Patent Application
20220400536
The invention relates to pipeline monitoring and management systems, and particularly to systems for controlling a pipeline heating system to maintain a desired temperature and/or to provide flow assurance of process fluid along the pipeline.
Pipeline systems are often used to transport a liquid, such as Sulphur, over large distances, such as from an extraction point to a processing facility. If the extraction location and/or the processing facility are located in a cold weather environment, or even a warm weather environment, it may be necessary to provide a heating element, or heat trace, to maintain the pipe at a desired temperature to prevent the fluid product from freezing or, in temperature sensitive operations, to maintain a temperature that allows for an efficient flow of the fluid product. The heating element along with any associated components can be known as an electric heating trace (EHT) circuit.
Currently, typical EHT circuits are energized to keep the fluid in the pipe from freezing. For example, if the fluid cools, the cooler temperature may result in the transported fluid freezing, becoming more viscous and/or the fluid not being able to flow properly. The increased viscosity or phase change may cause an unwanted pressure buildup in the pipeline system.
The management of liquid pipelines generally relies on highly manual and operator-dependent approaches, with limited or no real-time data used to drive decisions. Yet failing to utilize safe, reliable and repeatable re-melting methods of solidified process fluid in the pipeline could result in a plant shutdown due to a pipeline rupture or damage from excessive movement of solidified process fluid and/or pipe anchor failures.
It may therefore be desirable to provide improved pipeline re-melt systems and processes.
Embodiments of the invention provide a control system for use with a pipeline that transports a process fluid. The control system includes a distributed temperature sensing system, a heating system, and a management system. The distributed temperature sensing system records temperature data at a plurality of segments along the pipeline. The heating system heats the process fluid in the pipeline. The management system includes a controller in electronic communication with the distributed temperature sensing system and the heating system. The controller includes a processor and memory storing specific computer-executable instructions that, when executed by the processor, cause the controller to receive the temperature data from the distributed temperature sensing system and determine a first alarm condition for each segment of the plurality of segments along the pipeline. When the first alarm condition is present in adjacent segments, the controller merges the first alarm condition to create an extended segment first alarm condition encompassing the adjacent segments and displays, via a graphical user interface, a representation of the extended segment first alarm condition.
Some embodiments of the invention provide a control system for use with a pipeline that transports a process fluid. The control system includes a distributed temperature sensing system, a heating system, and a management system. The distributed temperature sensing system records temperature data at a plurality of segments along the pipeline, wherein the plurality of segments make up a total length of the pipeline. The heating system heats the process fluid in the pipeline. The management system includes a controller in electronic communication with the distributed temperature sensing system and the heating system. The controller includes a processor and memory storing specific computer-executable instructions that, when executed by the processor, cause the controller to receive the temperature data from the distributed temperature sensing system and determine a first condition for each segment of the plurality of segments along the pipeline. The controller further calculates a risk score of the pipeline based on the first alarm condition and displays, via a graphical user interface, a representation of the risk score.
Embodiments of the invention provide a control system for use with a pipeline that transports a process fluid. The control system includes a distributed temperature sensing system, a heating system, and a management system. The distributed temperature sensing system records temperature data at a plurality of segments along the pipeline. The heating system heats the process fluid in the pipeline. The management system includes a controller in electronic communication with the distributed temperature sensing system and the heating system. The controller includes a processor and memory storing specific computer-executable instructions that, when executed by the processor, cause the controller to receive the temperature data from the distributed temperature sensing system and map the plurality of segments to a virtual model of the pipeline by coordinating temperature data associated with ends of the distributed temperature sensing system to ends of the pipeline. The controller further determines a condition of each segment of the plurality of segments along the pipeline and displays, via a graphical user interface, the condition on the virtual model of the pipeline.
Before any embodiments of the invention are explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. Unless specified or limited otherwise, the terms “mounted,” “connected,” “supported,” and “coupled” and variations thereof are used broadly and encompass both direct and indirect mountings, connections, supports, and couplings. Further, “connected” and “coupled” are not restricted to physical or mechanical connections or couplings.
The following discussion is presented to enable a person skilled in the art to make and use embodiments of the invention. Various modifications to the illustrated embodiments will be readily apparent to those skilled in the art, and the generic principles herein can be applied to other embodiments and applications without departing from embodiments of the invention. Thus, embodiments of the invention are not intended to be limited to embodiments shown, but are to be accorded the widest scope consistent with the principles and features disclosed herein. The following detailed description is to be read with reference to the figures, in which like elements in different figures have like reference numerals. The figures, which are not necessarily to scale, depict selected embodiments and are not intended to limit the scope of embodiments of the invention. Skilled artisans will recognize the examples provided herein have many useful alternatives and fall within the scope of embodiments of the invention.
