SYSTEM, METHOD AND DEVICE FOR ABNORMALITY DETECTION IN TUBULAR STRUCTURE

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application Ser. No. 63/430,718, filed Dec. 7, 2022, and entitled “SYSTEM AND METHOD FOR ABNORMALITY DETECTION IN BORES”, the disclosure of which is incorporated herein by reference.

TECHNOLOGICAL FIELD

The present disclosure generally relates to abnormality detection systems, and more particularly relates to optical acoustic imaging systems and methods for detecting abnormalities.

BACKGROUND

A tubular structure may refer to an elongated physical structure having a hollow part inside it. In an example, a tubular structure may be drilled into earth's surface to aid exploration and recovery of natural resources. Examples of such natural resources may include, but are not limited to, oil, gas, water, ores and other naturally occurring elements. In other examples, the tubular structure may form a tunnel that may be used for, for example, oil and gas extraction, medicine or medical diagnosis and treatment, non-destructive testing, carbon sequestration, and geothermal energy generation.

For example, a tubular structure may be constructed by placing a series of pipes, for example, metal or steel pipes that are connected end-to-end using, for example, threaded ends or threaded connectors. In certain cases, such as in case of a tubular structure drilled into the earth's surface forming a wellbore, cement may be introduced or deposited into an exterior space between a casing of the tubular structure and a formation, or a hole dug or drilled into the earth from the earth's surface or from the sea floor into a subterranean region. Such pipes, also known as casing, increase the integrity and stability of the tubular structure or wellbore and provide a flow path, for example, between the earth's surface and a subterranean region. During the lifetime of the tubular structure or the wellbore, the casings may be exposed to high volumes of materials and fluids, such as chemically aggressive fluids, natural resources, environmental contaminants, etc. In harsh environments, the casings may be subject to abnormalities due to damage and defects, such as corrosion, leaks, cracks, voids, deformation due to stress, and so forth. This may affect the integrity of the tubular structure. Moreover, due to the length, volume, accessibility difficulties, and long time periods between the construction of such tubular structure and subsequent maintenance activities or the end of life abandonment process of a tubular structure used for natural resources extraction, the casings may not be monitored reliably. Therefore, techniques for easy and reliable detection of abnormalities and damage in a tubular structure are needed.

BRIEF SUMMARY

A system, a method and computer programmable product are provided herein that focuses on detecting abnormalities in a tubular structure. For example, the system, the method, and the computer programmable product provided herein focuses on detecting abnormalities and/or damage in walls of a casing of a wellbore, inner walls of heat exchangers, surfaces of metal or cement tubulars, and the like.

In one aspect, a system for detecting abnormalities is provided. The system for abnormality detection may include a sensing array, and a processor arranged in a tubular structure. In an example, the sensing array comprises at least a plurality of optical sensors to measure light rays and a plurality of acoustic sensors to measure acoustic responses. Further, the processor is configured to receive sensor data relating to the tubular structure from the sensing array, the sensor data comprising at least imaging data and acoustic response data. The processor is configured to determine properties of the tubular structure based on the sensor data, wherein the properties are associated with at least one of: material, or physical structure. In an example, the processor is configured to determine physical and mechanical properties of the casing that forms the walls of the tubular structure. The processor is configured to detect an abnormality in the tubular structure based on the determined properties. Further the processor is configured to determine one or more characteristics of the detected abnormality in the tubular structure.

In an example embodiment, the processor is further configured to detect, using the sensor array, an acoustic response of the detected abnormality in the tubular structure, and determine one or more characteristics of the detected abnormality in the tubular structure based on the detected acoustic response of the abnormality.

In an example embodiment, the acoustic response data comprises acoustic responses from one or more materials associated with the tubular structure. In an example one or more materials include materials used for construction as well as material flowing within and without the tubular structure.

In an example embodiment, the plurality of acoustic sensors may be positioned within a wavelength of a natural resonant frequency of at least one of: the sensing array, or a material of a casing of the tubular structure.

In an example embodiment, the plurality of optical sensors comprises at least one hyperspectral image sensor.

In an example embodiment, the imaging data comprises a plurality of 2D images of the tubular structure generated based on illumination of the tubular structure with broad band light.

In an example embodiment, the sensor data is relating to a casing of the tubular structure, and the processor is configured to determine properties relating to a material of at least one of: an inside wall or an outside wall, of the casing of the tubular structure; and determine one or more differences in the properties of at least one of: the inside wall or the outside wall, based on a set of predefined material properties. Further the processor is configured to detect, using the AI-based model, the abnormality in at least one of: the inside wall or the outside wall of the casing of the tubular structure, based on the one or more differences.

In an example embodiment, the casing of the tubular structure comprises a plurality of layers and wherein each of the plurality of layers of the casing is made of at least one of: steel, or cement.

In an example embodiment, the processor is configured to process, using the AI-based model, one or more images of the tubular structure to detect and characterize the abnormality in the tubular structure.

In an example embodiment, the sensing array may be organized into different configurations including, but not limited to, two dimensional or three-dimensional assemblies of sensors with combinations of one or more sparse, irregular, linear, curved or radially shaped arrays.

In an example embodiment, the tubular structure is a wellbore.

In an example embodiment, the casing of the tubular structure comprises a plurality of layers, wherein each of the plurality of layers of the casing is made of at least one of: steel, or cement.

In an example embodiment, the processor is further configured to measure flow of fluid within at least one of: an inside, or an outside, of the casing of the tubular structure, and a leakage of the fluid within at least one of: the inside, or the outside, of the casing of the tubular structure. In addition, the processor is configured to determine one or more properties of the tubular structure based on at least one of: the flow of fluid within an inside of the casing, or a leakage of the fluid in at least one if: the inside, or the outside of the casing.

In an example embodiment, the sensing array may also include one or more fiber Bragg grating (FBG) sensors, and one or more distributed acoustic sensors (DAS) that sense or detect the presence of leaks and determine chemical and physical properties of the tubular structure.

In another aspect, a method for abnormality detection is disclosed. The method comprising receiving sensor data relating to a tubular structure from the sensing array, the sensor data comprising at least one of: imaging data, or acoustic response data and determining properties of the tubular structure based on the sensor data, wherein the properties are associated with at least one of: material, or physical structure. Further, the method comprises detecting an abnormality in the tubular structure based on the determined properties, and determining one or more characteristics of the detected abnormality in the tubular structure.

In yet another aspect, a device for abnormality detection is provided. The device comprises a plurality of acoustic transducer arrays configured to direct an ultrasonic beam at the tubular structure to illuminate the tubular structure, the tubular structure comprising a plurality of layers of nested tubular structures. The device further comprises a plurality of sensing arrays, wherein the plurality of sensing arrays are positioned with respect to the each other. In an example, the at least one of the plurality of sensing arrays is configured to generate at least one image of at least one of: a casing of the tubular structure at a predefined resolution, or a layer between the casing and a surrounding surface, based on the ultrasonic beam, and receive and transmit sensor data to a processor. In an example, the sensor data comprises at least one of: imaging data, or acoustic response data, Moreover, the processor is configured to detect an abnormality in the tubular structure based on the sensor data.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described example embodiments of the disclosure in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

FIG. 1 illustrates a block diagram of a network environment comprising a system for abnormality detection, in accordance with one or more embodiments of the present disclosure;

FIG. 2 illustrates a block diagram of the system for abnormality detection, in accordance with one or more embodiments of the present disclosure;

FIG. 3 illustrates an exemplary schematic diagram of a device for abnormality detection, in accordance with an example embodiment;

FIG. 4 illustrates another exemplary schematic diagram of the device for abnormality detection, in accordance with an example embodiment;

FIG. 5 illustrates an example method for detecting an abnormality in a wellbore casing, in accordance with an example embodiment; and

FIG. 6 illustrates an example method for detecting an abnormality, in accordance with an example embodiment.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced without these specific details. In other instances, devices and methods are shown in block diagram form only in order to avoid obscuring the present disclosure.

Some embodiments of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments of the disclosure are shown. Indeed, various embodiments of the disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like reference numerals refer to like elements throughout. Also, reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Further, the terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced quantities. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not for other embodiments. As used herein, the terms “data,” “content,” “information,” and similar terms may be used interchangeably to refer to data capable of being displayed, transmitted, received and/or stored in accordance with embodiments of the present disclosure. Thus, use of any such terms should not be taken to limit the spirit and scope of embodiments of the present disclosure.

The embodiments are described herein for illustrative purposes and are subject to many variations. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient but are intended to cover the application or implementation without departing from the spirit or the scope of the present disclosure. Further, it is to be understood that the phraseology and terminology employed herein are for the purpose of the description and should not be regarded as limiting. Any heading utilized within this description is for convenience only and has no legal or limiting effect.

Definitions

The term “sensing array” refers to an organized arrangement of individual sensors and/or transducers that work collectively to capture, measure, or detect signals or physical phenomena across a spatial area. The sensors within the sensing array can be of various types, depending on the application, and they are often arranged in a specific pattern or configuration to achieve a desired sensing outcome. Sensing arrays are commonly used in fields such as imaging, remote sensing, environmental monitoring, and signal processing. The collective data from multiple sensors in the array provide a more comprehensive and detailed understanding of the measured environment or signal, compared to what could be obtained with a single sensor.

In an example, the sensing array may include a plurality of arrays. The plurality of arrays may not be any specific type of sensing array. In certain cases, the plurality of arrays may include individual sensors, or transducers, that are not only arranged in a spatial configuration but also equipped with the ability to dynamically control a direction and focus of a sensing beam.

According to an example, the sensing array is constructed based on a consideration of an application or environment in which the sensing array will be used. In this regard, the sensing array may be developed for different temperature ranges, pressure tolerance, interfacing fluid or material (such as mud, debris, fluid, etc.) and/or a range of a casing of a tubular structure to be inspected. To this end, a tool body or a housing of the sensing array may be oil filled and pressure compensated to prevent the sensing elements from being preloaded due to a pressure difference from a surface to a deep environment in a tubular structure. In certain cases, a piston style housing or tool body may be used for compensating for piston to accommodate the compression of the oil during pressure difference. For example, the tool body may be a pad type housing, including, for example, a micro-resistivity pad. In certain other cases, a worm-gear or a rod configuration may be used as a pressure feedback loop and spring to allow for variation in the pressure.

