CN113486579B - Oil-water separation prediction method and device based on droplet microscopic distribution derivatization - Google Patents
Oil-water separation prediction method and device based on droplet microscopic distribution derivatization Download PDFInfo
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Abstract
The invention relates to an oil-water separation prediction method and device based on droplet microscopic distribution derivatization, wherein the method comprises the following steps: 1) Acquiring basic structural parameters and common operating conditions of the three-phase separator based on design or on-site operation so as to determine boundary initial conditions of an oil-water droplet evolution process at an inlet of the three-phase separator; 2) Solving the evolution process of the average diameter of the oil-water emulsion liquid drops in the oil-water residence time of the three-phase separator based on demulsifier test data used by the three-phase separator in field operation or experimental test; 3) Solving the sedimentation separation efficiency of the liquid drops under different liquid drop diameters based on a Stokes formula sedimentation theory, the oil-water emulsion liquid drop diameter distribution and a liquid drop two-dimensional coalescence model; 4) And simulating by adopting a deep learning model and an SVM algorithm, and finally predicting the water content of crude oil at the outlet of the three-phase separator. The method integrates a mechanism model and an artificial intelligence algorithm, and considers the influence factors of the oil-water separation effect as comprehensively as possible.
Description
Technical Field
The invention relates to an oil-water separation prediction method, device, medium and equipment based on droplet microscopic distribution derivatization, and belongs to the technical field of oil field development.
Background
Along with the development cycle extension of the oil field in China, a plurality of oil fields enter a middle and high water content development stage, the content of produced water in the oil-gas-water of the well head produced liquid is continuously improved, and in order to be capable of rapidly demulsification, dehydration and processing crude oil, a horizontal three-phase separator, an oil-water sedimentation tank and the like become important mechanical equipment for processing oil-gas-water mixtures of an oil field combined station, which is also the first process of oil-gas field gathering and transportation processing engineering. The horizontal oil-gas-water three-phase separator mainly separates the oil field inlet produced liquid into wet natural gas, crude oil with low water content and sewage with low oil content; the three-phase separator or the vertical oil-water sedimentation tank connected in series in multiple stages can further carry out dehydration treatment on the crude oil with water so that the water content reaches the standard of external transmission. The high-efficiency oil-gas-water separation facilitates subsequent processing and production, and reduces the crude oil treatment and processing cost. The research of the three-phase separator is gradually improved from an oil-water separation tank to be a spherical, vertical and horizontal separator, and the horizontal separator is highly concerned by students at home and abroad with applicability and high efficiency, and is developed faster.
In the actual production process, the factors influencing the oil-water separation effect are numerous, and besides the structure of the separator and the working condition during operation, various factors such as the non-uniformity of crude oil, pH value, salt content, colloid asphalt content, the complexity of impurities in water, surface active compounds, demulsifiers and the like can possibly influence the water content of the crude oil at the outlet of the three-phase separator. For the predictive analysis of the oil-water separation effect of the horizontal three-phase separator, students at home and abroad mainly study through indoor dehydration experiments, sedimentation physical models, CFD numerical simulation and other modes, however, the indoor dehydration experiments neglect the influence of the structure of the three-phase separator, the sedimentation physical models, CFD simulation and other methods are difficult to describe complex factors such as demulsifiers, and meanwhile, the problems of complex modeling, difficult operation and the like exist. The machine learning algorithm adopting artificial intelligence can directly perform learning training through the on-site operation database, so that a prediction model capable of predicting the oil-water separation effect is obtained, but the prediction model is not supported by a complete physical theory, and has defects in the aspects of ductility, stability and the like. The current intelligent development trend is the fusion and improvement of physical models and intelligent algorithms.