Managing the temperature of a process fluid (e.g., oil, natural gas, molten materials) during transportation through a pipeline can be of key concern, particularly when the process fluid is a material that exhibits changing viscosity characteristics relative to temperature. For example, the physical properties of Sulphur and its narrow operating temperature zone create many design challenges in a Sulphur pipeline. More specifically, in a Sulphur pipeline, the fluid will experience three different flow regimes during its operational life: (1) flowing, i.e., moving or molten, Sulphur (temperature above freezing); (2) stagnant, i.e., liquid, Sulphur not flowing, but still in a molten state; and (3) plugged, in which portions of the pipeline have experienced Sulphur solidification, perhaps with formation of voids, which forms one or more plugs within the pipeline. A critical issue in the performance and operational life of a Sulphur pipeline is the safe and reliable re-melt of solidified Sulphur to re-establish flow.
Most attention has historically been placed on assuring that the required pipeline maintenance temperature is achieved during normal operations. However, first, localized thermal discontinuities, from a heat transfer perspective, create a complex and dynamic environment such that discrete temperature monitoring, on its own, may be insufficient for optimal pipeline maintenance. More specifically, while a 100% uniform thermal profile (i.e., with respect to the temperature of the process fluid) along the entire constructed pipeline is ideal, it is oftentimes not realistic due to discontinuities such as, but not limited to, pipeline void spaces (liquid-free zones), excessive heat loss zones (such as pipe supports/anchors) and the impact of elevational changes (peaks/valleys and/or vertical risers). For example, a section of the pipeline having a low elevation level and having comparatively high elevation adjacent pipeline sections ahead and behind will be likely to accumulate solidified process material due to its geometry. Considering the case of Sulphur, when Sulphur transitions from a liquid to a solid, the volume of the Sulphur decreases. When Sulphur in the low elevation section of the pipeline solidifies, the amount of volume taken up by this Sulphur decreases, allowing liquid Sulphur to flow from adjacent sections of pipe into gaps created by this decrease in volume. In this way, it is possible for a section of pipeline to become completely filled (e.g., plugged) with solid Sulphur.
Second, because the re-melting of Sulphur (i.e., from solid to liquid) in the pipeline can occur at different rates in various portions of the line, re-melt must be performed in a manner that does not overpressure the pipe or allow other pipeline failure modes to occur. For example, re-establishing flow in a plugged pipeline is a difficult endeavor because the solid-to-liquid phase change of Sulphur creates expansive forces from the volume increase that occurs when solid Sulphur melts and becomes liquid Sulphur. These expansive forces may over-pressurize the pipeline if not accounted for correctly, thereby potentially damaging the pipeline. If sufficient pressure is placed behind a plug of solidified Sulphur in a pipeline, the plug could break loose as a result of the pressure and move, uncontrolled, through the pipeline, potentially damaging the pipeline in the process (e.g., by forcefully coming into contact with sidewalls of the pipeline).
Historically, the management of liquid Sulphur pipelines has been left largely to a shift operator who uses judgement and experience to make appropriate decisions. This is a highly manual and operator-dependent approach, with limited or no real-time data used to drive decisions. It becomes, many times, a “best guess” manual approach to managing the pipeline, which can lead to failures due to human error. Poor or inexperienced planning may result in a non-homogenous thermal profile for the pipeline, with solidification of process fluid occurring at unknown locations.
Furthermore, pipeline failures may be caused by pressure build-up in a pipeline due to, for example: lack of pressure management; welded pipe shoes or faulty anchor design, causing areas of high heat loss; insufficient thickness and/or poor field installation of thermal insulation; inability to monitor pipeline temperature along the entire length of the pipeline; absence of any extra heat delivery capability during “emergency conditions” when localized heat losses create cold zones along the pipeline; excessive pipeline movements; “runaway heating” at voids/empty zones present in the pipeline from process fluid solidification; and/or absence of a clear and methodical re-melt procedure. The dynamics of these issues require a multi-disciplinary approach and in-depth experience with process fluid properties and pipeline operational behavior in order for these issues to be properly addressed.
The foregoing needs are met by the methods, apparatus, and/or systems herein for monitoring and managing a pipeline in order to maintain desired characteristics, such as temperature, of a process fluid in the pipeline. In some embodiments, a control system for a pipeline may include: one or more trace heating cables, such as skin-effect heat tubes, to provide heat to the pipeline (e.g., as part of a heating system); a fiber optic cable for distributed temperature sensing along the pipeline; optionally, a plurality of sensors for detecting and reporting pipeline operating data; pre-insulated pipe; thermally isolated pipe supports and anchors; and a monitoring and re-melt program implemented through a computerized management system.
The combined instrumentation along the pipeline may be used to gather key decision-making data, and programs of some embodiments operate on such data to determine whether to change operating parameters of the heating system, generate alarms in response to changes in sensed parameters, and/or generate reports and/or alerts to guide manual operations. For example, to combat thermal discontinuities, an accurate mapping of the rate of temperature change, along with other operational parameters, may yield a more sophisticated and predictable real-time model for process fluid re-melt. The development of specialized algorithms based on trends in measured or otherwise obtained data during commissioning, preliminary start-up, and/or operation could provide the early indication of potential failure modes and can serve to more precisely monitor and assess dynamic pipeline conditions, attributing to the successful implementation of a customized automated re-melt program. For example, by monitoring temperature trends along the pipeline, it is possible to predict and track the movement of freely-moving plugs in the pipeline. Accordingly, some embodiments can use predictive modeling, transient analysis, and improved software solutions to create a dynamic, real-time model for detecting and/or predicting solidification of Sulphur (or other process fluids) as it undergoes phase changes inside the pipeline.