The term “tubular structure” refers to a physical structure having a hollow part inside it. In an example, the tubular structure is encased by a casing. In other words, the casing may form walls of the tubular structure, and equivalently of an entity with closed sides and open or closed ends. For example, the casing may be a tube-shaped structure. In one example, the tubular structure may be drilled into earth's surface for exploration and recovery of natural resources, i.e., the tubular structure may be a wellbore. The tubular structure may then be used to extract the natural resource flowing through it. In other examples, the tubular structure may be, for example, wellbore for carbon sequestration, conduits, pipelines, subsea production equipment, tubes of heat exchangers, tubes of wellhead blowout preventers (BOPs), blood vessels, and so forth.

It may be noted that the tubular structure may be encased with a casing or may be uncased. In the case of an uncased tubular structure, a material in which the tubular structure 110 is drilled or created may form walls of the tubular structure.

The term “casing” refers to a material that may be used to create walls of the tubular structure. The casing may provide durability, stability, and integrity to the tubular structure. In case of a wellbore drilled into the earth's surface, the casing of the tubular structure may be made of metal, steel, and cement. For NDT purposes where the tubular structure may be a tank, tube or other enclosed or open ended chamber/container for example, it may be cylindrical or may have multiple facets to the sides of the tubular structure. In certain cases, a material of the casing may be same as a material in which the tubular structure is dug or drilled. In some cases, when the tubular structure is a pipe, the casing may be made of a plastic, a bio-plastic, an animal-origin shell, a cellulose-based shell, a muscle cell shell or blood vessel, etc. To this end, a material of the casing is dependent on a type of the tubular structure, such as a use of the tubular structure, and a place of creation of the tubular structure.

The term “abnormality” refers to any type of abnormal feature or characteristics in the tubular structure. For example, the abnormality may be damage caused due to corrosion, improper construction or drilling of the tubular structure, defect, or fault due to improper manufacturing or placement of casing, ageing, poor quality of material, degradation due to chemicals or contaminants, cement composition, deposition, etc. In certain cases, the term abnormality is used in conjunction with casing, i.e., to indicate an abnormality in the casing, such as outer walls of the casing, inner walls of the casing, structure of the casing, joints in the casing, etc.

The term “acoustic sensor” refers to a sensor that works based on a mechanical or an acoustic wave detection mechanism. In an example, the acoustic sensor may comprise an acoustic transmitter and an acoustic receiver. The acoustic transmitter may propagate an acoustic wave through the tubular structure, for example through the casing wall or multiple layers of casing of the tubular structure. In an example, the acoustic transmitter may use a piezoelectric material to generate the acoustic wave. Moreover, based on characteristics of the tubular structure, the velocity, frequency, phase and/or amplitude of the acoustic wave may be changed. Further, the acoustic receiver may measure the change in the velocity, frequency, phase and/or amplitude of received acoustic wave. The change may then be correlated with a corresponding physical quantity of the tubular structure to be measured. In an example, for the wellbore, the physical quantity may be density of material or metal in the casing, strength of cement, etc.

The term “optical sensor” refers to a sensor that converts light rays into an electronic signal. In an example, the optical sensor comprises an optical transmitter and an optical receiver. The optical transmitter may generate and transmit an electromagnetic light wave through the tubular structure wall. Further, the optical receiver may measure a change in physical quantity of the light wave frequency and convert the change into an electronic signal. The optical receiver might be passive. The electronic signal may then be correlated with a corresponding physical quantity of the tubular structure to be measured. Attributes for the light and sound signals received may be computed or processed separately or together.

End of Definitions

A system, a method and a computer programmable device are provided herein in accordance with an example embodiment for abnormality detection. The system, the method and the computer programmable device are disclosed herein enables generating a multi-dimensional image of a tubular structure for abnormality detection in the tubular structure. For example, the tubular structure wall may be several inches thick or may have a thickness in the order of nanometers or microns. To this end, the system disclosed herein scans through different types and possibly multiple layers of materials that may be used for construction of the tubular structure and identifies any abnormality in the tubular structure, such as in a casing of the tubular structure or uncased tubular structure. For example, the system is configured to scan through the steel and cement layers in the tubular structure wall or casing and determine physical properties and mechanical strength of the tubular structure or casing of the tubular structure. In the case of an ageing tubular structure, the system may be able to scan through the layers of the casing of the tubular structure and detect or determine changes in physical properties and mechanical strength of the tubular structure or the casing that may have developed over time.

The system, the method, and the computer programmable device disclosed herein may determine a flow of a fluid flowing through the tubular structure as well as flow of a fluid leaking through an abnormality, such as a crack or a crevice, in the tubular structure. The system is configured to quantify the abnormality identified in the tubular structure in order to accurately determine the location and quantity of leakage through the detected abnormality. The system is configured to determine flow through the tubular structure where no abnormalities exist.

It may be noted that the embodiments of the present disclosure may be described for detecting an abnormality within a tubular structure that is constructed under the earth's surface, such as a wellbore. However, this should not be construed as a limitation. In other embodiments of the present disclosure, the abnormality detection device may be used in any field of technology for detecting an abnormality in a tubular structure. In other examples, the abnormality detection device may be used in geothermal wells, water wells, oil and gas wells, steam-assisted gravity drainage (SAGD), carbon sequestration, for hydrogen stress crack prediction and detection, etc. Further, the abnormality detection system may also be used within tubes or tubular structures that may be, but not limited to, conduits, pipelines, subsea production equipment, wellhead blowout preventers (BOPs), and blood vessels.

Further, the abnormality detection system may measure flow of fluid both in the wellbore and through cracks that permeate the cement that holds the steel or metal pipe in place. The abnormality detection system may also detect perforations in the wellbore created for the purpose of enabling a process called fracking and show a three-dimensional map of perforations as well as tunnels into the formation created by the process of fracking.

The system, the method, and the computer programmable device disclosed in the present disclosure provides better capabilities for more efficient and detailed evaluations of materials of the tubular structures. Moreover, the technologies may also be applied to any industry including but not limited to oil and gas, medicine, and other non-destructive testing.

The system disclosed herein enable detection of any abnormality, such as anomalies, flaws, defect, damage and or changes due to corrosive or abrasive substances in tubular structures, such as wellbores, a casing or walls of the wellbore, heat exchangers, surfaces of metal or cement forming tubular structures, etc. The techniques disclosed herein may allow non-destructive testing applications for, for example, medical imaging application, and/or constructed structures.

FIG. 1 illustrates a block diagram of a network environment 100 comprising a system 102 for abnormality detection, in accordance with one or more embodiments of the present disclosure. The system 102 comprises a sensing array 104 and a processor 106. For example, the system 102, particularly sensing array 104, is configured to capture sensor data associated with a tubular structure 110. In this regard, the system 102 or the sensing array 104 may be located within the tubular structure 110. In an example, the system 102 may be configured to move down and up through the tubular structure 110. The tubular structure 110 may be created or constructed within a surface 114, for example, earth's surface, biological cells, etc. In an example, the tubular structure 110 is a wellbore drilled within the earth's surface. The system 102 is configured to detect any defect in the tubular structure 110.

According to an embodiment of the present disclosure, the tubular structure 110 may be drilled or constructed below the earth's surface. The tubular structure 110 may be constructed using a casing 108. In an example, the casing 108 may form a wall of the tubular structure. For example, the casing 108 may be made of a plurality of layers and each of the plurality of layers may be made of steel and/or cement. In an example, the casing 108 may be made of metal or steel pipes, such that the plurality of metal or steel pipes may be nested, concentric, or multilayered tubes which may be encased in cement. In an example, the plurality of pipes may be connected in an end-to-end manner using threads, and cement may form an outer layer surrounding the plurality of pipes. Such an arrangement of the plurality of pipes and the outer layer of cement may form the layers of the casing 108 of the tubular structure 110. To this end, the tubular structure 110 may be made of materials, such as metal and/or steel pipes, cement, mud/soil, rock, gravel, etc.

For example, the integrity of the tubular structure 110 will have to be analyzed to ensure prolonged life, safe operations, and reduced waste or leaks. If there is a void in the cement in the tubular structure 110, the chances of collapsing of the tubular structure 110 may increase. Further, due to ageing, exposure of the tubular structure 110 to contaminants and chemically aggressive fluids, improper construction, and/or fault in materials (such as fault in materials used for constructing the casing 108), the tubular structure 110 may suffer from faults, cracks, crevices, corrosion, pitting and so forth. This may affect integrity of the tubular structure 110. In certain cases, collapsing of the tubular structure 110 may be life-threatening for workers and exposure to fluids leaking through caracks and crevices in the tubular structure 110 may have prolonged effect on humans and animals. Therefore, it is crucial to identify any abnormality in the tubular structure 110, such as during construction, as well as during periodic assessments, especially for ensuring safety, planned abandonment of a tubular structure, and so forth. Moreover, there is a need to expedite the process of abnormality detection to ensure timely risk assessment and implementation of precautionary and/or preventative actions.

In an example, the system 102 of the present disclosure is capable of utilizing multiple types of sensors deployed on a single downhole tool, such as the system 102. These sensors may include a hyperspectral image sensor for generating hyperspectral images of the tubular structure 110 under investigation. Such hyperspectral images may be overlaid and/or processed with acoustic response data and other optical imaging data to identify abnormalities in the tubular structure 110. To this end, data fusion from multiple sensors is used for assessing or inspecting integrity, soundness, and safety of the tubular structure 110 and/or the casing 108. The system 102 allows objective measurements of irregularities or abnormalities of the casing 108 or the tubular structure 110.

