CN109827079B - Oil spill source detection method based on submarine petroleum pipeline information physical system - Google Patents
Oil spill source detection method based on submarine petroleum pipeline information physical system Download PDFInfo
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Abstract
The invention provides an oil spill source detection method based on a submarine petroleum pipeline information physical system, and relates to the technical field of oil spill detection of submarine pipeline information physical systems. The method comprises the following steps: establishing a submarine pipeline petroleum transmission model; establishing a steady-state model of the submarine pipeline; generating a pipeline CPS model; initially positioning a leakage point; judging whether the position of the primarily positioned leakage point is accurate according to the performance index of the leakage point, and if not, correcting; repeatedly adjusting and simulating the inaccurate leakage point position; and judging whether to continue adjustment according to the mismatch ratio of the simulation result and the implementation remote sensing observation value, and outputting the position of the leakage point after adjustment is not needed. The method combines the steady-state model of the submarine oil pipeline with remote sensing data, thereby integrating a new method for realizing the oil spill detection of the pipeline represented by the offshore oil CPS system. The defect of low positioning precision of the oil spilling detection leakage point in the prior art is overcome.
Description
Technical Field
The invention relates to the technical field of oil spill detection of submarine pipeline information physical systems, in particular to an oil spill source detection method based on a submarine oil pipeline information physical system.
Background
Pipeline transportation is an important transportation form and has a strategic position of playing a great role in national economy. However, the frequent occurrence of the pipeline leakage causes serious economic loss and environmental pollution, so that the research on the pipeline leakage detection and positioning method has important practical significance. Although the pipeline leakage detection and positioning based on the satellite remote sensing data have certain effect, the pipeline leakage detection and positioning based on the satellite remote sensing data are influenced by the longer return visit time of the satellite remote sensing data and the complexity of an underwater oil spill model, and the prior art has great defects in the aspect of accurate positioning of pipeline leakage points. Therefore, the oil spill detection method for realizing pipeline leakage detection and positioning based on the modeling of the information physical system of the seabed complex pipe network steady-state model is very important.
The submarine pipeline information physical fusion system provides convenience for oil exploration, production and management, and provides a new idea and an implementation approach for the purpose of detecting and reducing offshore oil leakage risks. The information physical system (CPS) is a novel system formed by deeply fusing computing resources and a physical system, and researches on theories, models, methods and algorithms of the pipeline CPS, calculation and implementation tools and the like need to be further developed and deepened, so that the submarine pipeline CPS model is combined with satellite remote sensing data detection to be integrated into a new method for realizing pipeline oil spill detection represented by the offshore oil CPS.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an oil spill source detection method based on a submarine petroleum pipeline information physical system, which utilizes satellite remote sensing data and a leading edge information technology to enhance the sensing and control capability of the submarine petroleum pipeline system, thereby identifying leakage points more accurately.
In order to achieve the purpose, the method for detecting the oil spilling source based on the submarine petroleum pipeline information physical system comprises the following specific steps:
step 1: combining the concept of an information physical system with the characteristics of a submarine oil pipeline system, providing a submarine pipeline information physical system framework, and establishing a submarine pipeline information system model;
step 1.1: establishing a steady-state model of the submarine petroleum pipeline;
step 1.1.1: analyzing the transient flow process of the simple pipeline, and establishing a differential form of a motion process and a continuous equation so as to establish a pipeline transmission model;
the formula of the pipeline transmission model is as follows:
wherein P is the average pressure of the cross section, t is time, L is displacement, rho is the average density of the fluid, a is the angle between the fluid and the horizontal axis, v is the average flow velocity of the cross section, g is the gravity acceleration, lambda is the hydraulic friction coefficient, D is the inner diameter of the pipeline, CvIs the heat energy of the liquid in the pipe, T is the temperature of the liquid, k is the ground heat conductivity, and T (r) isA temperature function, wherein r is the radial distance from a certain point in the pipeline to the center of the pipe diameter;
