Patent Description:
<CIT> discloses a system to provide pipeline damage alerts. <CIT> discloses inspecting pipelines using magnetic flux sensors.

After certain components, such as pipelines, are installed and commissioned, it may be difficult to ascertain the condition of the components over time as fit for operational service and the like. As such, improved systems and methods for monitoring the integrity of these components over time may be desirable.

Certain embodiments commensurate in scope with the original claims are summarized below. These embodiments are not intended to limit the scope of the claims, but rather these embodiments are intended only to provide a brief summary of possible forms of the disclosed embodiments. Indeed, the claims may encompass a variety of forms that may be similar to or different from the embodiments set forth below.

These and other features, aspects, and advantages of the disclosed embodiments will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:.

<FIG> an illustration of a system for performing predictive integrity analysis;.

<FIG> is a flow diagram of a process suitable for performing predictive integrity analysis;.

<FIG> is an illustration of a block diagram of various components of the system of <FIG>; and.

<FIG> is an illustration of a visualization of a portion of a pipeline at an element level, in accordance with an embodiment of the present invention.

Pipeline integrity can relate to the safety and/or operations of the pipeline through monitoring, assessment, and/or prevention of any issues that may affect the structural fit for service of a pipeline to transport its products (e.g., hydrocarbons, natural gas, oil, etc.) at desired operational levels (e.g. mass-flow, pressures). Various inline inspection (ILI) tools and sensors obtain data related to structural and physical characteristics of certain components, such as pipelines. For example, sensors may acquire corrosion data that indicates whether a wall of a pipeline has thinned, and other sensors may acquire cracking data that indicates whether the wall has split apart. Other sensor types may capture geometric information such as the cross-sectional shapes and/or centerline direction of the pipe. In addition, other sensor types may capture information related to the material properties of the pipe material. Still other sensors types may capture environmental properties of the pipeline operation as measured around the tool, such as local pressure, temperature, product density, and/or composition at any given point in the pipeline. The available datasets may also include the location and localized geographic information with reference to the pipelines as known for the pipeline from the inline inspection tools or above-ground geographic surveys. The available datasets may be examined to identify correlations between the datasets; however, this process may prove to be complex and time consuming.

Embodiments of the present disclosure generally relate to systems and methods for access and assessment of pipeline integrity and reliability using a virtual model generated in a distributed cloud-computing environment (e.g., a cloud-based computing system) from the available data sources. In embodiments, the systems and methods described herein may provide rapid and/or detailed access and assessment. It should be noted that one or more of one pipeline, many pipelines, a network of pipelines may be modeled. For example, a virtual set of pipeline networks (e.g., series of networks of pipelines) may be simulated and visualized using the disclosed techniques. Continuous and segmented data sets regarding the state of the pipelines, such as inline inspection (ILI), may be loaded, correlated, and used to record historical state information regarding a structural frame of the pipelines and/or current state information regarding the state of the structural frame. The virtual model of such state of the structural frame can be assessed against known engineering codes and practices, as well as advanced computational analysis techniques like FEA and structural design methods. This information may also be processed with change/growth methods to determine a variety of predicted future state(s) at different future time periods, to determine reliability characteristics of the structural frame at those time periods and to determine optimal preventative maintenance methods and timing as well as pipeline downtime for such activities (e.g., replacement of pipe, patchwork, decrease load conditions). As a result, the future reliability and predictive maintenance operations can be modeled and available for use in timely manner.

In some embodiments, the cloud-based computing system may correlate, manage, and/or compute structural information that is received from multiple ILI and other inspection/structural descriptive data sets obtained via ILI tools and/or sensors to form a virtual structure. The processing may be handled and managed in the cloud-based computing system by using a parallel high performance computing framework with a number of computing systems working together. High performance computing power can enable rapid computation of statistics, probabilistic reliability assessments, and quasi-real time structural evaluation at a detailed elemental level (e.g., anomalies may be identified and evaluated via preset industry assessment codes and/or via finite element analysis (FEA)/multi-physics engine or similar computational methods for structural assessment). Using a cloud-based computing system can also enable remote access functionality for select users internally to the given organization, and/or for select external stakeholders as well regarding such predictive analyses.

