High-Resolution Acoustic Imaging and Multi-Axis Robotics: Three-Dimensional Downhole Obstruction Evaluation in Real Time

High-resolution acoustic imaging technology has been developed and deployed to map the downhole location and orientation of fiber optic lines in unconventional oil and gas and carbon capture wells. Fiber optic installations are long term monitoring solutions providing continuous measurement of temperature, sound, or strain. These fiber lines provide significant insight into the operation and optimization of downhole assets but require a large capital investment, typically upwards of a million dollars. By accurately mapping fiber optic lines, operators can prevent damaging or perforating through these costly systems during completion and production operations. High-resolution acoustic imaging technology allows operators to directly locate and map in-situ fiber optic systems at logging speeds up to ten times faster, with high accuracy, and more efficiently than legacy technologies by overcoming the requirement to install additional costly detection components. The unique sensor probe employs a circumferential array design, comprised of up to 512 individual elements which are electronically controlled from advanced imaging software. The integration of machine vision algorithms has led to a 100% success rate at detecting, orientating, and mapping of fiber optic lines to prevent damaging these costly and critical monitoring installations. Through a series of validation tests and field applications, this paper details how the solid-state imaging probe was used to identify the submillimetric indentations made at each fiber clamps installation. These contact points indicate the depth and phase orientation of each clamp in a well, enabling the generation of a high-resolution fiber optics system map. While legacy ultrasonic tools rely on a direct reflection principle, this novel, intra-steel imaging technology measures diffuse acoustic reflections at any point on, or inside of, the casing steel. Diffuse reflections are highly sensitive to indentations and markings on the casing surfaces; this removes the legacy-technology requirements for excessively slow logging speeds and the installation of costly steel detection bars. Following successful validation testing, this technology was field deployed and successfully located, oriented, and mapped all the fiber optic clamps ahead of perforating the casing of a carbon capture well. The platform imaged and mapped over 2,060 clamp contact points with a sub-radian azimuthal or phase resolution. In addition to this, high- resolution acoustics have shown fiber optic systems wrap around the casing circumference multiple times, highlighting the inability for fiber optic systems to be accurately installed and oriented. Using this dataset, the operator effectively executed subsequent perforation activities without damaging the fiber optic lines used to monitor the well and reservoir.

High-resolution acoustic imaging was deployed for the first time in the Middle East to assess casedhole well integrity and completion performance in several different wells. These integrity-based inspections represent the first time this novel technology has been run in hole for the purpose of investigating well integrity risks. This paper introduces this new technology, showcases and validates its core applications in well integrity assessments, and highlights the findings from three wells logged in 2023. This solid-state acoustic imaging technology uses an array of up to 512 independent sensors arranged around the head of the tool. Each of these sensors captures amplitude or acoustic intensity signals and time of flight measurements that are then processed into three-dimensional (3D) renderings, conventional two-dimensional (2D) logs, and submillimetric measurements of all defects using proprietary surface detection and defect localization algorithms. The sensor array is controlled entirely electronically via software to focus the acoustic energy at the casing wall, and this focal distance can be adjusted on the fly, enabling multiple casings or tubing strings of varying diameters to be assessed in a single run. This technology inspects cased wells for inner and outer diameter corrosion and pitting, casing deformation, connection defects such as overtorquing, breaches, and perforated/engineered punches, resulting in more efficient and valuable casedhole integrity inspections. This paper showcases the results of laboratory validation of well integrity defects imaged using this technology. In addition, the paper presents an example of a well inspected throughout 2023. Several unique findings were observed, and the novel images from some of the most impactful findings are presented in this paper.

This paper presents recently introduced concept of Learning Entropy (LE) for time series and recalls the practical form of its evaluation in real time. Then, a technique that estimates the increased risk of prediction inaccuracy of adaptive predictors in real time using LE is introduced. On simulation examples using artificial signal and real respiratory time series, it is shown that LE can be used to evaluate the actual validity of the adaptive predicting model of time series in real time. The introduced technique is discussed as a potential approach to the improvement of accuracy of lung tumor tracking radiation therapy.

