Physics-Driven Machine-Learning-Based Borehole Sonic Interpretation in the Presence of Casing and Drillpipe

Abstract

Summary Interpretation of sonic data acquired by a logging-while-drilling (LWD) tool or wireline tool in cased holes is complicated by the presence of drillpipe or casing because those steel pipes can act as a strong waveguide. Traditional solutions, which rely on using a frequency bandpass filter or waveform arrival-time separation to filter out the unwanted pipe mode, often fail when formation and pipe signals coexist in the same frequency band or arrival-time range. We hence developed 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 on the basis of two assumed models: One is defined as a cemented cased hole for a wireline scenario, and the other is defined as 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 using 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 extended 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.