In order to accurately detect abnormalities in the tubular structure 110, the system 102 is employed. In an example, the sensing array 104 is configured to capture comprehensive sensor data about the tubular structure 110, such as the casing 108. In an example, the sensing array 104 may include a plurality of optical sensors, and a plurality of acoustic sensors. The optical sensors may measure light rays and generate high-resolution 2D images that provide visual insights into material composition and condition of the tubular structure 110, such as structure of the casing 108. For example, the optical sensors may include at least one hyperspectral image sensor. Further, the acoustic sensors may be positioned within a wavelength of a natural resonant frequency of the material of the tubular structure 110, such as materials of the casing 108 of the tubular structure 110 to detect subtle changes in acoustic responses, revealing anomalies in the tubular structure 110.

The sensing array 104 may be organized into different configurations, such as two-dimensional or three-dimensional assemblies of sensors with varied shapes, ensuring versatile coverage. In other words, the sensing array 104 may include multiple sensor arrays in which different or same type of sensors may be positioned. Further, these multiple sensor arrays may be positioned in varying configurations on a body of the sensing array 104 or the system 102. To this end, varying shapes of the sensor arrays may include, for example, sparse, irregular, linear, curved or radial.

Apart from the optical sensors and the acoustic sensors, the sensing array 104 may also include other sensors to detect an abnormality in the tubular structure 110. For example, the sensing array 104 may include one or more Fiber Bragg grating (FBG) sensors. In an example, the FBG sensor may be configured to measure or sense strain in structure of the tubular structure 110 for health monitoring of the tubular structure 110. The FBG sensor may also be used to measure or sense localized strain and temperature within the tubular structure and multiplex the multitude of sensor outputs from the sensors in the sensing array 104 with a single ingress/egress fiber to the processor 106 for processing. Further, the sensing array 104 may include one or more hyperspectral optical cameras or hyperspectral image sensors to capture one or more images of the tubular structure 110 at a very large number of wavelengths, such as corresponding to multiple colors. It may be noted, the hyperspectral image sensors may measure multitude of spectra to create a massive hyperspectral data cube comprising position, wavelength, and time-related information to produce images with high spatial and spectral resolution which enables the detailed characterization of the materials of the tubular structure 110. Further, the sensing array 104 or the plurality of acoustic sensors may include one or more distributed acoustic sensors (DAS) and/or distributed vibrations sensors (DVS). In an example, the DAS and/or DVS are configured to detect vibrations and capture acoustic energy along optical fibers. For example, existing fiber optic networks along the tubular structure 110 are utilized and turned into a distributed acoustic sensor for capturing real-time sensor data. In an example, classification algorithms may be used to detect and locate abnormalities such as leaks, cable faults, intrusion activities, or other abnormal sounds.

To this end, examples of sensors included in the sensing array may include, but are not limited to, acoustic sensors (such as, sonic and ultrasonic), acoustic imaging sensors, irregular acoustic sensors (such as, sensors for sensing frequencies between 250 kHz and 15 MHz), opto-acoustic sensors, distributed acoustic sensors, optical sensors, image sensors (such as cameras), hyperspectral image sensors or hyperspectral cameras, FBG sensors, noise sensors, logging sessors (such as induction sensor, sonic sensor, resistivity sensor, acoustic sensor, spontaneous potential sensor, gamma ray logs sensor, density sensor, neutron porosity sensor, electrode resistivity sensor, etc.), temperature sensor, accelerometer, gyroscope, and pressure sensor.

In certain cases, the system 102 may include an optic fiber cable for sensing using distributed acoustic system or sensors (DAS) and/or distributed thermal sensor or system (DTS), power and/or communication, such as between the sensing array 104 and the processor 106 when the sensing array 104 travels down the tubular structure 110.

It may be noted that the types of the sensors and/or sensor arrays arranged on a body of the sensing array 104 is only exemplary and should not be construed as a limitation. In other examples, the sensing array 104 may include different numbers of different types of sensors positioned at different positions and orientations on the body. Further, the illustration of the system 102 and the sensing array 104 in FIG. 1 as a block is only exemplary. To this end, in one example, the system 102, or the sensing array 104 may be cylindrical in shape and may have other parts. In this regard, the system 102 may have more and different types of sensors, such as optical sensors, acoustic sensors, FBG sensors, hyperspectral optical sensors, DAS, and so forth positioned on the sensing array 104 and/or on other parts of a body of the system 102.

The sensing array 104 is connected to the processor 106 via the communication network 112. The communication network 112 may be wired, wireless, or any combination of wired and wireless communication networks, such as cellular, Wi-Fi, internet, local area networks, or the like. In some embodiments, the communication network 112 may include one or more networks such as a data network, a wireless network, a telephony network, or any combination thereof. It is contemplated that the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks (for e.g. LTE-Advanced Pro), 5G New Radio networks, ITU-IMT 2020 networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof.

In operation, the system 102 is configured to receive sensor data relating to the tubular structure 110 from the sensing array 104. The sensing array 104 comprises the plurality of optical sensors to measure light rays and the plurality of acoustic sensors to measure acoustic responses. The sensor data received from sensing array 104 may comprise at least imaging data and acoustic response data. The sensor data may be received in a singular manner or a coordinated manner, for example, in a predefined order. Further, the imaging data may include, for example, 2D and/or 3D images of the tubular structure 110, such as inner wall or inner surface of the casing 108 of the tubular structure 110. Further, the acoustic response data may include, for example, graphical representation of electrical signals indicating sound waves or sound intensity reflected from the surface(s) of the tubular structure 110.

In one example, the system 102 is configured to lower the sensing array 104 into the tubular structure 110 until the sensing array 104 is at the bottom. Further, the sensing array 104 is raised towards the surface 114. For example, during raising transducer arrays may transmit waves, such as ultrasonic and/or sonic waves, whereby the sensors of the sensing array 104 are configured to collect or measure the sensor data. For example, the acoustic sensors may measure acoustic response data and optical sensors may measure imaging data. In certain cases, acoustic imaging sensors may also be used for generating the imaging data. In an example, a speed of the raising of the sensing array 104 may be in a range of 5 metres per minute to 10 metres per minute.

Based on the received sensor data, the processor 106 is configured to determine properties of tubular structures 110. In an example, the properties are associated with material, and/or physical structure of the tubular structure 110 or the casing 108. For example, based on the imaging data received from the plurality of optical sensors, such as hyperspectral image sensor(s), the processor 106 may generate one or more two-dimensional and/or three-dimensional images or three-dimensional views of the tubular structure 110. For example, the images may correspond to different sections of the tubular structure 110 from one or more viewing directions. Moreover, based on the acoustic response data received from the plurality of acoustic sensors, the processor 106 may generate an acoustic map for the tubular structure. The acoustic map may indicate acoustic response of the materials of the tubular structure 110 or the materials of the casing 108 across a length of the tubular structure 110. Based on the received sensor data, the processor 106 is configured to analyze physical and mechanical properties of materials (such as, casing or steel pipes, cement, mud, etc.) of the tubular structure 110.

In an example, based on processing of the imaging data or images, the processor 106 is configured to detect properties, such as shape, color, texture, and other physical properties associated with a surface of an inner wall as well as other layers of the casing 108. For example, based on the processing of the acoustic response data, the processor 106 is configured to detect properties, such as transition temperatures, pressure, morphology, cross-link density, casing condition, multiple-nested casing condition, fluid flow behind the casing 108 and other material properties associated with the layers of the casing 108 and other layers (such as hollow area, intermediate layer(s) of the casing 108, layer(s) between an outer layer of the casing 108 and a formation of the tubular structure under the surface 114, etc.) of the tubular structure 110.

Continuing further, the processor 106 is configured to detect an abnormality in the tubular structure 110 based on the determined properties of the tubular structure 110. In this regard, based on the determined properties of the materials, condition and physical structure of the tubular structure 110 and/or the casing 108 of the tubular structure 110, an abnormality may be detected. Pursuant to present examples, both imaging data as well as acoustic response data, say corresponding to a particular section of the tubular structure 110, may be analyzed in conjunction. In an example, any deviations from predefined expected structure of the section and identified structure of the section based on the imaging data corresponding to the section may be identified in order to detect an abnormality within the section of the tubular structure 110. Similarly, any deviations from predefined expected acoustic response form materials forming the section and identified acoustic properties of the section based on the acoustic response data corresponding to the section may be identified in order to detect the abnormality within the section of the tubular structure 110. For example, deviations identified based on the comparison of the imaging data and the acoustic responses may be compared to accurately identify the presence or absence of the abnormality.

In an example, the processor 106 may be configured to determine one or more characteristics of the detected abnormality in the tubular structure 110. For example, the processor 106 is configured to measure strength of the materials of the tubular structure 110 and quantify the abnormality in the tubular structure 110. For example, the processor 106 may quantify an amount of corrosion or degree of pitting both inside and outside of the tubular structure 110. Further, the processor 106 may quantify dimensions of a crack or a crevice in the tubular structure 110, and/or an amount of leakage of fluid through such cracks and crevices.

In an example, the processor 106 may utilize an artificial intelligence (AI)-based model to detect the abnormality. In an example, the processor 106 may utilize the AI-based model to determine the properties of the tubular structure 110, based on the obtained acoustic response data and the imaging data. The AI-based model may be trained using training data comprising acoustic responses and imaging data relating to normal operating conditions and operating conditions with abnormalities for different tubular structures. In an example, the trained AI-based model may find deviations in physical and/or material properties of the tubular structure 110 under investigation as compared to physical and/or material properties of the tubular structure 110 under normal operating conditions and/or as compared to physical and/or material properties of the tubular structure 110 from when it was originally constructed, to detect an abnormality in the tubular structure 110. In an example, the AI-based model may include a self-evolving algorithm.

In an example, the self-evolving algorithm of the AI-based model are a class of optimization algorithms inspired by a process of natural selection. These algorithms are designed to evolve and improve over time, adapting to changing conditions or requirements without direct human intervention. It may be noted, the self-evolving algorithm may include a cycle of evaluation, selection, and replacement continuing iteratively, allowing the algorithm to explore a solution space and converge towards optimal or near-optimal solutions. Self-evolving algorithms are applied in various domains, such as optimization problems, machine learning, and artificial intelligence. They are particularly useful when the solution space is complex, and traditional optimization methods may struggle to find the best solution.