Using the disclosed embodiments may enable data traceability for engineering decisions related to the data, predicting integrity of current state/reliability and future state reliability for one or more pipelines, and/or data organization and management in a cloud-based computing system. Further, shared resources and parallel computing enabled by the cloud-based computing system may enable rapid computations and reduced complexity and errors in time-lag/manual data handling transition within computation of integrity and engineering assessments. To that end, the disclosed embodiments may remove or reduce manual handling of data, correlation, and/or assessment. In some embodiments, the cloud-based computing system may use existing industry engineering codes and/or computational methods such as finite element analysis (FEA) to establish structural integrity predictions in a thorough (e.g., full pipeline) and timely manner.

In addition, "combined" or "multiple" threat cases may be considered by the nature of the virtual model structure within the cloud-based computing system. For example, the virtual model may illustrate that a bending strain area of a pipeline overlaps with a corrosion area, which forms a different structural situation than each of the individual threats alone. Corrosion and crack anomalies may be found and assessed in the same localized region, dented/deformed regions of the pipe also having corrosion or cracks in the pipe wall may be modeled for fatigue via FEA, and the like. Such modeling can include operational parameters such as product pressure, temperature as taken from the sensor data sources or as assumed values for the given location. Future state predictions may be achieved with the use of a baseline current state and change/growth models to each point of the virtual pipeline of each degrading mechanisms (e.g., crack growth, corrosion growth, dent fatigue, geotechnical movement).

With the foregoing in mind, <FIG> is an illustration of a system <NUM> for performing predictive integrity analysis, in accordance with an example of the present disclosure. The system <NUM> includes one or more pipelines <NUM>. As mentioned above, the pipelines <NUM> may include a single pipeline, a network of pipelines, or a series of networks of pipelines. The pipelines <NUM> may transport product (e.g., hydrocarbons, oil, natural gas, etc.) and may be disposed below and/or above ground. Determining the mechanical state of the pipelines <NUM> efficiently and in quasi-real time may enable the reduction of lost operational time of the pipeline(s) as well as optimization of the identification of the timing, type, and instructions related to repairs, among other things. Thus, some embodiments use a cloud-based computing system <NUM> that can use a virtual model of the pipelines <NUM> to enable the assessment of historic, current, and/or future structural and operational states of the pipelines. The cloud-based computing system <NUM> may determine whether to take one or more actions based upon the assessment (e.g., perform maintenance, replace a part, schedule maintenance, etc.). For example, the cloud-based computing system <NUM> may alter (e.g., reduce) operating parameters of the system <NUM> so as to reduce stress on an assessed portion of the pipeline(s) <NUM> so as to extend an amount of time the system <NUM> can operate before maintenance and/or part replacement occurs. In this manner, the cloud-based computing system <NUM> may operate to reduce lost operational time of the pipeline(s) <NUM> during periods of high demand and instead adjust maintenance and/or part replacement time periods to periods of lowered activity in the system <NUM>. Likewise, for example, the cloud-based computing system <NUM> may alter (e.g., increase or decrease) operating parameters of the system <NUM> so as to increase or reduce stress on an assessed portion of the pipeline(s) <NUM> so as to cause portions of the system <NUM> to have scheduled maintenance and/or part replacement at similar times (e.g., to match maintenance and/or part replacement schedules for the pipeline(s) <NUM> so that the frequency of maintenance and/or part replacement of the pipeline(s) <NUM> is reduced). A user or operator may thereafter carry out the one or more actions, such as by performing maintenance, replacing a part, scheduling/performing maintenance, etc..