While the present disclosure is presented with particular details relevant to the monitoring of liquid Sulphur and re-melting of solidified Sulphur in a Sulphur pipeline, it should be noted that the details described herein may also apply to other pipelines and other process fluids, including petroleum, various types of crude or processed oil, natural and highly volatile gasses, chemicals, and the like. The descriptions herein therefore are not limited in application to Sulphur pipelines.
In light of the above,
With reference to the pipeline system 102, in some embodiments, the pipeline system 102 can include one or more pipes 112 and can transport a fluid 114 such as, but not limited to, Sulphur. For example, as shown in
Additionally, in some embodiments, the pipeline system 102 can include one or more storage or transportation devices, fittings, and/or support structures. Storage or transportation devices may be devices other than pipes that are capable of storing and/or transporting fluids such as, but not limited to, tanks and/or storage vessels. Fittings may include, but are not limited to, adaptors, elbows, couplings, unions, nipples, reducers, tees, crosses, end caps, electrical or mechanical valves, flanges, and/or other devices interconnected with pipes 112 and storage or transportation devices. Support structures may include, but are not limited to, pipe anchors and/or pipe guides configured to hold the pipes 112 in place and/or prevent rotation of the pipes 112.
As an example, as shown in
Ideally, the pipeline system 102 has a “uniform thermal profile” in which there are no heat sinks along the pipeline system 102 that would cause excessive amounts of heat to be lost to surrounding areas. However, in reality, the fluid 114 may exhibit different temperatures along different locations within the pipeline system 102 due to heat sinks and other non-uniform heat loss. For example, certain components of the pipeline system 102 such as, but not limited to, valves, flanges, pipe anchors, and/or pipe guides may be more susceptible to heat loss and, thus, may be referred to as “high heat loss points,” such that fluid 114 may have a lower temperature adjacent these high heat loss points. In addition, poorly installed thermal insulation around the pipes 112 can jeopardize pipeline heat loss uniformity. For example, improperly installed insulation may be exposed to moisture, and wet insulation may result in excessive heat loss at such locations.
Accordingly, it may be desirable to monitor fluid temperatures at or along a pipe 112 near one or more high heat loss points and/or on the high heat loss points, where the fluid 114 may be more prone to freezing or dropping below a temperature setpoint as compared to other locations along the pipes 112. Referring now to the temperature sensing system 106, in some embodiments, the temperature sensing system 106 can comprise or include one or more linear temperature sensors. Further, in some embodiments, the temperature sensing system 106 can comprise a distributed temperature sensing (DTS) system, which can be configured to sense temperatures at multiple data points along the length of an optical fiber 126. Accordingly, as shown in
In some embodiments, the optical fiber 126 may be installed along substantially the full length of the pipeline system 102. Accordingly, with reference to
In some embodiments, as shown in
More specifically, as shown in
As shown in
While the systems 100 illustrated in
Accordingly, the temperature sensing system 106, in the form of a DTS system, can provide thermal intelligence by monitoring the temperature along the entire pipeline 102. More specifically, the DTS system can monitor temperatures, for example, at every meter segment of the pipeline 102 and, in some embodiments, may also monitor temperatures at one or more “off-pipe” areas, e.g., to be used as ambient temperature measurements.
Referring back to
With reference to the management system 108, in some embodiments, the management system 108 can include at least one controller. The controller can be any controller suitable for receiving inputs from one or more sensors, devices, or sources of data representing temperature or other parameters and can be capable of controlling components of the system 100 such as, but not limited to, the heating system 104, the pump 111, the temperature sensing system 106, etc. For example, in some embodiments, the controller can be a standalone controller such as a microcontroller that can include at least one processor and at least one memory or a programmable logic controller (PLC). The controller can be configured to execute a management program in accordance with one or more of the methods described below.
In some embodiments, the management system 108 can directly control the heating system 104 and/or the temperature sensing system 106, and/or can communicate with and/or provide instructions to dedicated controllers of the heating system 104 and/or the temperature sensing system 106. In some embodiments, the management system 108 can also communicate with other components of the system 100 or outside the system 100 to obtain certain operational parameter including, but not limited to, pump parameters, flow rates (e.g., at pipeline inlets and outlets), valve positions, weather conditions, heating cable parameters (e.g., voltage, current, power output or consumption, etc.), RTD temperature sensor values, etc.
For example, as shown in
As another example, as shown in
As yet another example,
Furthermore, in some embodiments, the management system 108 can be coupled to and in communication with a remote device 144, as shown in
Referring still to
Additionally, as shown in
Additionally, one or more discrete temperature sensors, such as resistance temperature detectors (RTDs, such as platinum RTDs) 168 may optionally be included along pipe 112. The RTDs 168 can generate RTD temperature data, separate from the temperature data generated by the DTS system, which may be used by the management system 108 for verification of the DTS data (e.g., to ensure that the DTS data is reasonably accurate). Furthermore, in some embodiments, temperature sensors (or optical fiber lines 126) may also be routed through heating cable splice boxes 156. Furthermore, it should be noted that the optical fiber line(s) 126 and associated hardware and circuitry in any of the above described embodiments may be installed during pipeline install or retrofit onto existing systems.