According to present disclosure, the self-evolving algorithms may be used to image processing in various ways to optimize and adapt image processing techniques automatically. In this regard, the self-evolving algorithms of the AI-based model may be configured to perform, for example, image enhancement, feature extraction, image segmentation, noise reduction, object recognition, etc. Similarly, the self-evolving algorithms of the AI-based model may also be implemented on acoustic response data, such as graphical representations of acoustic responses for feature extraction of the acoustic responses.

To this end, the self-evolving algorithm continuously evaluates the effectiveness of a current image and/or audio response processing strategy, explores alternative strategies through genetic operations like crossover and mutation, and adapts to changing conditions or requirements. This adaptability can lead to improved performance in image and acoustic processing tasks without the need for manual tuning of parameters of the AI-based model.

For example, the processor 106 or the AI-based model may characterize the abnormality in the tubular structure 110 due to, for example, corrosion on the surface of the inner wall of the casing 108, corrosion on a surface of an outer wall of the casing 108, pitting on the inner wall of the casing 108, cracks, holes, crevices, cement integrity failure, casing defect, flow irregularity, leaks, deformation, voids, casing stress, irregular cement-rock bond in the casing 108, and so forth.

In an example, the tubular structure 110 may be a wellbore made of steel and/or metal pipes and cement casing and having a large depth. The system 102 is able to scan properties of an entire length of the tubular structure 110 by using the sensing array 104 having a number of different types of sensors. The sensing array 104 is capable of determining features of the tubular structure 110 through multiple layers of metal, steel and/or cement. The processor 106 is configured to determine physical and mechanical properties of the casing of the tubular structure 110 made of steel and cement. Moreover, a multi-dimensional (e.g., three-dimensional) image may be generated of the tubular structure 110 from the inside of the tubular structure 110 to outer surface of the casing(s) where the casings come into contact with surroundings of earth's surface. For example, the three-dimensional image may be generated at a resolution in a range of 50 microns to 150 microns. Further, the processor 106 may measure the flow of fluid both in the tubular structure 110 and through cracks that permeate the cement that holds the steel or metal pipe in place. The processor 106 may also detect perforations in the tubular structure 110 created due to the process called fracking and show a three-dimensional map of the perforations and the fractures or fissures created as a result of fracking.

It may be noted that the system 102 is configured to combine response or sensor data from the different sensors of the sensing array 104 to analyze characteristics of the tubular structure 110. In an example, the processor 106 of the system 102 may implement an acoustic-optical scanning that combines sensor data from the acoustic sensors. Based on the acoustic responses and the imaging data, the processor 106 may determine physical properties of the tubular structure 110. For example, the processor 106 may be configured to determine characteristics of an inside wall of the casing 108 of the tubular structure 110, the intermediate casing walls, and the cement encasing the steel casings as well as the outer wall of the casing 108 of the tubular structure 110.

In an example, the processor 106 may be configured to perform one or more image processing techniques on the received imaging data and sound processing techniques on the acoustic response data to detect an abnormality in the tubular structure 110. The imaging data and the acoustic response data may be processed to create images of the abnormality as well as listening for sounds related to cracks, crevices, flow of fluid, leaks, etc. For example, based on pixels of an image of the tubular structure 110 generated by the optical sensors and/or the hyperspectral optical sensor, the processor 106 may detect the abnormality in the tubular structure 110, which may be verified, quantified and/or accurately located based on analyzing acoustic response data. To this end, more robust evaluation of the tubular structure 110 is performed with the combination of data acquired from multiple or all available sensors located in the sensing array 104 of the system 102.

Although not depicted, the system 102 may also include other components. Examples of such components may include, but are not limited to, conventional acoustic sensors, lock mechanism, hydraulic mechanism, rotate mechanism for rotating body for better sensor positioning relative to an abnormality, visual positioning system (VPS) to identify surroundings of the system 102, positioning capabilities and electrical and mechanical connectors.

FIG. 2 illustrates an exemplary block diagram 200 of the system 102, in accordance with one or more example embodiments. FIG. 2 is explained in conjunction with FIG. 1.

The system 102 may include the sensing array 104, the processor 106, a memory 202, and an I/O interface 204. The processor 106 is configured to collect and/or analyze data from the memory 202, and/or any other data repositories available over the communication network 112 to detect abnormalities in the tubular structure 110.

The I/O interface 204 may receive inputs and provide outputs for end user to view, such as detection of abnormalities in a tubular structure 110, etc. In an example embodiment, the I/O interface 204 may present any abnormality, such as leak, cracks, holes, crevices, cement integrity, casing defect, flow irregularity, leaks, deformation, voids, casing stress, casing cement-rock bond. It is further noted that the I/O interface 204 may operate over the communication network 112 to facilitate the exchange of information. As such, the I/O interface 204 may include a display and, in some embodiments, may also include a keyboard, a mouse, a joystick, a touch screen, touch areas, soft keys, one or more microphones, a plurality of speakers, or other input/output mechanisms. In one embodiment, the I/O interface 204 may comprise user interface circuitry configured to control at least some functions of one or more I/O interface elements such as a display and, in some embodiments, a plurality of speakers, a ringer, one or more microphones and/or the like.

In an example, the processor 106 may be embodied in a number of different ways. For example, the processor 106 may be embodied as one or more of various hardware processing means such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), a processing element with or without an accompanying DSP, or various other processing circuitry including integrated circuits such as, for example, an ASIC (application specific integrated circuit), an FPGA (field programmable gate array), a microcontroller unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like. As such, in some embodiments, the processor 106 may include one or more processing cores configured to perform independently. A multi-core processor may enable multiprocessing within a single physical package. Additionally, or alternatively, the processor 106 may include one or more processors configured in tandem via the bus to enable independent execution of instructions, pipelining and/or multithreading. Additionally, or alternatively, the processor 106 may include one or more processors capable of processing large volumes of workloads and operations to provide support for big data analysis. In an example embodiment, the processor 106 may be in communication with the memory 202 via a bus for passing information among components of the system 102.

The memory 202 may be non-transitory and may include, for example, one or more volatile and/or non-volatile memories. In other words, for example, the memory 202 may be an electronic storage device (for example, a computer readable storage medium) comprising gates configured to store data (for example, bits) that may be retrievable by a machine (for example, a computing device like the processor 106). The memory 202 may be configured to store information, data, content, applications, instructions, or the like, for enabling the system 102 to carry out various functions in accordance with an example embodiment of the present disclosure. For example, the memory 202 may be configured to store sensor data received from the sensing array 104 for processing by the processor 106. As exemplarily illustrated in FIG. 2, the memory 202 may be configured to store instructions for execution by the processor 106. In some example embodiment, the memory 202 functions as a repository within the system. The memory 202 is configured to store the sensor data 202A and an AI-based model 202B.

As such, whether configured by hardware or software methods, or by a combination thereof, the processor 106 may represent an entity (for example, physically embodied in circuitry) capable of performing operations according to an embodiment of the present disclosure while configured accordingly. Thus, for example, when the processor 106 is embodied as an ASIC, FPGA, or the like. The processor 106 may be specifically configured hardware for conducting the operations described herein.

Alternatively, as another example, when the processor 106 is embodied as an executor of software instructions, the instructions may specifically configure the processor 106 to perform the algorithms and/or operations described herein when the instructions are executed. However, in some cases, the processor 106 may be a processor specific device (for example, a mobile terminal or a fixed computing device) configured to employ an embodiment of the present disclosure by performing instructions for performing the algorithms and/or operations described herein. The processor 106 may include, among other things, a clock, an arithmetic logic unit (ALU) and logic gates configured to support operation of the processor 106. The network environment, such as, the network environment 100 may be accessed using the I/O interface 204 of the system 102. The I/O interface 204 may provide an interface for accessing various features and data stored in the system 102 or other connected data repositories.

In operation, the processor 106 is configured to receive the sensor data 202A relating to the tubular structure 110 from the sensing array 104. The sensor data 202A may include imaging data captured by optical sensors or hyperspectral image sensor(s), and acoustic response data captured by acoustic sensors or DAS. The sensor data 202A may also include other data collected, measured, or sensed by other sensors within the sensing array 104.

Further, the processor 106, specifically, an abnormality detection module 106A, may process the sensor data 202A to determine properties of the tubular structure 110. For example, the properties are associated with material, or physical structure of the tubular structure 110. In an example, based on the sensor data 202A, the abnormality detection module 106A may determine color, shape texture, material density, material elasticity, material compressibility, material viscosity, etc. For example, different sections of the tubular structure 110 as well as different layers of the tubular structure 110 may have similar or different corresponding properties. Further, the abnormality detection module 106A is configured to detect an abnormality in the tubular structure 110 based on the determined properties. In this regard, the abnormality detection module 106A may utilize the AI-based model 202B to detect the abnormality in the tubular structure 110.

For example, the properties of the materials and physical structure at different sections of the tubular structure 110 may be processed. For example, based on processing of properties identified from one or more image pixels, such as color and/or texture of the image pixels, presence or absence of corrosion may be identified. Alternately, by processing the properties identified from one or more image pixels, such as color, texture and/or shape of the image pixels, presence or absence of a crack, a crevice, etc. may be identified. Further, properties of the tubular structure 110 identified from the imaging data are processed in conjunction with properties of the tubular structure 110 identified from the acoustic response data to accurately detect any abnormality in the tubular structure 110.

Further, the processor 106, specifically, a characteristic determination module 106B, is configured to determine one or more characteristics of the detected abnormality in the tubular structure 110. For example, the characteristics may include, but are not limited to, location, size, shape, and other identification or quantification information relating to the detected abnormality.

In an example, the processor 106 or the characteristic determination module 106B is configured to detect an acoustic response associated with the detected abnormality in the tubular structure 110 using the sensing array 104. Subsequently, the characteristic determination module 106B is configured to analyze the detected acoustic response to ascertain specific characteristics of the identified abnormality. For example, when the tubular structure 110 is a wellbore, the abnormality detection module 106A may detect unusual acoustic patterns, which may indicate issues like corrosion or structural damage. Further, the characteristic determination module 106B may interpret these acoustic responses to determine the crucial characteristics such as the nature, location, and potential severity of the abnormality. This enhances the system's 102 capabilities for providing a more comprehensive understanding of the detected abnormality or defect within the tubular structure 110 and aiding in the effective decision-making for maintenance of the tubular structure 110.