step 1.1.2: according to the pipeline transmission model, a steady-state pipeline model is established by considering a continuity equation and a motion equation;
the formula of the steady-state pipeline model is as follows:
in the formula, when a is equal to 0, the effect of height difference is not generated, and when a is equal to 0, the effect of height difference is generated;
step 1.2: respectively establishing a network layer dynamic model and a transmission layer dynamic model aiming at a network layer and a transmission layer in an open system interconnection model of a communication network, carrying out hybrid solution on information-energy flow distribution of all established dynamic models, and establishing a pipeline information system model;
the dynamic model comprises an energy flow calculation model, an energy flow direction information flow conversion model, an information flow calculation model and an information flow direction energy flow conversion model;
step 1.2.1: establishing an energy flow calculation model;
the formula of the energy flow calculation model is as follows:
f(x(N+1)，u(N)，D(N+1)，p，A)＝0；
in the formula, A is a network structure variable, p is a network element parameter, D is an interference variable, u is a control variable, x is a compliance variable, and N is a time scale;
step 1.2.2: establishing an energy flow direction information flow conversion model according to the control requirement of an industrial field;
the formula of the energy flow direction information flow conversion model is as follows:
y(N)＝Hy·x(N)；
wherein y (N) is a dummy signal HyThe corresponding control requirements are met;
step 1.2.3: and regarding the information flow in the dynamic model as information mapping from the root node y to other nodes, and recording the information of the leaf node and other nodes at the tail end of the dynamic model as z ═ z respectively1，...，zn]TAnd w ═ wl，...，wl]TObtaining a dynamic model information flow calculation model g (y (N));
the formula of the dynamic model information flow calculation model is as follows:
step 1.2.4: mapping information z of each leaf node of the information network into an actual control quantity u, and establishing an information flow direction energy flow conversion model;
the formula of the information flow to energy flow conversion model is as follows:
u(N)＝Eu·z(N)；
wherein z (N) is node information, EuAs mapping parameters, u (N) is the actual control quantity;
step 1.2.5: selecting the size of a blocking window of each node and the size of each queue as state variables of a dynamic model, using the transmission delay and the data loss rate of each data stream as the output of the dynamic model, analyzing the information-physical coupling characteristics of the dynamic model, namely, performing hybrid solution on the information-energy stream distribution of all the established dynamic models, and establishing a pipeline information system model;
step 1.3: the submarine petroleum pipeline transmission model and the pipeline information system model are combined to obtain a pipeline information physical system model:
step 2: establishing a pipeline pressure gradient distribution curve according to a pipeline steady-state model, estimating the position of an oil overflow source, and calculating a time-varying path of a pipeline information system performance index within a time period t after pipeline leakage occurs according to the established pipeline information system model;
step 2.1: detecting leakage of the pipeline according to the flow difference of the two ends of the pipeline;
the formula for detecting the leakage of the pipeline is as follows:
GM-GC＞σ；
in the formula, GMFor monitoring the mass flow at the inlet of the pipeline, GCFor monitoringThe mass flow of the outlet of the pipeline, sigma is a set alarm threshold;
step 2.2: establishing a pipeline pressure gradient distribution curve according to a pipeline steady-state model, and determining the leakage position of the detection pipeline according to an inflection point in the pipeline pressure gradient distribution curve;
the formula for determining the leakage position of the detection pipeline according to the inflection point in the pipeline pressure gradient distribution curve is as follows:
where Lr is the distance between the source of the pipeline leak and the head end of the pipeline, PqIs the pressure, P, of the first section of the pipeline after pipeline leakage occurs in a steady statezPressure at the source of the leak, QqIs the flow of the first section of the pipeline Q after pipeline leakage occurs in a steady statezIs the flow at the leakage source, L is the total length of the pipeline, ρ is the average density of the fluid, g is the gravitational acceleration, λ is the hydraulic friction coefficient,
d is the inner diameter of the pipeline;
step 2.3: calculating a time-varying path of a performance index of the pipeline information system within a time period t after pipeline leakage occurs according to the pipeline information physical system model;
the performance indexes of the pipeline information system comprise transmission delay and data loss rate;
