An offshore platform water spray fire-fighting capacity evaluation method

Abstract

The application provides a kind of ocean platform water spray fire-fighting capacity evaluation method, belongs to ocean platform fire-fighting technical field.Its technical scheme is: a kind of ocean platform water spray fire-fighting capacity evaluation method, including identifying the risk factors of ocean platform fire accident;Determine the accident scene to be evaluated;Based on FDS, simulate modeling for the accident scene to be evaluated, generate numerical model;System carries out FDS fire extinguishing simulation under different conditions;According to the comparison of the heat radiation of each device before and after the start of water spray, judge whether the water spray system is effective;Establish the machine learning experience model of water spray system fire extinguishing performance, and evaluate the system;For failure scenarios, combined with the machine learning experience model, propose improvement direction and measures.The beneficial effects of the application are: a comprehensive evaluation model of ocean platform water spray fire-fighting capacity is constructed, so as to realize accurate ocean platform water spray system fire-fighting capacity reliability evaluation.

Description

Technical Field

[0001] This invention relates to the field of fire protection technology for marine platforms, and in particular to a method for assessing the fire-fighting capability of water spray systems on marine platforms. Background Technology

[0002] Offshore platforms routinely produce, store, and use large quantities of flammable oil and gas materials, operating in complex and challenging environments with inherent risks of oil and gas leaks and spread. Furthermore, the numerous electrical devices on the platforms, metal-on-metal collisions and friction, and on-site hot work operations contribute to a high risk of oil and gas leaks and fires. While water sprinkler fire suppression systems are used for fire prevention on offshore platforms, modifications, aging, and damage during platform operation have rendered their actual fire suppression capabilities unreliable and posed significant safety hazards. To ensure the safe and stable operation of offshore platforms and the safety of operators, it is necessary to conduct a comprehensive fire suppression capability assessment of offshore platform water sprinkler systems. Currently, fire suppression capability assessment methods in the petrochemical industry mainly include: compliance methods, logical analysis methods, comprehensive evaluation methods, and fire modeling methods. However, the compliance method, logical analysis method, and comprehensive evaluation method suffer from outdated assessment standards and procedures, subjective qualitative assessment processes, and low accuracy and reliability of assessment results, failing to meet the precision requirements for assessing the comprehensive fire suppression capability of offshore platform water sprinkler systems.

[0003] Given the unclear reliability of the fire-fighting capability of water sprinkler systems on offshore platforms and the technical deficiencies of existing fire-fighting capability assessment methods, there is an urgent need to invent a method for assessing the fire-fighting capability of water sprinkler systems on offshore platforms, so as to achieve accurate assessment of the fire-fighting capability of water sprinkler systems on offshore platforms and provide safety assurance for offshore platform operations. Summary of the Invention

[0004] To address the problems in the prior art, the present invention aims to provide a method for assessing the fire-fighting capability of a water sprinkler system on a marine platform. Based on FDS simulation, this method achieves an accurate assessment of the fire-fighting capability of the water sprinkler system on a marine platform, thereby providing safety assurance for marine platform operations.

[0005] The present invention is achieved through the following technical solution: a method for assessing the water spray fire-fighting capability of an offshore platform, comprising the following steps;

[0006] S1: Identify the risk factors of fire accidents on offshore platforms, determine the scenario units on offshore platforms where leakage and fire may occur, and assess the leakage risk of each scenario unit.

[0007] S2: Based on the leakage risk of the aforementioned scenario unit and the marine environment, and combined with the parameters of the water spray system, determine the accident scenario to be evaluated;

[0008] S3: Based on FDS, simulate and model the accident scenario to be evaluated, generate a numerical model, and set different parameter conditions for different accident scenarios.

[0009] S4: The system conducts FDS fire extinguishing simulations under different conditions, and the heat radiation received by each device is determined based on the simulation results;

[0010] S5: By comparing the heat radiation of each device before and after the water spray is started, and comparing it with relevant heat radiation standards, we can determine whether the water spray system is effective.

