Abstract
Interpretation of sonic data acquired by logging-while-drilling tool or wireline tool in cased holes is complicated by the presence of drill pipe or casing because those steel pipes can act as a strong waveguide. Traditional solutions, which rely on using frequency bandpass filter or waveform arrival-time separation to filter out the unwanted pipe mode, often fail when formation and pipe signals co-exist in the same frequency band or arrival-time range. We hence develop a physics-driven machine learning-based method to overcome the challenge. In this method, two synthetic databases are generated from a general root-finding mode-search routine based on two assumed models, one is defined as a cemented cased hole for wireline scenario, another with a steel pipe immersed in a fluid-filled borehole for the logging-while-drilling scenario. The synthetic databases are used to train neural network models, which are first used to perform global sensitivity analysis on all relevant model parameters so that the influence of each parameter on the dipole dispersion data can be well understood. A least-squares inversion scheme utilizing the trained model was developed and tested on synthetic cases. The scheme showed good results and a reasonable uncertainty estimate was made for each parameter. We then extend the application of the trained model to develop a method for automated labeling and extraction of the dipole flexural dispersion mode from other disturbances. The method combines the clustering technique with the neural network model-based inversion and an adaptive filter. Testing on field data demonstrates that the new method is superior to traditional methods because it introduces a mechanism from which unwanted pipe mode can be physically filtered out.
This novel physics-driven machine learning-based method improved the interpretation of sonic dipole dispersion data to cope with the challenge brought by the existence of steel pipes. Unlike data-driven machine learning methods, it can provide global service with just one-time offline training. Compared with traditional methods, the new method is more accurate and reliable because the processing is confined by physical laws. This method is less dependent on input parameters; hence a fully automated solution could be achieved.