CN113486579A - Oil-water separation prediction method and device based on droplet micro-distribution derivation - Google Patents
Oil-water separation prediction method and device based on droplet micro-distribution derivation Download PDFInfo
- Publication number
- CN113486579A CN113486579A CN202110727479.9A CN202110727479A CN113486579A CN 113486579 A CN113486579 A CN 113486579A CN 202110727479 A CN202110727479 A CN 202110727479A CN 113486579 A CN113486579 A CN 113486579A
- Authority
- CN
- China
- Prior art keywords
- oil
- water
- phase separator
- droplet
- emulsion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 153
- 238000000926 separation method Methods 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000009826 distribution Methods 0.000 title claims abstract description 41
- 238000009795 derivation Methods 0.000 title claims abstract description 14
- 239000007788 liquid Substances 0.000 claims abstract description 47
- 239000002569 water oil cream Substances 0.000 claims abstract description 43
- 239000010779 crude oil Substances 0.000 claims abstract description 34
- 230000000694 effects Effects 0.000 claims abstract description 31
- 230000008569 process Effects 0.000 claims abstract description 25
- 238000004581 coalescence Methods 0.000 claims abstract description 20
- 230000007246 mechanism Effects 0.000 claims abstract description 19
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 11
- 238000013461 design Methods 0.000 claims abstract description 8
- 238000013136 deep learning model Methods 0.000 claims abstract description 5
- 238000012360 testing method Methods 0.000 claims abstract description 5
- 239000012071 phase Substances 0.000 claims description 98
- 239000003921 oil Substances 0.000 claims description 34
- 239000000839 emulsion Substances 0.000 claims description 25
- 238000004062 sedimentation Methods 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 15
- 239000002245 particle Substances 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 6
- 230000008878 coupling Effects 0.000 claims description 4
- 238000010168 coupling process Methods 0.000 claims description 4
- 238000005859 coupling reaction Methods 0.000 claims description 4
- 230000005501 phase interface Effects 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 230000001133 acceleration Effects 0.000 claims description 2
- 239000008346 aqueous phase Substances 0.000 claims description 2
- 230000005484 gravity Effects 0.000 claims description 2
- 239000007762 w/o emulsion Substances 0.000 claims description 2
- 238000013473 artificial intelligence Methods 0.000 abstract description 5
- 230000018044 dehydration Effects 0.000 description 13
- 238000006297 dehydration reaction Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 9
- 238000004519 manufacturing process Methods 0.000 description 8
- 238000011161 development Methods 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 239000007789 gas Substances 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 230000000704 physical effect Effects 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 239000010426 asphalt Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000000084 colloidal system Substances 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004945 emulsification Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 239000000693 micelle Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000005191 phase separation Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000010865 sewage Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; Viscous liquids; Paints; Inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/2835—Specific substances contained in the oils or fuels
- G01N33/2847—Water in oils
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Oil, Petroleum & Natural Gas (AREA)
- Computer Hardware Design (AREA)
- Artificial Intelligence (AREA)
- General Chemical & Material Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Geometry (AREA)
- Medical Informatics (AREA)
- General Engineering & Computer Science (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Production Of Liquid Hydrocarbon Mixture For Refining Petroleum (AREA)
Abstract
The invention relates to an oil-water separation prediction method and device based on droplet micro-distribution derivation, and the method comprises the following steps: 1) obtaining basic structural parameters and common operation working conditions of the three-phase separator based on design or field operation 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 liquid drops of the oil-water emulsion in the oil-water retention time of the three-phase separator based on the demulsifier test data used by the three-phase separator operated on site or tested experimentally; 3) solving the settling separation efficiency of the liquid drops under different liquid drop diameters based on a Stokes formula settling theory, the liquid drop diameter distribution of the oil-water emulsion and a two-dimensional coalescence model of the liquid drops; 4) and (3) 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 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 micro-distribution derivation, and belongs to the technical field of oilfield development.
