Method and system for determining operation scheme of gas storage, medium and equipment

Abstract

The invention relates to the field of natural gas industry, and discloses a gas storage operation scheme determination method and system, a medium and equipment, and the method comprises the steps: obtaining historical operation big data of a gas storage under a given working condition, carrying out the preprocessing, and constructing an energy consumption prediction model of key equipment in the gas storage under each working condition according to the historical operation big data; establishing a processing cost prediction model of the gas storage under each working condition based on an actual processing cost model of the gas storage established by the historical operation big data and an energy consumption prediction model of key equipment in the gas storage under each working condition; and based on the processing cost prediction model under each working condition, optimizing an operation scheme of key equipment in the gas storage under each working condition. According to the method, the actual operation performance and the actual efficient operation interval of each key device in the gas storage can be determined, so that the operation cost of the gas storage is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas industry, and in particular to a method, system, medium and equipment for determining an operation plan of a gas storage. Background Art

[0002] Natural gas storage includes gas injection technology and gas production technology, and is an important part of natural gas development and utilization.

[0003] The daily gas injection and production volumes at gas storage facilities fluctuate significantly depending on the injection schedule or downstream user demand. Furthermore, injection and production conditions vary significantly depending on the stage of the injection and production cycle. These factors pose challenges to optimizing gas storage operations and reducing energy consumption. Furthermore, gas storage facilities rarely conduct analysis of the actual operating performance of key equipment, and lack a systematic understanding of the actual efficient operating ranges of each device. For key equipment such as compressors, air coolers, and hydrocarbon-water dew point control devices, where multiple similar devices operate in parallel, the start-up and shutdown sequencing of these devices is still empirical and random. Control of certain key process operating parameters, such as cooler and reboiler temperatures, is primarily based on experience to meet operating procedures, lacking scientific and systematic optimization methods and online platform guidance. Summary of the Invention

[0004] In response to the above problems, the purpose of the present invention is to provide a method, system, medium and equipment for determining the operation plan of a gas storage facility, which can clarify the actual operating performance and actual efficient operating range of each key equipment in the gas storage facility, thereby reducing the operating cost of the gas storage facility.

[0005] To achieve the above-mentioned objectives, in the first aspect, the technical solution adopted by the present invention is: a method for determining an operation plan of a gas storage reservoir, which comprises: obtaining historical operation big data of the gas storage reservoir under given operating conditions and performing preprocessing, and constructing an energy consumption prediction model for key equipment in the gas storage reservoir under each operating condition based on the historical operation big data; establishing a processing cost prediction model for the gas storage reservoir under each operating condition based on the actual processing cost model of the gas storage reservoir established by the historical operation big data and the energy consumption prediction model for key equipment in the gas storage reservoir under each operating condition; and optimizing the operation plan for key equipment in the gas storage reservoir under each operating condition based on the processing cost prediction model under each operating condition.

[0006] Furthermore, the historical operation big data of the gas storage under given operating conditions includes key parameters that affect the operation cost of the gas storage and energy and material consumption data of key equipment; among which:

[0007] Key parameters include controllable key parameters and uncontrollable key parameters;

[0008] Key equipment includes at least one or more types of filters and separators such as cyclone separators, three-phase separators and flash separators, compressors, coolers, shut-off valves, JT valves, reboilers, fans and pumps;

[0009] The controllable key parameters include at least one of the following: the start and stop status of key equipment, the opening of key valves, the cooling temperature of the exhaust gas at the outlet of the gas injection compressor, the lean TEG flow rate and reboiler temperature in the gas production system, and the injection volume of antifreeze methanol or ethylene glycol solution;

[0010] The uncontrollable key parameters include at least one of the following: daily gas injection or production volume, the pressure, temperature and composition of natural gas from the upstream pipeline during injection, the bottom hole pressure of a single well, the pressure, temperature and composition of natural gas entering the gathering and injection station during production, and the pressure of the gas transmission pipeline;

[0011] The energy and material consumption data include at least one of fuel gas consumption and electricity consumption.

[0012] Furthermore, the operation scheme of the gas storage is divided into operating conditions according to the uncontrollable key parameters in the historical operation big data to obtain the given operating conditions;

[0013] The given working conditions include gas injection, gas production cycle, gas injection and production volume, and injection and production pressure conditions.