To enable modeling the virtual pipelines, data from numerous sensors <NUM> and/or inline inspection (ILI) tools <NUM> may be used throughout the pipelines <NUM> to obtain data related to the pipelines <NUM>. The sensors <NUM> and/or ILI tools <NUM> may obtain magnetic, ultrasonic, radiographic and/or electromagnetic data regarding its surrounding environment, the condition of the pipelines <NUM>, and the like. The sensors <NUM> and/or ILI tools <NUM> may transmit that data to the cloud-based computing system <NUM>. The signal data from ILI tools <NUM> and sensors <NUM> may be analyzed and processed on a computing device <NUM>, or in the cloud-based computing system <NUM> on server <NUM> using processor <NUM>, memory <NUM>, or both via communication component <NUM>. Some signal ILI navigational data may be processed to generate a continuous pipeline centerline in global positioning system (GPS) coordinates to enable the cloud-based computing system <NUM> to map the measurements with various locations in the pipelines <NUM>. In some instances, the ILI tools <NUM> may traverse the pipelines <NUM> to obtain images of the pipelines <NUM>. The ILI tools <NUM> may include an inertial or positioning sensor probe that may or may not be attached to a wire. The data obtained by the ILI tools <NUM> and/or the sensors <NUM> may provide indications of one or more of volumetric wall loss by location, orientation of the pipeline <NUM> (e.g., length, width, height), cracking, pressure, flow, geometry of the pipeline (e.g., to determine whether external force has hit the pipeline <NUM>), a centerline, weld anomalies, bending strains, transition/fittings/facility, other strain loading, material properties, and the like.

The data obtained via the ILI tools <NUM> and sensors <NUM> may be received by one or more servers <NUM> of the cloud-based computing system <NUM> and stored in one or more memories <NUM> of the servers <NUM> or in one or more databases <NUM> included in the cloud-based computing system <NUM> that are external to the servers <NUM>. The servers <NUM> may be communicatively coupled to each other and may distribute various tasks between each other to perform the tasks more efficiently. The servers <NUM> may also include one or more processors <NUM> and a communication component <NUM>. The communication component <NUM> may be a wireless or wired communication component that may facilitate communication between the cloud-based computing system <NUM>, the ILI tools <NUM>, the sensors <NUM>, and/or a computing device <NUM>.

The processor <NUM> may be any type of computer processor or microprocessor capable of executing computer-executable code. The processor <NUM> may also include multiple processors that may perform the operations described below. The memory <NUM> may be any suitable article(s) of manufacture that can serve as non-transitory media to store processor-executable code, data, analysis of the data, or the like. These articles of manufacture may represent computer-readable media (e.g., any suitable form of memory or storage) that may store the processor-executable code used by the processor <NUM> to perform the presently disclosed techniques. Generally, the processor <NUM> may execute computer instructions that virtually model pipelines <NUM> based on a multitude of data received from the ILI tools <NUM> and/or the sensors <NUM> using various techniques (e.g., finite element analysis). In some embodiments, due to the distributed nature of the servers <NUM> in the cloud-based computing system <NUM>, the shared resources of the servers <NUM> can enable parallel processing of the modeling to enable quasi-real time feedback. For example, each server <NUM> may be responsible for processing a different portion of the model at substantially the same time and a single server <NUM>, that combines the results of the model and outputs the results to the computing device <NUM>, may collect the results. In this way, no one server <NUM> is inundated with the computationally expensive task of virtually modeling the entire pipeline <NUM> system and the processing time may be reduced.

