Effective area metal loss clustering

A method of in-line inspection of integrity of a pipeline includes identifying a first prospective cluster related to at least a first feature of the pipeline and a second prospective cluster related to at least a second feature of the pipeline. The method includes calculating an effective area using Length Adaptive Pressure Assessment (LAPA) techniques. The effective area corresponds to a lower calculated burst pressure than surrounding areas of the pipeline. LAPA techniques are used to determine if the first prospective cluster interacts with the second prospective cluster. The method includes combining the first and the second prospective cluster when the effective area includes the first and the second prospective cluster to form a resultant cluster. The method further includes generating an indication of an attribute of the resultant cluster.

BACKGROUND

The subject matter disclosed herein relates generally to piping integrity inspection, and, more specifically, to determining interaction of metal loss clusters.

Typically, pipelines exist to transport a wide variety of products, such as crude or refined petroleum, natural gas, water, or any other suitable liquid or gas. In order to ensure that pipelines continue running properly, operators perform various testing and maintenance on the pipelines, such as inspecting pipelines for features. A feature may be metal losses, dents, deformations, or other defects in the pipe. Operators can locate features of the pipeline in a variety of ways.

Direct measurements can be performed (e.g., pipeline excavation), which can be costly, time consuming, or impractical. Alternatively and/or additionally, in-line inspection may be used by operators to inspect pipelines. Tools can be sent through the pipeline to provide information about features of the pipeline. Surface pitting, corrosion, cracks, or other features are often detected by the tool to identify sections that may have lower burst pressures (i.e. the pressure at which the pipe may rupture). A section with a lower burst pressure (i.e. bursts at a lower pressure) may have a more severe feature or damage. However, sometimes in-line inspection does not provide accurate information about the pipeline. Accordingly, a need exists to improve in-line inspection of pipelines.

BRIEF DESCRIPTION

In a first embodiment, a method of in-line inspection of integrity of a pipeline includes identifying a first prospective cluster related to at least a first feature of the pipeline and a second prospective cluster related to at least a second feature of the pipeline, calculating an effective area using Length Adaptive Pressure Assessment (LAPA) techniques, wherein the effective area corresponds to a lower calculated burst pressure than surrounding areas of the pipeline, wherein LAPA techniques are used to determine if the first prospective cluster interacts with the second prospective cluster, combining the first and the second prospective cluster when the effective area includes the first and the second prospective cluster to form a resultant cluster, and generating an indication of an attribute of the resultant cluster.

In a second embodiment, a tangible, non-transitory computer-readable medium comprising instructions configured to be executed by a processor, the instructions comprising instructions to identify a first prospective cluster related to a first feature of the pipeline and a second prospective cluster related to a second feature of the pipeline, calculate an effective area using a technique, wherein the technique comprises a Length Adaptive Pressure Assessment (LAPA) technique, a Remaining Strength (RStreng) technique, or any combination thereof, wherein the technique is used to determine if the first prospective cluster interacts with the second prospective cluster, combine the first and the second prospective cluster, when the effective area comprises the first and the second prospective cluster, into a resultant cluster, and generate an indication of the resultant cluster.

In a third embodiment, an electronic device configured to assess features of a pipeline includes a processor operatively coupled to a memory, wherein the processor is configured to identify a first prospective cluster related to a first feature of the pipeline and a second prospective cluster related to a second feature of the pipeline, calculate an effective area using an interaction technique, wherein the technique comprises a Length Adaptive Pressure Assessment (LAPA) technique, a Remaining Strength (RStreng) technique, or any combination thereof, wherein the interaction technique is used to determine if the first prospective cluster interacts with the second prospective cluster, combine the first and the second prospective cluster when the effective area includes the first and the second prospective cluster to form a resultant cluster, and generate an indication of the resultant cluster.

DETAILED DESCRIPTION

The techniques described herein relate to predicting accurate burst pressures in a pipeline. Typically, an in-line inspection tool may be run through pipelines to detect features of the pipe. The tool may include bristles that contact the pipeline walls to form a magnetic circuit. In some cases, the tool may detect features (e.g., surface pitting, corrosion, cracks and defects) using magnetic flux leakage from the magnetic circuit. Alternatively, tools may also use acoustics or any other suitable technology for inspecting the pipeline. Other instruments, such as sensors with GPS capability, may be used to record the tool's passage through the pipeline. In some cases, the tool may track passage, time, or location. After the tool passes through the pipeline, the positional data may be combined with the pipeline feature data to provide a location-specific defect profile. While the tool described above may be used, any method suitable for detecting defects and locations may be used to provide a profile of the defects at distances.