Disclosure of Invention
The invention provides an oil-water separation prediction method, device, medium and equipment based on droplet microscopic distribution derivatization.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an oil-water separation prediction method based on droplet microscopic distribution derivatization comprises the following steps:
1) Acquiring basic structural parameters and common operating conditions of the three-phase separator based on design or on-site operation so as to determine boundary initial conditions of an oil-water droplet evolution process at an inlet of the three-phase separator;
2) Determining the service life of an oil-water emulsion liquid film under the current demulsifier dosage concentration based on demulsifier test data used by a three-phase separator in field operation or experimental test, and solving the evolution process of the average diameter of oil-water emulsion liquid drops in the oil-water residence time of the three-phase separator according to a liquid drop diameter evolution model of the oil-water emulsion liquid drops with time variation;
3) Based on Stokes formula sedimentation theory, oil-water emulsion droplet diameter distribution and droplet two-dimensional coalescence model, a mechanism model of oil-water droplet separation effect is established, and droplet sedimentation separation efficiency under different droplet diameters is solved by utilizing the droplet diameter of the discretized W/O type emulsion and the three-phase separator volume model coupling;
4) Based on the analysis of the mechanism evolution process of the oil-water emulsion droplet size distribution of the inlet and the outlet of the three-phase separator in the step 3), the initial particle size distribution state of the oil-water emulsion droplet outlet, the adding amount of the demulsifier and the initial water content of the incoming liquid are selected as four characteristic input parameters, the water content of the outlet crude oil produced on site is selected as a characteristic output parameter, a deep learning model and an SVM algorithm are adopted for simulation, and finally the water content of the crude oil at the outlet of the three-phase separator is predicted.
In the oil-water separation prediction method, preferably, the average diameter of the oil-water emulsion droplets at the inlet of the three-phase separator in the step 1) is calculated by adopting an oil-water emulsion droplet diameter model, and the ratio of the diameters of the droplets before and after the oil-water emulsion reverse phase satisfies the following formula (1):
wherein (d) 32 ) w/o The sauter mean diameter of the emulsion drops is w/o; (d) 32 ) o/w The sauter mean diameter is o/w emulsion droplets; mu (mu) w Is the viscosity of the water phase; mu (mu) o Is the viscosity of oil phase; ρ o Is the density of the water phase; ρ w Is oil phase density; epsilon w The water content is the water content;
when the distribution rule of the droplet diameter is subjected to logarithmic positive distribution, the method is as shown in the formula (2):
wherein delta i Is the current droplet diameter; sigma (sigma) g Is the geometric standard deviation of the droplet diameter distribution; delta m Is the average value of the droplet diameter.
In the oil-water separation prediction method, preferably, in the step 3), the known parameter is an oil-water interface height h w Height h of oil-gas interface o The size diameter D of the three-phase separator and the effective length L of oil-water separation are used for deducing an oil-water sedimentation wedge-shaped volume model of the three-phase separator, and the three-phase separator is shown in formulas (3) - (8):
wherein V is S (x) The volume formula is an oil-water sedimentation wedge-shaped volume formula; r is the radius of the horizontal three-phase separator; l (L) * The theoretical distance required for the sedimentation of emulsion droplets;
an included angle between the sedimentation track of emulsion liquid drops and the horizontal plane; v ov A sedimentation rate for emulsion droplets; g is gravity acceleration; l is the effective oil-water separation length of the three-phase separator; f (f) w/o (δ i ) Is the droplet diameter delta i Is a probability of distribution of (1); v (V) oil The total wedge-shaped volume of water drop sedimentation in the oil phase; />
Is an unseparated wedge volume; θo is the oil phase interface included angle; θw is the interface included angle of the water phase; epsilon i Is oil phase with diameter delta i Is a water drop separation rate of (2); e (E) w/o Is the total separation rate of water drops in the oil phase.
In the oil-water separation prediction method, preferably, in the step 4), the demulsifier plays a role in the oil-water demulsification separation process mainly in promoting coalescence of water droplets of the W/O emulsion in the oil phase so as to improve the oil-water separation efficiency epsilon, and a demulsifier coalescence effect model is shown in the following formulas (9) - (10):
wherein the demulsifier acts to alter the interfacial film lifetime τ of the emulsion 0 The general value range is 0.01 s-100 s; h P Is oil-water emulsificationLayer thickness; r is (r) i Is the radius of the liquid drop;
coalescing the average diameter of the emulsion after adding the demulsifier; d, d i Is the initial diameter of the emulsion; lambda is the coalescence coefficient; t is the coalescence time; τ 0 Is the lifetime of the emulsion interfacial film.
