Method and system for automated monitoring of the operational parameters and severity of the phenomenon of co2 stress corrosion cracking in flexible pipelines

Abstract

The present disclosure is applied to the field of flexible pipes, risers, or pipelines that are subject to the phenomenon of CO2 stress corrosion cracking (SCC-CO2). More specifically, the present disclosure relates to methods and systems for monitoring automated of the operational parameters and severity of the phenomenon of CO2 stress corrosion cracking in flexible pipelines. An embodiment of a method includes collecting real-time monitored operational data, determining an Operational Severity Index (OSI), and displaying the monitored OSI in real time in the computational tool.

Description

FIELD OF THE DISCLOSURE

[0001] The present disclosure is applied in the field of pipelines, risers or flexible pipelines that are subject to the phenomenon of CO2 stress corrosion cracking (Stress Corrosion Cracking by CO2-SCC-CO2). More particularly, the present disclosure belongs to the field of real-time management and monitoring of the integrity of flexible pipelines to assess the criticality of flexible pipelines subject to SCC-CO2 and optimize the assessment of these pipelines with regard to the extension of their useful life.BACKGROUND OF THE DISCLOSURE

[0002] Flexible unbonded pipes, as shown by way of example in FIG. 1, are structures widely used in the subsea oil production system and are responsible for transporting fluids (hydrocarbons, gas, or water) from the wells to the offshore production units. These structures are designed according to API Spec 17J and API RP 17B standards and consist of a sequence of layers of metallic, polymeric, and composite materials that perform specific structural functions. Operational records of these components show that they are subject to several failure modes during operation, with CO2 stress corrosion cracking (SCC-CO2) being one of the most relevant.

[0003] The SCC-CO2 phenomenon consists of a failure mechanism recently discovered by the oil and gas industry and not addressed to by the standards in force at the time. Specifically, it was discovered during an offshore operation to recover a flexible pipeline, in which there was identified the rupture of the tensile armor wires, which are the structures responsible for the pipeline's tensile load-bearing capacity.

[0004] In light of the incident, Petrobras set up an investigation team to assess the root cause of the failure and outline a strategy to define the scope and mitigating measures for the problem. Considering the results obtained in the assessment of the pipeline's life cycle (qualification, engineering, manufacturing, handling, installation, operation and recovery), in the dissections, analysis of laboratory tests, and bibliographic research, it was concluded that the failure that occurred was the result of a cracking phenomenon assisted by the medium activated by carbon dioxide (CO2) in the pipeline's annular space, described as CO2 stress corrosion cracking.

[0005] Reference is made to FIG. 2, which shows, by way of example, a schematic representation of the phenomena that occur in the annular space of a flexible pipeline subject to SCC-CO2. The annular space is characterized by being a region between two watertight but permeable polymer layers, where the metal layers subject to SCC-CO2 are located. During operation of the pipeline, the annular space may be exposed to corrosive gases that permeate from the transported fluid (such as CO2 and H2S, for example) and to water that permeates from inside the pipeline, through the barrier polymer. The eventual loss of watertightness of the outer layer also results in the presence of water in the annular space.

[0006] In this annular environment containing water and corrosive gases, different failure mechanisms may occur, such as corrosion-fatigue and hydrogen-induced cracking (HIC). In turn, for SCC-CO2 to occur, four main factors must interact, as follows: 1—Tensile stress acting on the metal structural layers of the flexible pipeline; 2—Corrosive environment containing CO2 and water; 3—Material of the structural layers that is susceptible to the phenomenon; and 4—Sufficient time for the incubation and subsequent growth of the crack to occur, which may lead to the rupture of the layer.

[0007] Currently, one of the strategies adopted to manage the operating risk of flexible pipelines within the scope of SCC-CO2 is based, among other actions, on conducting preliminary risk analyses (PRA), the main input of which is the analysis of the useful life of the structures.

[0008] Thus, it is known that the SCC-CO2 phenomenon depends on the operating conditions of the pipeline (pressure and temperature), the type of fluid transported, and the level of CO2 in the annular space. These variables are incorporated into a useful life calculation model through gas permeation analyses through the polymeric barriers, which establish the CO2 content in the annulus and, consequently, the severity of the environment to the SCC-CO2 failure mechanism. The use of such models is of great utility, since they provide an estimate of the gas fugacity in the annulus considering multiple variables.