Background
With the extension of the period of oil field development in China, many oil fields enter a medium-high water content development stage, the content of produced water in oil gas water of a wellhead produced liquid is continuously improved, and in order to quickly perform demulsification, dehydration and processing on crude oil, a horizontal three-phase separator, an oil-water settling tank and the like become important mechanical equipment for treating oil-gas-water mixtures in an oil field combined station, which is also the first process of an oil-gas field gathering and transportation treatment project. The horizontal oil-gas-water three-phase separator is mainly used for separating oil field inlet produced liquid into wet natural gas, crude oil with low water content and sewage with low oil content; the multi-stage series three-phase separator or the vertical oil-water settling tank can further dehydrate the crude oil containing water to ensure that the water content of the crude oil reaches the standard of output. The high-efficiency oil-gas-water separation facilitates subsequent processing and production, and reduces the processing cost of crude oil treatment. The research on the three-phase separator is gradually improved from an oil-water separation pool, and the three-phase separator is developed into a spherical separator, a vertical separator and a horizontal separator, and the horizontal separator is highly concerned by scholars at home and abroad with the applicability and high efficiency and is developed more quickly.
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, the non-uniformity of the crude oil, the pH value, the salinity, the content of colloid asphalt, the complexity of impurities in water, surface active compounds, demulsifiers and other factors can influence the water content of the crude oil at the outlet of the three-phase separator. For the prediction and analysis of the oil-water separation effect of the horizontal three-phase separator, domestic and foreign scholars mainly study the oil-water separation effect of the horizontal three-phase separator 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, the CFD simulation and other methods are difficult to describe complex factors such as demulsifiers and the like, and meanwhile, the problems of complex modeling, difficult operation and the like exist. The machine learning algorithm adopting artificial intelligence can be used for learning and training directly through a field 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 extensibility, stability and the like. The trend of intelligent development is the fusion and improvement of physical model and intelligent algorithm.
Disclosure of Invention
The invention provides an oil-water separation prediction method, device, medium and equipment based on droplet micro-distribution derivation.
In order to achieve the purpose, the invention adopts the following technical scheme:
an oil-water separation prediction method based on droplet micro-distribution derivation comprises the following steps:
1) obtaining basic structural parameters and common operation working conditions of the three-phase separator based on design or field operation 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 adding amount concentration based on demulsifier test data used by a three-phase separator operated on site or tested experimentally, and solving the evolution process of the average diameter of oil-water emulsion liquid drops in the oil-water retention time of the three-phase separator according to a liquid drop diameter evolution model of the oil-water emulsion liquid drops changing along with time;
3) based on a Stokes formula settlement theory, the droplet diameter distribution of the oil-water emulsion and a droplet two-dimensional coalescence model, establishing a mechanism model of the oil-water droplet separation effect, and solving the droplet settlement separation efficiency under different droplet diameters by utilizing the droplet diameter of the discretization W/O type emulsion and the three-phase separator volume model coupling;
4) based on the analysis of the mechanism evolution process of the particle size distribution of the oil-water emulsion liquid drops at the inlet and the outlet of the three-phase separator in the step 3), selecting an oil-water emulsion liquid drop initial particle size distribution state, an oil-water emulsion liquid drop outlet particle size distribution state, a demulsifier adding amount and an incoming liquid initial water content as four characteristic input parameters, selecting outlet crude oil water content produced on site as a characteristic output parameter, simulating by adopting a deep learning model and an SVM algorithm, and finally predicting the crude oil water content at the outlet of the three-phase separator.