[0014] Furthermore, based on historical operational big data, an energy consumption prediction model for key equipment in the gas storage facility under each operating condition is constructed, including:

[0015] The key parameters of key equipment in the historical operation big data of each working condition are used as input data, and the energy consumption and material consumption data of key equipment in the historical operation big data of each working condition are used as output data to construct an energy consumption prediction model for key equipment under each working condition.

[0016] Furthermore, the actual processing cost model of the gas storage facility established based on historical operation big data includes:

[0017] Based on historical operation big data, according to the operating mechanism of key equipment and the law of conservation of energy and quality, the basic energy and material consumption data of the gas storage under different operating conditions, the key parameters and energy and material consumption data of the key equipment in the gas storage are used as input variables, and the actual cost of processing 10,000 cubic meters of natural gas is used as the output variable to establish an actual processing cost model, and the actual cost value of processing 10,000 cubic meters of natural gas is calculated.

[0018] Furthermore, a gas storage processing cost prediction model under each operating condition is established, including:

[0019] Based on the actual processing cost model of the gas storage under each operating condition, the energy consumption prediction model of key equipment in the gas storage, and the basic energy and material consumption data of the gas storage, a processing cost prediction model for the gas storage under each operating condition is established:

[0020]

[0021] Among them, C YC To predict the processing cost; M NG The daily natural gas injection or production volume of the gas storage facility, in units of 10 4 Nm 3 / d;E i,YC The energy consumption and material consumption of each key equipment calculated by the energy consumption prediction model of each key equipment according to the working conditions; a i The unit price of energy or materials purchased for the gas storage facility; b i The maintenance cost of each key equipment opened for the gas storage is amortized to the daily unit price; N represents the total number of key energy-consuming equipment that needs to be opened according to the gas injection conditions on that day.

[0022] Furthermore, based on the processing cost prediction model under each operating condition, the operation plan of key equipment in the gas storage facility is optimized under each operating condition, including:

[0023] Based on the processing cost prediction model under each operating condition, a natural gas processing cost optimization model under each operating condition is constructed;

[0024] Based on the natural gas processing cost optimization model under each operating condition, the operation plan of key equipment in the gas storage facility is optimized, including:

[0025] Based on the corresponding controllable key parameters of the key equipment in the gas storage under each operating condition, the operating space of the gas storage under each operating condition is determined; the operating space represents the set of all possible operating schemes of the key equipment in the gas storage under each operating condition;

[0026] Based on the natural gas processing cost optimization model under each operating condition, the optimization objective function under each operating condition is constructed;

[0027] Based on the optimization objective function under each operating condition, the optimal operation plan for key equipment in the gas storage under each operating condition in the operation space is obtained.

[0028] In the second aspect, the technical solution adopted by the present invention is: a system for determining an operation plan of a gas storage reservoir, which includes: an acquisition and construction module, which obtains historical operation big data of the gas storage reservoir under given working conditions and performs preprocessing, and constructs an energy consumption prediction model for key equipment in the gas storage reservoir under each working condition based on the historical operation big data; a processing cost prediction module, which establishes a processing cost prediction model for the gas storage reservoir under each working condition based on the actual processing cost model of the gas storage reservoir established by the historical operation big data and the energy consumption prediction model of the key equipment in the gas storage reservoir under each working condition; an optimization module, which optimizes the operation plan of the key equipment in the gas storage reservoir under each working condition based on the processing cost prediction model under each working condition.

[0029] In a third aspect, the technical solution adopted by the present invention is: a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0030] In a fourth aspect, the technical solution adopted by the present invention is: a computing device, comprising: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0031] The present invention has the following advantages due to the adoption of the above technical solution:

[0032] This method obtains historical operational data from a gas storage facility under each operating condition and, based on this data, constructs an energy consumption prediction model for key equipment within the facility under each operating condition. Based on this prediction model, a processing cost prediction model for the facility is established under each operating condition. Finally, based on this processing cost prediction model, the operating plan for key equipment within the facility is optimized under each operating condition. Compared to existing methods, this method, based on the processing cost prediction model under each operating condition, more scientifically and systematically determines the actual operating performance and effective operating range of each key equipment within the facility, thereby reducing the facility's operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of a method for determining an operation plan of a gas storage facility in an embodiment of the present invention;

[0034] Figure 2 is a detailed flow chart of a method for determining an operation plan of a gas storage facility in an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of a typical process flow of a gas injection system for a gas storage facility according to an embodiment of the present invention;

[0036] Figure 4 3 is a schematic diagram of the relative error distribution of the compressor unit energy consumption prediction model in an embodiment of the present invention.