Furthermore, in some embodiments, the system 100 can further include one or more different types of sensors for generating pipeline data and other dynamic information, e.g., which may be sent to and received by the management system 108. These sensor inputs may include both distributed and/or discrete measurements, and may generate data describing the process fluid 114 and its flow, as well as the status of different system components such as the heating system 104, the temperature sensing system 106, pipe insulation 146, various sensors, and the pipe sections themselves. For example, the system 100 can include one or more sensors configured to generate pipeline data such as heating cable power outputs, pump status, pressure, flow rate, heating cable voltages, heating cable currents, alarm signals (e.g., pump alarm signals, high pressure alarm signals, etc.) or others.
Accordingly, the process 200 will be described below with reference to being executed by the controller 142 of the process automation system 110, but can instead be executed by another controller of the system 100 in some embodiments. Additionally, analysis of certain data values may be performed by a combination of one, two, or three controllers 128, 140, and/or 142. Furthermore, all values read, determined, or otherwise obtained by any of the controllers 128, 140, 142 can be stored in memory, such as non-transitory memory of the management system 108.
Generally, at step 202, the controller 142 can receive one or more operational parameter values. At step 204, the controller 142 can determine at least one predictive parameter value based on the operational parameter value(s). At step 206, the controller 142 can determine if any pipeline locations require heating based on the at least one predictive parameter value. If so, at step 208, the controller 142 can energize one or more heating cable circuits at the one or more pipeline locations. If not, or after energization commands are sent at step 208, the controller 142 can proceed to step 210 and generate a notification based on the operational parameter value(s) and/or the at least one predictive parameter value. At step 212, the controller 142 can output the notification to a display of the management system 108 or to one or more remote devices 144.
More specifically, with respect to step 202, the controller 142 can receive one or more operational parameter values. In some embodiments, the operational parameter values can include temperature values, e.g., received from the temperature sensing system 106. Other operational parameter values can include values of operational parameters such as alarm signals (e.g., pump alarm signals, high pressure alarm signals, etc.), heater cable power outputs, pump status, pressure, flow rate, heater cable voltages, heater cable currents, supplementary temperature readings, physical pipeline parameters (e.g., high or low elevations, elbows, curves, etc.) and other suitable parameters. In some embodiments, operational parameter values can be obtained from sensors or meters, calculated or estimated based on other parameters, or otherwise determined. For example, flow rate may be obtained from a flow meter within the pipeline 102, or otherwise obtained without requiring a flow meter. Additionally, in some embodiments, operational parameter values can include parameters outside the pipeline 102, such as ambient temperature or weather data.
At 204, the controller 142 can determine at least one predictive parameter value based on the operational parameter values. The at least one predictive parameter value can include one or more values of parameters such as percentage fill, phase status, flow status, pipeline plug formation detection and/or location, pipeline heat loss coefficient, pipeline insulation health, pipeline anchor health, pipeline rupture points, hot or cold spots, time-to-freeze, re-melt status and time until re-melt, time to reach operational limits, heater cable health, DTS system health, melt uniformity, and/or solid Sulphur distribution.
Certain predictive parameter values can be determined based on the temperature values with related time and locations (considered “DTS data”), such as by analyzing trends of temperature values over time at pipelines locations. For example, the controller 142 can determine hot spots, cold spots, pipeline plug formation detection, pipeline plug formation locations, time-to-freeze, freeze detection, rupture points, and/or percentage fill based, at least in part, on the temperature values. Other predictive parameter values can be determined based on the temperature values as well as additional information such as alarm signals, heater power outputs, pump status, pressure, flow rate, heater cable voltages, heater cable currents, and/or supplementary temperature readings.
For example, the controller 142 can determine percentage fill (e.g., pipe percentage fill) based on the temperature measurements, flow rate(s) of the pipeline 102, heater operational data, ambient temperature measurements, and/or weather data. More specifically, a cooling rate of the temperature values can vary based on how full of fluid the pipeline 102 is and, thus, percentage fill may be determined at least by looking at relative temperature rates of change and/or by also factoring in flow rate, heater operation, ambient temperature, and/or weather data. As another example, using a dual-optical fiber setup, if the pipeline 102 is full, a temperature sensed should match the actual fluid temperature. However, if the pipeline is not completely full, a temperature gradient may form between the top of the pipe 112 and the bottom of the pipe 112. In some embodiments, percentage fill may be output as a percentage between 0% and 100%, inclusive. In other embodiments, percentage fill may be output as a relative value, such as “full,” “partially full,” or “empty.”
In some embodiments, the controller 142 can determine phase status of the fluid 114 at locations along the pipeline 102 based on sensed temperature trends. This phase status may alternatively be considered freeze detection. For example, the controller 142 can determine that the process fluid 114 is beginning to solidify in the pipe 112 by comparing a latent heat signature stored in memory to the temperature data over a time period. That is, the controller 142 can identify one or more latent heat signatures in the extracted temperature data that match the stored latent heat signature. Latent heat signature-based analysis may be beneficial when used in conjunction with pipelines carrying process material, such as Sulphur, that does not freeze at a discrete temperature, but instead freezes at some temperature within a temperature range (e.g., 114-120° C. in the case of Sulphur). For example, monitored temperature accuracies, make-up of process fluid, size of pipe (considering that fluid does not freeze all at once) are but some considerations that affect the actual monitored temperature at which the process fluid will freeze.