and step 3: judging whether the time-varying path of the performance index of the pipeline information system is larger than a set threshold value, if so, indicating that the position of the oil spill source is inaccurate, continuing to the step 4, otherwise, indicating that the position of the oil spill source is accurate, and outputting the position of the oil spill source;
and 4, step 4: simulating an oil leakage scene for each candidate leakage source, calculating the oil leakage area in an iterative mode, and judging whether the position of the candidate leakage source is corrected or not according to the actual oil leakage area observed by satellite remote sensing;
step 4.1: initializing oil leakage by taking remote sensing data and geological parameters as inputSearch range of source and location of leakage source [ POS ]min，POSmax]The simulation time k is 1;
step 4.2: generating N candidate leakage source examples { X) according to the oil spilling point estimated in the step 21，X2，…XNIn which XiFor each candidate leakage source, simulating an oil spilling scene through simulation software, and obtaining a simulation result { S (X) } through simulation calculationi)}＝{S(X1)，S(X2)，…，S(XN) And the simulation result S (X)i) The values of the } are arranged from small to large;
step 4.3, the k-th simulation is carried out on the generated candidate leakage source examples, and the quantile of the objective function η is calculated, so that P (S (X)i)＜bk) η wherein bkFor the optimum value gamma of the objective function*Estimate in the kth simulation, η quantile S (X) of the library function value order statistic for comparisonηN)；
Step 4.4: comparing the simulation result of the kth time with the remote sensing data, evaluating the simulation result of the leakage source, and if the simulation result of the kth time of the leakage source is close to the remote sensing data, storing the simulation result of the kth time of the leakage source as a high-quality sample in a candidate set;
step 4.5: selecting a high-quality sample closest to the remote sensing data from the candidate set, continuing to the step 4.6, and updating the candidate leakage source example;
step 4.6: and judging whether the difference value between the simulation result of the kth time and the remote sensing data is smaller than a set threshold epsilon, if so, outputting the position of the leakage source, otherwise, making k equal to k +1, and returning to the step 4.2.
The invention has the beneficial effects that:
the invention provides an oil spill source detection method based on a submarine petroleum pipeline information physical system, which combines a submarine oil pipeline steady-state model with remote sensing data, thereby integrating a new method for realizing pipeline oil spill detection represented by a marine petroleum CPS system. The defect of low positioning precision of the oil spilling detection leakage point in the prior art is overcome.
Drawings
FIG. 1 is a flow chart of a method for detecting an oil spill source based on a subsea petroleum pipeline information physical system in an embodiment of the present invention;
FIG. 2 is a schematic view of a pipe model without elevation difference according to an embodiment of the present invention;
FIG. 3 is a schematic view of a pipe model with elevation difference according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a CPS model of a subsea pipeline in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments. The specific embodiments described herein are merely illustrative of the invention and are not intended to be limiting.
In the embodiment, an oil field in a certain sea area is taken as an example for analysis, and the analysis is applied to an offshore pipeline information physical system model. Since the oil pipeline is laid on the seabed, in the present embodiment, it is assumed that the depth of the oil leakage site is the average depth of the area. The geological model used to simulate the process of oil leakage is a three-dimensional estuary coast ocean pattern. The model can solve time-dependent three-dimensional equations of conservation of mass, momentum, salt, heat, and turbulence in the incompressible fluid. The model divides the oil field into a plurality of oil particles, and the oil level of each oil particle can be calculated by the following formula.
Wherein X0、Y0、Z0Is the three-dimensional coordinate of the current position coordinate of the oil particles. X1，Y1，Z1Is the coordinate position after the lapse of time Δ t. Sx，Sy，SzIs the velocity in the x, y, z directions. SωAnd DωIs the speed and angle of the wind in the xy plane. The angle a correction factor takes into account the influence of the wave-induced current. DzIs the angle of the wind in the z direction. R is a random number between 0 and 1. E is oil diffusion coefficient and can be measured by a hydrodynamic modelAnd (4) calculating. DrAnd DzIs an arbitrary spread angle in the x, y plane and z direction. The motion trail of each oil particle can be calculated by using the formula, so that the leakage process of the whole oil spilling field is simulated.
In this embodiment, the simulation accuracy is defined as the difference between the simulated oil stain area on the surface of the seawater and the image taken by the remote sensing satellite. For example, if the surface pollution area is represented by 10000 pixels, 8000 pixels cover the measured pollution area of the remote sensing satellite, and the simulation precision is 80%.