Preferably, in the oil-water separation prediction method, the average diameter of the droplets of the oil-water emulsion at the inlet of the three-phase separator in step 1) is calculated by using an oil-water emulsion droplet diameter model, and the ratio of the diameters of the droplets before and after the inversion of the oil-water emulsion satisfies the following formula (1):
wherein (d)32)w/oIs the Sott average diameter of the droplets of the w/o emulsion; (d)32)o/wIs the Sott average diameter of the emulsion droplets; mu.swThe viscosity of the water phase; mu.soThe viscosity of the oil phase; rhooIs the density of the aqueous phase; rhowIs the density of the oil phase; epsilonwThe water content is obtained;
when the distribution rule of the diameters of the liquid drops follows logarithmic positive-space distribution, the formula (2) shows that:
wherein, deltaiCurrent droplet diameter; sigmagIs the geometric standard deviation of the droplet diameter distribution; deltamThe average droplet diameter.
Preferably, in the oil-water separation prediction method, in the step 3), the known parameter is the oil-water interface height hwOil-gas interface height hoThe 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, which is shown in formulas (3) to (8):
wherein, VS(x) Is an oil-water sedimentation wedge-shaped volume formula; r is the radius of the horizontal three-phase separator; l is*The theoretical distance required for emulsion droplet sedimentation;
the included angle between the sedimentation track of the emulsion liquid drop and the horizontal plane is shown; v. ofovIs the emulsion droplet settling rate; g is the acceleration of gravity; l is the effective length of oil-water separation of the three-phase separator; f. ofw/o(δi) Is the droplet diameter deltaiThe distribution probability of (2); voilThe total wedge-shaped volume of water drop sedimentation in the oil phase;
an unseparated wedge volume; theta o is an included angle of an oil phase interface; theta w is the included angle of the water phase interface; epsiloniIs the diameter delta in the oil phaseiThe separation rate of water droplets of (a); ew/oThe total separation rate of water drops in the oil phase is shown.
Preferably, in the oil-water separation prediction method, in the step 4), the demulsifier mainly acts on the oil-water demulsification separation process to promote 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 represented by the following formulas (9) to (10):
wherein the action of the demulsifier is shown as changing the lifetime tau of the emulsion interfacial film0The general value range is 0.01 s-100 s; hPThe thickness of the oil-water emulsion layer; r isiIs the droplet radius;
the average diameter of the emulsion after the demulsifier is added is the average diameter of the emulsion coalescence; diIs the emulsion initial diameter; λ is the coalescence coefficient; t is the coalescence time; tau is0Is the emulsion interfacial film lifetime.
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 at the inlet of the three-phase separator should be equal to the sum of the crude oil flow and the water flow at the outlet; total flow rate Q, crude oil flow rate QoWater flow rate Qw and oil-water interface height hwEffective length L of oil-water separation and oil-gas interface height hoThe residence time t is expressed by the following equations (11) to (14):
Vo＝R2L(θo-0.5cosθo)-R2L(θw-0.5cosθw) (12)
wherein, VoIs the actual volume occupied by the oil phase in the three-phase separator.
The oil-water separation prediction methodPreferably, the three-phase separator is a conventional horizontal oil-gas-water three-phase separator, the whole separator is in a horizontal cylindrical shape, 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 weiroInlet pipe diameter dru。
The invention also provides an oil-water separation prediction device based on the derivative of the micro distribution of the liquid drops, which comprises:
the first processing unit is used for obtaining basic structure parameters and common operation working conditions of the three-phase separator based on design or field 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 liquid film of the oil-water emulsion under the current demulsifier adding amount concentration based on demulsifier test data used by the three-phase separator operated on site or tested experimentally, and solving the evolution process of the average diameter of the liquid drops of the oil-water emulsion in the oil-water retention time of the three-phase separator according to a liquid drop diameter evolution model of the liquid drops of the oil-water emulsion changing along with time;
the third processing unit is used for establishing a mechanism model of oil-water droplet separation effect based on a 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 discretization W/O type emulsion and the three-phase separator volume model coupling;
and the fourth processing unit is used for analyzing the mechanism evolution process of the particle size distribution of the oil-water emulsion liquid drops at 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 liquid drops, the particle size distribution state of the oil-water emulsion liquid drops at the outlet, the dosage of a demulsifier and the initial water content of incoming liquid as four characteristic input parameters, selecting the water content of outlet crude oil produced on site as a characteristic output parameter, and simulating by adopting a deep learning model and an SVM algorithm to finally predict 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 being executed by a processor, implements the steps of the above-mentioned oil-water separation prediction method based on droplet micro-distribution derivation.