In the oil-water separation prediction method, preferably, in the steps 1) to 4), the oil-water separation process of the three-phase separator is a steady-state operation process, that is, the oil-water mixed flow rate at the inlet of the three-phase separator should be equal to the sum of the crude oil flow rate and the water flow rate at the outlet; total flow rate Q and crude oil flow rate Q o Water flow Qw, oil-water interface height h w The effective length L of oil-water separation and the height h of an oil-gas interface o The residence time t, the corresponding relation of which is shown in the following formulas (11) - (14):
V o ＝R 2 L(θ o -0.5cosθ o )-R 2 L(θ w -0.5cosθ w ) (12)
wherein V is o Is the actual occupied volume of the oil phase in the three-phase separator.
In the oil-water separation prediction method, preferably, the three-phase separator is a conventional horizontal oil-gas-water three-phase separator, the whole shape is a horizontal cylinder, and the main structural parameters comprise the diameter D of the separator, the effective length L of oil-water separation and the height H of an oil weir o Inlet pipe diameter d ru 。
The invention also provides an oil-water separation prediction device based on droplet microscopic distribution derivatization, which comprises:
the first processing unit is used for acquiring basic structural parameters and common operating conditions of the three-phase separator based on design or on-site operation so as to determine boundary initial conditions of an oil-water droplet evolution process at the inlet of the three-phase separator;
the second processing unit is used for determining the service life of the oil-water emulsion liquid film under the current demulsifier adding amount concentration based on demulsifier test data used by the three-phase separator in field operation or experimental test, and solving the evolution process of the average diameter of the oil-water emulsion liquid drops in the oil-water residence time of the three-phase separator according to the evolution model of the liquid drop diameter of the oil-water emulsion liquid drops with time variation;
the third processing unit is used for establishing a mechanism model of oil-water droplet separation effect based on Stokes formula sedimentation theory, oil-water emulsion droplet diameter distribution and a droplet two-dimensional coalescence model, and solving droplet sedimentation separation efficiency under different droplet diameters by utilizing the droplet diameter of the discretized W/O type emulsion and the three-phase separator volume model coupling;
the fourth processing unit is used for analyzing the mechanism evolution process of the oil-water emulsion droplet size distribution of the inlet and the outlet of the three-phase separator based on the third processing unit, selecting the initial particle size distribution state of the oil-water emulsion droplet, the particle size distribution state of the outlet of the oil-water emulsion droplet, the adding amount of the demulsifier and the initial water content of the incoming liquid as four characteristic input parameters, selecting the water content of the crude oil at the outlet of the on-site production as a characteristic output parameter, simulating by adopting a deep learning model and an SVM algorithm, and finally predicting the water content of the crude oil at the outlet of the three-phase separator.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described droplet microscopic distribution derivatization-based oil-water separation prediction method.
The invention also provides a computer device which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the oil-water separation prediction method based on the derivation of the microscopic distribution of liquid drops when executing the computer program.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the three-phase separator volume model can characterize and describe design structural parameters (diameter, separation length and oil-water interface height) of the three-phase separator, and the three-phase separator oil-water droplet sedimentation model and the oil-water emulsion droplet diameter model can reflect the physical properties of crude oil and produced water and the influence of on-site working condition parameters of an oil field on thermochemical sedimentation dehydration in an oil-water separation effect.
2. The demulsifier coalescence effect model can correspondingly characterize the demulsification and dehydration effects of the demulsifiers, and the effect of the demulsifiers is expressed as promoting the coalescence of emulsion from small liquid drops to large liquid drops, so that the demulsifier coalescence effect model has higher sedimentation efficiency in thermochemical sedimentation of oil-water separation, and achieves the aim of improving the oil-water separation effect. Meanwhile, the coalescence effect of oil-water droplets of the demulsifier is closely related to the type and the adding concentration of the demulsifier, and the optimal demulsifier concentration generally exists. The relationship between the type, concentration and coalescence efficiency of the demulsifier can be obtained through indoor dehydration experiments or field experience, and can also be estimated through the critical micelle concentration CMC of the demulsifier, or can be predicted through a mode of combining artificial intelligence with a mechanism model.
3. The artificial intelligent machine is used as an emerging technical field with rapid development, and can directly perform continuous analysis through mass data so as to directly achieve the prediction effect of the mechanism model. For complex process conditions of the oilfield gathering and transportation engineering site and hidden influencing factors which are not covered in the mechanism model, knowledge and experience can be extracted from site production data through a machine analysis algorithm, and the knowledge and experience can be used for predicting the oil-water separation effect of the three-phase separator. The artificial neural network algorithm is suitable for simulating more sufficient field experience data, and the support vector machine is suitable for a small number of data samples. When field experience data and working condition parameters are absent (such as the design of a separator of a new oil field), the oil-water marketing effect can be predicted by adopting a mechanism model.
