CN108952637B - Underwater tree safety system and method for hydrate inhibition in deepwater operation - Google Patents

Underwater tree safety system and method for hydrate inhibition in deepwater operation Download PDF

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CN108952637B
CN108952637B CN201810722730.0A CN201810722730A CN108952637B CN 108952637 B CN108952637 B CN 108952637B CN 201810722730 A CN201810722730 A CN 201810722730A CN 108952637 B CN108952637 B CN 108952637B
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inhibitor
hydrate
thermodynamic
column
underwater
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CN108952637A (en
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孙维洲
关利军
田向东
杨昆
魏安超
茅春
蒋政达
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Cosl Expro Testing Services Tianjin Co ltd
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/01—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells specially adapted for obtaining from underwater installations
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B41/00—Equipment or details not covered by groups E21B15/00 - E21B40/00
    • E21B41/0099—Equipment or details not covered by groups E21B15/00 - E21B40/00 specially adapted for drilling for or production of natural hydrate or clathrate gas reservoirs; Drilling through or monitoring of formations containing gas hydrates or clathrates
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/04—Measuring depth or liquid level
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/06—Measuring temperature or pressure
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/06—Measuring temperature or pressure
    • E21B47/07—Temperature

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Abstract

The invention discloses an underwater tree safety system for hydrate inhibition in deepwater operation, which comprises: inhibitor injection points which are arranged between the underwater trees at equal intervals along the axial direction of the underwater trees; a first injection valve provided at the inhibitor injection point for injecting a thermodynamic inhibitor; a second injection valve provided at the inhibitor injection point for injecting a kinetic inhibitor; a winch connected to the subsea tree for controlling the subsea tree to move axially along the downhole string such that the inhibitor injection point covers the entire downhole string. The underwater pipe column can be monitored at multiple points, and efficient hydrate inhibition is achieved. The invention also discloses an underwater tree safety inhibition method for hydrate inhibition in deepwater operation, which is characterized in that the opening of the first injection valve and the second injection valve and the addition depth of the inhibitor are determined based on the BP neural network, the addition amount of the thermodynamic inhibitor and the kinetic inhibitor can be controlled, and the formation of the hydrate is effectively inhibited.

Description

Underwater tree safety system and method for hydrate inhibition in deepwater operation
Technical Field
The invention relates to the technical field of offshore deepwater oil and gas exploration, in particular to an underwater tree safety system and an underwater tree safety method for hydrate inhibition in deepwater operation.
Background
At present, the current technical situation is that petroleum and natural gas in the stratum are led to the ground for data recording during operation. However, in the process of leading the petroleum and the natural gas to the ground, due to the changes of pressure and temperature, particularly the temperature of seawater close to zero degree near the seabed, hydrates are easily formed in the pipe column, and once the hydrates are formed, the damage or the blockage of the oil pipe can be caused quickly, so that the safety of the whole operation platform is threatened, and the huge exploration investment is abandoned.
The existing tubular column process cannot monitor the temperature change of petroleum and natural gas, cannot inject hydrate inhibitor, and can only close the safety valve in emergency. However, once the pipe column is punctured below the safety valve, the safety valve cannot guarantee safety, so that the whole operation is exposed to danger.
According to research, the hydrate is changed from obvious temperature and pressure when being formed in an oil pipe, and the hydrate is an optimal inhibition point at the initial formation stage, but the existing technology cannot monitor the data change and misses the optimal inhibition period, so that huge hidden troubles are left for the safety of deepwater test operation.
Disclosure of Invention
The invention aims to design and develop an underwater tree safety system for hydrate inhibition in deepwater operation, which can carry out multi-point monitoring on an underwater pipe column and achieve efficient hydrate inhibition.
Another object of the present invention is to design and develop a safety inhibition method for underwater trees for hydrate inhibition in deep water operation, which determines the opening of the first injection valve and the second injection valve and the adding depth of the inhibitor based on the BP neural network, and effectively inhibits the formation of hydrate.
The invention can also control the adding amount of the thermodynamic inhibitor and the kinetic inhibitor according to the opening state of the first injection valve and the second injection valve, thereby effectively inhibiting the formation of hydrate.
The technical scheme provided by the invention is as follows:
an underwater tree safety system for hydrate inhibition in deepwater operations, comprising:
inhibitor injection points which are arranged between the underwater trees at equal intervals along the axial direction of the underwater trees;
a first injection valve provided at the inhibitor injection point for injecting a thermodynamic inhibitor;
a second injection valve provided at the inhibitor injection point for injecting a kinetic inhibitor;
a winch connected to the subsea tree for controlling the subsea tree to move axially along the downhole string such that the inhibitor injection point covers the entire downhole string.