[0011] S6: Establish a machine learning experience model for the fire extinguishing performance of water sprinkler systems, and evaluate the safety level and reliability of current water sprinkler systems based on the effects of water sprinkler systems and relevant fire protection standards.

[0012] S7: For the failure scenario of the water sprinkler system in S5, the parameters of the corresponding water sprinkler system in the numerical model are adjusted based on the FDS, in conjunction with the machine learning experience model.

[0013] Furthermore, S2 specifically refers to:

[0014] S21: Conduct a survey of data on the water spray system of offshore platforms to determine parameters such as spray start-up response time, spray flow rate, spray angle, and number of probes;

[0015] S22: Conduct research to determine the wind speed and wind direction environmental parameters of the sea area where the offshore platform is located, consider the leakage risk of each unit and the detailed parameters of water spray, establish a set of credible fire accident scenarios, and thus determine the accident scenarios to be evaluated.

[0016] Furthermore, S3 specifically refers to:

[0017] S31: Based on preliminary research and risk identification, the situation of leaked fuel oil and environmental conditions are determined, thereby determining the ignition source and wind speed and other environmental conditions set during FDS modeling; in addition, based on the actual situation of the platform, relevant standard requirements, simulation determination and other conditions, different triggering times of the water spray system are determined.

[0018] S32: Perform detailed FDS modeling based on the platform design drawings, wherein the relevant parameters are set according to the settings determined in S31, and generate the numerical model.

[0019] S31: Verify the effectiveness of the numerical model by combining existing water spray experimental data.

[0020] 1. The method for assessing the water spray firefighting capability of offshore platforms according to claim 1, wherein step S4 specifically comprises:

[0021] S41: Heat radiation monitoring points are set at each device in the numerical model so that the heat radiation of each device can be obtained after the fire simulation is completed.

[0022] S42: Conduct fire extinguishing simulations under various conditions, including different fire sources, different wind speeds, and different water spray activation times.

[0023] Furthermore, S6 specifically includes:

[0024] S61: Based on the water spray fire extinguishing simulations conducted in S4 and S5 under different fire accident scenarios, construct a fire reduction dataset of flame temperature, smoke concentration, and heat radiation intensity, establish a machine learning experience model of the fire extinguishing performance of the water spray system, and establish a regression relationship between accident scenario parameters and temperature and radiation intensity under the action of water spray.

[0025] S62: Based on machine learning experience models, solve the fire consequences of water sprinkler systems under typical fire accident conditions, and evaluate the safety level and reliability of current water sprinkler systems in conjunction with relevant fire protection standards.

[0026] Furthermore, S7 also includes performing fire extinguishing simulation again on the numerical model after parameter adjustment until the water sprinkler system reaches the fire extinguishing standard, and obtaining the parameters of the water sprinkler system at this time as improvement parameters to provide improvement measures for the water sprinkler system in the current platform.

[0027] This method employs a fire modeling approach, using a fire fluid numerical simulation model to numerically simulate the changes in various parameters during fire development, thereby estimating the mitigation effect of the water sprinkler system on the consequences of the fire. This invention utilizes FDS software for simulation numerical modeling, taking into account the complex spatial layout, shape, and scale characteristics of offshore platforms, as well as the variable marine environmental conditions. It accurately derives the main parameters related to changes in thermal radiation, fire shape, temperature, and smoke flow patterns during a fire on an offshore platform. The simulation results accurately reflect the actual fire situation. Simultaneously, a machine learning empirical model of the water sprinkler system's fire extinguishing performance is established. Based on a numerical surrogate model using machine learning regression methods, this model can more accurately fit the relationship between fire accident scenario parameters and fire simulation consequences in fluid simulation, reliably assessing the comprehensive fire extinguishing capability of the water sprinkler system and identifying areas for improvement.

