MCI Logging Inversion for Formation Anisotropy
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Solution Overview
Problem
Current multi-component induction (MCI) logging methods are prone to errors in complex borehole environments due to the use of a radial one-dimensional forward model, leading to inaccurate resistivity anisotropy, dip, and strike measurements.
Innovation Solution
The method combines MCI measurements with other logging data, such as multi-arm caliper and directional logging, and employs an adaptive low-pass filtering technique and Software Focusing (SWF) processing to improve the accuracy of formation property determination, using a more realistic borehole-formation model that includes dip and anisotropic parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a radial one-dimensional forward model is used for MCI logging inversion, then the processing is simple and fast, but the measurement precision of resistivity anisotropy, dip, and strike deteriorates in complex borehole environments
Solution Approach 1:
The inversion process is divided into two distinct segments: first, a rapid radial 1D inversion is performed to obtain initial estimates of formation parameters; second, these initial estimates serve as starting values for a more sophisticated 3D inversion that accounts for complex borehole effects. This segmentation allows the system to benefit from both the speed of simple models and the accuracy of complex models.
Solution Approach 2:
The radial 1D inversion is performed as a preliminary step before the final 3D inversion. This preliminary action provides initial parameter estimates that guide the subsequent more accurate inversion process, reducing computational complexity while maintaining measurement precision in complex environments.
2Device complexity
If a radial one-dimensional forward model is used for MCI logging inversion, then the device complexity is low, but the reliability of results in complex borehole environments deteriorates
Solution Approach 1:
The inversion algorithm is segmented into two stages: a simple radial 1D inversion stage that provides initial parameter estimates, and a more complex 3D inversion stage that refines these estimates while accounting for borehole geometry effects. This segmentation manages device complexity while improving reliability.
Solution Approach 2:
The radial 1D inversion results serve as an intermediary step, providing initial parameter estimates that bridge the gap between simple data collection and complex final inversion. This intermediary process simplifies the overall computational burden while maintaining final result reliability.
3Loss of time
If MCI data is processed using conventional methods, then the processing time is short, but the vertical resolution of resistivity logs deteriorates due to horn effects
Solution Approach 1:
An adaptive low-pass filter is applied as a preliminary processing step to remove horn effects from the MCI data before inversion. This preliminary action preserves vertical resolution by eliminating artifacts that would otherwise require more complex and time-consuming post-processing corrections.
Solution Approach 2:
The processing approach changes the frequency domain characteristics of the data by applying an adaptive low-pass filter in the frequency domain. This parameter transformation removes high-frequency horn effects while preserving the essential formation information, improving vertical resolution without significantly increasing processing time.
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 enhances the speed and accuracy of determining formation properties, reduces horn effects, and improves the vertical resolution of resistivity logs, resulting in more precise horizontal and vertical resistivity measurements and dip angle analysis.
Implementation Method 1
Multi-component induction (MCI) logging is one of the logging methods used to analyze subterranean formations
Data Source
AI summary
Method and system for improving the speed and accuracy of determining formation properties using multiple logging data are disclosed. Logging data relating to the formation of interest is obtained and used as an input. High frequency noise is then removed from the logging data and bed-boundary determination is performed using the logging data. An adaptive low pass filter is applied to the logging data and the logging data is inverted. The inverted logging data is then visually interpreted.