Machine Learning Assisted Cement Integrity Evaluation During Plugging and Abandonment Operations

Abstract

Summary Due to the growth of Plugging and Abandonment operations, the challenges of assessing the integrity of the cement layer and the quality of its bond to the casing and formation increase consequentially. Hence, it is paramount to ensure that the wellbore is hydraulically isolated from the surrounding environment before permanently sealing the well. However, nowadays, this process depends on the skills of a specialist interpreting a vast amount of complex data acquired through logging operations, which turns the task human-dependent, error-prone, and time-consuming. Motivated by that cement evaluation task, ouronova, in partnership with Repsol Sinopec Brazil, is developing a computational tool to interactively assist the specialist in interpreting cement integrity logging data and the operator in optimizing the planning and management of Plugging and Abandonment campaigns. The so-called P&A Assistant software uses machine learning techniques that, through the work done so far, have shown to be a promising alternative to improve the accuracy and reliability and reduce the time of the cement sheath integrity analysis. The software is also prepared to work with logging data acquired in a through-tubing configuration, which represents a reduction in operational cost and time. The paper presents the software's initial module, presenting three different unsupervised methods (K-means, Bisecting K-means, and Gaussian Mixture Model) and input feature combinations, with the aim of optimizing the model. The main results of the work indicate that the methods implemented using the Cement Bond Long channel and Bond Index channel have better results when compared to the models combined with Variable Density Log and AIBK, with values above 0.7 for Rand Index and 0.5 for Silhouette Coefficient. For the unsupervised methods, the K-mean model had the best performance.