Several light oil and gas reservoirs are present within an extensive Permian clastic sequence in the Middle East. Well logs and core data revealed numerous challenges for the petrophysical description of this formation. Rapidly changing depositional environments and diagenetic effects caused heterogeneities in grain size and sorting within clean sands. Consequently, gamma ray and conventional porosity logs have little sensitivity to rock quality variations. Secondly, an influx of meteoric water into the reservoir rocks decreased formation water salinities which adds uncertainties to the estimation of fluid saturations from resistivity logs. Finally, gas-oil contacts are present in some reservoirs where log-based in-situ hydrocarbon typing is of great practical value. The introduction of nuclear magnetic resonance (NMR) logs has successfully mitigated these issues as evidenced by petrophysical interpretations and formation testing in the area's exploration and delineation wells. Lateral facies variations and complicated reservoir structures warranted the deployment of NMR technology in horizontal development wells for better well placement and completion optimization with logging while drilling (LWD) NMR as the most preferable option. The NMR logs led geosteering decisions and proactive well planning to singificantly increase reservoir contacts in producer wells. Permeability predicted by the NMR logs is of great value in real-time well placement decisions and completion design including ICD installations. The low-gradient LWD NMR measurement gives rise to a very simple and robust real-time fluid identification thanks to the the good separation of water and oil signals in the NMR T2 spectrum. This fluid quantification, combined with bound fluid analysis, helps determine the well's position in the transition zone by detecting free water. This paper summarizes the experience with both wireline and LWD NMR technologies in the area. Lessons learned include considerations for deployment, tool activations and NMR interpretation.
Well integrity management is a prime global focus area for all oil and gas operators. Any field-wide corrosion challenge requires a substantial investment to manage the integrity of assets and, consequently, to maximize life expectancy and efficiency. Over decades, the industry has concentrated its efforts toward containing fluids from any unintentional release at the surface occurring as a result of corrosion. This paper highlights the most recent electromagnetic (EM) logging technology developments to address well integrity challenges. Three primary corrosion mechanisms occur in oil and gas wells: chemical, mechanical, and electrochemical. Electrochemical corrosion is the mechanism responsible for most of the failures in which the outermost casing is exposed to corrosive fluids and is consequently penetrated first. As the corrosion process continues, subsequent well barriers are progressively corroded until the inner casing or tubing is in direct contact with a corrosive environment and at direct risk of a major well integrity failure. As a result of this outside-to-inside corrosion mechanism, the early diagnosis of the outermost casing status is especially important as a proactive measure to identify any potential weak zones in the completion string. This early diagnosis is a major step to optimize well integrity intervention and to optimize workover operations costs. Cathodic protection and coated casing are used to extend the life of the well by controlling corrosion; however, these are only mitigation measures that slow down but do not eliminate corrosion. EM logging technology provides an effective method for monitoring and identifying the effectiveness of these corrosion mitigation measures. Time domain EM pulse eddy current (PEC) technology has facilitated corrosion evaluation by logging through tubing, thereby avoiding the cost of pulling completions solely for surveillance purposes. The latest EM PEC technology, the enhanced pipe detection tool (ePDT), provides individual barrier thickness measurements for four concentric pipe strings. The innovative features of ePDT include: (1) A fractal transmitter (Tx) coiled array that improves the performance of the tool with enhanced signal-to-noise ratio (SNR) covering a wide signal dynamic range, and adaptability for various logging speeds and spatial resolutions for varying pipes; (2) a synthetic aperture of the receiver (Rx) coil array for noise compensation from extraneous tool motion; and (3) a wide-spatial aperture Rx coil array which, when combined with (1) and (2), enables the compression of the inner pipe remnant magnetization interferences without sacrificing spatial resolution. This paper demonstrates ePDT benefits by benchmarking to other technologies and control environments. The results are discussed in detail to provide an overview of EM technology, as well as the advantages and limitations. Ultimately, the answer product from this technology is integrated with other current and historical information related to the well or field being evaluated as part of the well integrity management system (WIMS). Finally, it is important to expand the technology operating envelope beyond the standard applications to address larger completions challenges, such as gas wells and landing base inspection, by extending the tool capabilities while optimizing data acquisition and processing methodologies.