[0037] Figure 5 Schematic diagram of the structure of a device for determining an operation plan of a gas storage facility in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to fill the technical gap in the existing technology for analyzing the actual operating performance of key equipment in gas storage reservoirs, the present invention provides a method, system, medium and equipment for determining an operating plan of a gas storage reservoir. Among them, the method for determining an operating plan of a gas storage reservoir includes: obtaining historical operating big data of the gas storage reservoir under each operating condition; constructing an energy consumption prediction model for key equipment in the gas storage reservoir under each operating condition based on the historical operating big data; establishing a processing cost prediction model for the gas storage reservoir under each operating condition based on the energy consumption prediction model for key equipment in the gas storage reservoir under each operating condition; and optimizing the operating plan of key equipment in the gas storage reservoir under each operating condition based on the processing cost prediction model under each operating condition. The embodiments of this specification can more scientifically and systematically clarify the actual operating performance of each key equipment in the gas storage reservoir based on the processing cost prediction model under each operating condition, and determine the optimized operating plan for each key equipment, thereby reducing the operating cost of the gas storage reservoir.

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0040] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0041] In one embodiment of the present invention, a method for determining an operation plan of a gas storage is provided. In this embodiment, Figure 1 、 Figure 2 As shown, the method includes the following steps:

[0042] 1) Obtain and preprocess historical operating data of the gas storage under given operating conditions, and build an energy consumption prediction model for key equipment in the gas storage under each operating condition based on the historical operating data;

[0043] 2) Based on the actual processing cost model of the gas storage established by historical operation big data and the energy consumption prediction model of key equipment in the gas storage under each operating condition, a processing cost prediction model for the gas storage under each operating condition is established;

[0044] 3) Based on the processing cost prediction model under each operating condition, optimize the operation plan of key equipment in the gas storage under each operating condition.

[0045] In this embodiment, the typical process of the gas storage facility mainly consists of gas injection and gas production. During gas injection, natural gas enters the gathering and injection station from the upstream pipeline through the connecting line. After filtering and pressurization by the compressor unit, it is transported to the collection and distribution station through the gathering and transportation pipeline network. After distribution and metering, it is injected into each single well. The gas injection system mainly includes a gas injection filtration and separation unit, a pressurization unit and a gas injection well unit, as well as key equipment such as gas injection wells, cyclone separators, filter separators, reciprocating or centrifugal compressor units and coolers. During gas production, the produced gas from each well site is throttled through the single well pipeline and transported to the gathering and injection station through the distribution station. After filtering and separation, it enters the hydrocarbon water dew point control device. The treated natural gas enters the downstream pipeline through the connecting line. Currently, the gas production system mainly adopts two processes: for dry gas and salt cavern type gas reservoirs, the triethylene glycol dehydration process is used, which only needs to control the water dew point; for oil reservoirs and condensate gas reservoirs, the JT valve refrigeration + ethylene glycol antifreeze process is used to simultaneously control the water and hydrocarbon dew points of the natural gas. The gas production system of dry gas reservoirs and salt cavern gas storage facilities is relatively simple, mainly including gas well units, throttle valve group units, filtration and separation units for the injection station and triethylene glycol dehydration units, as well as key equipment such as gas wells, throttle valve groups, filtration separators, and dehydration devices (among which the dehydration device is relatively complex, mainly consisting of an absorption tower, a solvent circulation pump, a regeneration tower and a reboiler, a flash tank and a buffer tank, and a lean and rich liquid heat exchanger).

[0046] In the above step 1), the historical operation big data is preprocessed, including cleaning the data including deactivation and abnormal points, and standardizing different physical quantities. At the same time, the upper and lower limits of the historical data values ​​corresponding to each key parameter in the historical operation big data under given working conditions are determined, and combined with the on-site process operation specifications, a feasible change range is provided for the operation optimization of controllable key parameters.