[0009] However, such models are complex and require a lot of analysis time, in addition to not allowing real-time monitoring of the annulus conditions during the operation of the flexible pipeline, since their objective is to predict / estimate a useful life value for the pipeline.

[0010] In addition, useful life calculations consider projected future operating scenarios, whose predicted maximum values of pressure, temperature, and CO2 content in the flowed fluid establish operational limits for the parameters to be monitored during the operation of the pipeline, without taking into account that eventual deviations in these limits may occur, so that the correct assessment of the criticality of these events depends on useful life re-analyses, which consume significant engineering time.

[0011] Thus, one of the main challenges in the process of managing the integrity of flexible pipelines subject to the SCC-CO2 phenomenon consists of managing operational deviations of the monitored variables (pressure, temperature and CO2 content in the flowed fluid) throughout the useful life of the structure, since the failure phenomenon depends on the interaction between several factors, as described above.

[0012] In this way, in view of the presented problems and difficulties, it is necessary to advance the State of the Art towards the development of a method capable of quantifying, expressly and in real time, the operational severity of the flexible pipeline, jointly assessing the main variables that impact its useful life within the scope of SCC-CO2 (i.e., pressure, temperature and CO2 content).STATE OF THE ART

[0013] The search for the history led to some documents that disclose matters within the technological field of the present disclosure.

[0014] Document US20230221226A1 refers to a method for calculating the useful life of flexible pipelines subject to the SCC-CO2 phenomenon, wherein the method allows establishing the criticality level of each pipeline within the scope of the phenomenon, allowing the establishment of actions for the most critical cases. In addition, another important feature with the development of the aforementioned method, according to document US20230221226A1, is related to the fact that it allows safe operation, even if the pipe / pipeline is degraded.

[0015] Despite the developments presented by document US20230221226A1, this document focuses on the development of a method for estimating the useful life of flexible pipeline taking into account the calculation of the crack growth rate obtained by artificial intelligence (AI) techniques and the estimation of critical crack, whereas the present disclosure performs a calculation by an AI computational tool to determine an operational severity index that combines the three main variables that influence the useful life of pipelines subject to SCC-CO2 (pressure, temperature and CO2 content), also taking into account operational deviations during monitoring.

[0016] Document US20140212978A1 describes methods, systems, and computer-readable media for monitoring operational processes. An example method of this document includes monitoring an equipment parameter associated with an asset in an operational process unit and monitoring a process parameter associated with the asset. Said example method includes determining an asset integrity corresponding to the asset. Asset integrity is determined based on a potential corrosion state associated with the asset, by comparing a corrosivity index with a corrosion limit. The corrosivity index is a function of at least one of the current values or changes in the monitored equipment parameter, or in the monitored process parameter over time.

[0017] However, despite the development presented by US20140212978A1, the method implemented by this document is focused on a thermal stress index, which must be compared to a calculated thermal stress limit value, and, when the value exceeds this limit, an alarm is issued. The aforementioned document fails to disclose an operational severity index that considers three monitoring parameters together (pressure, temperature and CO2 content) and also the operational deviations that occur over time, to provide more precise control of the flexible pipeline conditions for real-time monitoring.

[0018] Document US20180275100A1 describes a system that includes one or more tools, sensors, or both, configured to obtain data related to one or more pipelines, wherein the data is ultrasonic data, electromagnetic data, or both, and a cloud-based computing system including at least one processor that receives the data from the one or more tools, sensors, or both, performs analysis to generate a virtual structural model of the one or more pipelines based on the data, determines one or more states of the one or more pipelines using the virtual structural model, and determines whether to perform one or more actions when one or more states indicate that the one or more pipelines violate a threshold operating boundary.

[0019] However, document US20180275100A1 does not disclose or suggest a methodology to be implemented in flexible pipelines subject to the SCC-CO2 phenomenon, which consists of developing an operational severity index, making real-time control of the involved variables more effective and, at the same time, considering any operational deviations detected by the monitoring tools.BRIEF DESCRIPTION OF THE DISCLOSURE

[0020] The present disclosure is applied to the field of flexible pipelines, risers or pipelines that are subject to the phenomenon of CO2 stress corrosion cracking (SCC-CO2) and, more specifically, the present disclosure pertains to the field of real-time management and monitoring of the integrity of flexible pipelines to assess the criticality of flexible pipelines subject to SCC-CO2 and optimize the assessment of pipelines with potential for extending their useful life.