In an example, the processor 106 is configured to assess properties pertaining to the material composition of either the inner wall or the outer wall of the casing 108 of the tubular structure 110. The processor 106 may evaluate properties, such as material type and structural integrity. By comparing these properties against a predefined set of standards, the processor 106 may identify one or more deviations in the material properties or physical properties of either the inner wall or the outer wall of the casing 108, and/or intermediate layers of the casing 108. Comparative analysis serves to pinpoint differences that may include the abnormalities within the casing 108, such as corrosion or the structural defects. Leveraging the AI-based model 202B, the processor 106 detects and categorizes abnormalities in either the inner or outer casing walls based on the identified difference. This enhances the capacity of the system 102 to discern subtle material deviation, enabling accurate anomaly detection and contributing to a comprehensive understanding of potential structural issues within the tubular structure 110.

In an example, the processor 106 is configured to assess fluid dynamic within an interior of the casing 108 of the tubular structure 110. The processor 106 is further configured to monitor a flow of fluid within the casing 108 and identify any instances of fluid leakage from the interior to the exterior wall or surroundings of the casing 108. This allows proactive detection of the potential abnormality or issue such as leaks within the tubular structure 110.

In an example, the processor 106 is configured to generate one or more images of the tubular structure 110 based on the imaging data received from the sensing array 104. Subsequently, the images may undergo processing utilizing the AI-based model 202B employing self-evolving processing techniques to identify abnormalities or anomalies within the tubular structure 110. The system 102 is configured to establish a correlation between the detected abnormality or anomaly and the generated image, contributing to the determination of specific characteristics associated with identified abnormality or anomaly. These characteristics encompass essential details such as the location and type of the abnormality or anomaly detected in the tubular structure 110.

The sensing array 104 comprises optical sensors and acoustic sensors strategically arranged to capture comprehensive data from the tubular structure 110. In applications like wellbores, optical sensors may generate high-resolution images, while acoustic sensors may detect subtle structural changes based on acoustic responses. The sensing array 104 provides crucial insights into the structural integrity of the tubular structure 110.

In an example, the acoustic sensors are placed within a wavelength of the natural resonant frequency of either the sensing array 104 or the material(s) used for the construction of the casing 108 of the tubular structure 110. This positioning ensures optimal sensitivity, allowing the acoustic sensors to detect subtle acoustic responses with precision.

In an example, the optical sensors of sensing array 104 are configured to capture images of the tubular structure 110 with broad-spectrum light. The system 102 may utilize the light across a wide range of wavelengths, providing a comprehensive visual representation of the tubular structure 110. In an application like wellbores, the system 102 is configured to enhance the imaging process to capture detailed and high-resolution 2D images. The utilization of broad-band light contributes to a thorough examination, allowing for precise analysis of the material composition, layering and overall condition of the tubular structure 110.

In an example, the arrangement of the sensing array 104 exhibits versatile configuration, encompassing two-dimensional or three-dimensional assemblies of the sensors. These configurations incorporate a combination of sparse, irregular, linear, curved, or radially shaped arrays. This ensures comprehensive coverage and data collection from various angles within the tubular structure 110.

In an example, the sensing array 104 may include fiber Bragg grating (FBG) sensors and distributed acoustic sensors (DAS). This sensor is used for detecting leaks and assessing chemical and physical properties of the tubular structure 110. In applications such as wellbores, the inclusion of FBG sensors enhances precision in monitoring structural changes, while DAS contributes by sensing and identifying potential leaks.

FIG. 3 illustrates an exemplary schematic diagram 300 of a device 302 for abnormality detection, in accordance with an example embodiment. In an example, the device 302 may correspond to the sensing array 104. For example, the device 302 may include additional elements.

In one example, the device 302 may include the sensing array 104 comprising various sensors, such that certain sensors are transducers that may transmit and/or receive signals. For example, the transducer (not shown) is configured to convert electrical energy to beams or signals, such as sonic beams, ultrasonic beams, visible light beams, color light beams, etc. In an example, the transducers are implemented as an acoustic transducer array configured to control and direct an ultrasonic beam (or waveform) at the tubular structure 110, such as to illuminate the ultrasonic beams to the casing 108 of the tubular structure 110. In an example, the device 302 includes at least a pair, such as 2, 4, 6, etc. of sensing arrays. For example, the device 302 may include a plurality of sensing arrays, say a first sensing array and a second sensing array. These sensing arrays may include acoustic transducers (also referred to as acoustic sensors) for acoustic imaging. For example, the acoustic transducer may produce ultrasonic beam in a range of 5 MHz to 6 MHz.

Pursuant to the present example, only one side, i.e., a front side of the device 302 is shown. To this end, the device 302 may also have other sides, such as back, sides, top and bottom. Subsequently, various sensors in the FIG. 3 may form, for example, the first sensing array. To this end, the pair of sensing arrays may be positioned adjacently on a body of the device 302. In this regard, the second sensing array may be positioned at the back side of the device 302.

In an example, the first sensing array and the second sensing array may generate at least one image of the casing 108 of the tubular structure 110 at a predefined resolution and/or at least an image of a layer between the casing 108 and a surrounding surface forming an enclosure of the casing 108 or the tubular structure 110. In an example, when the tubular structure is a wellbore, the layer(s) of the casing 108 made of steel, metal and cement is to be analyzed, as well as layer of, for example, cement, mud, rock, etc. outside an outer layer of the casing 108 is to be analyzed to accurately identify any abnormality and one or more characteristics of the abnormality.

In an example, each of the first sensing array and the second sensing array may include a number of curved sections that may further include a plurality of sensors. For example, the first sensing array and/or the second sensing array may include acoustic sensors, image sensors or cameras, optical sensors, DAS, FGB, hyperspectral sensors, etc. For example, based on the emitted ultrasound beam and/or other light beam, imaging data, such as 3D volumetric images, or 2D image of the casing 108 is captured. Moreover, the first sensing array may be rotated such that the curved sections in the second sensing array overlap the curved sections in the first sensing array by, for example, half or 50%.

While the device 302 may include the first sensing array and second sensing array, or more pairs of sensing array, for the sake of brevity, only one sensing array 304 in shown and described in the FIG. 3. For example, the depiction of the sensing array 304, such as positioning of sensors, types of sensors, etc. is only exemplary. In other embodiments, other architecture of the sensing array 304 may be used.

The device 302 includes the sensing array 304 that may be connected, such as communicatively coupled to the processor 106 located within the system 102. As an example, the device may include the sensing array 304, as well as transducers, transmitters, and/or other electronic devices for operation of the sensing array 304. The sensing array 304 may include a variety of sensors, lock and rotate capabilities, and/or optical fibers for the distributed acoustic sensors (DAS).

In an example, the device 302 is configured to move down and up through the tubular structure 110. For example, the device 302 may be positioned at the bottom of the tubular structure 110 and moved upward (from bottom hole to the surface 114) while acquiring data to evaluate the whole tubular structure 110 for defects, faults, abnormalities, or damage. Once positioned in the tubular structure 110, the device 302 is configured to measure or sense signals reflected from the casing 108 and other components associated with the tubular structure 110. These measured signals may be used to identify any abnormality in the tubular structure 110.

For example, the integrity of the tubular structure 110 may have to be analyzed to ensure prolonged life, safe operations, and reduced waste or leaks. If there is a void, a crack, a leak, etc. in the tubular structure 110, the chances of collapsing of the tubular structure 110 may increase. Further, due to exposure of tubular structure 110 to contaminants and chemically aggressive fluids, improper construction, and/or fault in materials (such as fault/defect in materials of the casing 108), the tubular structure 110 may suffer from faults, cracks, crevices, corrosion, pitting, fracking, and so forth. This may affect integrity of the tubular structure 110. In certain cases, collapsing of the tubular structure 110 may be life-threatening. Therefore, it is crucial to identify any abnormality in the tubular structure 110 timely, such as during construction, or prior to development of the abnormality into significant and/or fatal abnormality, especially for planned abandonment of a tubular structure 110 and so forth. Moreover, there is a need to expedite the process of abnormality detection to ensure timely risk assessment and implementation of precautionary or preventative actions.

In an example, the positioning of the sensors on the sensing array 304 may be sparse, irregular, or unsymmetrical. Further different sections of the sensing array 304 having different sensors or combination of sensors may work in a singular way or in a concerted manner. Moreover, the different sensors or combination of sensors in the sensing array 304 may be fixed or moveable.

According to embodiments of the present disclosure, the sensing array 304 may include a plurality of optical sensors, depicted as, for example, optical sensors 306a, 306b, 306c and 306d (collectively referred to as optical sensors 306, hereinafter). In an example, the optical sensors 306 are light-based sensors. In an example, the optical sensors may include image sensors, such as cameras. The optical sensors 306 may be configured to measure a measurement of wavelength of incoming light after interaction with the materials of the tubular structure 110 or the materials of the casing 108. In an example, the optical sensors 306 may be optical cameras.

The sensing array 304 may further include one or more acoustic sensors, depicted as, for example, acoustic sensors 308a, 308b, 308c and 308d (collectively referred to as acoustic sensors 308, hereinafter). The acoustic sensors 308 are acoustic wave-based sensors. For example, each of the acoustic sensors 308 may include one or more acoustic receivers to measure a change in measurement of frequency and/or wavelength of the transmitted acoustic wave after interaction with the materials or structure of the tubular structure 110 or the materials of the casing 108. In an example, wavelength of the transmitted acoustic wave from the acoustic transducers or the acoustic transmitters may be set to natural resonant frequency of the materials of the tubular structure 110 or the casing 108, or fluid flowing through the tubular structure 110. Further, the acoustic sensors 308 includes low-frequency acoustic sensors, such as acoustic sensors 308a and 308b, and high-frequency acoustic sensors, such as acoustic sensors 308c and 308d. To this end, the acoustic sensors 308 may be sparse, non-focused, non-beam-formed, and have the capability of random sensing or determining random measurement.