[0047] In step 1) above, the historical operating big data of the gas storage under given operating conditions includes key parameters affecting the operating cost of the gas storage and energy and material consumption data of key equipment; wherein:

[0048] Key parameters include controllable key parameters and uncontrollable key parameters;

[0049] Key equipment includes at least one or more types of filters and separators such as cyclone separators, three-phase separators and flash separators, compressors, coolers, shut-off valves, JT valves, reboilers, fans and pumps;

[0050] The controllable key parameters include at least one of the following: the start and stop status of key equipment, the opening of key valves, the cooling temperature of the exhaust gas at the outlet of the gas injection compressor, the lean TEG flow rate and reboiler temperature in the gas production system such as the dehydration device, and the injection volume of antifreeze methanol or ethylene glycol solution;

[0051] The uncontrollable key parameters include at least one of the following: daily gas injection or production volume, pressure, temperature and composition of natural gas from upstream pipelines during injection, bottom hole pressure of individual wells, pressure, temperature and composition of natural gas entering the gathering and injection station during production, and pressure of the gas transmission pipeline;

[0052] By analyzing historical operational data from gas storage facilities, we determined that the primary drivers of gas storage operating costs are energy and material consumption of key equipment. Energy and material consumption data includes at least one of fuel gas consumption and electricity consumption. Electricity consumption includes power, current, voltage, operating hours, electricity costs, and capacity costs. Fuel gas consumption includes gas fees and maintenance costs.

[0053] In this embodiment, uncontrollable key parameters are usually used to divide working conditions and known input conditions, and controllable key parameters are usually the parameters that are adjusted in the work of reducing costs and increasing efficiency. The daily operation mode of the gas storage is divided according to the working conditions of each day, and an energy-saving and cost-reducing operation plan is proposed for each working condition. At the gas storage site, energy consumption is mainly concentrated in the gas injection process, such as Figure 3 As shown, the energy consumption of the compressor unit generally accounts for more than 90% of the energy consumption of the entire gas storage.

[0054] In this embodiment, the gas injection system has an upstream gas pressure of 4.5-7.0 MPa, a gas injection pressure of 10-20 MPa, and a gas injection volume of 100-800×10 4 Nm 3 / d as an example to illustrate.

[0055] In the above step 1), the operating conditions of the gas storage operation plan are divided according to the uncontrollable key parameters in the historical operation big data to obtain the given operating conditions; the given operating conditions include gas injection, gas production cycle, injection and production gas volume and injection and production pressure conditions.

[0056] In the above step 1), based on the historical operation big data, an energy consumption prediction model for key equipment in the gas storage under each operating condition is constructed. Specifically, the key parameters corresponding to the key equipment in the historical operation big data of each operating condition are used as input data, and the energy consumption and material consumption data corresponding to the key equipment in the historical operation big data of each operating condition are used as output data to construct an energy consumption prediction model for the key equipment under each operating condition.

[0057] In this embodiment, a neural network algorithm is used to establish an energy consumption prediction model for key equipment such as compressors, fans, pumps, and reboilers. Specifically, an artificial intelligence algorithm can be used, or a hybrid modeling method that combines a mechanism model with historical big data can be used to establish an energy consumption prediction model. This embodiment uses the hybrid modeling method to establish an energy consumption prediction model for a compressor as an example. The functional expression of the relationship between the inlet and outlet pressures and flow rates of natural gas and the energy consumption of the compressor in the compressor hybrid model can be expressed as:

[0058] e COM,YC =f(p COM,in ,p COM,out ,t COM,in ,m COM );

[0059] Among them, e COM,YC The predicted comprehensive energy consumption of the compressor for processing 10,000 cubic meters of natural gas, in MJ / 10 4 Nm 3 ;p COM,in ,p COM,out are the pressures of natural gas at the inlet and outlet of the compressor, in MPa; t COM,in is the temperature of natural gas at the compressor inlet, unit: °C; m COM is the flow rate of natural gas in the compressor, in units of 10 4 Nm 3 / d.