A real-time recursive back projection (BP) imaging algorithm is presented in this paper. Real time imaging is an intense demand but a challenging task in impulse surface penetrating radar (ImpSPR)'s application. Based on 'delay-sum' operation in time domain, BP imaging algorithm can precisely focus the scattering intensity and obtain high quality subsurface profile. But it's heavy computation burden restricts it's application in ImpSPR's real-time processing. By minutely analyzing it's procedure, a recursive model of BP imaging algorithm is established and real-time BP imaging algorithm is educed subsequently. The computation complexity of both non real time BP imaging algorithm and real time BP imaging algorithm are analyzed. Through processing the experimental data obtained by a ImpSPR system- RadarEye, the imaging algorithm validates its capability at the aspect of ImpSPR's real time processing.

In order to realize real-time and high-speed distortion removal of camera images, a new improved high resolution and high frame rate image distortion based on FPGA is proposed. The traditional method uses four random access memory (RAM) to buffer the distorted image respectively, and then remaps it with the double rate synchronous dynamic random access memory (DDR), which will cause the system to be unable to process high-resolution images at high frame rates. A new RAM buffer architecture and an interpolation module more suitable for Field Programmable Gate Array (FPGA) implementation are proposed, which can improve the system’s ability to process high-speed and high-resolution distorted images in real time within the acceptable range of resources. Compared with the traditional FPGA de-distortion algorithm, the proposed method can de-distortion with the frame rate of 100 fps and the resolution of 3072 x 2048 x 8bit.

To propose a real-time 3D MRI technique called MR SIGnature MAtching (MRSIGMA) for high-resolution volumetric imaging and motion tracking with very low imaging latency. MRSIGMA consists of two steps: (1) offline learning of a database of possible 3D motion states and corresponding motion signature ranges and (2) online matching of new motion signatures acquired in real time with prelearned motion states. Specifically, the offline learning step (non-real-time) reconstructs motion-resolved 4D images representing different motion states and assigns a unique motion range to each state. The online matching step (real-time) acquires motion signatures only and selects one of the prelearned 3D motion states for each newly acquired signature, which generates 3D images efficiently in real time. The MRSIGMA technique was evaluated on 15 golden-angle stack-of-stars liver data sets, and the performance of respiratory motion tracking with the online-generated real-time 3D MRI was compared with the corresponding 2D projections acquired in real time. The total latency of generating each 3D image during online matching was about 300ms, including acquisition of the motion signature data (~138ms) and corresponding matching process (~150ms). Linear correlation assessment suggested excellent correlation (R2 =0.948) between motion displacement measured from the online-generated real-time 3D images and the 2D real-time projections. This proof-of-concept study demonstrates the feasibility of MRSIGMA for high-resolution real-time volumetric imaging, which shifts the acquisition and reconstruction burden to an offline learning step and leaves fast online matching for online imaging with very low imaging latency. The MRSIGMA technique can potentially be used for real-time motion tracking in MRI-guided radiation therapy.

Background: Confocal laser endomicroscopy (CLE) is a real-time microscopic imaging technique based on laser scanning technology. It enables subcellular histological visualization in vivo with a resolution of up to 1 μm and demonstrates over 90% sensitivity for detecting early gastrointestinal cancers and precancerous lesions. In recent years, the integration of CLE with fluorescence staining and artificial intelligence (AI) has further improved its image interpretation capabilities in identifying gastrointestinal inflammation and early cancer. Summary: This review provides an overview of CLE’s clinical applications in gastric and intestinal diseases. It also discusses the synergistic role of AI in enhancing CLE and highlights the clinical relevance of CLE in the detection of digestive tract disorders. Key Messages: CLE is a high-resolution real-time imaging technology that can identify early gastrointestinal cancers and precancerous lesions with high sensitivity, providing important basis for diagnosis and treatment. The integration of CLE with fluorescent staining and AI significantly improves its real time in vivo interpretation of inflammation and early cancerous lesions in the gastrointestinal tract. CLE is valuable in the auxiliary diagnosis and dynamic monitoring of gastrointestinal diseases, and the integration of AI further expands its clinical application potential.