After the tool is run, the data collected from the tool may be analyzed to make predictions regarding features of the pipeline. A technique, such as Length Adaptive Pressure Assessment (LAPA) further discussed below, may be used to assess features using the predicted depths from the tool. Once the features are analyzed, in some cases, dig verification is performed on the pipeline. The excavated in-the-ditch measured lengths are compared to the features predicted using the data collected from the tool. Another technique, such as Remaining Strength (RStreng) further discussed below, may be used to determine the burst pressure with excavated measurements of the features. Unfortunately, in many cases the actual measured values do not match properly with the predicted values of the features. One reason may be that the in-line inspection predictions are made to assess accurate feature length. However, the interaction rules between the features of the pipeline can play a significant part in predicting accurate burst pressures and/or locations. Accordingly, a need exists in the field for more accurate burst pressure predictions and/or locations.

The present disclosure is directed to a system and method that addresses the need for more accurate burst pressure predictions and/or locations. By using LAPA techniques to compare clusters, the system and method can account for features that interact due to close proximity. Further, LAPA techniques provide objective interaction results as opposed to various subjective interaction results using distances set by a customer (e.g., 3× wall thickness, 6× wall thickness, feature length, etc.), thereby providing predicted burst pressures that are more consistent with measured burst pressures from excavation. While embodiments of the present disclosure may include the advantageous features and/or advantages described herein, the advantageous features and/or advantages are given as examples, and some embodiments do not require that the advantageous features and/or advantages be incorporated.

Turning to the drawings,FIG. 1is a diagram of a system10for detecting more accurate burst pressure predictions and/or locations. The system may include a processor12or multiple processors operatively coupled to a memory14. The processor12may be operatively coupled to the memory14to execute instructions for carrying out the presently disclosed techniques. These instructions may be encoded in programs or code stored in a tangible non-transitory computer-readable medium, such as the memory14and/or other storage. The processor12may be a general purpose processor (e.g., processor of a desktop/laptop computer), system-on-chip (SoC) device, or application-specific integrated circuit, or some other processor configuration. The memory14, in the embodiment, includes a computer readable medium, such as, without limitation, a hard disk drive, a solid state drive, diskette, flash drive, a compact disc, a digital video disc, random access memory (RAM), and/or any suitable storage device that enables the processor12to store, retrieve, and/or execute instructions and/or data. The memory14may include one or more local and/or remote storage devices. The system10may include a wide variety of inputs/outputs (i.e. I/O16). The processor12of the system10may be configured to access data collected from the tool. As explained below, the processor12may be configured to predict burst pressures and/or locations with improved accuracy.

The system may include a display18. The display18may be used to display a wide variety of charts, graphs, or any information suitable to analyze the pipeline. As shown inFIG. 1, the top diagram20shows a two-dimensional area obtained from a grouping technique. The x-axis22shows distances in the pipeline, and the y-axis24shows corrosion areas of the pipeline. Various blocks26may be used to represent features in the pipeline. A dig site28may include features shown as blocks30. The dig site28may be located at a distance32in the pipeline. The bottom diagram34may show an example of a LAPA profile in accordance with an embodiment of the present disclosure. The diagram34may include distance shown on the x-axis36and depth of the features shown on the y-axis38. For instance, a feature, such as a corrosion pit may cause the depth to decrease at a certain distance40.

RStreng and LAPA may use similar techniques to determine an effective area42by utilizing predicted depths from the tool (e.g., with LAPA) or measured depths from the excavation (e.g., with RStreng). The effective area42may correspond to a section with a lower burst pressure than surrounding areas (e.g., a lowest burst pressure of the inspected pipeline). The processor12may be configured to utilize RStreng and/or LAPA to generate information related to the top diagram20and/or bottom diagram34. More particularly, the processor12may utilize RStreng and/or LAPA to represent a one-dimensional axial profile (e.g., diagram34) by taking a maximum depth at each distance of a two-dimensional profile (e.g., diagram20). Further, the processor may utilize RStreng and/or LAPA by calculating an average depth of one or more sections of the one-dimensional axial profile. By using the average depth of the sections, the processor12may calculate the effective area42using RStreng and/or LAPA techniques. The effective area42may correspond to a location44at a distance to be used to identify a potential dig site32, such as the area with lower burst pressure than surrounding areas. In addition to assessing existing features, LAPA and/or RStreng may be used to determine if two features should interact.

FIG. 2shows a diagram48of a section of a pipeline that may be shown on the display18of the system10. The processor12of the system10may use LAPA and/or RStreng techniques, such as those described above, for determining if two features should interact. While the interactions are explained and may be used with the display18, the display18is simply used to be illustrative, and the processor12may execute instructions (e.g., running code) as described below without displaying the information disclosed herein and provide indications to operators using any suitable method. The data displayed in the diagram48may be based on the data from the inspection tool. The inspection tool may detect individual features, such as corrosion pits, of the pipeline, and the individual features may be referred to as boxes. The processor12may determine whether various boxes interact. For example, as shown inFIG. 2, various boxes (e.g., boxes50,52,54,56,58,60,62, and64) are displayed on the display18.