[0060] like Figure 4 Shown is the relative error distribution of the compressor unit energy consumption prediction model.

[0061] In step 2) above, the actual processing cost model of the gas storage established based on historical operation big data is specifically:

[0062] Based on historical operation big data, in accordance with the operating mechanism of key equipment and the law of conservation of energy and quality, the basic energy consumption and material consumption data of the gas storage under different operating conditions, the key parameters and energy consumption and material consumption data of key equipment in the gas storage (for example, the start and stop of key equipment and flow distribution, combined with unit energy consumption cost and operation and maintenance costs, etc.) are used as input variables, and the actual cost of processing 10,000 cubic meters of natural gas is used as the output variable to establish an actual processing cost model, and the actual cost value of processing 10,000 cubic meters of natural gas is calculated.

[0063] In this embodiment, the actual cost of processing 10,000 cubic meters of natural gas in a gas storage facility is expressed as the ratio of the actual cost of operating key equipment in the gas storage facility to the amount of natural gas injected, expressed as C SJ The calculation expression is:

[0064]

[0065] Among them, M NG The daily natural gas injection volume of the gas storage facility, in units of 10 4 Nm 3 / d;E i,SJ The actual energy and material consumption of key equipment per unit material processing, in MJ / 10 4 Nm 3 , fuel gas unit is t / 10 4 Nm 3 etc. iThe unit price of purchasing electricity and fuel gas and other energy or materials for the gas storage; b. i The maintenance cost of each key equipment that is turned on in the gas storage facility is amortized to the daily unit price. N represents the total number of key equipment that needs to be turned on based on the gas injection conditions on that day.

[0066] In step 2) above, a gas storage processing cost prediction model for each operating condition is established based on the energy consumption prediction model for key gas storage equipment, the overall process operation mechanism of the gas storage, the law of conservation of energy and quality, and unit energy consumption and operation and maintenance costs. Under the same input conditions, the predicted values ​​calculated by the processing cost prediction model can accurately replicate the actual processing costs calculated by the actual processing cost model.

[0067] Specifically, a gas storage processing cost prediction model for each operating condition is established based on the actual processing cost model of the gas storage under each operating condition, the energy consumption prediction model of key equipment in the gas storage, and the basic energy and material consumption data of the gas storage:

[0068]

[0069] Among them, C YC The predicted processing cost of the processing cost prediction model for injecting 10,000 cubic meters of natural gas into the gas storage; M NG The daily natural gas injection volume of the gas storage facility, in units of 10 4 Nm 3 / d;E i,YC The energy consumption and material consumption of each key equipment calculated by the energy consumption prediction model of each key equipment according to the working conditions; a i The unit price of energy or materials purchased for the gas storage facility; b i The maintenance cost of each key equipment opened for the gas storage is amortized to the daily unit price; N represents the total number of key equipment that needs to be opened based on the gas injection conditions on that day.

[0070] In step 3) above, based on the processing cost prediction model under each operating condition, the operation plan of the key equipment in the gas storage facility under each operating condition is optimized, including the following steps:

[0071] 3.1) Based on the processing cost prediction model under each operating condition, a natural gas processing cost optimization model under each operating condition is constructed;

[0072] In this embodiment, a cost optimization model for processing 10,000 cubic meters of natural gas at a gas storage facility is established. Based on this cost optimization model, an artificial intelligence algorithm particle swarm method is used for modeling. The model input variables are the external transmission volume, unit operating costs, and maintenance costs under different operating conditions. The model output variable is the optimized cost value for processing 10,000 cubic meters of natural gas at the gas storage facility. The startup and shutdown of key equipment and flow distribution are also determined, providing optimization guidance for cost reduction and efficiency improvement at the gas storage facility. The basic function of the optimization model is shown in the following formula:

[0073]

[0074] Where C YH This represents the minimum cost required to operate and maintain key equipment while the gas storage completes its daily gas injection mission. This represents the optimized processing cost for the gas storage to process 10,000 cubic meters of natural gas. Y = 1 or Y = 0 represents the start or stop status of a key piece of equipment. In the processing cost optimization model, each piece of equipment connected in parallel must maintain mass conservation, and its key operating parameters must meet the initial performance and field operating specifications.

