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Analytical and numerical modeling of through-tubing acoustic logging

Published in: Dec. 01, 2020

Edited by: IBP - Instituto Brasileiro de Petróleo e Gás

Languages: English, Portuguese.

Abstract

Presently, prior to plug & abandonment (P&A) operations, inspection of cement quality executed through acoustic logging techniques requires the removal of the production tubing. Hence, there has been an increasing interest in the industry to provide technological solutions that allow the assessment of cement sealing quality by performing through-tubing logging runs, thus reducing costs associated with P&A operations. This contribution addresses the problem of modeling acoustic wave propagation in multistring wells using both analytical and numerical techniques. Analytical results obtained in the frequency domain for the dispersion spectra of guided waves propagating in the wellbore were employed to validate the numerical simulations via the Finite Element Method (FEM). The effect of different defects in the cement sheath on the dispersion of guided waves in the multistring wellbore and acoustic response to a monopole source were investigated. Results of simulations demonstrated that it is possible to distinguish data from cases with and without the defects.

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Machine learning-based cement integrity evaluation with a through-tubing logging experimental setup

Published in: Aug. 2023

Edited by: Science Direct

Languages: English.

Abstract

Assessing the integrity of the cement layer and the quality of its bond to the casing and formation is paramount to ensure that the wellbore is hydraulically isolated from the surrounding environment before permanently sealing the well. Such inspection is part of Plugging & Abandonment (P&A) operations, and it is usually achieved through well logging tools, which provide a vast amount of data that a skilled specialist interprets. The process is human-dependent, error-prone, and time-consuming. There has been an increasing interest in solutions that allow an automatic interpretation of the logging data since the recent panorama of the oil and gas industry indicates a growing P&A demand. Such solutions aim at reducing the dependence on human knowledge and consequently increasing the reliability and accuracy of the cement integrity evaluation. Therefore, this work presents an experimental setup capable of emulating defective cement layer configurations of real-world oil wells in single or multi-string arrangements. Such flexibility enables acquiring logging data for multiple well conditions and building a rich logging database to develop a supervised learning framework and define the most suitable model for performing the cement integrity evaluation. Several logging runs were performed, producing a database with 130 samples, including varied tubing eccentricity levels and cement layer conditions. A complete analysis of the data both in the time domain as well as in the frequency–wavenumber domain was performed, highlighting the complexity of the interpretation task. A resampling-based workflow was employed to evaluate machine learning models of different families. The models were tested under three scenarios, and accuracy and computational complexity metrics were computed to compare their performance. The results showed that shallow learning models can perform satisfactorily well even with less data available for training. The support vector machine stood out, achieving a mean accuracy score higher than 0.99 while being able to predict the cement sheath’s condition in less than 1 ms. This paper contributes to the research on the cement integrity evaluation by presenting a study that combines an experimental setup mimicking several oil well conditions and the employment of machine learning as a diagnostic tool, which has no precedents in recent literature regarding the acoustic logging knowledge field.

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Machine Learning Assisted Cement Integrity Evaluation During Plugging and Abandonment Operations

Published in: Oct. 5, 2023

Edited by: One Petro

Languages: English.

Presented at ADIPEC

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.

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Supervised Machine Learning Applied to Cement Integrity Assessment – A Comparison Between Models and Feature Extraction Techniques

Published in: Feb 27, 2024

Edited by: One Petro

Languages: English.