Preferably, the method further comprises the following steps:
the temperature sensors are arranged on the inner wall surface, the outer wall surface and the seabed mud surface of the underground pipe column at equal intervals and are used for detecting the temperature;
pressure sensors respectively provided at the temperature sensors for detecting pressure;
depth sensors provided at the temperature sensors, respectively, for detecting a depth;
and the controller is connected with the temperature sensor, the pressure sensor, the first injection valve, the second injection valve and the winch and is used for receiving detection data of the temperature sensor and the pressure sensor and controlling the first injection valve, the second injection valve and the winch to work.
Correspondingly, the invention also provides a method for safely inhibiting the underwater tree for hydrate inhibition in deepwater operation, which is used for determining the adding depth of the inhibitor and the states of the first injection valve and the second injection valve based on the BP neural network when the underground operation is carried out, and comprises the following steps:
step 1: measuring the internal temperature and pressure, the external temperature and pressure of the underground pipe column and the temperature and pressure of the seabed mud surface through sensors according to a sampling period;
step 2: determining input layer neuron vector x ═ { x) of three-layer BP neural network1,x2,x3,x4,x5,x6}; wherein x is1Is the internal temperature, x, of the downhole string2Is the internal pressure of the tubular string, x3Is the external temperature, x, of the downhole string4Is a wellOutside temperature of lower tubular column, x5Is the temperature at the mud surface of the sea bed, x6The pressure at the mud surface of the seabed is obtained;
and step 3: the input layer vector is mapped to a middle layer, and the number of neurons in the middle layer is m;
step 4: obtaining output layer neuron vector o ═ o1,o2,o3}; wherein o is1State of the first filling valve, o2State of the second filling valve, o3For the depth of addition of inhibitor, the output layer neuron value is
Figure BDA0001718929720000021
k is output layer neuron sequence number, k is {1,2}, when ok At 1, the injection valve is in the open state, when ok At 0, the fill valve is in a closed state.
Preferably, the number m of the intermediate layer nodes satisfies:
Figure BDA0001718929720000031
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
Preferably, the excitation functions of the intermediate layer and the output layer both adopt S-shaped functions fj(x)＝1/(1+e-x)。
Preferably, when o1＝1,o2When the content is 0, the amount of the thermodynamic inhibitor is controlled as follows:
Figure BDA0001718929720000032
wherein m ist0The amount of the thermodynamic inhibitor when the inhibitor is only the thermodynamic inhibitor, rho is the density of water, Delta T is the temperature drop for forming hydrate, α is the ratio of the concentration of the thermodynamic inhibitor in the substance to be collected to the concentration of the thermodynamic inhibitor in the aqueous solution, M is the molecular weight of the thermodynamic inhibitor, K is a constant, Q is the flow rate of the substance to be collected in the column, C is the molar concentration of the thermodynamic inhibitor, pi is the circumferential ratio, r is the inner diameter of the column, and l is the influence of the thermodynamic inhibitor added into the columnThe zone height.
Preferably, when o1＝0,o2When 1, the amount of the kinetic inhibitor is controlled to be:
Figure BDA0001718929720000033
wherein m isd0The amount of kinetic inhibitor when the inhibitor is only kinetic inhibitor, ρ is the density of water, π is the circumference ratio, r is the internal diameter of the column and l is the height of the zone of the column affected by the addition of the kinetic inhibitor.
Preferably, when o1＝1,o2When the ratio is 1, the amount of the thermodynamic inhibitor and the kinetic inhibitor is controlled to be respectively as follows:
Figure BDA0001718929720000034
Figure BDA0001718929720000035
wherein m ist1The amount of thermodynamic inhibitor in the inhibitor, md1The amount of the kinetic inhibitor in the inhibitor, rho is the density of water, Delta T is the temperature drop for forming hydrate, α is the ratio of the concentration of the thermodynamic inhibitor in the substance to be collected to the concentration of the thermodynamic inhibitor in the aqueous solution, M is the molecular weight of the thermodynamic inhibitor, K is a constant, Q is the flow rate of the substance to be collected in the column, C is the molar concentration of the thermodynamic inhibitor, pi is the circumferential ratio, r is the inner diameter of the column, and l is the height of the region in the column affected by the addition of the inhibitor.