[0028] The beneficial effects of this invention are as follows: This invention establishes a quantitative assessment method for the fire-fighting capability of water sprinkler systems in corresponding units of offshore platforms; addressing the shortcomings of existing fire-fighting capability assessment methods, such as subjective human judgment, poor applicability of assessment standards, non-quantitative assessment results, and low accuracy, this invention adopts the fire modeling method, using a fire fluid numerical simulation model to assess the fire-fighting capability of offshore platform water sprinkler systems, quantitatively calculating the fire reduction effect of the water sprinkler system, thereby achieving an accurate reliability assessment of the fire-fighting capability of offshore platform water sprinkler systems; and constructing a comprehensive assessment model for the fire-fighting capability of offshore platform water sprinkler systems, providing a clearer direction for improvement of water sprinkler systems and avoiding blind investment in upgrades that would lead to resource waste. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating the method. Detailed Implementation

[0030] To clearly illustrate the technical features of this solution, the following detailed implementation method will be used to explain the solution.

[0031] See Figure 1 The present invention is achieved through the following technical solution: a method for assessing the water spray fire-fighting capability of an offshore platform, comprising the following steps;

[0032] S1: Identify the risk factors of fire accidents on offshore platforms, determine the scenario units on offshore platforms where leakage and fire may occur, and assess the leakage risk of each scenario unit.

[0033] S2: Based on the leakage risk of the aforementioned scenario unit and the marine environment, and in conjunction with the parameters of the water spray system, determine the accident scenario to be evaluated; specifically:

[0034] S21: Conduct a survey of data on the water spray system of offshore platforms to determine parameters such as spray start-up response time, spray flow rate, spray angle, and number of probes;

[0035] S22: Conduct research to determine the wind speed and wind direction environmental parameters of the sea area where the offshore platform is located, consider the leakage risk of each unit and the detailed parameters of water spray, establish a set of credible fire accident scenarios, and thus determine the accident scenarios to be evaluated.

[0036] S3: Based on FDS, simulation modeling is performed for the accident scenarios to be evaluated, generating numerical models, and different parameter conditions are set for different accident scenarios; specifically:

[0037] S31: Based on preliminary research and risk identification, the situation of leaked fuel oil and environmental conditions are determined, thereby determining the ignition source and wind speed and other environmental conditions set during FDS modeling; in addition, based on the actual situation of the platform, relevant standard requirements, simulation determination and other conditions, different triggering times of the water spray system are determined.

[0038] S32: Perform detailed FDS modeling based on the platform design drawings, wherein the relevant parameters are set according to the settings determined in S31, and generate the numerical model;

[0039] S33: Verify the effectiveness of the numerical model by combining existing water spray experimental data;

[0040] S4: The system conducts FDS fire extinguishing simulations under different conditions, and the heat radiation received by each piece of equipment is determined based on the simulation results; specifically:

[0041] S41: Heat radiation monitoring points are set at each device in the numerical model so that the heat radiation of each device can be obtained after the fire simulation is completed.

[0042] S42: Conduct fire extinguishing simulation under various conditions, including different fire sources, different wind speeds, and different water spray activation times;

[0043] S5: By comparing the heat radiation of each device before and after the water spray is started, and comparing it with relevant heat radiation standards, we can determine whether the water spray system is effective.

[0044] S6: Establish a machine learning-based empirical model for the fire extinguishing performance of water sprinkler systems. Based on the effectiveness of water sprinkler systems and relevant fire protection standards, assess the current safety level and reliability of water sprinkler systems; specifically:

[0045] S61: Based on the water spray fire extinguishing simulations conducted in S4 and S5 under different fire accident scenarios, construct a fire reduction dataset of flame temperature, smoke concentration, and heat radiation intensity, establish a machine learning experience model of the fire extinguishing performance of the water spray system, and establish a regression relationship between accident scenario parameters and temperature and radiation intensity under the action of water spray.

[0046] S62: Solve the fire consequences of water sprinkler systems under typical fire accident conditions based on machine learning experience models, and evaluate the safety level and reliability of current water sprinkler systems in conjunction with relevant fire protection standards.