The invention also provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor executes the computer program to realize the steps of the oil-water separation prediction method based on droplet micro-distribution derivation.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the three-phase separator volume model can represent and describe design structure parameters (diameter, separation length and oil-water interface height) of the three-phase separator, and the three-phase separator oil-water droplet settling model and the oil-water emulsion droplet diameter model can reflect the influence of physical properties of crude oil and produced water and field working condition parameters of an oil field on thermochemical settling dehydration in the oil-water separation effect.
2. The demulsifier coalescence effect model can correspondingly represent the demulsification and dehydration effects of the demulsifier, and expresses the action of the demulsifier as promoting the emulsion to be coalesced into large droplets from small droplets, so that the higher sedimentation efficiency is achieved in the thermochemical sedimentation of oil-water separation, and the purpose of improving the oil-water separation effect is achieved. Meanwhile, the oil-water droplet coalescence effect of the demulsifier is closely related to the type and the adding concentration of the demulsifier, and the optimal demulsifier concentration generally exists. The relation between the type and concentration of the demulsifier and the coalescence efficiency can be obtained through indoor dehydration experiments or field experience, can also be estimated through the critical micelle concentration CMC of the demulsifier, or can be predicted in a mode of combining artificial intelligence and a mechanism model.
3. As a new technical field developing rapidly, the artificial intelligence machine can directly and continuously analyze mass data to directly achieve the prediction effect of a mechanism model. For complex process conditions of an oil field gathering and transportation project site and hidden influence factors which cannot be covered in a mechanism model, knowledge and experience can be extracted from site production data through a machine analysis algorithm and 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 lacked (such as the design of a separator of a new oil field), the oil-water distribution effect can be predicted by adopting a mechanism model.
4. The prediction method of the invention is based on the oil-water separation prediction method of the three-phase separator in the evolution process of the oil-water emulsified liquid drops, and can directly and quickly obtain the water content of the crude oil at the outlet of the three-phase separator based on a mature mechanism model of coalescence and sedimentation of the oil-water emulsion liquid drops and a machine learning algorithm. The mechanism model is based on modeling of the oil-water separation volume of the three-phase separator, describes the thermochemical settling demulsification separation process from the inlet to the outlet of the three-phase separator by using a settling model, a demulsification and coalescence effect model and an oil-water droplet diameter distribution model, comprehensively considers the influences of the design structure of the three-phase separator, the physical properties of crude oil and produced water, the field working condition parameters of the oil field and the like, and has more perfect physical significance and process principle; the artificial intelligence technology mainly adopts a neuron network (including a deep learning network and a BP neural network) and a support vector machine in machine analysis, and can further bring the influence of a plurality of complex influence factors into a mechanism model through different analysis of field experience data on the basis of the mechanism model, so that the oil-water separation effect prediction precision of the model is improved. Compared with the existing CFD numerical simulation, indoor dehydration experiment, simple settlement prediction model and other methods, the method has the advantages of high speed, high precision, strong expandability and the like.