The processor12may identify and form one or more prospective clusters (e.g., clusters66,68, and70) by applying clustering rules to determine if one or more of the boxes interact. For instances, one or more boxes of a first set of boxes50,52, and54may have attributes, such as depth, length, width, and location which correspond to attributes, such as depth, length, width, and location of features of the pipeline. The feature depth, length, width, or location may be represented by the length, width, color, or location of the boxes on the display18. As shown inFIG. 2, the boxes (e.g., the first set of boxes50,52, and54) are of different lengths, widths, and locations to represent the different features of the pipeline. Further, the boxes may be of different color to represent different depths of the features. Traditionally, the processor12may not cluster the boxes because the clustering would have been regarded as overly conservative. A conservative cluster may err on the side of indicating greater severity (i.e. deeper and/or longer features with a lower burst pressure). However, in an embodiment of the present disclosure, the processor12may begin by clustering the boxes using techniques thought to be conservative, because the conservative clusters are then compared using interaction rules. By using conservative clustering, the clusters may be predicted as more severe (i.e. deeper and/or longer with a lower burst pressure). That is, the processor12predicts features conservatively (e.g., more severe clusters) to increase the likelihood of excavation over predicting features such that a pipeline may rupture. Accordingly, the processor12may determine whether the boxes interact by determining whether a distance between the boxes is less than a multiple of pipe wall thickness or less than a multiple of the length of the shortest box. For instance, a distance72between boxes50and52may be less than three times the wall thickness or less than three times the length of the shortest box. Similarly, a distance74between boxes52and54may be less than the multiple of wall thickness and/or the multiple of a length of the shortest box. Accordingly, the processor12may identify a first prospective cluster66related to at least a first feature (e.g., the features represented by boxes50,52, and54) of the pipeline. Additionally and/or alternatively, the grouping/clustering may be determined by utilizing multiple of minimum extent rules for grouping larger (e.g., clusters with 5 or more boxes) together.

The processor12may identify a second prospective cluster68related to at least a second feature (e.g., the features represented by boxes56and58). The processor12may determine whether the second set of boxes56and58interact with one another (e.g., based on the distance between the boxes56and58) to form a second cluster68. While the second set of boxes56and58are in close proximity to one another (e.g., less than a multiple of wall thickness), the boxes56and58may not be within some multiples of wall thickness to boxes50,52, or54. Accordingly, the processor12may determine that the first set of boxes50,52, and54form a first cluster66, while the second set of boxes56and58form a second cluster68. Similarly, the processor12may determine that the distance to a third set of boxes60is too great. While the clusters shown inFIG. 2include two or three boxes, the processor12may determine clusters are any suitable number of boxes. Additionally, the processor12may identify one or more other prospective clusters. For instance, the processor12may determine that boxes60and62may interact, due to their proximity to one another, to form a third prospective cluster70. The processor12may identify any suitable number of prospective clusters and the clusters may not be shown on the display48. In some cases, the processor12may be configured to apply a tapering background noise level along edges of the clusters to model the difficulty of boxing certain features (e.g., low level features) in recovery areas.

The processor12may then calculate an effective area using the LAPA and/or RStreng techniques described above. As shown inFIG. 1, the effective area may be calculated based on the depths at distances and/or the average depth of one or more subsections. As such, the effective area may correspond to the area with lower burst pressure than the surrounding areas (e.g., lowest calculated burst pressure of the pipeline). In some embodiments, a background level of corrosion may be optionally added to account for noise using the tool. The processor12may then determine if the effective area includes the area of one or more of the clusters. In some cases, one cluster may be located within the effective area, such as area76. As such, the processor12may utilize the area76of the first prospective cluster66as the effective area when assessing the burst pressure, and the processor12may also utilize the first prospective cluster66as a resultant cluster (e.g., a final cluster resulting from the interaction of other clusters). Accordingly, the resultant cluster may be a group of features of the pipeline that are associated with a lower burst pressure than the surrounding areas. In such cases, the processor12may determine that the distance between the clusters is sufficiently large enough that the clusters can be treated separate with respect to pipeline integrity.