A method for detecting an oil spill source based on a submarine petroleum pipeline information physical system is disclosed, wherein the flow is shown in figure 1, and the method comprises the following steps:
step 1: combining the concept of an information physical system with the characteristics of a submarine oil pipeline system, providing a submarine pipeline information physical system architecture, and establishing a submarine pipeline information system model, which comprises the following specific steps:
step 1.1: the method comprises the following steps of establishing a steady-state model of the submarine petroleum pipeline:
step 1.1.1: analyzing the transient flow process of the simple pipeline, and establishing a differential form of a motion process and a continuous equation so as to establish a pipeline transmission model.
The flow state of the liquid in the crude oil conveying pipeline can be divided into two categories, namely stable and unstable. The parameters at any point in the pressure line are only related to the line position and time-independent flow is called steady flow, whereas unsteady flow or transient flow is called. In an actual oil pipeline, it is difficult to maintain absolutely stable flow in the pipe. In this embodiment, in order to simplify the model and facilitate the study of the influence of the main parameters on the leak detection positioning, assuming that the pipe and the fluid deform within the linear elastic deformation range, the pipe diameter, the wall thickness and the fluid property along the pipeline are the same, and the temperature, the pipe diameter and the oil density are constant, the formula for establishing the pipeline transmission model is as shown in formula (1):
wherein P is the average pressure of the cross section, t is the time, and L isDisplacement, rho is the average density of the fluid, a is the angle between the fluid and the horizontal axis, v is the average flow velocity of the cross section, g is the gravity acceleration, lambda is the hydraulic friction coefficient, D is the inner diameter of the pipeline, CvIs the heat energy of the liquid in the pipeline, T is the temperature of the fluid, k is the ground heat conductivity, T (r) is a function of the temperature, and r is the radial distance from a certain point in the pipeline to the center of the pipe diameter.
Step 1.1.2: and (4) according to the pipeline transmission model, considering a continuity equation and a motion equation, and establishing a steady-state pipeline model.
In this embodiment, the pipeline model without height difference is as shown in fig. 2, the oil flow in the pipeline is considered to be in a stable state, the change of the pressure flow is independent of time, the temperature is constant, only the continuity equation and the motion equation are considered, and assuming that the whole pipeline has no horizontal fall, the pipeline model without height difference is obtained according to the pipeline model established by the equation (1), that is, the pipeline model without height difference is obtained
In the actual operation of the oil pipeline, the trend of the pipeline changes along with the different terrain, and the pipeline has a certain height difference, so that the model can better meet the actual condition of the pipeline, and the upstream and downstream fall of the pipeline needs to be considered. The pipe model with height difference is shown in fig. 3.
When the height difference of the pipeline is considered according to the pipeline transmission model, a pipeline motion equation and a continuity equation can be obtained:
the basic models of the pipeline steady state with and without the height difference influence are the same, and in order to facilitate the solution of the models, the two models are unified into the form shown in the formula (4), namely the formula of the steady state pipeline model is shown in the formula (4):
in the formula, when a ≠ 0, it indicates that there is no effect of level difference, and when a ≠ 0, it indicates that there is an effect of level difference.
And (3) solving the linear equation set of the formula (4) by adopting a direct elimination method, and integrating the pressure and the flow velocity on a specific pipe section on the position of the pipeline to obtain the information of each point in the pipeline.
Step 1.2: the method comprises the steps of respectively establishing a network layer dynamic model and a transmission layer dynamic model aiming at a network layer and a transmission layer in an Open System Interconnection (OSI) model of a communication network, carrying out hybrid solution on information-energy flow distribution of all established dynamic models, and establishing a pipeline information system model.
The dynamic model comprises an energy flow calculation model, an energy flow direction information flow conversion model, an information flow calculation model and an information flow direction energy flow conversion model.
In this embodiment, an Open System Interconnection (OSI) model of a communication network is established by the International Standard Organization (ISO), and the communication network may be divided into 7 layers according to functions, and since a problem of interest in analysis and control of an oil pipeline system is a data delay and loss phenomenon caused by the communication network, a dynamic model is established only for a network layer and a transport layer in the OSI model; in a network layer and a transmission layer, modeling objects are a router, a communication line and a congestion control protocol for preventing network congestion, wherein the router and the communication line are provided with memory buffers, and when network congestion occurs, the size of a congestion window of each node and the size of each queue are selected as state variables of a dynamic model; and the output of the system is taken as the transmission delay and data loss rate of each data stream.