[0021] The present disclosure relates to embodiments of methods and systems for automated monitoring of the operational parameters and severity of the phenomenon of CO2 stress corrosion cracking in flexible pipelines.

[0022] By means of a method associated with the present disclosure, it is possible to perform a real-time assessment of the operational severity index of flexible pipelines, which results in a significant reduction in the engineering time spent on assessing the criticality of flexible pipelines subject to the phenomenon of SCC-CO2 and assessing their useful life.

[0023] Furthermore, a method developed by the present disclosure also allows operational deviations to be assessed in real time, reducing the chance of leaks due to possible structural failures.

[0024] It is also worth highlighting that embodiments of methods and systems of the present disclosure add robustness to the integrity management of flexible pipelines, consequently reducing the probability of failure of these structures during operation, which in turn will occur within previously validated safety limits.

[0025] Thus, the present disclosure significantly increases the operational reliability of the flexible pipelines subject to SCC-CO2, since operational deviations can be assessed in real time, significantly reducing the time between the occurrence of an operational event and the decision-making regarding the operational continuity of the pipeline.

[0026] Accordingly, an embodiment of a method for automated monitoring of the operational parameters and severity of the CO2 stress corrosion cracking phenomenon in flexible pipelines includes collecting real-time monitored operational data, by means of a digital twin computational tool, from a server that stores monitored operational data from sensors installed in a subsea assembly; determining an Operational Severity Index (OSI), based on the collected monitored operational data, by means of OSI calculation performed by the computational tool; and displaying the monitored OSI in real time in the computational tool.

[0027] Furthermore, the present disclosure also provides a system for automated monitoring of the operational parameters and severity of the CO2 stress corrosion cracking phenomenon in flexible pipelines, wherein the system includes: sensors installed in a subsea assembly to monitor operational parameters of a pipeline; a server that stores monitored operational data; and a digital twin computational tool configured to: collect the monitored operational data; determine an Operational Severity Index (OSI) based on the collected monitored operational data, by means of OSI calculation; and display the monitored OSI in real time.BRIEF DESCRIPTION OF THE FIGURES

[0028] The foregoing brief description, as well as the detailed description below of the preferred embodiments of the disclosure in question, will be better understood when read in conjunction with the accompanying drawings. It should be understood, however, that the disclosure in question is not limited to the precise arrangements and instruments shown.

[0029] Accordingly, the present disclosure will be described below with reference to its typical embodiments and also with reference to the accompanying drawings, in which:

[0030] FIG. 1 shows a schematic representation of the internal layers of a typical unbonded flexible pipe.

[0031] FIG. 2 shows a schematic representation of the conditions in the annulus of a flexible pipeline subject to SCC-CO2.

[0032] FIG. 3A shows a schematic representation, by means of graphs, of the monitored operational conditions, and of the projected future scenario, as detected by sensors of a subsea assembly, according to an exemplary configuration of the present disclosure.

[0033] FIG. 3B shows a schematic representation, by means of graphs, of the calculation of the Operational Severity Index (OSI) and the Cumulative Operational Severity Index (COSI), demonstrating the real-time comparison between the monitored indices and the reference indices, according to an exemplary configuration of the present disclosure.

[0034] FIG. 4A shows a real example of operational conditions monitored by means of sensors of a subsea assembly, according to an exemplary configuration of the present disclosure.

[0035] FIG. 4B shows a real-time example of the calculation of OSI and COSI for an interconnection operating within the scope of the SCC-CO2, according to an exemplary configuration of the present disclosure.

[0036] FIG. 5 shows a schematic representation of the system implementing the method for data capture, calculation of the Operational Severity Index (OSI) and display of the results using a digital twin computational tool, according to an exemplary configuration of the present disclosure.DETAILED DESCRIPTION OF THE DISCLOSURE

[0037] In the following, reference is made in detail to the preferred embodiments of the present disclosure illustrated in the accompanying drawings. Whenever possible, the same or similar reference numerals will be used throughout the drawings to refer to the same or similar features. It should be noted that the drawings are in simplified form and are not represented to a precise scale, so that small variations are anticipated.

[0038] It should be highlighted that the terms “riser(s)”, “pipeline(s)”, “pipe(s)” and “tubing(s)” are used throughout the text as synonyms, and here they should be understood as referring generically to tubular structures used in the offshore oil and gas segment to transport fluids through their interior, also encompassing their most different configurations along a line, such as, for example, flowline, riser and jumper.