The invention has the following beneficial effects:
(1) the underwater tree safety system for hydrate inhibition in deepwater operation can monitor underwater pipe columns at multiple points, and achieves efficient hydrate inhibition.
(2) The underwater tree safety inhibition method for hydrate inhibition in deepwater operation disclosed by the invention determines the opening of the first injection valve and the second injection valve and the addition depth of the inhibitor based on the BP neural network, so that the hydrate formation is efficiently inhibited. And the addition amount of a thermodynamic inhibitor and a kinetic inhibitor can be controlled according to the opening state of the first injection valve and the second injection valve, so that the formation of hydrate is effectively inhibited.
Drawings
Fig. 1 is a schematic diagram of an underwater tree safety system for hydrate inhibition in deep water operation according to the invention.
FIG. 2 is a temperature pressure phase curve for simulation check of hydrate formation according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1, the present invention provides an underwater tree safety system for hydrate inhibition in deepwater operations, comprising: inhibitor injection points which are arranged between the underwater trees at equal intervals along the axial direction of the underwater trees; a first injection valve provided at the inhibitor injection point for injecting a thermodynamic inhibitor; a second injection valve provided at the inhibitor injection point for injecting a kinetic inhibitor; a winch connected to the subsea tree for controlling the subsea tree to move axially along the downhole string such that the inhibitor injection point covers the entire downhole string. Further comprising: the temperature sensors are arranged on the inner wall surface, the outer wall surface and the seabed mud surface of the underground pipe column at equal intervals and are used for detecting the temperature; pressure sensors respectively provided at the temperature sensors for detecting pressure; depth sensors provided at the temperature sensors, respectively, for detecting a depth; and the controller (a bottom electric control panel) is connected with the temperature sensor, the pressure sensor, the first injection valve, the second injection valve and the winch and is used for receiving the detection data of the temperature sensor and the pressure sensor and controlling the first injection valve, the second injection valve and the winch to work.
The kinetic inhibitors are water soluble or water dispersible polymers that inhibit hydrate formation only in the aqueous phase, and are added at very low concentrations (typically less than 1% in the aqueous phase) without affecting the thermodynamic conditions of hydrate formation. In the initial stage of hydrate crystal nucleation and growth, they are adsorbed on the surface of hydrate particles, and the cyclic structure of the inhibitor is combined with the crystals of the hydrate through hydrogen bonds to delay the hydrate crystal nucleation time or prevent further growth of the crystals so that the fluid in the column flows at a temperature lower than the hydrate formation temperature (i.e., at a certain supercooling degree) without hydrate blockage, and PVP (polyvinylpyrrolidone), ethyl methacrylate, N-acylpolyolefin imine, N-vinyl caprolactam, N-alkylacrylamide, polyisopropylmethacrylamide, 2-propyl-2-imidazoline, and the like are generally used.
The thermodynamic inhibitor reduces the activity coefficient of water by changing the thermodynamic generation conditions of three-phase equilibrium of the substance to be exploited, water and hydrate, so that higher pressure or lower temperature is required for generating the hydrate, and the hydrate is not easily formed under the temperature and pressure conditions of a common oil and gas pipeline. Thermodynamic inhibitors are mainly alcohols (methanol, ethylene glycol, diethylene glycol) and sodium chloride solution.
The underwater tree safety system for hydrate inhibition in deepwater operation can monitor underwater pipe columns at multiple points, and achieves efficient hydrate inhibition.
The invention also provides an underwater tree safety inhibition method for hydrate inhibition in deepwater operation, which is used for determining the adding depth of the inhibitor and the states of the first injection valve and the second injection valve based on the BP neural network when the underground operation is carried out, and comprises the following steps:
step one, establishing a BP neural network model.
Fully interconnected connections are formed among neurons of each layer on the BP model, the neurons in each layer are not connected, and the output and the input of neurons in an input layer are the same, namely oi＝xi. The operating characteristics of the neurons of the intermediate hidden and output layers are:
Figure BDA0001718929720000051
opj＝fj(netpj)
where p represents the current input sample, ωjiIs the connection weight from neuron i to neuron j, opiIs the current input of neuron j, opjIs the output thereof; f. ofjIs a non-linear, slightly non-decreasing function, generally taken as a sigmoid function, i.e. fj(x)＝1/(1+e-x)。
The BP network system structure adopted by the invention comprises three layers, wherein the first layer is an input layer, n nodes are provided in total, n detection signals representing underground pipe columns are correspondingly provided, and the signal parameters are given by a data preprocessing module; the second layer is an intermediate layer (hidden layer) which has m nodes and is determined by the training process of the network in a self-adaptive mode; the third layer is an output layer, p nodes are provided in total, and the output is determined by the response actually needed by the system.