Drawings
Fig. 1 is a flow chart of a mechanism model of a three-phase separator according to an embodiment of the present invention;
FIG. 2 is a flow chart of a mechanism model of another three-phase separator according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of the oil-water separation process of the three-phase separator according to the embodiment of the present invention;
fig. 4 is an evolution process of the average diameter of oil-water emulsion drops in the three-phase separator under the liquid film life influenced by different demulsifiers according to the embodiment of the present invention;
FIG. 5 is a graph showing the predicted results of different dewatering residence times for the three-phase separator according to this embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described clearly and completely below, and it is obvious that the described embodiments are some, not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention will be further explained by taking the case that the oil-water separation prediction method of the three-phase separator based on the evolution process of the oil-water emulsion droplets is used 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: separation effect for predicting residence time change of three-phase separator
In a certain high-water-content oil field, a horizontal three-phase separator is used as main equipment for oil-gas-water three-phase separation, the three-phase separator can quickly remove most of free water in liquid coming from a wellhead, and if the crude oil is poor in emulsification stability or good in demulsification effect, the three-phase separator can reduce the water content of the crude oil to 0.5% of the water content standard of the exported crude oil in a heating or residence time prolonging mode.
In the embodiment, the three-phase separator dehydrates for 30min at the dehydration temperature of 30 ℃, the station crude oil belongs to medium waxy crude oil and has the density of 840kg/m3Viscosity of 5mpa · s, oil-water interfacial tension of 12mN/m, separator diameter of 2.5m, effective length of oil-water separation of 9m, separator inlet diameter of 0.2m, oil-water interfacial height of 1.50m, oil weir height of 2.25m, inlet flow of 0.500m3Min, outlet water flow of 0.150m3Min, outlet oil flow 0.350m3And/min, the dosage of the demulsifier is 150ppm of the optimal dosage of the demulsifier.
In this station, the prediction method of the invention is used to predict the dehydration effect of the three-phase separator under different dehydration residence times (0-30min), and the result is shown in fig. 5.
As shown in figure 5, the water content can be reduced to below 20% after dehydration for 10min, 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 experimental rules of site production operation and indoor dehydration.
The embodiment shows that in the actual production process of oil-water emulsion breaking and separation, the oil-water separation prediction method of the three-phase separator based on the oil-water emulsion droplet evolution process has a good effect of predicting the oil-water separation effect (the water content of the crude oil outlet) under different residence times, and can provide support for field production operation or prediction of the separation effect of the three-phase separator.
Example 2: predicting the separation effect of different influencing factors
The initial parameters are basically consistent with those in the embodiment 1, the retention time is 30min, the initial temperature of demulsification and dehydration is respectively set to be 40 ℃, the oil-water interfacial tension is reduced to be 10mN/m, and the density of the increased crude oil is 850kg/m3And predicting the water content of the crude oil after oil-water separation, wherein the prediction result is shown in a table 1:
TABLE 1 prediction of oil-water separation efficiency under different basic parameters
The calculation result of the prediction method in the embodiment 2 shows that, for the influence factors influencing the oil-water separation effect, the prediction method provided by the invention can well reflect the influence of the change of the influence factors on the oil-water separation effect, and the water content of the outlet crude oil is increased due to adverse factors (such as reduction of oil-water density difference, reduction of temperature, reduction of oil-water interface tension and the like) influencing the oil-water separation, otherwise, the water content of the outlet crude oil is reduced due to the beneficial effects.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the steps of the above-mentioned oil-water separation prediction method based on droplet micro-distribution derivation.
The invention also provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor executes the computer program to realize the steps of the oil-water separation prediction method based on droplet micro-distribution derivation.
The present invention is described in terms of flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to specific embodiments. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 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 of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. An oil-water separation prediction method based on droplet micro-distribution derivation is characterized by comprising the following steps:
1) obtaining basic structural parameters and common operation working conditions of the three-phase separator based on design or field operation 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 adding amount concentration based on demulsifier test data used by a three-phase separator operated on site or tested experimentally, and solving the evolution process of the average diameter of oil-water emulsion liquid drops in the oil-water retention time of the three-phase separator according to a liquid drop diameter evolution model of the oil-water emulsion liquid drops changing along with time;
3) based on a Stokes formula settlement theory, the droplet diameter distribution of the oil-water emulsion and a droplet two-dimensional coalescence model, establishing a mechanism model of the oil-water droplet separation effect, and solving the droplet settlement separation efficiency under different droplet diameters by utilizing the droplet diameter of the discretization W/O type emulsion and the three-phase separator volume model coupling;
4) based on the analysis of the mechanism evolution process of the particle size distribution of the oil-water emulsion liquid drops at the inlet and the outlet of the three-phase separator in the step 3), selecting an oil-water emulsion liquid drop initial particle size distribution state, an oil-water emulsion liquid drop outlet particle size distribution state, a demulsifier adding amount and an incoming liquid initial water content as four characteristic input parameters, selecting outlet crude oil water content produced on site as a characteristic output parameter, simulating by adopting a deep learning model and an SVM algorithm, and finally predicting the crude oil water content at the outlet of the three-phase separator.