[0039] The process of managing the integrity of flexible pipelines depends directly on the calculated useful life of these structures considering the SCC-CO2 failure mechanism. This analysis, in turn, is conducted considering a projected future operational scenario, which establishes limit values of the main variables that influence this failure mechanism (such as pressure, temperature and CO2 content).

[0040] In the traditional monitoring approach, if one of the three monitored variables is above the limit established in the life calculation, a new gas permeation calculation must be performed to quantify the CO2 content in the annular space and, subsequently, a new life calculation must be performed if the new calculated CO2 content is higher than the projected limit value. The steps described above are complex, require a lot of engineering time and generate an increase in operational risk, given that there is a significant period between the occurrence of the operational deviation and the decision-making regarding the operational continuity of the pipe.

[0041] Additionally, the lack of an efficient method for assessing operational severity prevents prioritization of reanalysis of the interconnections that operate under more severe conditions than those foreseen in the design, generating an increase in the associated operational risk.

[0042] In this way, the present disclosure relates to a method and a system that collect monitored operational data, perform processing by using an Operational Severity Index (OSI) calculation method, and display the results to aid in decision-making regarding the operational continuity of the flexible pipe subject to SCC-CO2.

[0043] Reference is made to FIG. 5, which shows a schematic of the system implementing the method for obtaining an Operational Severity Index (OSI), according to an embodiment of the present disclosure. Thus, the method for automated monitoring of the operational parameters and severity of the CO2 stress corrosion cracking phenomenon in flexible pipelines of the present disclosure comprises the steps of: collecting real-time monitored operational data 201, 202, 203, by means of a digital twin computational tool 300, from a server 200 that stores monitored operational data 201, 202, 203 from sensors 101, 102, 103 installed in a subsea assembly 100 for monitoring one or more pipelines; determining an operational severity index (OSI) 303, based on the collected monitored operational data 201, 202, 203, by means of OSI calculation 302 performed by the computational tool 300; and displaying the monitored OSI in real time on the computational tool 300.

[0044] In one embodiment of the present disclosure, in the data collection step, the monitored operational data collected in real time 201, 202, 203 are considered as the input data of the method implemented in a computational environment. These data 201, 202, 203 are necessary for the calculation of the monitored OSI 302 and are automatically collected by a digital twin type computational tool, from a database integrated to a server 200. More specifically, in a subsea assembly 100, which represents a real physical entity in relation to the digital twin computational tool 300, sensors 101, 102, 103 are installed that monitor operational parameters, as well as their variations, over time. These sensors 101, 102, 103 are configured to monitor the main variables s that influence the useful life of pipelines subject to SCC-CO2, that is, pressure, temperature and CO2 content.

[0045] In one embodiment of the present disclosure, the data monitored by the sensors 101, 102, 103 relating to pressure, temperature and CO2 content are sent to the database integrated with the server 200 and form individualized histories of each of the parameters corresponding to the data monitored in the subsea assembly 100 over time. Thus, pressure sensors 101 send data relating to the internal pressure of the flexible pipeline to the desired pipeline sections for analysis of the OSI, which, accumulated over time, form a pressure history 201; temperature sensors 102 send data relating to the internal temperature of the flexible pipeline to the desired analysis sections of the OSI, which, accumulated over time, form a temperature history 202; and sensors that capture the CO2 content 103 send data relating to the CO2 content in the fluid transported inside the flexible pipeline, which, accumulated over time, form a CO2 content history 203.

[0046] Next, in the OSI determination step, the pressure history 201, temperature history 202, and CO2 content history 203 data, stored in the server 200, are automatically collected by the digital twin computational tool 300. Subsequently, the computational tool 300 is configured to calculate the OSI 302, by means of an OSI calculation method 301 integrated therein, through the following expression:ISOCO⁢2=(1.8711·e-0.005⁢%⁢CO⁢2·PP·PP-(0.0031·T+0.534)·0.0002·e-281138.314·(T+273.15))*108(1)wherein:

[0048] PP corresponds to the partial pressure of CO2 in the flowed fluid; and

[0049] T corresponds to the internal temperature of the pipeline.