4. The prediction method of the invention is based on a three-phase separator oil-water separation prediction method in the evolution process of oil-water emulsion liquid drops, and can directly and quickly obtain the water content of crude oil at the outlet of the three-phase separator based on a more mature mechanism model of coalescence sedimentation of the oil-water emulsion liquid drops and a machine learning algorithm. The mechanism model is based on three-phase separator oil-water separation volume modeling, a sedimentation model, a demulsification and coalescence effect model and an oil-water droplet diameter distribution model are used for describing a thermochemical sedimentation demulsification and separation process from an inlet to an outlet of the three-phase separator, and the influences of crude oil and produced water physical properties, oilfield on-site working condition parameters and the like are comprehensively considered, so that the three-phase separator has relatively perfect physical significance and technological principle; the artificial intelligence technology mainly adopts a neural network (including a deep learning network and a BP neural network) and a support vector machine in machine analysis, and can be analyzed differently through field experience data on the basis of a mechanism model, so that the influence of a plurality of complex influence factors is further brought into the mechanism model, and the prediction precision of the oil-water separation effect of the model is improved. Compared with the existing CFD numerical simulation, indoor dehydration experiment, simple sedimentation prediction model and other methods, the method has the advantages of being rapid, high in precision, strong in expansibility and the like.
Drawings
FIG. 1 is a schematic diagram of a three-phase separator mechanism model in accordance with one embodiment of the present invention;
FIG. 2 is a schematic diagram of another three-phase separator mechanism model provided in this embodiment of the present invention;
FIG. 3 is a schematic diagram of the three-phase separator oil-water separation process according to the embodiment of the present invention;
FIG. 4 shows the average diameter evolution process of oil-water emulsion droplets in the three-phase separator under the influence of different demulsifiers in the life of the liquid film according to the embodiment of the invention;
FIG. 5 is a graph showing the predicted results of different dewatering residence times for the three-phase separator provided in this example of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
The invention is further described by taking an example of the oil-water separation prediction method of the three-phase separator based on the evolution process of oil-water emulsion liquid drops for predicting the oil-water separation effect of the oil-water separation process of the three-phase separator with high water content in a certain oil field.
Example 1: predicting separation effect of residence time change of three-phase separator
A horizontal three-phase separator is adopted as main equipment for oil-gas-water three-phase separation in a middle-high water-containing oilfield, the three-phase separator can rapidly remove most of free water in wellhead liquid, and if the crude oil has poor emulsion stability or good emulsion breaking effect, the three-phase separator can reduce the water content of the crude oil to 0.5% of the water content standard of the externally-conveyed crude oil in a heating or residence time prolonging mode.
In this example, the three-phase separator was used for 30 minutes at a dehydration temperature of 30℃and the yard crude oil was a waxy crude oil having a density of 840kg/m 3 The viscosity is 5 mpa.s, the oil-water interfacial tension is 12mN/m, the diameter of the separator is 2.5m, the effective length of oil-water separation is 9m, the diameter of the inlet of the separator is 0.2m, the height of the oil-water interface is 1.50m, the height of the oil weir is 2.25m, and the inlet flow is 0.500m 3 Per min, outlet water flow of 0.150m 3 Per min, outlet oil flow of 0.350m 3 And/min, wherein the adding amount of the demulsifier is 150ppm of the adding amount of the optimal demulsifier.
In this yard, the dewatering effect of the three-phase separator at different dewatering residence times (0-30 min) was predicted by the prediction method of the present invention, and the result is shown in fig. 5.
As shown in FIG. 5, the water content can be reduced to below 20% after 10min dehydration, which is consistent with the actual production experience on site, and the water content of the dehydrated crude oil can be reduced to lower after heating and prolonging time, which is consistent with the operation of on site production and the experimental rule of indoor dehydration.
The embodiment shows that in the actual production process of oil-water demulsification separation, the three-phase separator oil-water separation prediction method based on the oil-water emulsion droplet evolution process provided by the invention has a good effect of predicting the oil-water separation effect (the water content of a crude oil outlet) under different residence time, and can provide support for on-site production operation or the separation effect prediction of the three-phase separator.