Step 1.2.1: and establishing an energy flow calculation model.
The formula of the energy flow calculation model is shown as formula (5):
f(x(N+1)，u(N)，D(N+1)，p，A)＝0 (5)
in the formula, A is a network structure variable, p is a network element parameter, D is an interference variable, u is a control variable, x is a compliance variable, and N is a time scale.
Step 1.2.2: and establishing an energy flow direction information flow conversion model according to the control requirement of the industrial field.
The formula of the energy flow direction information flow conversion model is shown as the formula (6):
y(N)＝Hy·x(N) (6)
wherein y (N) is a dummy signal HyFor corresponding control requirements.
In this embodiment, the control requirements of the industrial site may be modified according to different operating standards of the industrial site.
Step 1.2.3: and regarding the information flow in the dynamic model as information mapping from the root node y to other nodes, and recording the information of the leaf node and other nodes at the tail end of the dynamic model as z ═ z respectively1，...，zn]TAnd w ═ wl，...，wl]TAnd obtaining a dynamic model information flow calculation model g (y (N)).
The formula of the dynamic model information flow calculation model is shown as formula (7):
step 1.2.4: mapping information z of each leaf node of the information network into an actual control quantity u, and establishing an information flow direction energy flow conversion model.
The formula of the information flow-to-energy flow conversion model is shown as the formula (8):
u(N)＝Eu·z(N) (8)
wherein z (N) is node information, EuAs mapping parameters, u (N) is the actual control quantity;
in this embodiment, in order to handle the conversion of the information system between various discrete operating states, a finite automaton is introduced as a mathematical tool, and forms a mathematical model of the communication network together with a differential equation set; simulating discrete state conversion of the system by using the finite automata, wherein the dynamic behavior of the system is simulated by using a corresponding micro equation set corresponding to each discrete state of the finite automata; different communication networks adopt different network protocols, and the congestion control mechanisms of the different communication networks are different, so that the characteristics of the congestion control protocols need to be considered when a dynamic model of the communication network is established.
Step 1.2.5: selecting the size of a blocking window of each node and the size of each queue as state variables of the dynamic model, using the transmission delay and the data loss rate of each data stream as the output of the dynamic model, analyzing the information-physical coupling characteristics of the dynamic model, namely, carrying out hybrid solution on the information-energy stream distribution of all the established dynamic models, and establishing a pipeline information system model.
Step 1.3: and (4) combining the submarine petroleum pipeline transmission model with the pipeline information system model to obtain a pipeline information physical system model.
Step 2: the method comprises the following steps of establishing a pipeline pressure gradient distribution curve according to a pipeline steady-state model, estimating the position of an oil overflow source, and calculating a time-varying path of a pipeline information system performance index in a period of t after pipeline leakage occurs according to the established pipeline information system model, wherein the specific steps are as follows:
step 2.1: and carrying out leakage detection on the pipeline according to the flow difference at the two ends of the monitoring pipeline.
The formula for detecting the leakage of the pipeline is shown as the formula (9):
GM-GC＞σ (9)
in the formula, GMFor monitoring the mass flow at the inlet of the pipeline, GCAnd sigma is a set alarm threshold value for monitoring the mass flow of the outlet of the pipeline.
In this embodiment, if the influence of the fluctuation of the interference factor is ignored, when the difference between the outlet and inlet mass flows of the pipeline exceeds the alarm threshold, it indicates that the pipeline leaks. The selection of the alarm threshold value needs to be set according to the actual situation on site.
Step 2.2: and establishing a pipeline pressure gradient distribution curve according to the pipeline steady-state model, and determining the leakage position of the detection pipeline according to the inflection point in the pipeline pressure gradient distribution curve.
The formula for determining the leakage position of the detection pipeline according to the inflection point in the pipeline pressure gradient distribution curve is shown as a formula (10):
where Lr is the distance between the source of the pipeline leak and the head end of the pipeline, PqIs the pressure, P, of the first section of the pipeline after pipeline leakage occurs in a steady statezPressure at the source of the leak, QqIs the flow of the first section of the pipeline Q after pipeline leakage occurs in a steady statezIs the flow at the leakage source, L is the total length of the pipeline, ρ is the average density of the fluid, g is the gravitational acceleration, λ is the hydraulic friction coefficient,
and D is the inner diameter of the pipeline.