Laser aided deposition is a material additive based manufacturing process by metallurgically bonding the deposited material to the substrate. For a precise study of the process, a mathematical model was established to investigate thermal and mass transportation phenomena, which includes substrate melting and solidification as well as the powder heating process. The detailed process together with the temperature distribution is simulated. The simulation results of the melt pool length, width and peak temperature are validated for the laser deposition process through the measurements on the melt pool geometry and surface peak temperature by the CMOS camera and the dual-wavelength temperature sensor. A smart embedded vision system using CMOS camera is used to measure the size of the melt pool. The length and width of the melt pool are measured in real time using CMOS camera with an on-board DSP processing system which processes the images in real time and provides the output through a serial port to a real-time controller. A dual wavelength non-contact temperature sensor is used for measuring the temperature of the melt pool in real time. The comparison of the melt pool length and peak temperature was conducted between simulation and experiment and shows a good agreement.Laser aided deposition is a material additive based manufacturing process by metallurgically bonding the deposited material to the substrate. For a precise study of the process, a mathematical model was established to investigate thermal and mass transportation phenomena, which includes substrate melting and solidification as well as the powder heating process. The detailed process together with the temperature distribution is simulated. The simulation results of the melt pool length, width and peak temperature are validated for the laser deposition process through the measurements on the melt pool geometry and surface peak temperature by the CMOS camera and the dual-wavelength temperature sensor. A smart embedded vision system using CMOS camera is used to measure the size of the melt pool. The length and width of the melt pool are measured in real time using CMOS camera with an on-board DSP processing system which processes the images in real time and provides the output through a serial port to a real-t...

The main advantage of confocal microscopes over their conventional counterparts arises from their ability to optically 'section' nearly transparent materials; the thin image slices thus obtained can be used to reconstruct three-dimensional images, a capability which is particularly useful for the study of biological specimens. Confocal microscopes have previously used either a single laser-illuminated point-source and single point-detector (which are scanned in tandem across the object) or white-light illumination with multiple point-sources and detectors. Single-point-source systems, however, do not usually form images in real time and are restricted to using available laser wavelengths. Multiple-point-source systems, on the other hand, produce images in real time but use light very inefficiently--typically 1% or less is used for imaging. Here we demonstrate a white-light, multiple-point-source method which can in principle produce images in real time with light efficiencies as high as 50%. This system is likely to find broad practical application, particularly in the imaging of weakly reflecting or weakly fluorescent specimens.

Damage in composite laminates evolves through complex interactions of different failure modes, influenced by load type, environment, and initial damage, such as from transverse impact. This paper investigates damage growth in cross-ply polymeric matrix laminates under tensile load, focusing on three primary failure modes: transverse matrix cracks, delaminations, and fiber breaks in the primary loadbearing 0-degree laminae. Acoustic emission (AE) techniques can monitor and quantify damage in real time, provided the signals from these failure modes can be distinguished. However, directly observing crack growth and related AE signals is challenging, making numerical simulations a useful alternative. AE signals generated by the three failure modes were simulated using modified step impulses of appropriate durations based on incremental crack growth. Linear elastic finite element analysis (FEA) was applied to model the AE signal propagating as Lamb waves. Experimental attenuation data were used to modify the simulated AE waveforms by designing arbitrary magnitude response filters. The propagating waves can be detected as surface displacements or surface strains depending upon the type of sensor employed. This paper presents the signals corresponding to surface strains measured by surface-bonded piezoelectric sensors. Fiber break events showed higher-order Lamb wave modes with frequencies over 2 MHz, while matrix cracks primarily exhibited the fundamental S0 and A0 modes with frequencies ranging up to 650 kHz, with delaminations having a dominant A0 mode and frequency content less than 250 kHz. The amplitude and frequency content of signals from these failure modes are seen to change significantly with source–sensor distance, hence requiring an array of dense sensors to acquire the signals effectively. Furthermore, the reasonable correlation between the simulated waveforms and experimental acoustic emission signals obtained during quasi-static tensile test highlights the effectiveness of FEA in accurately modeling these failure modes in composite materials.