[0047] S7: For the failure scenario of the water sprinkler system in S5, the parameters of the corresponding water sprinkler system in the numerical model are adjusted based on the FDS, combined with the machine learning experience model; for the numerical model after parameter adjustment, fire extinguishing simulation is performed again until the water sprinkler system reaches the fire extinguishing standard, and the parameters of the water sprinkler system at this time are obtained as improvement parameters to provide improvement measures for the water sprinkler system in the current platform.

[0048] Example 2, using the above method and combined with actual conditions, is implemented as follows:

[0049] S1: Identify fire accident risk factors for the No. 1 platform at the Chengdao Center and determine that the oil storage tanks on the storage tank platform and the three-phase separator on the new production platform have the risk of oil and gas leakage.

[0050] S2: Based on the leakage risk of the aforementioned scenario unit and the marine environment, and in conjunction with the parameters of the water spray system, determine the accident scenario to be evaluated; specifically:

[0051] S21: The survey obtained that the tank platform area and the three-phase separator area share the same set of water spray design parameters, obtained its spray flow rate, spray angle, and number of probes, and determined that the water spray response time is ≤5 minutes, and the actual response time is 3 minutes.

[0052] S22: The wind speeds in the sea area where the No. 1 platform is located are determined to be 6 m / s, 10 m / s, and 16 m / s. The wind directions are determined to be along the wind and against the wind in the platform's wind rose diagram. The fire accident in the storage tank area is determined to be a pool fire, with liquid pool sizes of radii of 2.096 m, 4.687 m, and 6.016 m. The three-phase separator area is determined to be a jet fire, with leakage rates of 40 kg / s and 60 kg / s. Considering the leakage risk of each unit and the detailed parameters of the water spray, a set of credible fire accident scenarios is established to determine the accident scenario to be evaluated.

[0053] S3: Based on FDS, simulation modeling is performed for the accident scenarios to be evaluated, generating numerical models, and different parameter conditions are set for different accident scenarios; specifically:

[0054] S31: Based on preliminary research and risk identification, the situation of leaked fuel oil and environmental conditions are determined, thereby determining the ignition source and wind speed and other environmental conditions set during FDS modeling; in addition, based on the actual situation of the platform, relevant standard requirements, simulation determination and other conditions, different triggering times of the water spray system are determined.

[0055] S32: Based on the platform design drawings, perform detailed FDS modeling to establish a three-dimensional numerical simulation model of the marine platform's local unit storage tank platform and three-phase separator, wherein the relevant parameters are set according to the settings determined in S31, and generate the numerical model.

[0056] S33: Obtain simulation results such as the temperature and thermal radiation intensity of different equipment, and verify the effectiveness of the numerical model by combining them with existing historical experimental data of water spraying.

[0057] S4: The system conducts FDS fire extinguishing simulations under different conditions, and the heat radiation received by each piece of equipment is determined based on the simulation results; specifically:

[0058] S41: Heat radiation monitoring points are set at each device in the numerical model so that the heat radiation of each device can be obtained after the fire simulation is completed.

[0059] S42: Under various conditions such as different fire sources, different wind speeds, and different water spray start times, 3 types of pool fire radii, 6 types of wind speeds, and 3 types of water spray times were determined, resulting in a total of 54 sets of fire extinguishing simulations.

[0060] S5: By comparing the heat radiation received by each device before and after the water spray system is started, and comparing it with relevant heat radiation standards, the effectiveness of the water spray system can be determined; a simple example is given using a three-phase separator:

[0061] Result 1: Under the condition of a fire radius of 2m in the pool, the thermal radiation received by the production separator is within 10 kW / m2.

[0062] Result 2: Under the condition of a pool fire radius of 4.6m, the thermal radiation received by the production separator is still within 10 kW / m2.