The servers <NUM> may receive data from one or more of the ILI tools <NUM> and/or sensors <NUM> and generate a virtual pipeline by using various modeling techniques (e.g., mathematic, physics-based). The servers <NUM> may transform the received data into a different format that can be used to create virtual pipeline. Using the modeled virtual pipelines <NUM>, the servers <NUM> may evaluate the pipeline state (e.g., past, current, future) by using finite element analysis. The servers <NUM> may predict future timeframes of when certain conditions may occur and subsequently when additional actions may be taken, such as repair, pipeline depressurization, or shutdown. In some instances, the servers <NUM> may correlate numerous identified issues, such as corrosion, cracks, complex features, geometry, weld anomalies, bending strain, stress, material properties, and the like within a full structural analyses, as opposed to analyzing each issue individually in a simpler but potentially limited engineering calculation. Using the current data, the servers <NUM> may extrapolate future growth or future states of when the pipelines <NUM> may stop operating within a desired range. The desired ranges may be predetermined ranges for any pipeline <NUM> and the desired ranges may relate to product throughput (flow), pressure, and the like. The servers <NUM> may perform the computations of various processes described below in near real time either on demand from users and/or automatically based on certain data change triggers, as received from data updates via sensors <NUM>, ILI tools <NUM>, and/or data sets on record in database <NUM> and/or memory <NUM>. Interested stakeholders and users may access the results via computing device <NUM>.

The databases <NUM> may be related to various aspects of the pipelines <NUM>. For example, the databases <NUM> may include information regarding various regulations related to how the pipelines <NUM> should be maintained. Additionally, the regulations may be related to how maintenance operations should be documented by the user of the computing device <NUM>. The databases <NUM> may also include data related to warranty information for the pipelines <NUM>, service contact information related to the pipelines <NUM>, and other information that may be useful to an operator of the pipelines <NUM>. Further, the databases <NUM> and/or the memory <NUM> may store historical sensor and/or ILI data, as well as historical state data related to the pipelines <NUM> determined by the processors <NUM>.

The computing device <NUM> may store an application that provides a graphical user interface (GUI) that displays the visualization of the modeled pipelines <NUM>, as well as any predictions and/or actions (e.g., maintenance, repair, replacement, etc.) to be taken. That is, the application may not perform any computationally intensive processing. Instead, in some embodiments, the application may function as a front-end display of data and results of the integrity predicting modeling performed by the cloud-based computing system <NUM>. For example, in a client-server architecture, a website may be accessed via a browser on the computing device <NUM> and the website may function as a thin-client in that it just displays information provided by the cloud-based computing system <NUM> without actually performing any modeling.

Although the components described above have been discussed with regard to the servers <NUM> of the cloud-based computing system <NUM>, it should be noted that similar components may make up the computing device <NUM>. Further, it should be noted that the listed components are provided as example components and the embodiments described herein are not to be limited to the components described with reference to <FIG>.

<FIG> is a flow diagram of a process <NUM> suitable for performing predictive integrity virtual analysis, in accordance with an example of the present disclosure. Although the following description of the process <NUM> is described with reference to the processor <NUM> of one or more servers <NUM> of the cloud-based computing system <NUM>, it should be noted that the process <NUM> may be performed by one or more other processors disposed on other devices that may be capable of communicating with the cloud-based computing system <NUM>, such as the computing device <NUM>, or other components associated with the system <NUM>. Additionally, although the following process <NUM> describes a number of operations that may be performed, it should be noted that the process <NUM> may be performed in a variety of suitable orders and all of the operations may not be performed. It should be appreciated that the process <NUM> may be distributed between the servers <NUM> of the cloud-based computing system <NUM>, distributed between local devices and the servers <NUM>, individually, or any combination of the devices.

Referring now to the process <NUM>, the processor <NUM> may receive (block <NUM>) data related to the one or more pipelines <NUM> from the ILI tools <NUM> and/or the sensors <NUM>. In some instances, the amount of data may be large due to the frequency of measurements and the sheer size of the pipeline networks being measured. The data may relate to material properties obtained from inspection data or other physical operational data available for a region of interest. The data may include ultrasonic and/or electromagnetic measurements, among other things. In some embodiments, the data may be stored in the one or more databases <NUM> in the cloud-based computing system <NUM>. Additionally or alternatively, the data may be stored in the memories <NUM> of each or in some of the servers <NUM> in the cloud-based computing system <NUM>. In this way, the cloud-based computing system <NUM> enables storing the ILI data and/or sensor data in a single location. Further, historical data may be maintained in the databases <NUM> and/or the memories <NUM>.