The mathematical model of the network is:
inputting a vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a vector: o ═ o (o)1,o2,...,op)T
In the invention, the number of nodes of the input layer is n equals to 6, the number of nodes of the output layer is p equals to 3, and the number of nodes of the hidden layer is determined according to
Figure BDA0001718929720000061
And determining that m is 5.
The input layer 6 parameters are respectively expressed as: x is the number of1Is the internal temperature, x, of the downhole string2Is the internal pressure of the tubular string, x3Is the external temperature, x, of the downhole string4Is the external temperature, x, of the downhole string5Is the temperature at the mud surface of the sea bed, x6The pressure at the mud surface of the seabed is obtained;
the output layer has 3 parameters expressed as: o1State of the first filling valve, o2State of the second filling valve, o3For the depth of addition of inhibitor, the output layer neuron value is
Figure BDA0001718929720000062
k is output layer neuron sequence number, k is {1,2}, when okAt 1, the injection valve is in the open state, when okAt 0, the fill valve is in a closed state.
And step two, training the BP neural network.
After the BP neural network node model is established, the training of the BP neural network can be carried out. And obtaining a training sample according to historical experience data of the product, and giving a connection weight between the input node i and the hidden layer node j and a connection weight between the hidden layer node j and the output layer node k.
The historical empirical data is obtained according to the hydrate formation simulation check, the depth of the position is determined according to the temperature and the pressure of the formed hydrate, and the formation of the hydrate can be effectively prevented by adding an inhibitor at the position. The temperature and pressure at which hydrates are formed are shown in table 1, and the temperature pressure phase diagram is shown in fig. 2.
TABLE 1 temperature and pressure for hydrate formation
Figure BDA0001718929720000063
Figure BDA0001718929720000071
(1) Training method
Each subnet adopts a separate training method; when training, firstly providing a group of training samples, wherein each sample consists of an input sample and an ideal output pair, and when all actual outputs of the network are consistent with the ideal outputs of the network, the training is finished; otherwise, the ideal output of the network is consistent with the actual output by correcting the weight; the output samples for each subnet training are shown in table 2.
TABLE 2 output samples for network training
Figure BDA0001718929720000072
(2) Training algorithm
The BP network is trained by using a back Propagation (Backward Propagation) algorithm, and the steps can be summarized as follows:
the first step is as follows: and selecting a network with a reasonable structure, and setting initial values of all node thresholds and connection weights.
The second step is that: for each input sample, the following calculations are made:
(a) forward calculation: for j unit of l layer
Figure BDA0001718929720000081
In the formula (I), the compound is shown in the specification,
Figure BDA0001718929720000082
for the weighted sum of the j unit information of the l layer at the nth calculation,
Figure BDA0001718929720000083
is the connection weight between the j cell of the l layer and the cell i of the previous layer (i.e. the l-1 layer),
Figure BDA0001718929720000084
is the previous layer (i.e. l-1 layer, node number n)l-1) The operating signal sent by the unit i; when i is 0, order
Figure BDA0001718929720000085
Is the threshold of the j cell of the l layer.
If the activation function of the unit j is a sigmoid function, then
Figure BDA0001718929720000086
And is
Figure BDA0001718929720000087
If neuron j belongs to the first hidden layer (l ═ 1), then there are
Figure BDA0001718929720000088
If neuron j belongs to the output layer (L ═ L), then there are
Figure BDA0001718929720000089
And ej(n)＝xj(n)-oj(n)；
(b) And (3) calculating the error reversely:
for output unit
Figure BDA00017189297200000810
Pair hidden unit
Figure BDA00017189297200000811
(c) Correcting the weight value:
Figure BDA00017189297200000812
η is the learning rate.
The third step: inputting a new sample or a new period sample until the network converges, and randomly re-ordering the input sequence of the samples in each period during training.
The BP algorithm adopts a gradient descent method to solve the extreme value of a nonlinear function, and has the problems of local minimum, low convergence speed and the like. A more effective algorithm is a Levenberg-Marquardt optimization algorithm, which enables the network learning time to be shorter and can effectively inhibit the network from being locally minimum. The weight adjustment rate is selected as
Δω＝(JTJ+μI)-1JTe
Wherein J is a Jacobian (Jacobian) matrix of error to weight differentiation, I is an input vector, e is an error vector, and the variable mu is a scalar quantity which is self-adaptive and adjusted and is used for determining whether the learning is finished according to a Newton method or a gradient method.