[0075] Table 1 shows the operating ranges of key parameters for the compressor unit in this example. Table 2 compares the actual and optimized costs for processing 10,000 cubic meters of natural gas at the gas storage facility under three export conditions, as well as the start-up and shutdown allocation of key equipment. Using the processing cost optimization model, the system can reduce costs by 2.4-7.9%.

[0076] Table 1

[0077]

[0078] Table 2

[0079]

[0080] 3.2) Based on the natural gas processing cost optimization model under each operating condition, optimize the operation plan of key equipment in the gas storage facility. Specifically, the following steps are included:

[0081] 3.2.1) Determine the operating space of the gas storage facility under each operating condition based on the corresponding controllable key parameters of the key equipment in the gas storage facility under each operating condition; the operating space represents the set of all possible operating scenarios for the key equipment in the gas storage facility under each operating condition;

[0082] 3.2.2) Based on the natural gas processing cost optimization model under each operating condition, construct the optimization objective function under each operating condition;

[0083] 3.2.3) Based on the optimization objective function under each operating condition, the optimal operation plan for key equipment in the gas storage facility under each operating condition in the operation space is obtained.

[0084] like Figure 5 As shown, in one embodiment of the present invention, a system for determining an operation plan of a gas storage is provided, which includes:

[0085] Acquisition building module, obtains and pre-processes the historical operation big data of the gas storage under given operating conditions, and builds the energy consumption prediction model of key equipment in the gas storage under each operating condition based on the historical operation big data;

[0086] The processing cost prediction module establishes a processing cost prediction model for the gas storage under each operating condition based on the actual processing cost model of the gas storage established by historical operation big data and the energy consumption prediction model of key equipment in the gas storage under each operating condition;

[0087] The optimization module optimizes the operation plan of key equipment in the gas storage under each operating condition based on the processing cost prediction model under each operating condition.

[0088] In the above embodiment, the historical operating big data of the gas storage under given operating conditions includes key parameters affecting the operating cost of the gas storage and energy and material consumption data of key equipment; wherein:

[0089] Key parameters include controllable key parameters and uncontrollable key parameters;

[0090] Key equipment includes at least one or more types of filters and separators such as cyclone separators, three-phase separators and flash separators, compressors, coolers, shut-off valves, JT valves, reboilers, fans and pumps;

[0091] The controllable key parameters include at least one of the following: the start and stop status of key equipment, the opening of key valves, the cooling temperature of the exhaust gas at the outlet of the gas injection compressor, the lean TEG flow rate and reboiler temperature in the gas production system, and the injection volume of antifreeze methanol or ethylene glycol solution;

[0092] The uncontrollable key parameters include at least one of the following: daily gas injection or production volume, the pressure, temperature and composition of natural gas from the upstream pipeline during injection, the bottom hole pressure of a single well, the pressure, temperature and composition of natural gas entering the gathering and injection station during production, and the pressure of the gas transmission pipeline;

[0093] The energy and material consumption data include at least one of fuel gas consumption, electric power, current, voltage, operating time, electricity charges, capacity charges, gas charges, and maintenance costs.

[0094] In the above embodiment, the operating scheme of the gas storage is divided into operating conditions according to the uncontrollable key parameters in the historical operating big data to obtain a given operating condition;

[0095] The given working conditions include gas injection, gas production cycle, gas injection and production volume, and injection and production pressure conditions.

[0096] In the above embodiment, based on historical operation big data, an energy consumption prediction model for key equipment in the gas storage facility under each operating condition is constructed, including:

[0097] The key parameters of key equipment in the historical operation big data of each working condition are used as input data, and the energy consumption and material consumption data of key equipment in the historical operation big data of each working condition are used as output data to construct an energy consumption prediction model for key equipment under each working condition.

[0098] In the above embodiment, the actual processing cost model of the gas storage established based on historical operation big data includes:

[0099] Based on historical operation big data, according to the operating mechanism of key equipment and the law of conservation of energy and quality, the basic energy and material consumption data of the gas storage under different operating conditions, the key parameters and energy and material consumption data of the key equipment in the gas storage are used as input variables, and the actual cost of processing 10,000 cubic meters of natural gas is used as the output variable to establish an actual processing cost model, and the actual cost value of processing 10,000 cubic meters of natural gas is calculated.