Accordingly, the controller 142 can identify the latent heat signature of the actual phase transition, independent of the process fluid's measured temperature, from the DTS data in order to identify phase transitions as they occur in the pipeline 102. That is, a latent heat signature can be a particular rate of change (e.g., within a certain temperature range) that is specific to the process fluid and indicative of a phase change. As an example of a latent heat signature associated with a liquid-to-solid phase change of Sulphur, a transient upward temperature spike may be detected at a location along the pipeline at which Sulphur is transitioning from liquid to solid (e.g., freezing). As an example of a latent heat signature associated with a solid-to-liquid phase change of Sulphur, a continuous temperature decrease may be detected at a location along the pipeline at which Sulphur is transitioning from solid to liquid (e.g., melting). In some embodiments, the latent heat signature unique to the process material 114 can be determined and generated during initial deployment of the pipeline 102 as a process material undergoes its phase changes within the pipeline, and at different points along the pipeline 102.
In some embodiments, the controller 142 can determine flow status based on DTS data. For example, the controller 142 can determine if the pipelines 102 is, or has recently been, flowing using only the DTS temperature data. For example, the controller 142 can analyze DTS data to monitor variations from segment to segment (e.g., meter to meter segments) across a fixed length of pipeline. This determination can work well for applications where there is sufficient heat loss variation along the pipeline 102, and where the pipeline temperature is sufficiently higher than ambient so that the temperature distribution along the pipeline 102 varies by several degrees when the pipeline 102 is in a non-flowing thermal equilibrium state.
Returning back to step 204 of
In some embodiments, the controller 142 can determine an effective pipe heat loss coefficient based on the temperature values, flow rate(s) of the pipeline 102, and/or heater operational measurements. For example, by examining steady-state periods in the pipeline 102, the controller 142 can determine the heat loss coefficient based on linear regression to determine mean effective ambient temperature, mean pipeline temperature, and mean power consumption, and identify if an element changes due to non-ambient conditions (e.g., a pipe going through tunnel suddenly gets buried/surrounded by sand). For example, mean effective ambient temperature can be calculated by taking into account weather conditions from weather data (e.g., wind, precipitation, and/or solar effects) or from off-pipe temperature measurements (e.g., from the DTS system). The heat loss coefficient can be equal to mean power consumption divided by (mean pipe temperature minus effective ambient temperature). Such calculations can be done on a meter-by-meter basis, for example, for each meter of the pipeline 102. Additionally, in some embodiments, the above calculations can be separately performed on spatially continuous sections a respective heater zone of the heating system 104.
In some embodiments, the controller 142 can determine pipeline insulation health or performance integrity, e.g., in order to identify and/or locate pipe insulation failures and/or potential or pending failures. According to one example, pipeline insulation health can be determined based on the determined effective pipe heat loss coefficient, e.g., comparing the heat loss coefficient to a stored baseline value, such as on a meter-by-meter or segment-by-segment basis. In some applications, the stored baseline value may be updated, for example, to reflect normal wear on the pipeline. As another example, the controller 142 can identify the location of wet insulation 146 along the pipeline 102 based on the temperature data. As yet another example, pipeline insulation health for pipeline segments can be determined based on thermal conductivity and temperature rates-of-change, e.g., against a stored baseline value.
In some embodiments, the controller 142 can determine pipeline anchor health based on a spatial shift in temperature values. More specifically, pipeline anchors 122 can “pop” off anchor points and physically move, which may be reflected in the temperature values. For example, as described above, anchors 122 are generally cool spots in the pipeline 102. Therefore, if a measured cool spot shifts to a different segment, this observed shifting pattern can indicate an issue with the anchor 122. Thus, the controller 142 can determine that an anchor 122 has shifted based on the temperature values illustrating a specific pattern (e.g., stored in memory) or, more specifically, by comparing changes in temperature values at and/or adjacent to a known anchor segment to a stored pattern.
In some embodiments, the controller 142 can determine pipeline ruptures and/or other failures using the temperature values. For example, air flowing (e.g., backfilling) into a pipeline 102 can cool the pipeline 102 in a detectable pattern. The controller 142 can thus analyze past temperature values at a given segment along the pipeline 102 (e.g., on a meter-by-meter basis) and compare the temperature values to a predetermined (i.e., stored in memory) pattern indicative of a rupture. If the past temperature values are similar to the predetermined pattern, the controller 142 can determine that a rupture has occurred and/or a location of the rupture. Such patterns, for example, can reflect extreme changes in temperature over a short time period over a specific segment.