Example 2: predicting separation effects of different influencing factors
The initial parameters were substantially the same as those in example 1, the residence time was 30min, the initial temperature for demulsification and dehydration was set to 40℃respectively, the oil-water interfacial tension was reduced to 10mN/m, and the crude oil density was increased to 850kg/m 3 The water content of crude oil after oil-water separation is predicted, and the predicted result is shown in table 1:
TABLE 1 prediction results of oil-water separation effect under different basic parameters
The calculation result of the prediction method in embodiment 2 shows that, for the influence factors influencing the oil-water separation effect, the prediction method of the invention can well reflect the influence of the change of the influence factors on the oil-water separation effect, and adverse factors (such as reduced oil-water density difference, reduced temperature, reduced oil-water interfacial tension and the like) influencing the oil-water separation can cause the water content of the export crude oil to rise, otherwise, the adverse influence can cause the water content of the export crude oil to fall.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described droplet microscopic distribution derivatization-based oil-water separation prediction method.
The invention also provides a computer device which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the oil-water separation prediction method based on the derivation of the microscopic distribution of liquid drops when executing the computer program.
The present invention is described in terms of flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. The oil-water separation prediction method based on droplet microscopic distribution derivatization is characterized by comprising the following steps of:
1) Acquiring structural parameters and operation conditions of the three-phase separator based on design or on-site operation so as to determine boundary initial conditions of an oil-water droplet evolution process at an inlet of the three-phase separator;
2) Determining the service life of an oil-water emulsion liquid film under the current demulsifier dosage concentration based on demulsifier test data used by a three-phase separator in field operation or experimental test, and solving the evolution process of the average diameter of oil-water emulsion liquid drops in the oil-water residence time of the three-phase separator according to a liquid drop diameter evolution model of the oil-water emulsion liquid drops with time variation;
3) Based on Stokes formula sedimentation theory, oil-water emulsion droplet diameter distribution and droplet two-dimensional coalescence model, a mechanism model of oil-water droplet separation effect is established, and droplet sedimentation separation efficiency under different droplet diameters is solved by utilizing the droplet diameter of the discretized W/O type emulsion and the three-phase separator volume model coupling;
4) Based on the analysis of the mechanism evolution process of the oil-water emulsion droplet size distribution of the inlet and the outlet of the three-phase separator in the step 3), selecting an initial particle size distribution state of the oil-water emulsion droplet, an initial particle size distribution state of the oil-water emulsion droplet outlet, the addition amount of a demulsifier and the initial water content of incoming liquid as four characteristic input parameters, selecting the water content of the outlet crude oil produced on site as a characteristic output parameter, adopting a deep learning model and an SVM algorithm to simulate, and finally predicting the water content of the crude oil at the outlet of the three-phase separator;
the average diameter of the oil-water liquid drops at the inlet of the three-phase separator in the step 1) is calculated by adopting an oil-water emulsion liquid drop diameter model, and the ratio of the diameters of the liquid drops before and after the reverse phase of the oil-water emulsion meets the following formula (1):
wherein (d) 32 ) w/o The sauter mean diameter of the emulsion drops is w/o; (d) 32 ) o/w The sauter mean diameter is o/w emulsion droplets; mu (mu) w Is the viscosity of the water phase; mu (mu) o Is the viscosity of oil phase; ρ o Is the density of the water phase; ρ w Is oil phase density; epsilon w The water content is the water content;
when the distribution rule of the droplet diameter is subjected to logarithmic positive distribution, the method is as shown in the formula (2):
wherein delta i Is the current droplet diameter; sigma (sigma) g Is the geometric standard deviation of the droplet diameter distribution; delta m Is the average value of the diameter of the liquid drop;
in the step 3), the known parameter is set as the oil-water interface height h w Height h of oil-gas interface o The size diameter D of the three-phase separator and the effective length L of oil-water separation are used for deducing an oil-water sedimentation wedge-shaped volume model of the three-phase separator, and the three-phase separator is shown in formulas (3) - (8):
wherein V is S (x) The volume formula is an oil-water sedimentation wedge-shaped volume formula; r is the radius of the horizontal three-phase separator; l (L) * The theoretical distance required for the sedimentation of emulsion droplets;
an included angle between the sedimentation track of emulsion liquid drops and the horizontal plane; v ov A sedimentation rate for emulsion droplets; g is gravity acceleration; l is the effective oil-water separation length of the three-phase separator; f (f) w/o (δ i ) Is the droplet diameter delta i Is a probability of distribution of (1); v (V) oil The total wedge-shaped volume of water drop sedimentation in the oil phase; />
Is an unseparated wedge volume; θo is the oil phase interface included angle; θw is the interface included angle of the water phase; epsilon i Is oil phase with diameter delta i Is a water drop separation rate of (2); e (E) w/o The total separation rate of water drops in the oil phase;
in the steps 1) to 4), the oil-water separation process of the three-phase separator is a steady-state operation process, namely, the oil-water mixed flow rate at the inlet of the three-phase separator is equal to the sum of the crude oil flow rate and the water flow rate at the outlet; total flow rate Q and crude oil flow rate Q o Water flow Qw, oil-water interface height h w The effective length L of oil-water separation and the height h of an oil-gas interface o The residence time t, the corresponding relation of which is shown in the following formulas (11) - (14):
V o ＝R 2 L(θ o -0.5cosθ o )-R 2 L(θ w -0.5cosθ w ) (12)
wherein V is o Is the actual occupied volume of the oil phase in the three-phase separator.