[0100] In the above embodiment, a gas storage processing cost prediction model is established under each operating condition, including:

[0101] Based on the actual processing cost model of the gas storage under each operating condition, the energy consumption prediction model of key equipment in the gas storage, and the basic energy and material consumption data of the gas storage, a processing cost prediction model for the gas storage under each operating condition is established:

[0102]

[0103] Among them, C YC To predict the processing cost; M NG The daily natural gas injection volume of the gas storage facility, in units of 10 4 Nm 3 / d;E i,YC The energy consumption and material consumption of each key equipment calculated by the energy consumption prediction model of each key equipment according to the working conditions; a i The unit price of energy or materials purchased for the gas storage facility; b i The maintenance cost of each key equipment opened for the gas storage is amortized to the daily unit price; N represents the total number of key equipment that needs to be opened based on the gas injection and production conditions on that day.

[0104] In the above embodiment, based on the processing cost prediction model under each operating condition, the operation plan of the key equipment in the gas storage facility under each operating condition is optimized, including:

[0105] Based on the processing cost prediction model under each operating condition, a natural gas processing cost optimization model under each operating condition is constructed;

[0106] Based on the natural gas processing cost optimization model under each operating condition, the operation plan of key equipment in the gas storage facility is optimized, including:

[0107] Based on the corresponding controllable key parameters of the key equipment in the gas storage under each operating condition, the operating space of the gas storage under each operating condition is determined; the operating space represents the set of all possible operating schemes of the key equipment in the gas storage under each operating condition;

[0108] Based on the natural gas processing cost optimization model under each operating condition, the optimization objective function under each operating condition is constructed;

[0109] Based on the optimization objective function under each operating condition, the optimal operation plan for key equipment in the gas storage under each operating condition in the operation space is obtained.

[0110] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0111] In one embodiment of the present invention, a computing device is provided. The computing device may be a terminal and may include: a processor, a communications interface, a memory, a display screen, and an input device. The processor, communications interface, and memory communicate with each other via a communications bus. The processor is configured to provide computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. When executed by the processor, the computer program implements the methods described in the above embodiments. The internal memory provides an environment for the operating system and computer program in the non-volatile storage medium to run. The communications interface is configured to communicate with an external terminal via wired or wireless communication. The wireless communication may be achieved via Wi-Fi, a network management service provider, NFC (near field communication), or other technologies. The display screen may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen layer covering the display screen, or may be buttons, a trackball, or a touchpad provided on the computing device housing, or may be an external keyboard, touchpad, or mouse. The processor may invoke logic instructions stored in the memory.

[0112] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0113] In one embodiment of the present invention, a computer program product is provided, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0114] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium stores server instructions. The computer instructions enable a computer to execute the methods provided in the above embodiments.

[0115] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effects are similar to those of the above method embodiment, and will not be repeated here.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for determining an operation plan of a gas storage facility, characterized in that: include: Obtain and pre-process the historical operating big data of the gas storage under given operating conditions, and build an energy consumption prediction model for key equipment in the gas storage under each operating condition based on the historical operating big data; Based on the actual processing cost model of the gas storage reservoir established by historical operation big data and the energy consumption prediction model of key equipment in the gas storage reservoir under each operating condition, a processing cost prediction model of the gas storage reservoir under each operating condition is established; Based on the processing cost prediction model under each operating condition, the operation plan of key equipment in the gas storage facility is optimized under each operating condition.

2. The method for determining an operation plan of a gas storage facility according to claim 1, wherein: The historical operation big data of the gas storage under given operating conditions includes key parameters that affect the operation cost of the gas storage and energy and material consumption data of key equipment; among which: Key parameters include controllable key parameters and uncontrollable key parameters; Key equipment includes at least one or more types of filters and separators such as cyclone separators, three-phase separators and flash separators, compressors, coolers, shut-off valves, JT valves, reboilers, fans and pumps; The controllable key parameters include at least one of the following: the start and stop status of key equipment, the opening of key valves, the cooling temperature of the exhaust gas at the outlet of the gas injection compressor, the lean TEG flow rate and reboiler temperature in the gas production system, and the injection volume of antifreeze methanol or ethylene glycol solution; The uncontrollable key parameters include at least one of the following: daily gas injection or production volume, the pressure, temperature and composition of natural gas from the upstream pipeline during injection, the bottom hole pressure of a single well, the pressure, temperature and composition of natural gas entering the gathering and injection station during production, and the pressure of the gas transmission pipeline; The energy and material consumption data include at least one of fuel gas consumption and electricity consumption.