In some embodiments, the data may be indicative of certain issues, such as corrosion (e.g., metal loss resulting in wall thinning), cracking (e.g., pipeline split open due too much pressure), pipeline geometry (e.g., abnormal radius), stress loading (e.g., earthquake, flood, excavation, construction in area), pressure within pipelines <NUM>, flow, weld anomalies, material properties, and the like. In some embodiments, the data may be formatted to enable finite element analysis. For example, the data may be formatted using a convention related to a "box listing" or "location listing. " The box listing or location listing may include a physical location (e.g., geographic, such as a distance from a reference number) of the box in the pipelines <NUM>, an identifier, and/or certain details of the box (e.g., orientation (length, width, height)), that define the physical feature attributes represented by that box. The box listing or location listing may be represented concurrently in a spreadsheet and the listings may enable an operator to find the box on the pipeline <NUM> to repair it, replace it, or the like as a parallel data set to the results of the more detailed FEA analyses. That is, the box listing or location may enable virtually representing the pipeline <NUM> in elemental structure using a computer-aided design (CAD) image that represents defects or loading conditions on the pipeline <NUM> to enable advanced assessments, as described below.

The processor <NUM> may also perform (block <NUM>) analysis to model one or more physical states of the one or more pipelines <NUM> based on the data. In particular, in some embodiments, the processor <NUM> may use the data formatted as described above to perform finite element analysis or similar computational structural analyses. For example, the processor <NUM> may correlate the element level data (e.g., generated from corrosion by location, cracking by location, stress loading by location, etc.) as represented from data sources of inline inspection <NUM>, sensors <NUM>, to map properties of the pipelines <NUM> for each elemental location to generate a virtual model of the entire pipeline. In some embodiments, the processor <NUM> may use modeling (e.g., set of mathematical equations, physics-based) to generate the virtual structural model of the pipelines <NUM>. The virtual structural model may include a visualization of the physical pipelines <NUM>. The finite element analysis may be performed at the element level to enable a more detailed assessment of correlated issues (e.g., corrosion, cracking, stress loading, flow, pressure, etc.).

The processor <NUM> may use the virtual structural model to determine historic states of the pipelines <NUM>, current states of the pipelines <NUM>, and/or predicted future states (e.g., with the use of change/growth methods). For example, performing finite element analysis on the formatted data may enable predicting how the pipelines <NUM> react to real-world forces, corrosion, cracking, pressure, vibration, heat, fluid flow, and other physical effects. In addition, the finite element analysis may be subdivided in smaller computations (e.g., algebraic equations, partial differential equations, etc.) between servers <NUM> so portions of the pipelines <NUM> may be modeled separately. Breaking the problem domain into smaller portions may enable faster compute time and more accurate representation of complex geometry and physical state of the pipelines <NUM>.

Further, in some embodiments, the various current states may be stored as historic states in the databases <NUM> and/or the memories <NUM>. The processor <NUM> may generate overlapping representations of the historic states of the pipelines <NUM> with the current states of the pipelines <NUM> to enable visualizing the change in the structural composition of the pipelines <NUM> over time. In some embodiments, the historical state data may be maintained for extended periods of time, such as decades.

The processor <NUM> may determine the pipeline state and any future states to determine or predict when the pipelines <NUM> or portions of the pipelines <NUM> stop operating within a desired threshold boundary or range (e.g., detected level of corrosion). For example, if the ILI data or sensor data indicates that a threshold boundary is violated, then the processor <NUM> may perform (block <NUM>) one or more actions based on the one or more states of the one or more pipelines <NUM>. Additionally, after performing finite element analysis and the processor <NUM> predicting that the flow of product in any portion of the pipeline <NUM> may fall below the threshold boundary, the processor <NUM> may also perform (block <NUM>) the one or more actions based on the one or more states of the one or more pipelines <NUM>. The assessment may change when conditions of the pipelines change to enable detailed analysis, and may be recalculated using the cloud-based computing system <NUM>.

