Microstructure Refinement on Crevice Corrosion of High-Speed Rail Steel U75V Visualized by an In Situ Monitoring System

Rail foot covered by a fastener will suffer from crevice corrosion, leading to thinning and localized attack of crevice interior posing a risk of failure. This work investigated crevice corrosion behavior of a typical pearlitic high-speed rail steel U75V, focusing for the first time on the effect of pearlitic microstructure refinement achieved by heat treatment with different cooling rates 2, 5, and 10°C/s. Under anodic polarization, localized dissolved spots presented on the as-received sample, where crevice corrosion mostly initiated from. For cooling rates 2 and 5°C/s, localized dissolved spots were also observed but crevice corrosion was mostly presented as general corrosion instead of from local spots, ascribed to enhanced tendency of uniform dissolution due to microstructure refinement and homogenization. For cooling rate 10°C/s, crevice corrosion expanded flocculently, ascribed to preferential dissolution of pearlitic nodules with entangled cementite due to over refinement. Crevice corrosion was obviously accelerated by microstructure refinement. Cooling rates 5 and 10°C/s led to the fastest and slowest expansion of the corroded area, respectively, while the corrosion depth was just the opposite based on the same amount of metal loss. This work provides important information regarding the effect of pearlitic microstructure refinement on crevice corrosion and introduces a facile method for in situ monitoring of crevice corrosion.

The Effect of Both Moisture and Clay Content on The Soil Corrosion Process for Different Periods of Time as A Geomorphological Study in Al-Kut City

Soil corrosion is a major hazard to subterranean infrastructure including gas and oil transmission pipes, underground storage tanks and others. The impacts of soil engineering characteristics on buried mild steel coupons’ metal loss are investigated in this work. Soil characteristics such as soil clay and moisture content are the focus of the present research in Al-Kut city near Tigris River. For a twelve month period, 100 pieces of mild steel coupons were put underground in five different sites across to look into the effects of the aforementioned variables on loss of metal owing to corrosion of soil. Every three months, the samples were recovered to evaluate the rate of weight loss and corrosion rate development. The data show that the high moisture content of the soil is linked to rapid corrosion development. Corrosion on clay soil, on the other hand, takes longer to start. According to the qualitative assessment, soil moisture content has a greater impact on corrosion dynamics than clay content.

A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.

Thermal Wellbore Failure Detection Case Studies: A High Temperature Multi-Finger Caliper Surveillance Approach

Cooling of thermal wellbores such as steam assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) wells, is a common prerequistite to allow deployment of logging instruments due to the temperature limitation of imaging instruments’ electronics (<150°C). This paper presents a memory caliper technology housed in a thermoshield that can perform at up to 220°C, with the acquired data used to evaluate the integrity of tubulars and completion items (metal loss, deposition, deformation, and gap/hole damage), negating the need for cooling before deployment. Two cases are presented. One is a SAGD well with liner screens across the lateral section. The memory multi-finger caliper was deployed using coiled tubing and the data were successfully obtained across the lateral section with a maximum recorded temperature of 169°C. The second example is a vertical well in a steam flood field. Because of the uncertainty over the downhole temperature at the time of the well intervention, a temperature sensor was deployed in surface read-out mode above the caliper. This ensured the 220° temperature limit of the caliper would not be breached, and a maximum temperature of 208°C was recorded. The data confirm the feasibility of acquiring high accuracy/high resolution data from thermal wellbores without having to resort to manipulative cooling techniques to attain a temperature below 150°C. Enlargement of a limited entry perforation (LEP) was observed in the horizontal well and buckling was clearly detected in the vertical well. The broad measurement range of the caliper – 1.85" – 7.2" – enabled both the tubing and liner to be logged in a single well intervention, which facilitated a swift resumption of of steam injection activities. Ultimately, the high temperature MFC's ability to minimize deliberate cooling the thermal wellsbore before deployment, has time and cost saving implications throughout the life cycle of the well. Much of the existing literature examining downhole data acquistion in thermal wells, for the diagnosis of wellbore integrity issues, has relied on technologies that are unable to withstand temperatures much greater than 150°C. The ability to execute well interventions for data acquistion at higher temperatures offers the potential for empirical studies that compare the status and integrity of the wellbore completion in thermal and cooled conditions.

Signatures of AGN-induced Metal Loss in the Stellar Population

One way the active galactic nuclei (AGN) are expected to influence the evolution of their host galaxies is by removing metal content via outflows. In this article we present results that show that AGN can have an effect on the chemical enrichment of their host galaxies using the fossil record technique on CALIFA galaxies. We classified the chemical enrichment histories of all galaxies in our sample regarding whether they show a drop in the value of their metallicity. We find that galaxies currently hosting an AGN are more likely to show this drop in their metal content compared to the quiescent sample. Once we separate the sample by their star-forming status we find that star-forming galaxies are less likely to have a drop in metallicity but have deeper decreases when these appear. This behavior could be evidence for the influence of either pristine gas inflows or galactic outflows triggered by starbursts, both of which can produce a drop in metallicity.