In this embodiment, when the pipeline normally operates, the average value of the friction coefficient of the pipeline is taken to obtain a pressure gradient distribution curve, at this time, the pressure gradient of the pipeline is an oblique line, when the pipeline leaks, the pressure gradients of the upstream and downstream pipeline sections at the leakage point are different, and the intersection point of the two is the leakage position. According to the formula (10), when the pipeline leaks, the leakage position can be determined according to the pressure and the flow at two ends of the pipeline.
Step 2.3: and calculating the time-varying path of the performance index of the pipeline information system in a time period t after the pipeline leakage occurs according to the pipeline information physical system model.
The performance indexes of the pipeline information system comprise transmission delay and data loss rate.
And step 3: and (4) judging whether the time-varying path of the performance index of the pipeline information system is larger than a set threshold value, if so, indicating that the position of the oil overflow source is inaccurate, continuing to the step 4, otherwise, indicating that the position of the oil overflow source is accurate, and outputting the position of the oil overflow source.
In the present embodiment, the model of the subsea pipeline CPS obtained by the above steps is shown in fig. 4.
And 4, step 4: and simulating an oil leakage scene for each candidate leakage source, calculating the oil leakage area in an iterative mode, and judging whether to correct the position of the candidate leakage source according to the actual oil leakage area observed by satellite remote sensing.
In the embodiment, in order to estimate the coordinates of the crude Oil leakage position more accurately, the invention provides a CE-based Oil field Spill Source Detection (codds) method based on iterative solution, which is a method based on reliability analysis and random optimization design and has high efficiency and adaptability. In each iteration, an oil spilling scene is simulated for each candidate leakage source according to inaccurate leakage sources judged by field personnel, and a polluted area on the surface of the seawater is obtained through simulation. And applying the simulation result to leakage source evaluation. And determining a mismatch value between the simulated polluted sea area and the actual polluted sea area by analyzing the remote sensing image. Candidate leakage sources with small mismatch values are considered reliable leakage sources and the solution may be updated in the next iteration. The method comprises the following specific steps:
step 4.1: initializing the search range of the leakage source and the position of the leakage source [ POS ] by taking the remote sensing data and the geological parameters as inputmin，POSmax]And the simulation time k is 1.
Step 4.2: generating N candidate leakage source examples { X) according to the oil spilling point estimated in the step 21，X2，…XNIn which XiFor each candidate leakage source, simulating an oil spilling scene through simulation software, and obtaining a simulation result { S (X) } through simulation calculationi)}＝{S(X1)，S(X2)，…，S(XN) And the simulation result S (X)i) The values of which are arranged from small to large.
Step 4.3, the k-th simulation is carried out on the generated candidate leakage source examples, and the quantile of the objective function η is calculated, so that P (S (X)i)＜bk) η wherein bkFor the optimum value gamma of the objective function*Estimate in the kth simulation, η quantile S (X) of the library function value order statistic for comparisonηN)。
In this embodiment, because simulation scenes of the individual leakage sources in the leakage source example are independent of each other, parallel simulation is possible, and time is saved.
Step 4.4: and comparing the simulation result of the kth time with the remote sensing data, evaluating the simulation result of the leakage source, and if the simulation result of the kth time of the leakage source is close to the remote sensing data, storing the simulation result of the kth time of the leakage source as a high-quality sample in a candidate set.
Step 4.5: the selection of the high quality sample from the candidate set that is closest to the telemetry data continues with step 4.6, while the candidate leakage source instance is updated.
Step 4.6: and judging whether the difference value between the simulation result of the kth time and the remote sensing data is smaller than a set threshold epsilon, if so, outputting the position of the leakage source, otherwise, making k equal to k +1, and returning to the step 4.2.
In this embodiment, the size of the threshold value epsilon can be adjusted according to the field requirements.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions as defined in the appended claims.