3. The method for determining an operation plan of a gas storage facility according to claim 2, wherein: The gas storage operation scheme is divided into operating conditions based on the uncontrollable key parameters in the historical operation big data to obtain the given operating conditions; The given working conditions include gas injection, gas production cycle, gas injection and production volume, and injection and production pressure conditions.

4. The method for determining an operation plan of a gas storage facility according to claim 1, wherein: Based on historical operational big data, an energy consumption prediction model for key equipment in the gas storage facility under each operating condition is constructed, including: The key parameters of key equipment in the historical operation big data of each working condition are used as input data, and the energy consumption and material consumption data of key equipment in the historical operation big data of each working condition are used as output data to construct an energy consumption prediction model for key equipment under each working condition.

5. The method for determining an operation plan of a gas storage facility according to claim 1, wherein: The actual processing cost model of the gas storage facility, established based on historical operational big data, includes: Based on historical operation big data, according to the operating mechanism of key equipment and the law of conservation of energy and quality, the basic energy and material consumption data of the gas storage under different operating conditions, the key parameters and energy and material consumption data of the key equipment in the gas storage are used as input variables, and the actual cost of processing 10,000 cubic meters of natural gas is used as the output variable to establish an actual processing cost model, and the actual cost value of processing 10,000 cubic meters of natural gas is calculated.

6. The method for determining an operation plan of a gas storage facility according to claim 1, wherein: Establish a gas storage processing cost prediction model under each operating condition, including: Based on the actual processing cost model of the gas storage under each operating condition, the energy consumption prediction model of key equipment in the gas storage, and the basic energy and material consumption data of the gas storage, a processing cost prediction model for the gas storage under each operating condition is established: Among them, C YC To predict the processing cost; M NG The daily natural gas injection or production volume of the gas storage facility, in units of 10 4 Nm 3 / d;E i,YC The energy consumption and material consumption of each key equipment calculated by the energy consumption prediction model of each key equipment according to the working conditions; a i The unit price of energy or materials purchased for the gas storage facility; b i The maintenance cost of each key equipment opened for the gas storage is amortized to the daily unit price; N represents the total number of key energy-consuming equipment that needs to be opened according to the gas injection conditions on that day.

7. The method for determining an operation plan of a gas storage facility according to claim 1, wherein: Based on the processing cost prediction model under each operating condition, the operation plan of key equipment in the gas storage facility is optimized under each operating condition, including: Based on the processing cost prediction model under each operating condition, a natural gas processing cost optimization model under each operating condition is constructed; Based on the natural gas processing cost optimization model under each operating condition, the operation plan of key equipment in the gas storage facility is optimized, including: Based on the corresponding controllable key parameters of the key equipment in the gas storage under each operating condition, the operating space of the gas storage under each operating condition is determined; the operating space represents the set of all possible operating schemes of the key equipment in the gas storage under each operating condition; Based on the natural gas processing cost optimization model under each operating condition, the optimization objective function under each operating condition is constructed; Based on the optimization objective function under each operating condition, the optimal operation plan for key equipment in the gas storage under each operating condition in the operation space is obtained.

8. A system for determining an operation plan of a gas storage facility, characterized in that: include: Acquisition building module, obtains and pre-processes the historical operation big data of the gas storage under given operating conditions, and builds the energy consumption prediction model of key equipment in the gas storage under each operating condition based on the historical operation big data; The processing cost prediction module establishes a processing cost prediction model for the gas storage under each operating condition based on the actual processing cost model of the gas storage established by historical operation big data and the energy consumption prediction model of key equipment in the gas storage under each operating condition; The optimization module optimizes the operation plan of key equipment in the gas storage under each operating condition based on the processing cost prediction model under each operating condition.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7 .

10. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods according to claims 1 to 7.

Images