Combination of New Acoustic and Electromagnetic Frequency Technologies Detects Leaks Behind Multiple Casings. Case History

Although leakages in well tubulars have always existed, their occurrence has become very frequent as the number of active wells in mature fields increases. The catastrophic risk of these leaks is an increase in the number of environmental accidents in the oil and gas industry. One of the fundamental causes of leaks is corrosion, which plays a negative role in the productive life of the wells. Generally, these environmental events are associated with surface or near-surface sources. Since multiple casing strings exist within this depth range, the identification of the leak location becomes extremely difficult. In view of this, the industry has put much effort in improving and new technology to be more precise and comprehensive in diagnosing these leaks. The evolution of two of such technologies will be addressed in this paper. The first one is a new electromagnetic high-definition frequency tool for pipes and multiples casing for metal loss detection. This state-of-the-art technology is a noticeable improvement over existing tools, due to an important increase in the number of sources, number of detectors and wide range of working frequencies. The combination of these changes allows for the evaluation of metal loss in up to 5 concentric casings in a single run. Furthermore, the tool is small in diameter which makes it compatible with production pipes without the need of a workover rig. This versatility obviously helps in the preworkover diagnosis before deciding to move a rig to location to eventually remedy any leak problems. The electromagnetic technology is complemented, with the latest leak detection acoustic technology. A spontaneous audio source is normally associated with downhole fluid movements. The tool has an array of 8 hydrophones with a working frequency range from 100 Hz to 100 KHz. These two different technologies based on independent fundamental principles, allows for the detection of leaks in multiple concentric pipes with great vertical and radial precision to identify the exact location of leaks as small as to 0.02 L/min. the depth of investigation of the system is up to 10 feet. Therefore, it is possible to detect fluid movement within the formation. Pulsed neutron technology was included in the study to detect water movement behind the casing to establish the flow path to the surface in addition to the leak point. A very complex acquisition program was established that was undoubtedly a key success factor in the results obtained. The electromagnetic tool determined the depth of severe casing metal loss in 7-inch casing, also the acoustic tool detected the noise of fluid movement in the 7-inch annulus, and the pulsed-neutron tool showed the beginning of water movement at the same interval the temperature log, also included in the same tool string showed a considerable change that correlated with all these logs, indicating the point of communication in this well. After establishing the uniqueness of the solution, this diagnosis helped the operator define an intervention plan for this well, and to make the appropriate corrections in the field development strategy.

EM-Based 2D Corrosion Azimuthal Imaging using Physics Informed Machine Learning PIML

In the wake of today's industrial revolution, many advanced technologies and techniques have been developed to address the complex challenges in well integrity evaluation. One of the most prominent innovations is the integration of physics-based data science for robust downhole measurements. This paper introduces a promising breakthrough in electromagnetism-based corrosion imaging using physics informed machine learning (PIML), tested and validated on the cross-sections of real metal casings/tubing with defects of various sizes, locations, and spacing. Unlike existing electromagnetism-based inspection tools, where only circumferential average metal thickness is measured, this research investigates the artificial intelligence (AI)-assisted interpretation of a unique arrangement of electromagnetic (EM) sensors. This facilitates the development of a novel solution for through-tubing corrosion imaging that enhances defect detection with pixel-level accuracy. The developed framework incorporates a finite-difference time-domain (FDTD)-based EM forward solver and an artificial neural network (ANN), namely the long short-term memory recurrent neural network (LSTM-RNN). The ANN is trained using the results generated from the FDTD solver, which simulates sensor readings for different scenarios of defects. The integration of the array EM-sensor responses and an ANN enabled generalizable and accurate measurements of metal loss percentage across various experimental defects. It also enabled the precise predictions of the defects’ aperture sizes, numbers, and locations in 360-degree coverage. Results were plotted in customized 2D heat-maps for any desired cross-section of the test casings. Further analysis of different techniques demonstrated that the LSTM-RNN could show higher precision and robustness compared to regular dense NNs, especially in the case of multiple defects. The LSTM-RNN is validated using additional data from simulated and experimental data. The results show reliable predictions even with limited training data. The model accurately predicted defects of larger and smaller sizes that were intentionally excluded from the training data to demonstrate generalizability. This highlights a major advance toward corrosion imaging behind tubing. This novel technique paves the way for the use of similar concepts on other sensors in multiple barriers imaging. Further work includes improvement to the sensor package and ANNs by adding a third dimension to the imaging capabilities to produce 3D images of defects on casings.