[0050] Next, with the determination of the calculated OSI 303, also called monitored OSI 303, as it is calculated in real time based on the monitored operational data 201, 202, 203, the digital twin computational tool 300 displays the calculated OSI 303 in real time, over time, for access by integrity managers, in order to assist in decision-making regarding the need to recalculate the useful life of the flexible pipeline.

[0051] Furthermore, according to an embodiment of the present disclosure, the computational tool 300 compares the monitored OSI 303 with a reference OSI, which corresponds to an upper limit of the graph displayed in display step, as shown in FIG. 3B, calculated based on limit values of pressure, temperature and CO2 content, in a future scenario condition, estimated in pipeline useful life calculations.

[0052] The reference OSI uses the same calculation method as OSI 301 integrated into the computational tool 300 to calculate the monitored OSI 303; however, it is calculated by the computational tool 300 based on future scenario values, which are projected / estimated values in pipeline useful life calculations. For example, in a future scenario condition, the pipeline useful life is estimated based on calculations that establish limit values of the main variables or parameters that influence the SCC-CO2 failure mechanism, in this case, pressure, temperature and CO2 content, as shown by way of example in FIG. 3A. These useful life projection approaches are commonly used in the State of the Art to assess the variables mentioned individually, wherein if one of the three monitored variables is above the limit established in the life calculation, a new gas permeation calculation must be performed to quantify the CO2 content in the annular space and, subsequently, a new life calculation must be performed if the new calculated CO2 content is higher than the projected limit value, as described above. Thus, the reference OSI implemented by the present disclosure combines the three variables that influence the useful life of the SCC-CO2 based on the individual limits of each one, calculated through methods of the State of the Art, such as crack growth methodologies for assessing useful life, among others.

[0053] Thus, the Operational Severity Index (OSI) of the present disclosure is a numerical indicator that allows assessing the operational severity of the pipeline regarding the level of CO2 permeation, being directly associated with the potential for CO2 flow through the pressure barrier of a flexible pipeline. The index expresses, in an analytical and rapid manner, the criticality of the operational conditions of a flexible pipeline, aiming at analyzing the adherence of these conditions to those predicted in the future scenario projected for the calculation of useful life. This index allows for the joint assessment of the variables with the greatest influence on the failure mechanism, namely pressure, temperature and CO2 content in the fluid being flown.

[0054] As previously mentioned, the application in the management routine of the method and system of the present disclosure, which take into account both the monitored OSI and the reference OSI, over time, is done in a relative manner, that is, by comparing the real-time operating condition (monitored OSI) with the reference defined with the values of the future scenario projected in the calculation of the pipeline's useful life (reference OSI).

[0055] Furthermore, the OSI of the present disclosure allows monitoring, over time, the operation of the same pipeline based on a limit reference, considering different operational situations, even if one or more monitored variables (pressure, temperature or CO2 content) are above the individual limit value, as shown in FIGS. 3A and 3B, since in its calculation method 301 integrated with the computational tool 300, the operational deviations that are perceived in the monitoring by the sensors 101, 102, 103 of each parameter individually are taken into account, to make the assessment of when it is in fact necessary to perform maintenance on the assessed pipeline more precise. It is worth mentioning, therefore, that both the monitored OSI and the reference OSI are given as a function of the temperature, pressure and CO2 content, and both consider in their calculations the operational deviations that are perceived in the monitoring by the sensors of each parameter individually.

[0056] Furthermore, in one embodiment of the present disclosure, the digital twin computational tool 300 determines a cumulative operational severity index (COSI), which is a sum of OSI. As with the monitored OSI and the reference OSI, the computational tool 300 provides a monitored COSI that is also compared with a reference COSI, also provided by the computational tool 300 and predicted in the calculation, wherein it is possible to observe the cumulative effect of the operational severity on the pipeline, as shown in FIG. 3B. The COSI, therefore, presents the accumulated index of OSI over time and serves to indicate the severity of the operational conditions, in relation to the scenario predicted for the projected future conditions. Thus, the general expression of the COSI calculation by the digital tool 300 is given as follows:IASO=Σ⁢ISO.(2)

[0057] FIG. 3B, therefore, presents a schematic representation of the application of OSI and COSI, in accordance with the method and system of the present disclosure. It can be observed that, although the operational records, shown in FIG. 3A, captured from sensors 101, 102, 103 of the subsea assembly 100, show deviations over time, that is, point values of pressure and temperature above the reference of the future scenario (horizontal line), the calculation of OSI and COSI was below the reference OSI and COSI values, respectively, since the CO2 content measured for this pipeline remained within the maximum limits, compensating for the deviations of the other variables.