In some embodiments, the controller 142 can determine hot spots and/or cold spots (heat sinks) using the temperature values. For example, the controller 142 can compare past temperature values at a given segment along the pipeline (e.g., on a meter-by-meter basis) and compare the temperature values directly or to a predetermined pattern or trend. Based on the comparison (e.g., based on temperatures changing at a predefined rate), the controller 142 can determine hot or cold spots present at specific segments, and/or predict that a hot or cold spot is likely to form at a specific segment. The controller 142 can also consider ambient temperature and/or heating system status in hot and cold spot determinations, such that different patterns or trends may be used in the temperature comparison based on ambient temperature values and/or heater energization status. Such information can assist with adjusting physical attributes of the pipeline 102 before bigger failures occur, such as a rupture.
In some embodiments, the controller 142 can determine a time to freeze based on DTS data alone or in combination with ambient temperature, and/or heating system status (e.g., energy input to the heating cables). For example, time to freeze may be a predicted number (e.g., hours and/or minutes) for a pipeline segment to reach a liquid-to-solid phase change temperature. For example, the controller 142 can determine the predicted number by extrapolating a time to reach a liquid-to-solid phase change temperature based on determined trends in the DTS data and, optionally, while also taking into account ambient temperature and/or heating system status.
The controller 142 can also determine a re-melt status or time until re-melt is complete based on an energy input to the heating cables and a predetermined temperature loss profile. For example, time until re-melt is complete may be a predicted number (e.g., hours and/or minutes) for a pipeline segment to reach a liquid temperature.
The controller 142 can further determine a time to reach operational temperature limits. For example, the controller 142 can remove noise and fit a line to monitored temperature gradients over time using linear regression so that extrapolation can be performed to determine a time that an operational temperature limit is reached (e.g., assuming outside conditions do not significantly change or accounting for changes in outside conditions, such as weather). Such information can be used, for example, for localized heating optimization of the fluid 114 within the pipeline 102 to deliver the fluid 114 (e.g., to a plant) within an optimal temperature window.
In some embodiments, the controller 142 can determine skin effect heating system health based on DTS data, heater cable voltages, heater cable currents, and/or heater cable power consumption. For example, skin effect heating cable impedance can change with cable temperature. The controller 142 can compare observed heater power consumption with predetermined “ideal” or “expected” heater power consumption (e.g., based on temperature data), to identify poor skin effect heater system health. Additionally, in some embodiments, the controller 142 can determine skin effect heating system health based on a temperature of a splice box housing a portion of the skin effect heating cable, as higher splice box temperatures can indicate relatively poor skin effect heater system health.
Additionally, in some embodiments, the controller 142 can monitor heater cable operation to detect areas that are not properly heating. For example, the controller 142 can compare temperature trends in individual segments against overall temperature trends of the full heater cable length. If the segment trends do not follow the overall trends, such anomalies may be indicative of the segment not properly heating.
In some embodiments, the controller 142 can determine DTS system health in order to determine an amount of performance degradation of the optical fiber(s) over extended periods of time. For example, the controller 142 can measure fiber attenuation (e.g., signal fidelity) against a commissioned baseline value for the entire length of the optical fiber line over time. As another example, the controller 142 can record historical process fluid temperatures during routine operations and excursion events. As new temperature data is generated and obtained, this new temperature data may be verified in order to ensure that the measured temperatures are within a reasonable range based on predefined ranges or baselines that may be stored in the non-transitory memory. This verification may be performed on the new temperature data before the new temperature data undergoes further analysis as described above and before the new temperature data is stored as part of the historical temperature data in the non-transitory memory. If the new temperature data is successfully verified, the analysis and storage continues normally. Otherwise, if the new temperature data does not pass verification (e.g., the new temperature data is outside of the predefined ranges), the new temperature data may be discarded and does not undergo further processing or storage.
Additionally, in some embodiments, the controller 142 can determine melt uniformity as a comparative analysis of phase changes along the pipeline 102. For example, the controller 142 can determine an amount of process fluid 114 in the pipeline 102 that is fluid or solid and approximate locations of such phases based on DTS data (such as percent fill data). Furthermore, in some embodiments, the controller 142 can determine a solid Sulphur distribution, for example, as aggregated locations of frozen process fluid 114 (e.g., Sulphur) in the pipeline. More specifically, the controller 142 can determine segments of frozen Sulphur and aggregate adjacent segments to output section lengths of frozen Sulphur. This allows the controller 142 to communicate to an operator whether, for example, a one-meter section of pipeline 102 is frozen or a full 50-meter section of pipeline 102 is frozen.
Furthermore, generally, in light of the above, the controller 142 can determine trends (e.g., historical patterns) of any of the operational parameter values and/or the predictive parameter values described herein.
Referring back to the process 200 of
At step 208, the controller 142 can energize one or more heating cable circuits at the one or more pipeline segment locations as determined at step 206. For example, the controller 142 can determine one or more heating cable commands to implement controlled re-melting of frozen areas of the pipeline 102 based on the temperature values in order to prevent pipeline rupture. As another example, the heating cable commands can include staged temperature increase to prevent rupture based on a predefined (e.g., stored) profile that factors in one or more operational parameters.