The sensing array 304 may further include one or more fiber Bragg grating (FBG) sensors, depicted as, for example, FBG sensors 312a and 312b (collectively referred to as FBG sensors 312, hereinafter). The FBG sensors 312 may be capable of measuring multiple physical parameters, such as temperature, pressure, and acoustic waves. In an example, the FBG sensors 312 may include laterally extending optic fibers. Such optic fibers may be exposed to a periodic pattern of light, such as laser light. The exposure to the optic fibers may result in a change in the measurement of refractive index of each of the optic fibers according to an exposure pattern. Based on the change in the measurement of each of the refractive index, properties of the tubular structure 110 or materials of the tubular structure 110 and the casing 108 may be analyzed.

Further, the sensing array 304 may include one or more hyperspectral optical cameras or hyperspectral image sensors, depicted as a hyperspectral image sensor 310. The hyperspectral image sensor 310 may also be a light-based sensor. The hyperspectral image sensor 310 may be configured to measure a wavelength of light after interaction with the tubular structure 110 or materials of the casing 108. It may be noted that the hyperspectral image sensor 310 may assign a wide spectrum of light or colors to each pixel and analyze the accurate light or color to identify characteristics of the tubular structure 110 and the casing 108 accurately. In an example, the hyperspectral image sensor 310 may be a hyperspectral camera. For example, the hyperspectral camera may illuminate the tubular structure 110 with broad-band or specific frequencies of light to determine a measurement of incoming light.

In an example, the sensing array 304 may also include one or more distributed acoustic sensors (DAS) 316. The DAS 316 may utilize optical fiber cables to provide distributed strain sensing. In particular, the optical fiber cables become sensing element and measurements may be processed, using an attached optoelectronic device. The DAS 316 may allow acoustic frequency strain signals to be detected over large distances and in harsh environments.

In an example, the positioning of the sensors on the sensing array 304 may be sparse, irregular, or unsymmetrical. Further different sections of the sensing array 304 having different sensors or combination of sensors may work in a singular way or in a concerted manner. Moreover, the different sensors or combination of sensors in the sensing array 304 may be fixed or moveable.

In an example, the processor 106 of the system 102 may implement an acoustic-optical scanning system that combines sensor data (referred to as acoustic response data) from the acoustic sensors 308 for the materials of the tubular structure 110 with sensor data (referred to as imaging data) from the optical sensors 306 and/or acoustic imaging sensors (for example, ultrasound imaging) and sensor data from the hyperspectral image sensor 310 to perform analysis of physical and material properties of the tubular structure 110 and/or the casing 108.

For example, the processor 106 is configured to detect an acoustic response indicating leaks by combining acoustic responses from the acoustic sensors 308 with output of FBG sensors 312, and output of DAS 316. By combining the output from various types of sensors, particularly, acoustic sensors 308, FBG sensors 312 and the DAS 316, the processor 106 may be able to characterize features of every abnormality, such as the leak, in the tubular structure 110. Further, using the optical sensors 306, the hyperspectral image sensor 310, and a noise sensor 314, the processor 106 may be configured to capture one or more images of the features of the leak in the tubular structure 110. In an example, the processor 106 may be configured to generate a multi-dimensional, such as three-dimensional image of the leak at the same time and location when the leak is detected allowing for real-time evaluation as to the degree of leak. This may eliminate over-detection of minor abnormalities by providing a way to categorize them while still at the site of the abnormality.

In an example, the flow or leakage of the fluid through the tubular structure 110 and/or the any leakage may be calibrated precisely by using the optical sensors 306 to accurately capture measurements such as, frequencies of light, by providing high signal to noise contrast values. In this regard, the use of the noise sensor 314 may enable the sensors, such as hyperspectral image sensor 310 and the optical sensors 306 in the sensing array 304 to easily pick up its measurements during the high frame rate imaging.

In an example, the processor 106 may be configured to perform one or more image processing techniques on the received imaging data and sound processing techniques on the acoustic response rata to detect an abnormality, such as a leak, a crack, corrosion, a void, or other defect or deformity, in the tubular structure 110. For example, based on pixels of image of the tubular structure 110 or the casing 108 generated by the optical sensors 306, acoustic image sensors, and/or the hyperspectral image sensor 310, the processor 106 may detect the abnormality in the tubular structure 110.

In an example, the processor 106 may be configured to measure a flow of fluid within an inside of the tubular structure 110. Further, the processor 106 is configured to measure a flow or leak of the fluid though cracks in the tubular structure 110. In another example, the processor 106 may be configured to determine a volume of a cavity in the tubular structure 110 and/or dimensions of the leaks. In an example, the processor 106 may measure the flow and the leakage of the fluid using the imaging data generated by the hyperspectral image sensor 310 and the optical sensors 306. For example, multi-dimensional high frame rate volumetric imaging data generated by the hyperspectral image sensor 310, the optical sensors 306 and/or the acoustic sensors 308, may be processed to determine flow velocity and direction of the flow of the fluid. Further, the flow or leakage of the fluid may be calibrated precisely by using the optical sensors 306 to accurately capture measurements such as frequencies of light, by providing high signal-to-noise contrast values.

In an example, the processor 106 may utilize the AI-based model 202B to determine the physical and/or material properties of the tubular structure 110, detect an abnormality in the tubular structure 110, and determine characteristics of the abnormality based on processing of the sensor data 202A captured by the sensors of the sensing array 304. The AI-based model 202B may be trained based on training acoustic responses and training imaging data relating to normal operating conditions and operating conditions with abnormalities. In an example, the trained AI-based model 202B may find differences in physical and material properties of the tubular structure 110 under investigation as compared to physical and material properties of tubular structures under normal operating conditions and/or as compared to physical and material properties of the tubular structure 110 when it was constructed to characterize abnormalities in the tubular structure 110. In an example, the artificial intelligence based algorithm may be a self-evolving genetic algorithm.

In an example, the present disclosure provides a tool, such as the device 302 designed and configured in such a way so as to allow the optical sensors 306 and the hyperspectral image sensor 310 to image through multiple layers of the casing 108, i.e., multiple layers of steel and cement, while being placed within the tubular structure 110, such as wellbores. The device 302 comprises of a series of irregular sensor arrays of the acoustic sensors 308 with frequencies varying between, for example, 250 Kilohertz (kHz) and 15 megahertz (MHz). The sensor arrays of the acoustic sensors 308 may be sparse in in one dimension, 2D, 3D and/or 4D. In other words, a distance from a center of the acoustic sensor array to one sensor, say 308A in the sensor array, and a distance from the center of the sensor array to a nearest adjacent sensor, say 308B in the sensor array, is greater than a wavelength of a frequency chosen for a selected target on the tubular structure 110 to be imaged.

The sensing array 304 is configured in such a manner so as to provide a 3D slice from inside the tubular structure 110 and through the casing 108 and into layers surrounding the tubular structure 110, such as layers of mud and rock surrounding wellbore tubular structure. In this regard, a primary objective is to detect and generate an image of flow paths of methane flowing through a leakage or a crack in the casing 108 in an area between the casing 108 and the rock or surrounding of the wellbore tubular structure. The device 302 may also be configured to measure flow behind the casing 108.

In an example, the sensing array 304 is configured to encircle a housing or a body (referred to as tool body and described in conjunction with FIG. 4) of the device 302 with either curved or flat sensor arrays of sensors to allow for a 360 degree field of view of the tubular structure 110. The device 302 may also be configured to look into and behind perforations in the tubular structure 110 and measure a size of these perforation channels that goes from the casing 108 to the surrounding layers, such as rock face. The device 302 also includes fiber-optic sensing fibers that are configured to sense pressure, noise, and temperature in the tubular structure 110.

According to embodiments of the present disclosure, the device 302 forming a tool is designed to be placed in the tubular structure 110, such as a wellbore. The device 302 contains hyperspectral image sensor 310 and high resolution acoustic imaging/sensing devices. The device 302 is used to determine changes in surface or physical properties of the inner surface of the casing 108. The device 302 transmits the data to the processor 106 and then uses multi sensor material characterization that is described as opto-acoustics. This combination does not work like traditional cellular acoustic optical systems but rather has no fluorescence and uses only the spectral components of both the illumination source and the acoustic sensors 308. The device 302 then computes the uniqueness of the combined information in any area under investigation. The objective is to find changes in the inner and/or outer surfaces of the tubular structure 110 prior to critical failure due to, for example, corrosion, methane emissions or hydrogen stress cracking.

The device 302 may fit inside the tubular structure 110. The device 302 is configured to detect a flow path of methane emissions or any other such gas or liquid that are present on the outside of the tubular structure 110. This may indicate a flow path and a flow rate of flow of a fluid that is leaking from the tubular structure 110 due to an abnormality, such as a leakage, a crack, etc. The device 302 accomplishes this by listening for the sounds created by leaks via acoustic sensors 308 and fiber Bragg grated (FBG) sensors 312 separated by some distance that allows the device 302 to position itself adjacent to the sound. At that time, the device 302 may uses a high resolution acoustic imaging sensors and/or optical sensors 306 to see or capture an image of the crack/leakage in the casing 108 or see the cracks in the layers, such as cement or rock layers, behind the casing 108 that will allow the gases or liquids to find their way to the surface 114.

In an example, the device 302 or the sensors of the first sensing array and the second sensing array are configured to generate and transmit sensor data to the processor 106. The sensor data may include imaging data, and/or acoustic response data. In addition, the sensor data may also include other data, such as temperature, pressure, resistivity, induction, acceleration, orientation, etc. Subsequently, the processor 106 is configured to detect an abnormality in the tubular structure 110 based on the sensor data.

