Generating Value from Mature Gas Fields by Quantifying Well Integrity Assurance with a Critical Analysis of Multiple Logs & Retrieved Tubular Surface Inspection

Sharjah National Oil Corporation (SNOC) operates 4 onshore gas condensate reservoirs of which 3 are very mature consisting of 50+ wells producing corrosive hydrocarbons for over 30 years. The integrity of these legacy wells is frequently questioned before any development is conceptualized, thus making it critical to evaluate the well integrity. The cost associated with pulling completions for their evaluation and running logs in all wells is significant and the availability of various emerging technologies for corrosion analysis in the market makes it challenging to choose the most reliable one. This paper focuses on the detailed analysis and comparison of electromagnetic thickness logs run in 10% of the well stock from 2016 to post-workover surface inspection of the downhole recovered tubing's in 2020/21. It also quantifies how correlating different logging technologies for well integrity increases the reliability of the electromagnetic technology applied on offset wells. The paper also showcases a comparison between mechanical and electromagnetic thickness evaluation of the production casing in-situ. Data from all the available logs from past 5 years was compiled for 6 wells. On recovery of the downhole completion tubings via a hydraulic workover, an ultrasonic (UT) inspection was performed on them at surface. Both sets of results (logs and surface inspection) were analyzed on the same logging track to give a comprehensive comparison of actual observation on surface vs the measurement by in-situ logging. Another multi-barrier corrosion and caliper log were run in the production casing to analyze their outcomes alongside older results. The final step was a comparison of all available data to create a broad well integrity profile. It was observed that the remaining production tubing metal thickness detected by electromagnetic tool (logs) and surface ultrasonic measurements were in good conformance (+/-10%). In the corrosion evaluation of the production casing, the electromagnetic tool matched extremely well with the caliper log results. This shows a large reliability of this technology to quantify corrosion in offset wells. The correlation of logs with surface inspection results across wells in the same reservoir did not indicate a strong presence of external corrosion. The study enables the management to make critical business decisions on utilizing the well stock for the future. This work is the first time a comprehensive and critical analysis on the electromagnetic thickness logging technology has been done, comparing their results of remaining wall thickness to various technologies in-situ and on surface. The analysis not only compares technology from various providers, but also mechanical vs electromagnetic measurements along with their respective advantages in quantifying well integrity assurance. The paper also gives an idea on the condition of L-80 tubulars under service for 30+ years.

Understanding Eddy Current Array for High-Performance Inspections

Eddy current testing (ECT) has been used for quite a while now and has been proven a reliable surface inspection technique for conductive materials. In the last 15 to 20 years, this technique has evolved toward the use of eddy current arrays (ECAs), and many applications can now benefit from this configuration to improve data quality, inspection speed, and ease of deployment, and considerably reduce operator dependency. The physics principle behind ECT and ECA is the same and was addressed in a previous issue of Materials Evaluation (Wassink et al. 2021). In this paper, we will discuss the main differences between ECT and ECA as well as how the arrangement of coils in an array can allow for optimized detection capabilities on different materials or types of defects. Common applications where ECA has demonstrated its strength will also be discussed.

Defect segmentation for multi-illumination quality control systems

Thanks to recent advancements in image processing and deep learning techniques, visual surface inspection in production lines has become an automated process as long as all the defects are visible in a single or a few images. However, it is often necessary to inspect parts under many different illumination conditions to capture all the defects. Training deep networks to perform this task requires large quantities of annotated data, which are rarely available and cumbersome to obtain. To alleviate this problem, we devised an original augmentation approach that, given a small image collection, generates rotated versions of the images while preserving illumination effects, something that random rotations cannot do. We introduce three real multi-illumination datasets, on which we demonstrate the effectiveness of our illumination preserving rotation approach. Training deep neural architectures with our approach delivers a performance increase of up to 51% in terms of AuPRC score over using standard rotations to perform data augmentation.

REVIEW ON PHOTOGRAMMETRIC SURFACE INSPECTION IN AUTOMOTIVE PRODUCTION

When purchasing a premium car for a substantial sum, first impressions count. Key to that first impression is a flawless exterior appearance, something self-explanatory for the customer, but a far greater challenge for production than one might initially assume. Fortunately, photogrammetric technologies and evaluation methods are enabling an ever greater degree of oversight in the form of comprehensive quality data at different automotive production stages, namely stamping, welding, painting and finishing. A drawback lies in the challenging production environment, which complicates inline integratability of certain technologies. In recent years, machine vision and deep learning have been applied to photogrammetric surface inspection with ever increasing success. Given comprehensive surface quality information throughout the entire production chain, production parameters can be dialed in ever tighter in a data-driven fashion, leading to a sustainable increase in quality. This paper provides a review of current and potential contributions of photogrammetry to this end, discussing several recent advances in research along the way. Particular emphasis will be placed on early production stages, as well as the application of machine vision and deep learning to this challenging task. An outline for further research conducted by the authors will conclude this paper.