Claims (1)
1. A method for detecting an oil spill source based on a submarine petroleum pipeline information physical system is characterized by comprising the following steps:
step 1: combining the concept of an information physical system with the characteristics of a submarine oil pipeline system, providing a submarine pipeline information physical system architecture, and establishing a submarine pipeline information system model, wherein the specific method comprises the following steps:
step 1.1: establishing a steady-state model of the submarine petroleum pipeline;
step 1.1.1: analyzing the transient flow process of the simple pipeline, and establishing a differential form of a motion process and a continuous equation so as to establish a pipeline transmission model;
the formula of the pipeline transmission model is as follows:
wherein P is the average pressure of the cross section, t is time, L is displacement, rho is the average density of the fluid, a is the angle between the fluid and the horizontal axis, v is the average flow velocity of the cross section, g is the gravity acceleration, lambda is the hydraulic friction coefficient, D is the inner diameter of the pipeline, CvThe heat energy of liquid in the pipeline is shown, T is the temperature of the fluid, k is the ground heat conductivity, T (r) is a temperature function, and r is the radial distance from a certain point in the pipeline to the center of the pipe diameter;
step 1.1.2: according to the pipeline transmission model, a steady-state pipeline model is established by considering a continuity equation and a motion equation;
the formula of the steady-state pipeline model is as follows:
in the formula, when a is equal to 0, the effect of height difference is not generated, and when a is equal to 0, the effect of height difference is generated;
step 1.2: respectively establishing a network layer dynamic model and a transmission layer dynamic model aiming at a network layer and a transmission layer in an open system interconnection model of a communication network, carrying out hybrid solution on information-energy flow distribution of all established dynamic models, and establishing a pipeline information system model;
the dynamic model comprises an energy flow calculation model, an energy flow direction information flow conversion model, an information flow calculation model and an information flow direction energy flow conversion model;
step 1.2.1: establishing an energy flow calculation model;
the formula of the energy flow calculation model is as follows:
f(x(N+1),u(N),D(N+1),p,A)＝0；
in the formula, A is a network structure variable, p is a network element parameter, D is an interference variable, u is a control variable, x is a compliance variable, and N is a time scale;
step 1.2.2: establishing an energy flow direction information flow conversion model according to the control requirement of an industrial field;
the formula of the energy flow direction information flow conversion model is as follows:
y(N)＝Hy·x(N)；
wherein y (N) is a dummy signal HyThe corresponding control requirements are met;
step 1.2.3: and regarding the information flow in the dynamic model as information mapping from the root node y to other nodes, and recording the information of the leaf node and other nodes at the tail end of the dynamic model as z ═ z respectively1,...,zn]TAnd w ═ w1,...,wl]TObtaining a dynamic model information flow calculation model g (y (N));
the formula of the dynamic model information flow calculation model is as follows:
step 1.2.4: mapping information z of each leaf node of the information network into an actual control quantity u, and establishing an information flow direction energy flow conversion model;
the formula of the information flow to energy flow conversion model is as follows:
u(N)＝Eu·z(N)；
wherein z (N) is node information, EuAs mapping parameters, u (N) is the actual control quantity;
step 1.2.5: selecting the size of a blocking window of each node and the size of each queue as state variables of a dynamic model, using the transmission delay and the data loss rate of each data stream as the output of the dynamic model, analyzing the information-physical coupling characteristics of the dynamic model, namely, performing hybrid solution on the information-energy stream distribution of all the established dynamic models, and establishing a pipeline information system model;
step 1.3: the method comprises the steps that a submarine petroleum pipeline transmission model and a pipeline information system model are combined to obtain a pipeline information physical system model;
step 2: establishing a pipeline pressure gradient distribution curve according to a pipeline steady-state model, estimating the position of an oil overflow source, and calculating a time-varying path of a pipeline information system performance index within a time period t after pipeline leakage occurs according to the established pipeline information system model;
step 2.1: detecting leakage of the pipeline according to the flow difference of the two ends of the pipeline;
step 2.2: establishing a pipeline pressure gradient distribution curve according to a pipeline steady-state model, and determining the leakage position of the detection pipeline according to an inflection point in the pipeline pressure gradient distribution curve;