The actions may include displaying an alert on the graphic user interface (GUI) of an application installed on the computing device <NUM>. The alert may highlight a portion of the pipeline <NUM> where the violation of the threshold boundary is predicted or detected, or may include a graphic (e.g., a flashing exclamation mark) that is overlaid on the portion of the pipeline <NUM> where the violation of the threshold boundary is predicted or detected. The alert may provide details as to the issues related to the portion of the pipelines <NUM> and a timeframe for when any undesirable condition (e.g., cracking or bending of the pipelines <NUM> that causes the reduced flow) may occur. Additionally, the actions may include scheduling maintenance, repair, and/or replacement of portions of the pipelines <NUM>. In some embodiments, the scheduling may be performed via the GUI of the application executing on the computing device <NUM>. Further, in some embodiments, the actions may include stopping the flow of product in the pipelines <NUM> when the current state or predicted state indicates a sufficiently severe condition.

<FIG> is an illustration of a block diagram of various components of the system <NUM> of <FIG>, in accordance with an example of the present disclosure. As depicted, various ILI tools <NUM> may include electronics and/or mechanics and vehicles that are used to obtain ILI data. For example, a probe may be attached to a wire that is dispensed through the pipeline networks to obtain ultrasonic and/or electromagnetic data indicative of properties of the pipelines <NUM>. Also, various sensors <NUM> (e.g., pressure, flow, vibration, thermal, etc.) may be used as part of the ILI tool <NUM> or as individual externally configured sensors on the pipelines <NUM>. The data may be sent to the cloud-based computing system <NUM> that performs data analysis and synthesis (block <NUM>). Data analysis and synthesis may include performing multiple sensor <NUM> technology data interpretation and synthesis (block <NUM>), which uses ILI <NUM> technology per threat interpretation (block <NUM>).

During block <NUM>, the processor <NUM> may also perform (block <NUM>) multiple-threat alignment and IE assessment. That is, the processor <NUM> may correlate the various issues, such as corrosion, cracking, strain loading, and the like, at the element level for each section of the pipelines <NUM> to build the virtual structural model used for determining the states of the pipelines <NUM>. The processor <NUM> may perform finite element analysis to predict whether the pipeline <NUM> future state violates a threshold operating boundary or range. The multiple-threat alignment and assessment may enable and streamline systematic combinational threat assessment in a highly detailed way using element level data in a finite element model. The sensor data and/or the state data (e.g., historic, current, and/or future) may be stored in the shared and compatible data repository (e.g., databases <NUM>). The data that is stored in the databases <NUM> may be monitored (block <NUM>) to enable taking one or more actions when a desired operating threshold range or boundary is violated. In some embodiments, the processor <NUM> may periodically clean (block <NUM>) the data by purging certain records.

The database <NUM> may maintain data for an extended period of time (e.g., decades) to enable visualizing the entire virtual history of the pipe by overlapping the states on a virtual structural model representation of the pipelines <NUM>. The historical visualization may illustrate the historical states of the pipeline <NUM> to show the transformation of the pipeline <NUM> over time. The database <NUM> may also store other vendor data (block <NUM>) and/or third party/dig verification data (block <NUM>).

The processor <NUM> may perform reporting (block <NUM>) by outputting the results of the predictive integrity virtual analysis that used the virtual structural model to determine states to an application installed on the computing device <NUM> or a website hosted by one of the servers <NUM>. The processor <NUM> may perform combined or standalone analysis reporting (block <NUM>). Combined analysis reporting may refer to integrity virtual analysis that combines and correlates the various threats (e.g., corrosion, cracking, and stress loading) in the virtual structural model to make predictions using finite element analysis, or the like. Combined analysis reporting may enable providing each threat related to a particular joint of the pipelines <NUM>. Standalone analysis reporting may refer to just analyzing the integrity of the pipelines <NUM> based on a single threat (e.g., corrosion) selected by an operator. Visualization and interpretation prioritization modules (block <NUM>) may be used by the processor <NUM> to enhance interfacing and interaction with the data for the operators. That is, the modules may draw the virtual structural model of the pipelines <NUM>, the alerts on the virtual structural model, arrange various information on the GUI, and the like for example, for easy viewing by the operator.