In other cases, the processor12may determine that the first prospective cluster66and the second prospective cluster68may interact, because the effective area includes the first prospective cluster66and the second prospective cluster68. For instance, the processor12may calculate an effective area78that includes (e.g., overlaps) the first prospective cluster66and the second prospective cluster68. As such, the processor12may combine the first prospective cluster66with the second prospective cluster68into a resultant cluster84. The resultant cluster84, for instance, may be used to assess burst pressure. In such cases, the calculated burst pressure may not be the LAPA burst pressure, and instead the pressure may incorporate the first prospective cluster, the second prospective cluster, and the cluster interaction.

The processor12may iteratively, on a feature by feature basis, perform the steps described above to determine if one or more of the prospective clusters interact, such as whether the resultant cluster84interacts with the third prospective cluster70. For instance, once the first prospective cluster66and the second prospective cluster68are combined by the processor12to form the resultant cluster84, the processor12may proceed to calculate another effective area80using LAPA and/or RStreng and treating the resultant cluster84as a single cluster. The processor12may then determine if the resultant cluster84and the third prospective cluster70should be combined. If the effective area80includes the resultant cluster84and the third prospective cluster70, then the processor12may combine the resultant cluster84with the prospective cluster70to form another resultant cluster (e.g., a final cluster). On the other hand, if the effective area does not include the third prospective cluster70, then the processor12may continue by checking the next cluster or proceeding to calculate the burst pressure by utilizing an indication of an attribute of the resultant cluster.

Once the processor12combines the appropriate prospective clusters, the processor12may proceed to generate an indication of an attribute of the resultant cluster. For instance, the attribute may be the location44at the distance described inFIG. 1. Alternatively and/or additionally, an attribute of the resultant cluster may be used in or be the burst pressure calculation of the pipeline. As further example, the attribute of the resultant cluster may also identify the features/boxes (e.g., boxes50,52,54,56, and58) associated with the burst pressure of the pipeline. The process described above may be performed by the processor12being configured to execute instructions.

FIG. 3is a process90performed by the processor12in accordance with an embodiment of the present disclosure. As will be appreciated, processor12may be configured to execute instructions encoded in programs or code stored in a tangible non-transitory computer-readable medium, such as the memory14and/or other storage. The processor12may begin the process90by accessing (e.g., loading) data from a tool that was run in a pipeline and that indicates one or more features at different locations (e.g., a first feature at a first location and a second feature at a second location) in the pipeline. The processor12may continue by identifying the first prospective cluster66and the second prospective cluster68(block92). For instance, the processor12may begin by identifying one or more boxes. The boxes may be clustered based on the distance between a box and the nearby boxes. The processor12may group the first and second clusters to form a conservatively large region (block93). That is, the processor12initially may cluster using grouping techniques that were traditionally found to be conservative. The processor12may then calculate an effective area using LAPA and/or RStreng techniques (block94). The LAPA techniques may be applied to the conservatively large region of block93. The effective area may correspond to an area of the pipeline that has a lower burst pressure than the surrounding areas. Then the processor12may form a resultant cluster by combining the first prospective cluster66and second prospective cluster68when the effective area includes the first prospective cluster66and second prospective cluster68(block96). If the first prospective cluster66and second prospective cluster68overlap the effective area, the resultant cluster may result in a different burst pressure due to the interactions between the first prospective cluster66and second prospective cluster68, as well as the depth of the space between the two features. The processor12may then generate the indication of the attribute of the resultant cluster (block98). As such, by forming a conservatively large region (block93), shorter features would be non-conservative. Additionally, by using LAPA techniques, extra length would not increase the severity of the feature. Accordingly, the length of features correspond to more accurate burst pressures, as a minimum length is used to predict burst pressure using LAPA.

By using the LAPA and/or RStreng techniques (e.g., calculating an effective area) as described herein, one of the advantages that may occur is that processor12may be used to calculate more accurate burst pressures in pipe segments by accounting for interactions between clusters of features. Bear in mind, the final calculated burst pressure may not use the LAPA burst pressure (e.g., calculated by assessing existing features), but instead use interactions between one or more clusters using LAPA techniques when calculating the final burst pressure. While some advantages may be described herein, some embodiments of the present disclosure may not incorporate some or all of such advantages.

Technical effects of the disclosed embodiments relate to generating an indication of an attribute of a resultant cluster. The resultant cluster may be created by using LAPA and/or RStreng techniques to determine interaction between one ore more prospective clusters. In one embodiment, a system may include a processor that accesses data from a tool that passes through a pipeline. The processor identifies a first and second prospective cluster. The processor then calculates an effective area using LAPA and/or RStreng. The processor then forms a resultant vector by combining the first and the second prospective clusters when the clusters fall within the effective area. The processor then generates an indication of an attribute of the resultant cluster. The indication may be a minimum burst pressure and/or location of the pipeline. The minimum burst pressure and/or location may then be used by operators to perform excavation of the pipeline.