[0063] Result 3: Under the condition of a fire radius of 6m in the pool, due to the spread of fuel to the bottom of the separator, the heat radiation received by the separator reached between 100 and 150 kW / m2, the equipment was completely damaged, and the water spray was completely ineffective.

[0064] Based on the comparison of the three water spray trigger times, it was found that the equipment temperature was in a state of continuous rise before the water spray was triggered, indicating that the damage to the equipment was continuously aggravated before the water spray was started.

[0065] Comparing with the thermal radiation damage standard, the following conclusions were drawn: as the radius of the pool fire continued to increase, the damage to the production separator continued to worsen, and the water spray system gradually failed.

[0066] S6: Establish a machine learning-based empirical model for the fire extinguishing performance of water sprinkler systems. Based on the effectiveness of water sprinkler systems and relevant fire protection standards, assess the current safety level and reliability of water sprinkler systems; specifically:

[0067] S61: Conduct water spray fire extinguishing simulations for all accident scenarios in the storage tank platform area and the three-phase separation area, based on the different fire accident scenarios carried out in S4 and S5, construct a fire reduction dataset of flame temperature, smoke concentration, and heat radiation intensity, establish a machine learning experience model of the fire extinguishing performance of the water spray system, and establish a regression relationship between accident scenario parameters and temperature and radiation intensity under the action of water spray.

[0068] S62: Based on machine learning experience models, solve the fire consequences of water sprinkler systems under typical fire accident conditions. Combined with relevant fire protection standards, evaluate the safety level and reliability of the current water sprinkler system. Obviously, the water sprinkler system of the No. 1 central platform has a certain suppression effect on the fire, but it cannot achieve complete suppression. Moreover, the fire protection system response is too slow, and some components are damaged before the fire protection system is activated. In addition, with the increase of wind speed and fire radius, the damage to surrounding equipment caused by the fire is also increasing.

[0069] S7: For the failure scenario of the water sprinkler system in S5, based on the machine learning experience model, adjust the parameters of the corresponding water sprinkler system in the numerical model according to FDS; for the numerical model after parameter adjustment, conduct fire extinguishing simulation again until the water sprinkler system reaches the fire extinguishing standard, and obtain the parameters of the water sprinkler system at this time as improvement parameters to provide improvement measures for the water sprinkler system in the current platform; specific improvement directions: first, consider increasing the spray intensity of the water sprinkler system of the storage tank platform or increasing the number of sprinklers; second, under the condition of a large radius pool fire, the production separator has been damaged before the water sprinkler is activated, so it is necessary to consider shortening the water sprinkler trigger time to suppress the further spread of the fire, or add protective facilities to the equipment to improve the heat resistance of the equipment.

[0070] In the description of this invention, various embodiments of the apparatus and / or process have been illustrated using block diagrams, flowcharts, and / or examples. To the extent that such block diagrams, flowcharts, and / or examples contain one or more functions and / or operations, those skilled in the art will understand that each function and / or operation within such block diagrams, flowcharts, or examples can be implemented individually and / or collectively by a great many different hardware, software, firmware, or virtually any combination thereof.

[0071] There is little difference between the hardware and software implementations of various aspects of the system; the use of hardware or software is often (but not always, as the choice between hardware and software may become important in some situations) a design choice representing a trade-off between cost and efficiency. Various means (e.g., hardware, software, and / or firmware) by which the processes and / or systems and / or other technologies described herein can be implemented exist, and preferred means will vary depending on the context in which the processes and / or systems and / or other technologies are deployed. For example, if the implementer determines that speed and accuracy are extremely important, then the implementer may choose a primarily hardware and / or firmware approach; if flexibility is extremely important, then the implementer may choose a primarily software implementation; or, but equally alternatively, the implementer may choose a combination of hardware, software, and / or firmware.

[0072] The technical features of this invention not described can be implemented by or using existing technology, and will not be repeated here. Of course, the above description is not a limitation of this invention, and this invention is not limited to the examples above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of this invention should also be within the protection scope of this invention.