Presented at IADC (USA)

Summary

The analysis of the interpretation of the integrity of the bond of the cement layer between the casing and the formation in oil wells has grown significantly as plugging and Abandonment operations also grow. Interpreting this analysis is important to ensure that the well is hydraulically isolated from the surrounding environment before permanently sealing the well. However, this interpretation depends on a specialist's ability to analyze a large demand of data, which is complex and acquired through logging operations. This fact makes this process prone to errors, human dependence, and time-consuming. These three challenges motivated Ouronova, in partnership with Repsol Sinopec Brazil, to develop software to help interpret acoustic profiling. Also inspired by the objective of optimizing the Plugging and Abandonment operations completely, the software also optimizes the planning and management of Plugging and Abandonment campaigns. The so-called plug and abandonment (P&A) Assistant software has proven to be a good tool that optimizes the Plugging and Abandonment process, using machine learning (ML) techniques to improve the accuracy and reliability and reduce the time for cement sheath integrity analysis. This paper then presents some supervised method techniques implemented in the software, such as Logistic Regression, k-Nearest Neighbors, Decision Trees, Random Forest, and Gaussian Naive Bayes. The results show that combining features derived from Cement Bond Log and Acoustic Impedance Log enables the construction of efficient models. The Gaussian model was the one with the best overall performance, achieving a Balanced Precise Accuracy equal to 0.50 and a Balanced Adjacency Accuracy around 0.88.

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AI-Based Cement Bond Quality Assessment: A First Step for Optimizing P&A Design

Published in: May 08, 2024

Edited by: One Petro

Languages: English.

Presented at OTC (USA)

Summary

As decommissioning operations continue to expand, the challenges associated with evaluating the integrity of the cement layer and its bond to casing and formation become more pronounced. Ensuring hydraulic isolation of the wellbore from the surrounding environment is crucial before permanently sealing the well. However, the current methodology relies on the expertise of specialists who interpret extensive and intricate data obtained through logging operations. Recognizing the challenges inherent in cement evaluation, Ouronova, in collaboration with Repsol Sinopec Brazil, is developing a computational solution to help specialists interpret cement integrity logging data. Simultaneously, the developed tool aims to assist operators in optimizing the planning and management of decommissioning campaigns. The innovative software employs machine learning techniques that have exhibited significant promise in enhancing accuracy, reliability, and efficiency in the analysis of cement sheath integrity. Thus, the objective of this paper is to present some results obtained with the software by using Convolutional Neural Networks to predict the cement condition in two wellbore regions. The acquired dataset was used to generate Variable Density Logs diagram and plots here referred to as 2D Combined Signals, which were used as inputs to train the model. The main results indicate good accuracy in predicting the cement condition using the Variable Density Log and the 2D Combined Signals. In special, the latter showed to be a more promising option because its accuracy value tended to be more stable as the database was increased, in comparison with the Variable Density Log case. As a metric for the comparisons, the Balanced Adjacency Accuracy was used. For the results based on the Variable Density Log, we found a value of 0.810, while for the ones based on the 2D Combined Signals, we found 0.958.

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

Published in: May 03, 2024

Edited by: Science Direct

Languages: English.

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.

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Computational method for detecting and estimating cementing failures in oil well casings by acquiring acoustic profile signals through the production column based on machine learning and high fidelity simulations

INPI BR 10 2021 018581 3

Summary

The present invention proposes the use of high-performance numerical simulation fidelity of the guided wave phenomenon through the production string, in conjunction with supervised learning methods, in a fully automated and data-driven approach, for detecting the quality of casing cement in oil wells through the production pipe. For this purpose, a set of simulations is adopted, under the most common conditions of cement quality failure, in order to carry out predictive modeling based on machine learning. In this way, the model created is capable of interpreting the complex patterns generated from this physical phenomenon, generating value to assist the decision maker in the task of interpreting acoustic profiling data. The ultimate objective is to isolate and identify cement defects in wells, based on acoustic waves guided through the production column, which generate signals that are difficult to interpret when compared to cases of simple casings, which therefore require highly qualified professional training, in an intensive and error-prone task.

EN

PT

Published in: Dec. 01, 2020

Abstract

Published in: Aug. 2023

Abstract

Published in: Oct. 5, 2023

Summary

Published in: Feb 27, 2024

Summary

Published in: May 08, 2024

Summary

Published in: May 03, 2024

Abstract

Summary