the formula for determining the leakage position of the detection pipeline according to the inflection point in the pipeline pressure gradient distribution curve is as follows:
where Lr is the distance between the source of the pipeline leak and the head end of the pipeline, PqIs the pressure, P, of the first section of the pipeline after pipeline leakage occurs in a steady statezPressure at the source of the leak, QqIs the flow of the first section of the pipeline Q after pipeline leakage occurs in a steady statezIs the flow at the leakage source, L is the total length of the pipeline, ρ is the average density of the fluid, g is the gravitational acceleration, λ is the hydraulic friction coefficient,
d is the inner diameter of the pipeline, △ h is L sina, when a is 0, the hydraulic model representing the steady state of the pipeline has no elevation difference influence, and when a is not equal to 0, the hydraulic model representing the steady state of the pipeline has elevation difference influence;
step 2.3: calculating a time-varying path of a performance index of the pipeline information system within a time period t after pipeline leakage occurs according to the pipeline information physical system model;
the performance indexes of the pipeline information system comprise transmission delay and data loss rate;
and step 3: judging whether the time-varying path of the performance index of the pipeline information system is larger than a set threshold value, if so, indicating that the position of the oil spill source is inaccurate, continuing to the step 4, otherwise, indicating that the position of the oil spill source is accurate, and outputting the position of the oil spill source;
and 4, step 4: for each candidate leakage source, simulating an oil leakage scene, calculating the oil leakage area in an iterative mode, and judging whether to correct the position of the candidate leakage source according to the actual oil leakage area observed by satellite remote sensing, wherein the specific method comprises the following steps:
step 4.1: initializing the search range of the leakage source and the position of the leakage source [ POS ] by taking the remote sensing data and the geological parameters as inputmin,POSmax]The simulation time k is 1;
step 4.2: generating N candidate leakage source examples { X) according to the oil spilling point estimated in the step 21,X2,…XNIn which XiFor each candidate leakage source, simulating an oil spilling scene through simulation software, and obtaining a simulation result { S (X) } through simulation calculationi)}＝{S(X1),S(X2),…,S(XN) And the simulation result S (X)i) The values of the } are arranged from small to large;
step 4.3, the k-th simulation is carried out on the generated candidate leakage source examples, and the quantile of the objective function η is calculated, so that P (S (X)i)<bk) η wherein bkFor the optimum value gamma of the objective function*Estimate in the kth simulation, η quantile S (X) of the library function value order statistic for comparisonηN)；
Step 4.4: comparing the simulation result of the kth time with the remote sensing data, evaluating the simulation result of the leakage source, and if the simulation result of the kth time of the leakage source is close to the remote sensing data, storing the simulation result of the kth time of the leakage source as a high-quality sample in a candidate set;
step 4.5: selecting a high-quality sample closest to the remote sensing data from the candidate set, continuing to the step 4.6, and updating the candidate leakage source example;
step 4.6: and judging whether the difference value between the simulation result of the kth time and the remote sensing data is smaller than a set threshold epsilon, if so, outputting the position of the leakage source, otherwise, making k equal to k +1, and returning to the step 4.2.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19542890C1 (en) * | 1995-11-17 | 1997-07-17 | Hansaconsult Ingenieurgesellsc | Fluid leakage detection method for pipeline |
| CN103939749A (en) * | 2014-04-28 | 2014-07-23 | 东北大学 | Oil pipeline network leakage intelligent self-adaptation monitoring system and method based on big data |
| CN106015948A (en) * | 2016-05-15 | 2016-10-12 | 芦慧 | Method and device for rapidly and accurately positioning leakage point of long oil delivery pipeline |
| CN106899664A (en) * | 2017-02-15 | 2017-06-27 | 东北大学 | Oil pipeline distributed collaboration leak detection system and method based on multiple agent |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19542890C1 (en) * | 1995-11-17 | 1997-07-17 | Hansaconsult Ingenieurgesellsc | Fluid leakage detection method for pipeline |
| CN103939749A (en) * | 2014-04-28 | 2014-07-23 | 东北大学 | Oil pipeline network leakage intelligent self-adaptation monitoring system and method based on big data |
| CN106015948A (en) * | 2016-05-15 | 2016-10-12 | 芦慧 | Method and device for rapidly and accurately positioning leakage point of long oil delivery pipeline |
| CN106899664A (en) * | 2017-02-15 | 2017-06-27 | 东北大学 | Oil pipeline distributed collaboration leak detection system and method based on multiple agent |
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