2. The oil-water separation prediction method according to claim 1, wherein the average diameter of the droplets of the oil-water emulsion at the inlet of the three-phase separator in the step 1) is calculated by using an oil-water emulsion droplet diameter model, and the ratio of the diameters of the droplets before and after the inversion of the oil-water emulsion satisfies the following formula (1):
wherein (d)32)w/oIs the Sott average diameter of the droplets of the w/o emulsion; (d)32)o/wIs the Sott average diameter of the emulsion droplets; mu.swThe viscosity of the water phase; mu.soThe viscosity of the oil phase; rhooIs the density of the aqueous phase; rhowIs the density of the oil phase; epsilonwThe water content is obtained;
when the distribution rule of the diameters of the liquid drops follows logarithmic positive-space distribution, the formula (2) shows that:
wherein, deltaiCurrent droplet diameter; sigmagIs the geometric standard deviation of the droplet diameter distribution; deltamThe average droplet diameter.
3. The method for predicting oil-water separation as defined in claim 1, wherein the known parameter in the step 3) is an oil-water interface height hwOil-gas interface height hoThe 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, which is shown in formulas (3) to (8):
wherein, VS(x) Is an oil-water sedimentation wedge-shaped volume formula; r is the radius of the horizontal three-phase separator; l is*The theoretical distance required for emulsion droplet sedimentation;
the included angle between the sedimentation track of the emulsion liquid drop and the horizontal plane is shown; v. ofovIs the emulsion droplet settling rate; g is the acceleration of gravity; l is the effective length of oil-water separation of the three-phase separator; f. ofw/o(δi) Is the droplet diameter deltaiThe distribution probability of (2); voilThe total wedge-shaped volume of water drop sedimentation in the oil phase;
an unseparated wedge volume; theta o is an included angle of an oil phase interface; theta w is the included angle of the water phase interface; epsiloniIs the diameter delta in the oil phaseiThe separation rate of water droplets of (a); ew/oThe total separation rate of water drops in the oil phase is shown.
4. The method for predicting oil-water separation as claimed in claim 1, wherein in the step 4), the demulsifier mainly acts on the oil-water demulsification process to promote coalescence of W/O emulsion droplets in the oil phase so as to improve the oil-water separation efficiency ε, and a demulsifier coalescence effect model is represented by the following formulas (9) to (10):
wherein the action of the demulsifier is shown as changing the lifetime tau of the emulsion interfacial film0The general value range is 0.01 s-100 s; hPThe thickness of the oil-water emulsion layer; r isiIs the droplet radius;
the average diameter of the emulsion after the demulsifier is added is the average diameter of the emulsion coalescence; diIs the emulsion initial diameter; λ is the coalescence coefficient; t is the coalescence time; tau is0Is the emulsion interfacial film lifetime.
5. The oil-water separation prediction method as claimed in claim 1, wherein in the steps 1) to 4), the oil-water separation process of the three-phase separator is a steady operation process, i.e. the oil-water mixed flow at the inlet of the three-phase separator should be equal to the sum of the crude oil flow and the water flow at the outlet; total flow rate Q, crude oil flow rate QoWater flow rate Qw and oil-water interface height hwEffective length L of oil-water separation and oil-gas interface height hoThe residence time t is expressed by the following equations (11) to (14):
Vo＝R2L(θo-0.5cosθo)-R2L(θw-0.5cosθw) (12)
wherein, VoIs the actual volume occupied by the oil phase in the three-phase separator.
