HOG-CNN based evaluation of cement integrity using 2D dispersion curves from an experimental through tubing logging setup

Abstract

In oil wells, adequate cementation is essential to guarantee that the well has support for the casing and is able to isolate the well from groundwater. Operations known as Plugging & Abandonment (P&A) must carry out inspections in the cement layer of the well to ensure that the bond quality of the cement with the formation and the hydraulic isolation is adequate before well sealing. The interpretation of data logs during the decommissioning processes is made exclusively for a petrophysicists team, which makes the process susceptible to human errors. Furthermore, it requires a lot of analysis time, increasing the search for alternative solutions that help reduce errors in the interpretation of data logs. So far, the use of machine learning has proven to be a strong candidate in this search. This paper deals with frequency domain images (dispersion curves and Cepstrum) that are acquired from acoustic pressure signals obtained in the time domain and along the vertical axis of an experimental setup. These images are used to train machine learning models to test and compare the accuracies obtained. An adjustment of the hyperparameters was carried out to find those that best describe the simulated experimental characteristics. Furthermore, the convolution neural network (CNN) model was combined with the oriented gradient histogram technique (HOG) to verify its efficiency by analyzing experimental data. Two different databases were used in this work, one with 130 samples relating to the average results of the four hydrophones used in the experiment and one with 520 samples relating to all results. The results found demonstrated that when using the dispersion curves as input with the CNN the accuracy hits 100%. Other combinations of models were also tested hitting similar results. This article contributes to the current research scenario as it presents an innovative combination of machine learning techniques applied to the oil and gas field (HOG-CNN) with the proposal to compare different types of databases, such as using Cepstrum instead of dispersion curves and image processing.