The processor <NUM> may deliver (block <NUM>) the results to various personnel in an organization. For example, the results may be delivered to a sales team, a project manager with oversight of the pipelines <NUM>, technical or project managers as stakeholders of the results and/or a pipeline operator's engineering team. Each of the personnel may have installed the application on their computing device <NUM> or have secure access to the website. That is, role-based security may be enabled where just personnel with proper role and credentials are allowed to see the results of the predictive integrity virtual analysis. In some embodiments, the application that is used on the computing device <NUM> may be downloaded from a software distribution platform, such as an application store, that authorizes the personnel prior to downloading the application. In some embodiments, the personnel provide their credentials at a login screen, and the website authenticates the user prior to providing access to the results.

An integrity management (IM) plan (block <NUM>) for the pipelines <NUM> may be generated based on the results of the predictive integrity analysis. The IM plan may consider maximizing throughput of product in the pipelines <NUM>, minimizing interruption to operation, and performing these objectives cost effectively. The IM plan may provide instructions to the operator to perform maintenance on certain portions of the pipelines <NUM> according to certain timing based on the results from the predictive integrity virtual analysis. For example, the virtual structural model of the pipeline <NUM> may enable the operator to visualize that a portion of the pipe includes an abnormal radius and general metal loss (e.g., wall thinning), which affects the product carrying capacity of the pipeline <NUM> under full operating conditions. Based on the correlated threat information, the IM plan may include replacing that portion of the pipeline <NUM> when product delivery is expected to be reduced to minimize an overall interruption of pipeline operation. In addition, a risk protocol and historic IM data (block <NUM>) may include a procedure to deal with pipeline condition that is included in the threat assessment.

An illustration of a visualization of a virtual structural model <NUM> of a pipeline <NUM> is depicted in <FIG>, in accordance with an embodiment of the present invention. The virtual structural model <NUM> may be generated by the processors <NUM> of the various servers <NUM> in the cloud-based computing system <NUM>. As depicted, the virtual structural model <NUM> depicts a representation of the actual structure of the pipeline <NUM>. The operator selects a portion <NUM> of the virtual structural model <NUM> to receive more information of that portion <NUM> at a zoomed-in view. As depicted, the portion <NUM> in the zoomed-in view may provide the operator with various information, such as centerline (X, Y, Z), or GPS coordinates (N, E, H), azimuth orientation angle of the pipeline <NUM>, transverse/circumferential, radial, and axial angles, among others. The centerline may be local earth frame reference. The pipeline <NUM> radius may be given by caliper (e.g., dent deflection as function of azimuth, etc.).

The operator drills down further into a more granular portion <NUM> of the pipeline <NUM> to see information visualized using finite element analysis at the element level. The granular portion <NUM> at the element level provides a three-dimensional (3D) spatial grid representation of the data provided in a spreadsheet <NUM> that is based on the data <NUM> formatted in the databases <NUM> and/or the memories <NUM>. The granular portion <NUM> provides an actual physical representation of the various threats indicated by the data. That is, the granular portion <NUM> reconstructs the threats and the physical structure of the pipelines <NUM> using the data that is formatted for finite element analysis (FEA) or computer-aided design (CAD). In some embodiments, the FEA and/or CAD formatted data may include material properties assigned.