6. The method of claim 1, wherein the three-phase separator is a conventional horizontal oil-gas-water three-phase separator, the overall shape of the three-phase separator is a horizontal cylinder, and the main structural parameters include the diameter D of the separator, the effective length L of oil-water separation, and the height H of an oil weiroInlet pipe diameter dru。
7. An oil-water separation prediction device based on droplet micro-distribution derivation, comprising:
the first processing unit is used for obtaining basic structure parameters and common operation working conditions of the three-phase separator based on design or field 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 liquid film of the oil-water emulsion under the current demulsifier adding amount concentration based on demulsifier test data used by the three-phase separator operated on site or tested experimentally, and solving the evolution process of the average diameter of the liquid drops of the oil-water emulsion in the oil-water retention time of the three-phase separator according to a liquid drop diameter evolution model of the liquid drops of the oil-water emulsion changing along with time;
the third processing unit is used for establishing a mechanism model of oil-water droplet separation effect based on a 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 discretization W/O type emulsion and the three-phase separator volume model coupling;
and the fourth processing unit is used for analyzing the mechanism evolution process of the particle size distribution of the oil-water emulsion liquid drops at 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 liquid drops, the dosage of a demulsifier and the initial water content of incoming liquid as four characteristic input parameters, selecting the water content of outlet crude oil produced on site as a characteristic output parameter, and simulating by adopting a deep learning model and an SVM algorithm to finally predict the water content of the crude oil at the outlet of the three-phase separator.
8. A computer-readable storage medium, having stored thereon a computer program, wherein the computer program, when being executed by a processor, is adapted to carry out the steps of the method for predicting oil-water separation based on droplet micro-distribution derivation of claims 1-6.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program performs the steps of the method for predicting oil-water separation based on droplet micro-distribution derivation of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110727479.9A CN113486579B (en) | 2021-06-29 | 2021-06-29 | Oil-water separation prediction method and device based on droplet microscopic distribution derivatization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110727479.9A CN113486579B (en) | 2021-06-29 | 2021-06-29 | Oil-water separation prediction method and device based on droplet microscopic distribution derivatization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113486579A true CN113486579A (en) | 2021-10-08 |
| CN113486579B CN113486579B (en) | 2023-06-09 |
Family
ID=77936588
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110727479.9A Active CN113486579B (en) | 2021-06-29 | 2021-06-29 | Oil-water separation prediction method and device based on droplet microscopic distribution derivatization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113486579B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024006509A1 (en) * | 2022-06-30 | 2024-01-04 | Saudi Arabian Oil Company | Method for development of smart sensor using real time hybrid ai with physics driven machine learning as advisory for oil in water |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101260802A (en) * | 2008-04-22 | 2008-09-10 | 天津大学 | Oil, gas and water three phase oil well continuous metering device and its measurement method |
| CN102925203A (en) * | 2012-10-30 | 2013-02-13 | 中国石油化工股份有限公司 | Chemical-flooding complex emulsion integrated module processing system |
| WO2016127274A1 (en) * | 2015-02-09 | 2016-08-18 | 华东理工大学 | Step-by-step oil-water mixture separation method coupled with oil gas washing |
-
2021