In one example, the processor 106 is configured to generate one or more images based on imaging data and acoustic data of the tubular structure 110 under investigation. For example, the images may be 3D volumetric images generated based on sensor data or imaging data from optical sensors 306, image sensors, acoustic image sensors, hyperspectral image sensor 310, and/or acoustic imaging sensors. Further, the processor 106 is configured to process the one or more images, for example, using the AI-based model 202B to determine at least one attribute from the images that may be used to determine the one or more properties of the tubular structure 110, evaluate integrity of the tubular structure 110 and detect any abnormality. The measurement of properties of the tubular structure 110 and its comparison with predefined standards may provide objective measurements of the integrity of the tubular casing 110 and any abnormality in the tubular structure 110. In an example, these attributes indicating the properties of the tubular structure 110 may include, attributes of images highlighting irregularities in a layer, such as an inner wall or a surface of the inner wall of the casing 108, indicating corrosion, buildup of asphaltenes, cracks, or breaks. In another example, the attributes of images evaluated for identifying an abnormality may include evaluating images corresponding to casing joints, i.e., joints between metal or steel pipes of the casing 108, to determine if the joints are sealed, the threads are fully intact, or the joints have come apart. These images relating to the casing joints may be processed and evaluated to check the joints to confirm if there is a proper seal, and if there is a presence of break, crack, hole, void, corrosion, burst or collapse of the casing joint. In yet another example, the attributes of images evaluated for identifying an abnormality may include evaluating images associated with the tubular structure to check for, for example, integrity of the cement, presence of cracks, presence of micro annulus, cement bond with the casing and outer formation of the tubular structure 110. Based on the position of the imaging or optical sensor arrays on the device 302, characteristics, such as location, depth, azimuthal position with respect to a center of the tubular structure 110, size, etc. may be identified for the detected anomaly or abnormality.

To this end, the acquired sensor data 202A and reconstructed images of the tubular structure 110 from the sensor data are used to evaluate structural integrity of the casing 108 by detecting abnormalities and to determine the characteristics, effectiveness of perforations or cracks, shape of the perforations or cracks, and signs of damage due to the perforations or other identified abnormalities. For example, based on the determined characteristics of the abnormalities, a determination is made to see if the casing 108 is still intact and usable. In certain cases, the characteristics of the casing 108 may be provided as an output for display or provided to downstream processing modules that may further generate remediation methods for restoring efficiency or perform maintenance work the tubular structure 110.

In an example, the system 102 is configured to analyze and assess integrity of each of plurality of layers, such as inner wall, outer wall, intermediate layer, a layer outside the outer layer of the casing, etc. In this regard, the device 302 enables imaging of a number of layers, such as three layers of the casing 108 within the tubular structure 110. In an example, the first sensing array and the sensing array may include radially shaped plurality of multi-frequency arrays. In other words, the first sensing array and the second sensing array may diverge from a common location, such as a bottom part of the device 302, extending upwards. In one example, the first sensing array and the second sensing array may be located at distinct spatial location such that relative phases of the sensors in the first sensing array and the second sensing array is varied to for effective propagation of signals in desired directions. In an example, ultrasound imaging system operating between 5 MHz and 6 MHz may be used for controlling an ultrasonic beam electronically to generate an image of the casing(s) at high resolution to assess the condition of the casing(s) and the cement between the outer casing and the formation.

In this manner, the device 302 along with the processor 106 are configured to form the system 102 for determining issues with walls or layers of the casing 108, casing joints or casing threads, and/or a layer of material between an outer layer of the casing and a formation in which the tubular structure 110 is constructed. For example, the tubular structure is inspected or analyzed based on reconstructed images and reconstructed data from sensor data acquired by the multiple sensors onboard the sensing array 304 of the device 302. Further, analysis or processing techniques are applied on the reconstructed images and reconstructed data. In operation, the device 302 may be lowered into the tubular structure 110, say wellbore or borehole. During raising of the device 302 from a bottom of the tubular structure 110 towards the surface 114, an upward movement of the device 302 for a defined distance below the casing threads of a first casing joint (i.e., a joint made of cement between two metal or steel pipes forming the casing 108) is performed in a slow manner to enable acquisition of sensor data at a higher frame rate while moving the device upward past the threads until the device 302 is a defined distance above a top of the threads of the first casing joint.

In certain cases, transmission frequency of light waves and/or acoustic waves from transducers or transmitters of the device 302 may be changed to facilitate acquiring higher resolution data.

In an example, the system 102 is configured to analyze and assess integrity of each of casing joints of the casing 108. In this regard, for each casing joint encountered while raising the device 302, the device 302 is slowed and the data acquisition frame rate is increased for a defined distance covering each casing joint. Furthermore, the acquired sensor data, such as imaging data and acoustic response data may be reconstructed to create high resolution images to provide improved analysis of the casing joints of the casing 108. In an example, analysis of the high-resolution reconstructed images and data allows for the identification of any deviations in properties of the tubular structure 110 and corresponding abnormalities. In an example, based on the analysis of the reconstructed data and images, areas along the casing joints that have separated, burst, broken apart, or collapsed are identified. In this manner, an output is generated to confirm if each of the casing joints of the casing 108 are not damaged or not.

FIG. 4 illustrates another exemplary schematic diagram 400 of the device 302 for detecting abnormality within the tubular structure 110, in accordance with an example embodiment.

In an example, the device 302 may include a tool body 402 or body for housing various components, such as the first sensing array, the second sensing array, etc. of the device 302. For example, the tool body 402 may provide a surface for attaching the sensing array 304 including the optical sensors 306, acoustic sensors 308, hyperspectral image sensor 310, FBG sensors 312, DAS 316, and noise sensor 314. Details of the sensing array 304 are described in conjunction with, for example, FIG. 3.

The tool body 402 may further include, for example, a data laser electrical mechanism 404, a hydraulics mechanism 406, a rotating mechanism 408, and a lock mechanism 410.

The tool body 402 of the device 302 features a sophisticated data laser electrical mechanism 404 designed for the efficient data transfer between the device 302 and external entities, such as servers or the memory storage. The data laser electrical mechanism 404 leverage laser technology, for its precision and high-speed data transmission capabilities. As part of the tool body 402, it enables seamless and rapid transfer of information, ensuring optimal performance and responsiveness. The utilization of a data laser mechanism 404 enhances the ability of the device 302 to transmit large volumes of data reliably, which is crucial in scenarios where real-time or high-throughput data transfer is essential. Whether facilitating communication with a central server or storing data in the memory 202, the advanced data transfer mechanism contributes to the overall efficiency and functionality of the device 302.

The tool body 402 may comprise the hydraulics mechanism 406 for the purpose of moving the device 302 within the confines of the tubular structure 110. The hydraulics mechanism 406 facilitates controlled and precise movement of the carrier and its associated components. Utilizing fluid power, the hydraulics mechanism 406 enables efficient navigation through the intricate and confined spaces of the tubular structure 110. For example, in wellbores, where maneuverability is essential for tasks like inspection, maintenance, or data collection, the hydraulics mechanism 406 is used to move the device 302 within the tubular structure 110. The hydraulics mechanism 406 ensures a responsive and adaptable tool body 402 enhancing its capabilities for seamless operation within the tubular structure 110.

The tool body 402 may further include the rotating mechanism 408 to enable rotation of the device 302 within the confines of the tubular structure 110. The rotational capability is important for enhancing the versatility and effectiveness of the tool body 402 especially in scenarios like wellbore or pipeline where flexibility of the movement is crucial. The rotating mechanism 408 allows the device 302 to dynamically adjust its orientation, facilitating a comprehensive view of the inner walls or interior surface of the tubular structure 110.

The tool body 402 may include the lock mechanism 410 to secure the device 302 at specific positions with the tubular structure 110. The lock mechanism 410 provides a means to stabilize and immobilize the carrier at desired location, adding precision and control to its operational capabilities. In applications like wellbore or pipeline, where accurate positioning is essential for tasks such as data acquisition or maintenance, the lock mechanism 410 ensures that the tool body 402 remains securely locked at particular positions and focused operations may be performed by preventing unintended movement.

In another embodiment the lock mechanism 410 may be employed for organizing and maintaining the components of the device 302 within the tool body 402 when the device 302 moves up and down in the tubular structure 110. The application of the lock mechanism 410 ensures that all elements remain in a predetermined configuration, orientation, and position in or on the tool body 402 or body of the device 302, contributing to the overall organization and structure of the device 302 during movement within the tubular structure 110.

As may be noted, the device 302 for detecting abnormalities in the tubular structure 110 includes the sensing array 304 comprising various types of sensors that can detect different signals or sensor data. For instance, the sensing array 304 includes optical sensors 306 that can measure light intensity and wavelength, acoustic sensors 308 that can capture sound waves and vibrations, and the noise sensor 314 that can filter out unwanted noises. The sensing array 304 may also have fiber Bragg grating (FBG) sensors 312 that can sense changes in temperature, strain, and pressure, as well as hyperspectral image sensor 310 that can provide high-resolution images of the spectrum. Moreover, the sensing array 304 includes the optical sensors 306 that can record visual information and distributed acoustic sensor (DAS) 316 that can use fiber optic cables to monitor acoustic signals along the length of the cable. The sensing array 304 can thus collect a variety of data from the environment and transmit it to the processor 106 for further processing and analysis.

The system 102 act as a central hub featuring component for the data transmission, sensing, and anomaly detection. Leveraging advanced electrical system and the sensing array 104 or 304, the system 102 is capable of efficiently processing and analyzing data relevant to the tubular structure's integrity and environmental conditions.

Complementing the capabilities of the device 302, the tool body 402 is highlighted with its hydraulic, rotational, and looking mechanism. The hydraulics mechanism 406 enables controlled movement through the intricate spaces of the tubular structure 110, ensuring adaptability and precision. The rotating mechanism 408 allows the device 302 to dynamically adjust its orientation, providing a comprehensive view of the interior surfaces or inner walls of the tubular surface 110. Furthermore, the lock mechanism 410 not only secures the tool body 402 at specific position but also contributes to organizing and maintaining the arrangement of components within the tool body 402.