When the system is designed, the system model is a network which is only initialized, the weight needs to be learned and adjusted according to data samples obtained in the using process, and therefore the self-learning function of the system is designed. Under the condition of appointing learning samples and quantity, the system can carry out self-learning so as to continuously improve the network performance.
When determining the depth of addition of inhibitor and the state of the first and second injection valves:
(1) when o is1＝1,o2When the content is 0, the amount of the thermodynamic inhibitor is controlled as follows:
Figure BDA0001718929720000091
wherein m ist0The amount (kg) of the thermodynamic inhibitor when the inhibitor is only the thermodynamic inhibitor, and ρ is the density of water (kg/m)3) Where Δ T is the temperature drop (. degree. C.) for forming a hydrate, α is the ratio of the concentration of the thermodynamic inhibitor in the substance to be collected to the concentration of the thermodynamic inhibitor in the aqueous solution, M is the molecular weight (kg/mol) of the thermodynamic inhibitor, K is a constant, and Q is the flow rate (M) of the substance to be collected in the column (M)3) C is the molar concentration (mol/m) of the thermodynamic inhibitor3) And pi is the circumferential ratio, r is the inner diameter (m) of the column, and l is the height (m) of the region affected by the thermodynamic inhibitor added into the column.
(2) When o is1＝0,o2When 1, the amount of the kinetic inhibitor is controlled to be:
Figure BDA0001718929720000092
wherein m isd0The amount (kg) of kinetic inhibitor when the inhibitor is kinetic inhibitor alone, ρ is the density of water (kg/m)3) And pi is the circumferential rate, r is the inner diameter (m) of the column, and l is the height (m) of the region affected by the kinetic inhibitor added into the column.
(3) When o is1＝1,o2When 1, thermodynamic inhibitor and kinetic force are controlledThe amounts of chemical inhibitors were:
Figure BDA0001718929720000101
Figure BDA0001718929720000102
wherein m ist1Amount of thermodynamic inhibitor in inhibitor (kg), md1The amount (kg) of kinetic inhibitor in the inhibitor, and ρ is the density of water (kg/m)3) Where Δ T is the temperature drop (. degree. C.) for forming a hydrate, α is the ratio of the concentration of the thermodynamic inhibitor in the substance to be collected to the concentration of the thermodynamic inhibitor in the aqueous solution, M is the molecular weight (kg/mol) of the thermodynamic inhibitor, K is a constant, and Q is the flow rate (M) of the substance to be collected in the column (M)3) C is the molar concentration (mol/m) of the thermodynamic inhibitor3) And pi is the circumference ratio, r is the inner diameter (m) of the column, and l is the height (m) of the area affected by the inhibitor added into the column.
The method for safely suppressing underwater trees for hydrate suppression in deepwater operation provided by the invention is further described with reference to specific examples.
The downhole operation was simulated, and 16 sets of different temperatures and pressures corresponding to hydrate formation were simulated for testing, the specific data being shown in table 3.
TABLE 3 simulation data
Figure BDA0001718929720000103
Figure BDA0001718929720000111
According to the principle of the detection evaluation model established in the foregoing, the addition depth of the inhibitor and the states of the first injection valve and the second injection valve are determined, and the results are shown in table 4.
Table 4 output results
Grouping First filling valve Second filling valve Depth (m) of inhibitor addition
1 0 1 500
2 0 1 550
3 0 1 600
4 0 1 650
5 1 0 700
6 1 0 750
7 1 0 800
8 1 0 850
9 1 0 900
10 1 1 950
11 1 1 1000
12 1 1 1100
13 1 1 1200
15 1 1 1300
15 1 1 1400
16 1 1 1500
And the addition of the inhibitor in accordance with the amount of the inhibitor determined above was followed to observe whether or not hydrate was generated, and the results are shown in table 5.
TABLE 5 hydrate formation results
Grouping Whether or not to form hydrate
1 Whether or not
2 Whether or not
3 Whether or not
4 Whether or not
5 Whether or not
6 Whether or not
7 Whether or not
8 Whether or not
9 Whether or not
10 Whether or not
11 Whether or not
12 Whether or not
13 Whether or not
15 Whether or not
15 Whether or not
16 Whether or not
As can be seen from table 5, no hydrate was formed, indicating that this method effectively suppressed the formation of hydrate.