- 2021-06-29 CN CN202110727479.9A patent/CN113486579B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101260802A (en) * | 2008-04-22 | 2008-09-10 | 天津大学 | Oil, gas and water three phase oil well continuous metering device and its measurement method |
| CN102925203A (en) * | 2012-10-30 | 2013-02-13 | 中国石油化工股份有限公司 | Chemical-flooding complex emulsion integrated module processing system |
| WO2016127274A1 (en) * | 2015-02-09 | 2016-08-18 | 华东理工大学 | Step-by-step oil-water mixture separation method coupled with oil gas washing |
Non-Patent Citations (5)
| Title |
|---|
| LANRE M.OSHINOWO 等: "Modeling of oil–water separation efficiency in three-phase separators: Effect of emulsion rheology and droplet size distribution" * |
| 庞帅 等: "油水分离器中液滴流动模拟方法研究" * |
| 李志明 等: "基于SVM的软测量在原油含水率估算中的应用" * |
| 肖荣 等: "对油气水三相分离器的认识与实践" * |
| 陈家庆 等: "高含水油井采出液预分水技术发展现状与展望" * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024006509A1 (en) * | 2022-06-30 | 2024-01-04 | Saudi Arabian Oil Company | Method for development of smart sensor using real time hybrid ai with physics driven machine learning as advisory for oil in water |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113486579B (en) | 2023-06-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dudek et al. | Colloid chemistry and experimental techniques for understanding fundamental behaviour of produced water in oil and gas production | |
| JP6676090B2 (en) | Dynamic emulsification breaking system for gas oil separator | |
| Frising et al. | The liquid/liquid sedimentation process: from droplet coalescence to technologically enhanced water/oil emulsion gravity separators: a review | |
| Mahdi et al. | Characterization and modeling of a crude oil desalting plant by a statistically designed approach | |
| US11760946B2 (en) | Process control systems and methods for simultaneous crude oil dehydration, desalting, sweetening, and stabilization | |
| Ishak et al. | Predicting the efficiency of the oil removal from surfactant and polymer produced water by using liquid–liquid hydrocyclone: Comparison of prediction abilities between response surface methodology and adaptive neuro-fuzzy inference system | |
| US11267730B2 (en) | Achieving water release zone for dewatering thick fine tailings based on shearing parameter such as camp number | |
| CN113486579A (en) | Oil-water separation prediction method and device based on droplet micro-distribution derivation | |
| CN109135819B (en) | System and method for treating crude oil of oil well | |
| CN201309911Y (en) | Oil-water high-efficiency processor | |
| Luo et al. | Modeling of the phase inversion point of crude oil emulsion by characterization of crude oil physical properties | |
| Solov’eva et al. | Influence of Design of Baffles in a Gravity-Dynamic Separator Model on Water-Oil Emulsion Separation Efficiency | |
| Arnold et al. | Droplet-settling vs. retention-time theories for sizing Oil/Water separator | |
| Sellami et al. | Optimization of operating parameters of oil desalting in southern treatment unit (HMD/Algeria) | |
| Aleem et al. | Experimental study on the effect of parameters on sedimentation and coalescing profiles in liquid-liquid batch settler | |
| Melo et al. | Velocity gradient optimization in a perforated tray-type flocculator using OpenFOAM: CFD as a tool in water treatment | |
| Ramajo et al. | Numerical and experimental evaluation of skimmer tank technologies for gravity separation of oil in produced water | |
| Hussein et al. | Optimising the chemical demulsification of water-in-crude oil emulsion using the Taguchi method | |
| US2679470A (en) | Process and apparatus for continuously treating a fluid with an immiscible liquid | |
| Forero et al. | Design and development of a high efficiency tank for crude oil dehydration (I) | |
| Wang et al. | Prediction of sump oil growth rate towards gravitational settling of produced water in oilfield based on machine learning | |
| Dmitrieva et al. | Separation of water-oil emulsions in device with enlarged throughflow capacity | |
| RU2282658C1 (en) | Method of determining readiness of crude oil emulsions for separation | |
| Ajogbeje | Semi-empirical Framework for the Design of Fibrous Bed Coalescers and Application to Diluted Bitumen | |
| Wang et al. | Prediction on Filtration Period of Granular Bed Filter Used in Purifying Produced Water in Oil Field |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |