electrode arrays with large spacings to obtain rough estimates of formation and invasion resistivity values. These preliminary results are then used as initial guesses for the iterative optimization procedure, significantly reducing the number of iterations needed and thereby decreasing processing time while maintaining measurement precision.
Solution Approach 2:
The optimization procedure uses feedback from measured resistivity values to iteratively adjust and refine the estimated formation and invasion resistivity parameters. By continuously comparing measured data with calculated values and adjusting parameters accordingly, the system converges on accurate results more efficiently, reducing processing time through intelligent feedback-driven optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides accurate and efficient estimation of invasion depth and resistivity values, even with inaccurate initial guesses, and accelerates convergence in optimization processes, enhancing computational efficiency and robustness.
Implementation Method 1
Some embodiments relate particularly to methods and systems for determination of formation resistivity using multi-array resistivity measurement data
Implementation Method 2
The tool drives auxiliary currents between the guard electrodes and the central electrode to focus the current from the center electrode
Data Source
AI summary
An estimated value for invasion depth of an invasion zone in a subsurface measurement zone is calculated in a one-dimensional optimization procedure based on multi-array laterolog measurement data. A one-dimensional optimization problem is defined as having the invasion depth as a sole variable measurement zone parameter. The one-dimensional optimization problem is then solved by automated, iterative modification of the invasion depth value. The one-dimensional optimization problem can be a function to minimize a misfit error between (a) multi-array measurement values for resistivity of the subsurface measurement zone, and (b) predicted measurement values calculated in accordance with a simulated measurement zone model based at least in part on the invasion depth. In one embodiment, the optimization function defines a misfit error between (1) normalized differences between respective measurements of neighboring measurement arrays of the multi-array laterolog tool, and (2) normalized differences between respective predicted measurement values for neighboring measurement arrays.