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.
Historically, only total metal thickness measurement was possible using frequency domain electromagnetic (EM) logging tools. With advances in technology, it is critical to develop a frequency domain alternative answer using a multi-frequency array EM pipe inspection tool to accurately estimate the individual wall thicknesses of as many as five concentric pipes. Results from yard testing in a special design mockup, as well as field logs, are demonstrated as part of the technology assessment process. The new multi-frequency array EM tool uses the eddy current principle and includes two transmitters and eight receivers. It operates in continuous wave mode at multiple frequencies. Optimized transmitter-receiver spacing configurations and multi-frequency operation provide sufficiently diverse information to help assess the metal loss in each individual pipe for a wide range of configurations. The tool uses a sophisticated workflow of data processing and inversion algorithms to decouple individual thickness information from the measured data. The capabilities of the tool are demonstrated using two 400-ft long pipe mockups, each having 18 different combinations of overlapping and non-overlapping defects in five-, four-, and three-pipe sections. The configurations of the pipes used in the mockups were chosen to cover typical well completions commonly used in the Middle East. Data from the mockups are validated using synthetic data generated using two-dimensional (2D) computer models. The tool has delivered unprecedented accurate assessments of the fourth and fifth pipes, as well as an accurate assessment of the commonly evaluated first, second, and third pipes. The sensitivity of the inversion to model mismatches, such as those introduced by decentralized pipes, is studied by deliberately decentralizing one of the mockup pipes over a length of the log. An algorithm designed to correct for pipe eccentricity is also demonstrated. The results from the surface testing are discussed along with the performance of the tool in a test well with pipe configurations similar to the mockups. In the studied test well, the tool was able to identify defects in the outermost strings. This solution utilizes a novel inversion algorithm of big data from multi-frequency array sensors to derive individual pipes corrosion. This technology can significantly improve proactive decision making for mature well operations, especially in areas with high corrosion rates and shallow outer casing corrosion.
With the evolving sensor technologies and advances in integrated solutions, routine surveys and interventions in oil and gas fields are going through a major revamp. The most recent developments in autonomous and untethered devices set a new paradigm shift in such crucial and frequent well operations. In this paper, field implementation and deployment of the novel Sensor-Ball technology is discussed to highlight success, challenges and lessons learned. Sensor-Ball is a small device, almost a tennis-ball size that enables autonomous and untethered logging of pressure, temperature, and tri-axial magnetic field amplitude. This intelligent device is self-powered using a battery pack with a battery life that suffices logging a dozen wells in a raw including logging time and data transfer time. The internal memory is designed for large and high definition data rates for high resolution and extended recording. Sensor-Ball is encapsulated in a ruggedized housing that can withstand downhole conditions as the device travels on a free-fall down to the programmed depth, as well as while floating back to the surface. This housing is light enough to enable efficient and flawless return of the Sensor-Ball exclusively under bouncy effect once the attached weight is dropped off. For the deployment of this innovative technology, new procedures and guidelines are developed to ensure successful journey of the Sensor-Ball. Despite the failsafe features, prejob plan, and risk assessment procedures complement this user-friendly technology and make it reliable, efficient, and easy to use. The results of the field trial of Sensor-Ball in water supply wells revealed a superior data quality of both log-down and log-up. In fact, during the mission time of three hours only, thousands of feet of high-resolution data were collected. This operation would have taken double the time and a much more wellsite footprint, in addition to increased HSE risk, if a standard wireline/slickline unit was mobilized for this routine operation. Sensor-Ball is a reliable and more advanced alternative to traditional well surveillance methods considering the operational efficiency and comparison with benchmark data. In fact, the footprint, cost and time savings are substantial, especially in an offshore environment where barges are mobilized and operations depend on weather conditions. This technology is a major breakthrough in the surveillance and logging world as it enables a fully autonomous and untethered acquisition of high-resolution data. Sensor-Ball offered more with less and will ultimately replace traditional surveillance and intervention methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.