The underwater tree safety inhibition method for hydrate inhibition in deepwater operation disclosed by the invention determines the opening of the first injection valve and the second injection valve and the addition depth of the inhibitor based on the BP neural network, so that the hydrate formation is efficiently inhibited. And the addition amount of a thermodynamic inhibitor and a kinetic inhibitor can be controlled according to the opening state of the first injection valve and the second injection valve, so that the formation of hydrate is effectively inhibited.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (6)

1. A safety restraining method for underwater trees for hydrate restraint in deepwater operation is characterized in that when the operation is carried out underground, the adding depth of an inhibitor and the states of a first injection valve and a second injection valve are determined based on a BP neural network, and the safety restraining method comprises the following steps:
step 1: measuring the internal temperature and pressure, the external temperature and pressure of the underground pipe column and the temperature and pressure of the seabed mud surface through sensors according to a sampling period;
step 2: determining input layer neuron vector x ═ { x) of three-layer BP neural network1,x2,x3,x4,x5,x6}; wherein x is1Is the internal temperature, x, of the downhole string2Is the internal pressure of the tubular string, x3Is the external temperature, x, of the downhole string4Is the external temperature, x, of the downhole string5Is the temperature at the mud surface of the sea bed, x6The pressure at the mud surface of the seabed is obtained;
and step 3: mapping the input layer neuron vectors to a middle layer, wherein the number of the neurons in the middle layer is m;
step 4: obtaining output layer neuron vector o ═ o1,o2,o3}; wherein o is1State of the first filling valve, o2State of the second filling valve, o3Is the depth of addition of the inhibitor, soThe output layer neuron value is
Figure FDA0002454640840000011
k is output layer neuron sequence number, k is {1,2}, when okAt 1, the injection valve is in the open state, when okAt 0, the fill valve is in a closed state.
2. The underwater tree safety suppression method for hydrate suppression in deepwater operation as claimed in claim 1, wherein the number m of intermediate layer nodes satisfies:
Figure FDA0002454640840000012
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
3. The underwater tree safety suppression method for hydrate suppression in deepwater operation as claimed in claim 2, wherein the excitation functions of the intermediate layer and the output layer both adopt an S-shaped function fj(x)＝1/(1+e-x)。
4. The underwater tree safety suppression method for hydrate suppression in deepwater operation as claimed in claim 1,2 or 3, wherein o is1＝1,o2When the content is 0, the amount of the thermodynamic inhibitor is controlled as follows:
Figure FDA0002454640840000013
wherein m ist0The amount of the thermodynamic inhibitor when the inhibitor is only the thermodynamic inhibitor, rho is the density of water, Delta T is the temperature drop for forming hydrate, α is the ratio of the concentration of the thermodynamic inhibitor in the substance to be collected to the concentration of the thermodynamic inhibitor in the aqueous solution, M is the molecular weight of the thermodynamic inhibitor, K is a constant, Q is the flow rate of the substance to be collected in the column, C is the molar concentration of the thermodynamic inhibitor, pi is the circumferential ratio, r is the inner diameter of the column, and l is the height of the region in the column affected by the thermodynamic inhibitorAnd (4) degree.
5. The underwater tree safety suppression method for hydrate suppression in deepwater operation as claimed in claim 1,2 or 3, wherein o is1＝0,o2When 1, the amount of the kinetic inhibitor is controlled to be:
Figure FDA0002454640840000021
wherein m isd0The amount of kinetic inhibitor when the inhibitor is only kinetic inhibitor, ρ is the density of water, π is the circumference ratio, r is the internal diameter of the column and l is the height of the zone of the column affected by the addition of the kinetic inhibitor.
6. The underwater tree safety suppression method for hydrate suppression in deepwater operation as claimed in claim 1,2 or 3, wherein o is1＝1,o2When the ratio is 1, the amount of the thermodynamic inhibitor and the kinetic inhibitor is controlled to be respectively as follows:
Figure FDA0002454640840000022
Figure FDA0002454640840000023
wherein m ist1The amount of thermodynamic inhibitor in the inhibitor, md1The amount of the kinetic inhibitor in the inhibitor, rho is the density of water, Delta T is the temperature drop for forming hydrate, α is the ratio of the concentration of the thermodynamic inhibitor in the substance to be collected to the concentration of the thermodynamic inhibitor in the aqueous solution, M is the molecular weight of the thermodynamic inhibitor, K is a constant, Q is the flow rate of the substance to be collected in the column, C is the molar concentration of the thermodynamic inhibitor, pi is the circumferential ratio, r is the inner diameter of the column, and l is the height of the region in the column affected by the addition of the inhibitor.
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