Claims

1. A method for assessing the fire-fighting capability of a water sprinkler system on an offshore platform, characterized in that, Includes the following steps; S1: Identify the risk factors of fire accidents on offshore platforms, determine the scenario units on offshore platforms where leakage and fire may occur, and assess the leakage risk of each scenario unit. S2: Based on the leakage risk of the aforementioned scenario unit and the marine environment, and combined with the parameters of the water spray system, determine the accident scenario to be evaluated; S3: Based on FDS, simulate and model the accident scenario to be evaluated, generate a numerical model, and set different parameter conditions for different accident scenarios. S4: The system conducts FDS fire extinguishing simulations under different conditions, and the heat radiation received by each device is determined based on the simulation results; S5: By comparing the heat radiation of each device before and after the water spray is started, and comparing it with relevant heat radiation standards, we can determine whether the water spray system is effective. S6: Establish a machine learning experience model for the fire extinguishing performance of water sprinkler systems, and evaluate the safety level and reliability of current water sprinkler systems based on the effects of water sprinkler systems and relevant fire protection standards. S7: For the failure scenario of the water sprinkler system in S5, the parameters of the corresponding water sprinkler system in the numerical model are adjusted based on the FDS, in conjunction with the machine learning experience model.

2. The method for assessing the fire-fighting capability of a marine platform water sprinkler system according to claim 1, characterized in that, Specifically, S2 is: S21: Conduct a survey of data on the water spray system of offshore platforms to determine parameters such as spray start-up response time, spray flow rate, spray angle, and number of probes; S22: Conduct research to determine the wind speed and direction environmental parameters of the sea area where the offshore platform is located, consider the leakage risk of each unit and the detailed parameters of water spray, establish a set of credible fire accident scenarios, and thus determine the accident scenarios to be evaluated.

3. The method for assessing the fire-fighting capability of a marine platform water sprinkler system according to claim 2, characterized in that, Specifically, S3 is: S31: Based on preliminary research and risk identification, the situation of leaked fuel oil and environmental conditions are determined, thereby determining the ignition source and wind speed conditions set during FDS modeling; in addition, based on the actual platform conditions, relevant standard requirements, and simulation determination conditions, different trigger times for the water spray system are determined. S32: Perform detailed FDS modeling based on the platform design drawings, wherein the relevant parameters are set according to the settings determined in S31, and generate the numerical model; S31: Verify the effectiveness of the numerical model by combining existing water spray experimental data.

4. The method for assessing the fire-fighting capability of a marine platform water sprinkler system according to claim 1, characterized in that, Specifically, S4 is: S41: Heat radiation monitoring points are set at each device in the numerical model so that the heat radiation of each device can be obtained after the fire simulation is completed. S42: Conduct fire extinguishing simulations under various conditions, including different fire sources, different wind speeds, and different water spray activation times.

5. The method for assessing the fire-fighting capability of a marine platform water sprinkler system according to claim 4, characterized in that, Specifically, S6 is: S61: Based on the water spray fire extinguishing simulations conducted in S4 and S5 under different fire accident scenarios, construct a fire reduction dataset of flame temperature, smoke concentration, and heat radiation intensity, establish a machine learning experience model of the fire extinguishing performance of the water spray system, and establish a regression relationship between accident scenario parameters and temperature and radiation intensity under the action of water spray. S62: Based on machine learning experience models, solve the fire consequences of water sprinkler systems under typical fire accident conditions, and evaluate the safety level and reliability of current water sprinkler systems in conjunction with relevant fire protection standards.

6. The method for assessing the fire-fighting capability of a marine platform water sprinkler system according to claim 5, characterized in that, The S7 also includes performing fire extinguishing simulation again on the numerical model after parameter adjustment until the water sprinkler system reaches the fire extinguishing standard, obtaining the parameters of the water sprinkler system at this time as improvement parameters, and providing improvement measures for the water sprinkler system in the current platform.

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