[0058] The present disclosure may be applied to flexible pipelines that are subject to the SCC-CO2 phenomenon. Currently, the scope of this phenomenon is quite extensive in Petrobras' offshore operations, totaling approximately 16 platforms operating in the Brazilian Pre-Salt, which represents more than 100 composed interconnections of flexible pipelines.

[0059] Thus, in one embodiment, the present disclosure relates to a method for automated monitoring of the operational parameters and severity of the CO2 stress corrosion cracking phenomenon in flexible pipelines, the method comprising: collecting real-time monitored operational data 201, 202, 203, by means of a digital twin computational tool 300, from a server 200 that stores monitored operational data 201, 202, 203 from sensors 101, 102, 103 installed in a subsea assembly 100; determining an Operational Severity Index (OSI) 303, based on the collected monitored operational data 201, 202, 203, by means of OSI calculation 302 performed by the computational tool 300; and displaying monitored OSI 303 in real time on the computational tool 300.

[0060] Additionally, in one embodiment, the present disclosure relates to a system for automated monitoring of the operational parameters and severity of the CO2 stress corrosion cracking phenomenon in flexible pipelines, the system comprising: sensors 101, 102, 103 installed in a subsea assembly 100 for monitoring operational parameters of a pipeline; a server 200 that stores monitored operational data 201, 202, 203; and a digital twin computational tool 300 configured to: collect the monitored operational data in real time 201, 202, 203; determine an Operational Severity Index (OSI) 303, based on the collected monitored operational data 201, 202, 203, by calculating OSI 302; and display monitored OSI 303 in real time.EXAMPLES

[0061] The following examples are presented in order to more fully illustrate the predictability of the present disclosure and the way to practice the same, without, however, being considered as limiting the content thereof. The aim is to analyze the response of the method by comparing the results predicted by the method with the actual values.

[0062] FIGS. 4A and 4B illustrate a real case of the application of the operational severity index (OSI) integrated into the digital twin computational tool of the present disclosure. In FIG. 4A, the operational parameters monitored in real time (pressure, temperature and CO2 content) by the sensors are observed, as well as point deviations in relation to the reference defined in the useful life calculation. In turn, in FIG. 4B, the OSI and COSI calculated for the flexible pipe are observed, for a case wherein the index exceeded the reference for a period and, subsequently, returned to the operational envelope, indicating that the point deviations in the operational parameters did not generate the need to reassess the useful life.

[0063] Furthermore, it is worth highlighting that the present disclosure is widely used at Petrobras for pipelines subject to the SCC-CO2 phenomenon and, among the results obtained, there can be mentioned the prioritization of reanalysis of the useful life of pipelines that operate in more severe conditions than those initially predicted by usual methods of estimating useful life, identification of the potential for extending the life of pipelines that operate below the established limits, real-time analysis of the adequacy of the operational scenario predicted in the useful life calculation to the real operational conditions.

[0064] It is worth highlighting that a digital twin computational tool, as described herein, should be understood as a digital model corresponding to physical entities, oriented to reproduce the entire life cycle of the entity, bidirectionally linking physical and information spaces with a single data source. The digital twin technology can track all information of the method and system for automated monitoring of the operational parameters and severity of the CO2 stress corrosion phenomenon in flexible pipelines, using this information to assist in the decision-making process.

[0065] Furthermore, it will be apparent to a technician skilled on the subject that this disclosure can be implemented using one or more suitable general-purpose computers with appropriate hardware and instructed to execute the processes, methods, and systems of the present disclosure. The instruction can be conducted through the use of one or more storage devices readable by the computer processor and containing one or more instructions executable by the computer to perform the operations described above. A computer-readable instruction storage device may take the form of, for example, a CD-ROM or other optical disk, a magnetic tape, a read-only memory (ROM) chip, and other forms well known in the state of the art or later developed.

[0066] The matter described herein may be implemented in digital electronic circuits or in computer software, firmware, or hardware, including the structural means disclosed in this specification and their structural equivalents, or in combinations thereof. The matter described herein may be implemented as one or more computer program products, such as one or more computer programs tangibly incorporated in an information carrier (e.g., in a computer-readable storage device), or incorporated in a propagated signal, for execution by, or for controlling the operation of, data processing apparatus (e.g., a programmable processor, a computer or multiple computers).