As may be appreciated, the 3D spatial grid representations is used to enable visually representing cracks <NUM> (discontinuity between elements). For example, the cracks may have a length and depth but no width and the crack is localized by distance and azimuth. The 3D spatial grid representation may also visualize welds and other features that can be represented accordingly with geometric and noted material changes. Also, the 3D spatial grid representation may visualize abnormal radius (e.g., within dent/deformation is function of distance azimuth). Also, the 3D spatial grid representation may represent mid-wall features <NUM>, as well as metal loss <NUM> where the elements are not comprised of steel but of air (in 3D). The pipeline <NUM> will contain physical fittings such as branches (e.g., off takes of line to other pipelines), stopples, taps and operational equipment such as valves, pumps, compressors, tanks which may be included in the virtual model to varying levels of precise representation in a virtual model according to the information from the data sources and basic user choice. Default representations of the main pipeline, fittings and equipment could be preselected from a preset library of representations for that type of item for use in any virtual model analyses.

Technical effects include providing systems and methods for predictive integrity virtual analysis of pipelines <NUM> to enhance life of the pipelines <NUM>, reduce costs of servicing and repair, and so forth. The techniques disclosed herein may remove and/or reduce manual handling of data, correlation, and assessment. The techniques may use industry engineering codes and/or computational methods, such as FEA, to establish structural integrity predictions in a very thorough (full pipeline) and timely manner (using high performance computing provided by the cloud-based computing system <NUM>). Combined threats may be considered by the nature of the virtual structural model generated. For example, the virtual structural model may illustrate that bending strain area overlaps with a corrosion area and/or crack anomalies are present in the same area. Further, dented and/or deformed regions with corrosion or cracks may be modeled for fatigue via FEA. Future state predictions may be enabled with the use of baseline current states and addition of change/growth models to each point of the virtual pipeline for each degrading mechanism (e.g., crack growth, corrosion growth, dent fatigue, geotechnical movement). Further, visualization of the entire history of the states of the pipelines <NUM> may be enabled by overlapping the states in the virtual structural model.

This written description uses examples to disclose embodiments, including the best mode, and also to enable any person skilled in the art to practice the embodiments, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the embodiments is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

In the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints or preferences, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but could nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments, the articles "a," "an," "the," and "said" are intended to mean that there are one or more of the elements.

The subject matter described herein can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structural means disclosed in this specification and structural equivalents thereof, or in combinations of them.

Claim 1:
A system for obtaining a predictive integrity virtual analysis of structural and operational conditions of one or more pipelines, the system comprising:
inline inspection tools (<NUM>) and sensors (<NUM>) configured to obtain data related to structural and physical characteristics of one or more pipelines (<NUM>), wherein the data comprises inspection tool and sensor data; and
a cloud-based computing system (<NUM>) comprising at least one processor (<NUM>) configured to:
receive the data from the inline inspection tools (<NUM>) and sensors (<NUM>);
perform an analysis on the inspection tool and sensor data to generate a virtual structural model (<NUM>) of the one or more pipelines (<NUM>) based on the data including receiving more information of an operator selected portion (<NUM>) of the virtual structural model (<NUM>) at a zoomed-in view, drilling down further into a more granular portion (<NUM>) of the one or more pipelines (<NUM>) to visualize information using finite element analysis at the element level, the granular portion (<NUM>) at the element level providing a three-dimensional spatial grid representation of the data provided in a spreadsheet (<NUM>) that is based on the data formatted in the database and / or the memories, the granular portion (<NUM>) providing an actual physical representation of the various threats indicated by the data, the granular portion (<NUM>) reconstructing the threats and the physical structure of the one or more pipelines (<NUM>) using the data that is formatted for finite element analysis;
determine one or more physical states of the one or more pipelines (<NUM>) using the virtual structural model; and
determine whether to take one or more actions when the one or more physical states indicate that the one or more pipelines (<NUM>) violate a threshold operation boundary; and
a computing device (<NUM>) configured to receive the three-dimensional spatial grid representation from the cloud-based computing system (<NUM>) and display a representation of the information as part of a graphical user interface,
wherein the three-dimensional spatial grid representation is used to enable visually representing cracks (<NUM>) and the cracks (<NUM>) are localized by distance and azimuth.