Step 208, as described above, indicates an automated process for pipeline heating and re-melt. In some embodiments, however, the process 200 may provide assistance for manual heating operations. In such embodiments, at step 208, the controller 142 may instead determine optimal heating cable commands to maintain a uniform thermal profile along the pipeline and/or to achieve a staged temperature increase to prevent rupture based on a predefined (e.g., stored) profile that factors in one or more operational parameters. In such embodiments, the optimal heating cable commands become predictive parameter values for use in steps 210 and 212 below.
Following step 208 or if no heating is required as determined at step 206, the controller 142 can proceed to step 210 and can generate one or more notifications based on the at least one predictive parameter value and/or the at least one predictive parameter value. For example, the controller 142 can generate a notification regarding detected ruptures, temperatures, pipeline component health (e.g., insulation health and/or anchor health), frozen areas, or any other suitable parameter value determined at step 204 and/or received or otherwise obtained at step 202. In some embodiments, the notification(s) can include graphs, charts, text instructions, and/or other infographics generated based on the at least one predictive parameter value and/or the at least one operational parameter value. The notification may be in the form of a report, chart, graph, alert, alarm, warning, etc.
At step 212, the controller 142 can output the notification to one or more remote devices 144 and/or to a display of the management system 108 (e.g., a display 143 as shown in
For example,
In some embodiments, as noted above, a GUI can provide one or more pipeline model views to allow an operator to better understand exactly what parts of the physical installation may be indicated. The pipeline model can include lengths, positions, material components, and/or other notable features of the pipeline 102 so that the operator can correlate to the physical installation. The controller 142, therefore, can map raw data from operation or predictive parameters to the pipeline model. For example, a length of fiber optic cable 126 may not necessarily match a measured length of pipeline 102 (e.g., due to stretching, loosening, snaking, loop-backs, lead-ins and lead-outs, pull-boxes and splice-boxes, etc.), the fiber optical cable 126 may shift after initial commissioning. Also, not all of the fiber optic cable 126 is on the pipeline 102 itself, such that some DTS data refers to pipeline temperatures, some DTS data refers to off-pipe (e.g., ambient temperatures), and some DTS data may not reference usable temperature data. Furthermore, the temperature sensing system 106 may sense temperatures at different length intervals. In light of these variables,
More specifically, with reference to the process 260 of
Still referring to
In addition to a pipeline model, in some embodiments, a heater status can be displayed via a GUI (such as one of the GUIs 250, 252 described above). More specifically, a heater status can include pipeline heating system energization charted relative to real-time temperature profiles of segments of the pipeline 102. Such a view can allow an operator to better understand potential causes of certain temperature profiles.
Furthermore, in some embodiments, a health gauge can be displayed via a GUI (such as one of the GUIs 250, 252 described above). More specifically, the health gauge can provide an instantaneous “risk level” or “score” of the pipeline 102. For example, the controller 142 can execute a process, such as process 290 illustrated in
More specifically, with reference to the process 290 of
With further reference to alarms, in some embodiments, notifications in the form of alarms indicate certain conditions such as out-of-range pipeline temperatures, which could jeopardize the flow of process fluid in the pipeline, other pipeline alarms, pump alarms, heater cable alarms, DTS system alarms, communication alarms, etc. While the controller 142 may individually display each alarm notification for each segment along the pipeline, it can be beneficial to aggregate alarms so that an operator has a cleaner view of what is occurring thermally along the pipeline 102, allowing the operator to more quickly determine what segments of the pipeline 102 may require attention. For example, when multiple alarms are determined along adjacent segments, the controller 142 can create a larger segment showing the extended length of the alarm condition along the pipeline 102.
More specifically,
Additionally, in some embodiments, the controller 142 can output a notification in the form of a shift report. For example, a shift report can be a summary report for a predetermined time increment (e.g., a past shift interval), in which historical data is summarized, such as high/low temperature values, locations, times, etc. This can be beneficial for operators to obtain key operating data for a specific period in a short summary without having to study historical temperature profiles and other concurrent data. A past shift interval may be a prior time increment between, for example, 5 and 24 hours, such as 5 hours, 10 hours, 12 hours, 18 hours, 24 hours, or another appropriate interval. For example, in order to optimally manage the operation of the system 100, a smooth and seamless handover of key information to an oncoming shift of operating personnel can be beneficial. That is, understanding what has occurred in the last shift can be especially helpful to the shift personnel about to inherit management of the pipeline 102. Furthermore, any unusual conditions or areas of concern can be provided so as to alert the new personnel to a higher level of vigilance. As sifting through information from a past shift can require significant efforts, a shift report can save operators hours of time.
Accordingly,
Accordingly, notifications can be in the form of status displays providing information. Some notifications can instruct an operation to take certain actions. For example, the controller 142 can display a notification indicating that wet insulation at a location needs to be repaired or replaced. Such notifications allow for maximizing the efficiency of the thermal envelope around the pipe network to reduce areas of heat loss. As another example, the controller 142 can display a pipeline anchor alert (e.g., based on temperature patterns, as described above, and/or time-to-freeze values), in which a pipeline anchor may need to be serviced.