[0067] The processes and logic flows described in this specification, including the method steps of the matter described herein, may be performed by one or more programmable processors executing one or more computer programs to perform functions of the matter described herein, operating on input data, and generating results. The processors suitable for executing a computer program include, by way of example, general and special purpose microprocessors and any one or more processors of any type of digital computer. Generally, a processor will receive instructions and data from a read-only memory, or a random access memory, or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include or be operatively coupled for receiving data from or transferring data to, or both, one or more mass storage devices for storing data, for example, magnetic, magneto-optical, or optical disks.

[0068] Those skilled in the art will appreciate the knowledge presented herein and will be able to reproduce the disclosure in the embodiments presented and in other variants, encompassed by the scope of the attached claims.

Claims

1. A method for automated monitoring of the operational parameters and severity of the phenomenon of CO2 stress corrosion cracking in flexible pipelines, the method comprising:collecting real-time monitored operational data, by means of a digital twin computational tool, from a server that stores monitored operational data from one or more sensors installed in a subsea assembly;determining an Operational Severity Index (OSI), based on the collected monitored operational data, by means of OSI calculation performed by the computational tool; anddisplaying monitored OSI in real time in the computational tool.

2. The method according to claim 1, wherein the monitored operational data comprise the following operational parameters: pipeline internal pressure data stored over time in a pressure history, pipeline internal temperature data stored over time in a temperature history, and CO2 content data in the fluid transported inside the pipeline stored over time in a CO2 content history.

3. The method according to claim 1, wherein the computational tool is configured to perform the OSI calculation, by means of an OSI calculation method integrated therein, through the following expression:ISOCO⁢2=(1.8711·e-0.005⁢%⁢CO⁢2·PP·PP-(0.0031·T+0.534)·0.0002·e-281138.314·(T+273.15))*108wherein:PP corresponds to the partial pressure of CO2 in the flowed fluid, andcorresponds to the internal temperature of the pipeline.

4. The method according to claim 2, whereincomputational tool is configured to perform the OSI calculation, by means of an OSI calculation method integrated therein, through the following expression:ISOCO⁢2=(1.8711·e-0.005⁢%⁢CO⁢2·PP·PP-(0.0031·T+0.534)·0.0002·e-281138.314·(T+273.15))*108wherein:PP corresponds to the partial pressure of CO2 in the flowed fluid, andT corresponds to the internal temperature of the pipeline.

5. The method according to claim 1, further comprising:comparing the monitored OSI with a reference OSI, which corresponds to an upper limit calculated based on limit values of pressure, temperature and CO2 content, in a future scenario condition, estimated in calculations of the useful life of the pipeline.

6. The method according to claim 2, further comprising:comparing the monitored OSI with a reference OSI, which corresponds to an upper limit calculated based on limit values of pressure, temperature and CO2 content, in a future scenario condition, estimated in calculations of the useful life of the pipeline.

7. The method according to claim 3, further comprising:comparing the monitored OSI with a reference OSI, which corresponds to an upper limit calculated based on limit values of pressure, temperature and CO2 content, in a future scenario condition, estimated in calculations of the useful life of the pipeline.

8. The method according to claim 1, further comprising:determining a monitored Cumulative Operational Severity Index (COSI), which is a sum of monitored OSI, and a reference COSI, which is a sum of reference OSI.

9. The method according to claim 2, further comprising:determining a monitored Cumulative Operational Severity Index (COSI), which is a sum of monitored OSI, and a reference COSI, which is a sum of reference OSI.

10. The method according to claim 3, further comprising:determining a monitored Cumulative Operational Severity Index (COSI), which is a sum of monitored OSI, and a reference COSI, which is a sum of reference OSI.

11. The method according to claim 5, further comprising:determining a monitored Cumulative Operational Severity Index (COSI), which is a sum of monitored OSI, and a reference COSI, which is a sum of reference OSI.

12. A system for automated monitoring of the operational parameters and severity of the phenomenon of CO2 stress corrosion cracking in flexible pipelines, the system comprising:one or more sensors installed in a subsea assembly to monitor operational parameters of a pipeline;a server that stores monitored operational data; anda digital twin computational tool configured to:collect the monitored operational data,determine an Operational Severity Index, OSI, based on the collected monitored operational data, by means of OSI calculation, anddisplay monitored OSI in real time.

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