Real-time ultrasonic and acoustic emission monitoring of damage in graphite/epoxy laminates

As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure.

The Uses of Fan-Aquadrones in Case of Floods or Tsunami Rescue and Relief Operations: MAR21 Prototype and MAR27 Prototype

Purpose of the paper is to present an innovative application inside the nondestructive testing field based on vibrations measurements, developed, at the Department of Aeronautical Engineering of the University of Naples Federico II (Italy), by the authors during the last three years, and already tested for analysing damages of many structural elements. The aim has been the development of a nondestructive test (NDT) which meet to most of the mandatory requirements for effective systems, simultaneously reducing as much as possible the complexity of the data analysis algorithm and of the experimental acquisition instrumentation; these peculiarities may, in fact, not be neglected for an operative implementation of such a system. The proposed new method is based on the acquisition and comparison of frequency response functions (FRFs) of the monitored structure before and after an occurred damage. Structural damages modify the dynamical behaviour of the structure such as mass, stiffened and damping, and consequently the FRFs of the structure in comparison with the FRFs of the sound structure, making possible to identify, to localize and quantify a structural damage. The activities, presented in the paper, mostly focused on a new FRFs processing technique based on the determining of a representative index for identifying and analysing damages both on real scale aeronautical structural components, like large-scale fuselage reinforced panels, and on aeronautical composite panels. Besides it has been carried out a dedicated neural network algorithm aiming at obtaining a recognition-based learning; this kind of learning methodology permits to train the neural network in order to let it recognise only examples discarding as a consequence the negative ones. Within the structural NDT a example means state of the analysed structural component and, obviously, a negative one means a damaged or perturbed state. With this object in view the neural network has been trained making use of the same FRFs of the healthy structure used for the determining of the damage index, as positive examples. From an architectural point of view magnetostrictive devices have been tested as actuators, and piezoceramic patches as actuators and sensors. Besides it has been used a laser-scanning vibrometer system to validate the behaviour of the piezoceramic patches and define their technical parameters in order to lay the bases for design a light and reliability system. These techniques promise to bring a step forward for the implementation of an automatic health monitoring system which will be able to identify a structural damage in real time, improving the safety and reducing maintenance costs

Clostridium difficile toxin B (TcdB) has been studied extensively by using cell-free systems and tissue culture, but, like many bacterial toxins, the in vivo targets of TcdB are unknown and have been difficult to elucidate with traditional animal models. In the current study, the transparent Danio rerio (zebrafish) embryo was used as a model for imaging of in vivo TcdB localization and organ-specific damage in real time. At 24 h after treatment, TcdB was found to localize at the pericardial region, and zebrafish exhibited the first signs of cardiovascular damage, including a 90% reduction in systemic blood flow and a 20% reduction in heart rate. Within 72 h of exposure to TcdB, the ventricle chamber of the heart became deformed and was unable to contract or pump blood, and the fish exhibited extensive pericardial edema. In line with the observed defects in ventricle contraction, TcdB was found to directly disrupt coordinated contractility and rhythmicity in primary cardiomyocytes. Furthermore, using a caspase-3 inhibitor, we were able to block TcdB-related cardiovascular damage and prevent zebrafish death. These findings present an insight into the in vivo targets of TcdB, as well as demonstrate the strength of the zebrafish embryo as a tractable model for identification of in vivo targets of bacterial toxins and evaluation of novel candidate therapeutics.