According to another example, notifications can be in the form of indicating partial or full re-melt procedures in response to detecting solidified process fluid in the pipeline. A challenge in a re-melt is to allow the process fluid 114 (e.g., Sulphur) to move unimpeded throughout the pipeline 102 as it expands through the phase change, and to find those void spaces that were created during freezing (from shrinking Sulphur). The reclaiming of the void spaces with melted Sulphur can prevent significant internal pipe pressure building from occurring. As there are generally insufficient pressure indicators along a pipeline to identify this building pressure, temperature can be the mechanism to monitor and manage pressure build-up.
For example,
At step 378, the controller 142 can identify potential “confined zones” where Sulphur may become trapped on re-melt. At step 380, the controller 142 can monitor temperature during re-melt as it approaches the theoretical phase change temperature. At step 382, the controller 142 can utilize a latent heat signature to identify phase changes, as described above. At step 384, the controller 142 can aggregate melted “zones” and identify any confined zones sections of trapped Sulphur. At step 386, the controller 142 can display an alarm indicating confined zones for intervention. At step 388, the controller 142 can predict (and/or display) time-to-melt for frozen sections on both ends of a confined zone. For example, the alarms and/or the times can be displayed via a GUI 250, 252 (e.g., such as on one of the pipeline models 256, 258 in the GUI 252 of
In some embodiments, the system 100 may take required actions automatically, without user intervention. For example, when it is determined that process fluid 114 is beginning to solidify or has solidified, the controller 142 can automatically execute a re-melt process by instructing specific heat tubes 154 to provide additional heat (e.g., beyond that which is needed to maintain the temperature of pipeline 102 at a setpoint temperature) to sections of pipe 112 in which solidification of process fluid is detected to be occurring. This may be considered a closed-loop re-melt approach.
In further embodiments, the system 100 may incorporate a partial or fully closed-loop approach, in which the controller 142 can establish optimum temperature setpoints for the heating system 104 based on the DTS data and data from the heating system 104. Such optimum setpoints can be indicated to an operator via a notification, as described above, or can be used by the controller 142 to directly control the heating system 104 based on real-time temperature values.
For example,
At step 406, the controller 142 can retrieve heating system high limits and control setpoints (e.g., from the controller 140). At step 408, the controller 142 can retrieve all alarm states. At step 410, the controller 142 determines if any alarm states indicate hot alarms. If so, the controller 142 reduces a temperature setpoint at step 412. That is, as shown in
Referring back to
Additionally, referring back to
At step 442, the controller 142 determines if detal_ts is greater than a first stored threshold (e.g., 0.5). If so, at step 444, the controller 142 sets a new setpoint as the last setpoint plus the lesser of delta_ts and delta_hi. If not, the controller 142 determines if delta_ts is less than a second stored threshold (e.g., −0.5) at step 446. If so, the controller 142 sets a new setpoint as the last setpoint plus the higher of delta_ts and delta_lo.
Following steps 416, 428, 444, or 448, the controller 142 determines if there is a new setpoint at step 450. If so, the controller 142 then stores the new setpoint at step 452. For example, the new setpoint may be displayed as a notification in some embodiments. In other embodiments, the new setpoint may be communicated to the heating system 104 for automatic heating system control.
In light of the above, an “intelligent” pipeline as provided herein seeks to maintain a uniform thermal profile along the pipeline, even in plugged and re-melt situations. To achieve a homogenous thermal profile for the entire pipeline, the systems of some embodiments can integrate existing pipeline heating technology, pre-insulated piping, a sensor network (e.g., a fiber optic based Distributed Temperature Sensing (DTS) system) to monitor pipeline temperature along the entire length of the pipeline, engineered pipe supports and anchors that minimize localized heat loss, and computational modelling and transient analysis. Together, all of these system components and customized procedures can create synergies in the operation of process fluid transport pipelines.
Accordingly, using the systems and methods described herein, automated or assisted re-melt may be performed based on DTS data for the pipeline and other dynamic information gathered for the pipeline. As such, re-melt processes may become more predictable, with less left to chance. Furthermore, the data processing associated with methods of some embodiments extends beyond traditional pipeline temperature monitoring, which is generally limited to providing pre-alarms or alarms when the pipeline temperature has moved out of the acceptable range for some portion of the pipeline. Instead, the systems and methods of some embodiments provide data analysis modules that can be used in the support of the day-to-day operation and maintenance of the pipeline.
It should be noted that the controllers described herein comprise a processor and memory storing specific computer-executable instructions that, when executed by the processor, carry out the steps of any of the methods described above. Furthermore, while the methods herein are shown and described as steps in a particular order, in some embodiments, certain steps may be eliminated, added, or rearranged in a different order.
While the invention has been illustrated and described in detail in the foregoing drawings and description, the same is to be considered as illustrative and not restrictive in character, it being understood that only illustrative embodiments thereof have been shown and described and that all changes and modifications that come within the spirit of the invention are desired to be protected. For example, any of the features or functions of any of the embodiments disclosed herein may be incorporated into any of the other embodiments disclosed herein. Various features and advantages of the invention are set forth in the following claims.
This application claims priority under 35 U.S.C. § 119 to U.S. Provisional Patent Application No. 63/209,648 filed on Jun. 11, 2021, the entire contents of which is incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63209648 | Jun 2021 | US |