2. The method according to claim 1, wherein in the step 4), the demulsifier plays a role in the demulsification and separation process of oil and water mainly in promoting coalescence of droplets of W/O type emulsion in the oil phase to improve the oil-water separation efficiency epsilon, and the demulsifier coalescence effect model is represented by the following formulas (9) - (10):
wherein the demulsifier acts to alter the interfacial film lifetime τ of the emulsion 0 The value range is 0.01 s-100 s; h P The thickness of the oil-water emulsion layer is; r is (r) i Is the radius of the liquid drop;
coalescing the average diameter of the emulsion after adding the demulsifier; d, d i Is the initial diameter of the emulsion; lambda is the coalescence coefficient; t is the coalescence time; τ 0 Is the lifetime of the emulsion interfacial film.
3. The oil-water separation prediction method according to claim 1, characterized in thatThe three-phase separator is a conventional horizontal oil-gas-water three-phase separator, the whole shape is a horizontal cylinder, and the main structural parameters comprise the diameter D of the separator, the effective length L of oil-water separation and the height H of an oil weir o Inlet pipe diameter d ru 。
4. An oil-water separation prediction device based on droplet microscopic distribution derivatization for realizing the oil-water separation prediction method according to claim 1, comprising:
the first processing unit is used for acquiring structural parameters and operation conditions of the three-phase separator based on design or on-site operation so as to determine boundary initial conditions of an oil-water droplet evolution process at an inlet of the three-phase separator;
the second processing unit is used for determining the service life of the oil-water emulsion liquid film under the current demulsifier adding amount concentration based on demulsifier test data used by the three-phase separator in field operation or experimental test, and solving the evolution process of the average diameter of the oil-water emulsion liquid drops in the oil-water residence time of the three-phase separator according to the evolution model of the liquid drop diameter of the oil-water emulsion liquid drops with time variation;
the third processing unit is used for establishing a mechanism model of oil-water droplet separation effect based on Stokes formula sedimentation theory, oil-water emulsion droplet diameter distribution and a droplet two-dimensional coalescence model, and solving droplet sedimentation separation efficiency under different droplet diameters by utilizing the droplet diameter of the discretized W/O type emulsion and the three-phase separator volume model coupling;
and a fourth processing unit, configured to analyze the mechanism evolution process of the oil-water emulsion droplet size distribution at the inlet and the outlet of the three-phase separator based on the step 3), select an initial oil-water emulsion droplet size distribution state, an oil-water emulsion droplet outlet size distribution state, a demulsifier addition amount, and an initial water content of the incoming liquid as four characteristic input parameters, select an outlet crude oil water content produced on site as a characteristic output parameter, simulate the characteristic output parameter by adopting a deep learning model and an SVM algorithm, and finally predict the crude oil water content at the outlet of the three-phase separator.
5. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor realizes the steps of the oil-water separation prediction method based on droplet microscopic distribution derivatization according to claims 1-3.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the droplet micro distribution derivatization based oil-water separation prediction method according to claims 1-3 when the computer program is executed.
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