In an example, the tubular structure 110 is a wellbore that may be used for extracting natural resources, oil and gas extraction, non-destructive testing, carbon sequestration, and/or geothermal energy generation. In this regard, the device 302 provides techniques for imaging the wellbore using a plurality of arrays of the sensing array, such as ultrasound optical or imaging sensors. The plurality of ultrasound arrays may include and utilize an array of sensor elements to produce and receive ultrasound waves. In particular, plurality of array transducers may employ multiple elements that can be controlled electronically to steer and focus the ultrasound beam dynamically. This may enable high resolution real-time imaging of the wellbore for the purpose of evaluating the casing(s) 108 for defects prior to initiating remediation or abandonment procedures for the wellbore. The plurality of arrays of ultrasound imaging device may be integrated into the tool with additional sensors such as positioning sensors, LED camera, temperature sensor and pressure sensors.

Further, the device 302 may form a downhole tool that is configured to acquire acoustic, image and other sensor data 202A by imaging through up to three layers of the casing 108 or walls of the casing 108 and into the surrounding layers beyond the outer wall of the casing 108. Some of the capabilities of the tool include the ability to detect and identify an existence of an abnormality as well as an extent of the abnormality, such as an extent of corrosion on the inner wall of the casing 108, other defects of the inner wall or innermost layer of the casing 108, defects in intermediate layer(s) of the casing 108 and/or surface or outer wall or outer layer of the casing 108. Moreover, any defect or abnormality in the thickness and integrity of cement bond surrounding the outermost layer or wall of the casing 108 may also be evaluated to check the quality and effectiveness of the wellbore, based on identified abnormalities, such as perforations, defects, and poor connections/joints between stringers of the casing 108.

For example, the device 302 uses acoustic transducers, such as the acoustic sensors 308 to transmit and receive unfocused ping-based acoustic waves. Using this, multiple concentric casings may be imaged by mathematically focusing the received acoustic response data at different depths orthogonally to a longitudinal axis of the device 302. The device 302 may also include multiple sensors to collect positioning and environmental measurements, an optical camera, and various logging tools selected according to the specifications of activity or use of the tubular structure 110.

According to an example embodiment, the sensing array 304 may include acoustic sensors or an acoustic transducer array for generating, controlling, and sensing acoustic signals, such as ultrasound waves, sonic waves, etc. In an example, the acoustic transducer array is a radially shaped array comprising a plurality o acoustic sensors 308.

For example, the acoustic transducer array may include two sets of transmitter arrays, wherein each set further comprises at least two axially adjacent circular rings of transmitter arrays. In particular, the acoustic transducer array may include, for example, 4 to 6 curved transmitter arrays corresponding to the two sets of transmitter arrays with two circular rings, say first ring and second ring, in each of them. In an example, each circular ring of the curved transmitter arrays may have a radius of, for example, 5 to 6 inches, and, for example, a plurality of transmitting elements for transmitting ultrasound beam. For example, the two axially adjacent circular rings of transmitter arrays are configured to generate the ultrasonic beam along a longitudinal axis of the device 302.

Moreover, the acoustic transducer array may include two sets of receiver arrays, wherein each set further comprises at least two axially adjacent circular rings of receiver arrays. In particular, the acoustic transducer array may include, for example, 4 to 6 curved receiver arrays corresponding to the two sets of receiver arrays with two circular rings, say first receiver ring and second receiver ring, in each of them. In an example, each circular ring of the curved receiver arrays may have a radius of, for example, 5 to 6 inches, and, for example, a plurality of receiving or sensing elements for measuring ultrasound beam reflected from surfaces of the tubular structure 110, such as surfaces of the casing 108, flowing, fluid, etc. For example, the two axially adjacent circular rings of the receiver arrays are configured to detect reflected ultrasonic beam or acoustic response data from the materials of the tubular structure 110. In an example, the receiver arrays are longitudinally separated from the transmitter arrays. For example, the receiver arrays are also referred to as acoustic sensors 308 throughout the present disclosure.

To this end, the acoustic transducer array may include transmitter arrays and receiver arrays that are radially shaped array. These transmitters and receivers may form the acoustic sensors 308. Each of the transmitter arrays and the receiver arrays may include two sets of two rings comprising transmitters and sensors, respectively. In an example, a first set of two rings of the transmitter arrays and/or the receiver arrays, curved arrays in a ring 1 are shifted by 50% compared to curved arrays in ring 2 of the first set of two rings. Further, for a second set of two rings of the transmitter arrays and/or the receiver arrays, curved arrays in a ring 1 are shifted by 50% compared to curved arrays in ring 2 of the second set of two rings. Further, a center frequency of the first set of two rings is approximately, for example, 1 MHz with a resulting bandwidth of 500 kilohertz (KHz) to 1.5 megahertz (MHz), to ensure penetration into the layers of the casing and layers outside the casing 108 in the formation of the tubular structure. Moreover, a center frequency of the second set of rings is approximately 3.5 MHz, to ensure imaging of an inside or intermediate layers of the casing 108.

FIG. 5 illustrates an exemplary method 500 for measuring or evaluating integrity of a wellbore or a casing 108 of the wellbore, according to one or more example embodiments.

At 502 the method 500 may include exciting or triggering acoustic transducers and/or acoustic transmitting elements of the sensing array 304. In an example, the transducers may be triggered to excite ping-based waveforms. Subsequently, the ping-based waveforms are transmitted from the rings of the transmitter arrays into the innermost layers of the casing of the borehole. In an example, the ping-based waveforms may travel perpendicular to the device 302 and beam of the ping-based waveforms may be emitted through all 360 degrees around a longitudinal axis of the device 302.

It may be noted, the device 302 is an acoustic tool with transmitter arrays comprising at least two sets of axially adjacent circular rings of curved transmitter arrays and receiver arrays comprising at least two sets of longitudinally adjacent circular rings of curved receiver arrays. The curved receiver arrays are axially or longitudinally separated from the curved transmitter arrays.

At 504, acoustic signals or ping-based waveforms are detected by the acoustic sensors 308. In an example, the acoustic sensors 308 may form the receiver arrays. These acoustic sensors 308 may detect the ping-based waveforms. In an example, echoes may also be received by the receiver arrays.

At 506, the received waveforms are processed for interpreting properties of the casing of the wellbore. The properties may include, for example, material properties of the casing, and physical properties of the casing. In certain cases, one or more acoustic images may be reconstructed from received the waveforms received by the acoustic sensors 308. For example, the one or more acoustic images may form 3D volume of the imaged casing. In one example, the inner wall or the inner surface of the casing may also be reconstructed, for example, in the form of a flat 2D image. Further, seismic properties of the casing may be computed from the 2D image.

At 508, abnormalities in the casing are detected. In an example, based on the 3D volume of the imaged casing, the 2D image and the computed seismic properties, the areas of the casing wall that have an abnormality, such as crack, crevice, leakage, corrosion, void, disintegrated joint, etc. is detected.

FIG. 6 illustrates a flowchart 600 of a method for detecting abnormality in a tubular structure, in accordance with an example embodiment. Fewer, more, or different steps may be taken. In an example, the method flowchart 600 may disclose steps for detecting an abnormality or anomaly in the tubular structure, or changes in physical and mechanical properties of the tubular structure prior to or during formation of stress cracks or any other abnormality.

Accordingly, blocks of the method support combinations of means for performing the specified functions and combinations of operations for performing the specified functions. It will also be understood that one or more blocks of the method can be implemented by special purpose hardware-based/firmware computer systems which perform the specified functions, or combinations of special purpose hardware and computer instructions. The computer may be located within the system 102 or for use with a wellbore, the computer may be located above ground and not enclosed in or affixed directly to the tool.

At 602, sensor data relating to the tubular structure is received from the sensing array 104. The sensors of the sensing array 104 may include a plurality of sensors including, but not limited to, the plurality of optical sensors 306, the hyperspectral image sensors 310, to measure light ray and plurality of acoustic sensors 308 to measure acoustic response. Further, the sensor data 202A may include imaging data and acoustic responses data, providing a comprehensive dataset for the subsequent analysis and detection of abnormalities in the tubular structure.

At 604, properties of the tubular structure 110 based on the sensor data 202A may be determined. The determined properties are critical for understanding and assessing the condition of the tubular structure 110 and are associated with at least one of two fundamental aspects: material composition and physical structure of the tubular structure 110. For example, properties or characteristics of materials (such as, casing or steel pipes, cement, mud, etc.) of the tubular structure 110 may be determined. In an example, the processor 106 may combine acoustic response data from the acoustic sensors 308 and imaging data from the optical sensors 306 and the hyperspectral image sensor 310 to determine mechanical and physical properties of the materials of the tubular structure 110 and/or the casing 108.

At 606, an abnormality in the tubular structure 110 is detected. For example, the abnormality may be detected using the AI-based model 202B applied on the determined properties of the tubular structure 110. For example, if the determined properties reveal unusual variation in material composition or structural integrity of the tubular structure 110, the AI-based model 202B can identify anomalies indicative of corrosion, crack, or other form of damage. The AI-based model 202B leverages its learning capabilities to discern its learning capabilities to discern pattern and deviation from expected norms, enhancing timely intervention, allowing for preventive maintenance or repair measures to be implemented before the abnormalities escalate, thereby contributing to the overall integrity and longevity of the tubular structure, such as a wellbore casing.

At 608, one or more characteristics of the detected abnormality in the tubular structure is determined. For example, if the abnormality is identified as corrosion in a tubular structure casing, then the processor 106 is configured to determine characteristics such as the extent of corrosion, the specific location of corrosion within the casing 108, and whether it poses an immediate risk to structural integrity. This step enhances the system's diagnostics capabilities and facilitates a comprehensive understanding of the detected abnormalities in the tubular structure.

Many modifications and other embodiments of the disclosures set forth herein will come to mind to one skilled in the art to which these disclosures pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosures are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the present disclosure. Moreover, although the foregoing descriptions and the associated drawings describe example embodiments in the context of certain example combinations of elements and/or functions, it should be appreciated that different combinations of elements and/or functions may be provided by alternative embodiments without departing from the scope of the present disclosure. In this regard, for example, different combinations of elements and/or functions than those explicitly described above are also contemplated as may be set forth in some of the embodiments of the present disclosure. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.