CN112507638A - Deep condensate gas reservoir multiphase and multi-flow state discrimination method and device - Google Patents

Deep condensate gas reservoir multiphase and multi-flow state discrimination method and device Download PDF

Info

Publication number
CN112507638A
CN112507638A CN202011507563.1A CN202011507563A CN112507638A CN 112507638 A CN112507638 A CN 112507638A CN 202011507563 A CN202011507563 A CN 202011507563A CN 112507638 A CN112507638 A CN 112507638A
Authority
CN
China
Prior art keywords
condensate
flow
flow state
seepage
shape parameters
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
Application number
CN202011507563.1A
Other languages
Chinese (zh)
Other versions
CN112507638B (en
Inventor
朱维耀
夏静
孔德彬
岳明
李保柱
卜文港
黄堃
宋智勇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Science and Technology Beijing USTB
Original Assignee
University of Science and Technology Beijing USTB
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by University of Science and Technology Beijing USTB filed Critical University of Science and Technology Beijing USTB
Priority to CN202011507563.1A priority Critical patent/CN112507638B/en
Publication of CN112507638A publication Critical patent/CN112507638A/en
Application granted granted Critical
Publication of CN112507638B publication Critical patent/CN112507638B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Geometry (AREA)
  • Fluid Mechanics (AREA)
  • Algebra (AREA)
  • Computing Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a method and a device for discriminating multiphase and multi-flow state of a deep condensate gas reservoir, wherein the method comprises the following steps: acquiring seepage images of a required number of condensate gas in the seepage experiment process; processing the obtained seepage image to obtain the numerical ranges of various shape parameters corresponding to the flow state of each condensate oil; establishing a corresponding relation between each condensate flow state and a dimensionless number numerical range; obtaining a value of the dimensionless times corresponding to the condensate flow state according to the newly obtained seepage image; and determining the condensate oil flow state in the newly acquired seepage image according to the corresponding relation between the dimensionless number range and each condensate oil flow state. The method forms a corresponding relation with the flow state of each condensate oil by introducing the numerical range of the non-factor times, and distinguishes different condensate oil forms by the numerical value of the non-factor times obtained by subsequent calculation; the calculation efficiency is high, and the condensate gas reservoir multiphase and multi-flow state can be accurately judged.

Description

Deep condensate gas reservoir multiphase and multi-flow state discrimination method and device
Technical Field
The invention relates to the technical field of oil-gas seepage, in particular to a method and a device for discriminating multiphase and multi-flow states of a deep condensate gas reservoir.
Background
At present, most of research objects are gas-liquid two-phase flow, and flow pattern division methods mainly comprise two types, namely division according to the appearance shape of the fluid and division according to the distribution characteristics of the phases. According to the first method, the horizontal pipeline gas-liquid two-phase flow type includes stratified flow, wavy flow, gas cluster flow, slug flow, bubble flow and annular flow; the vertical pipeline gas-liquid two-phase flow pattern comprises bubble flow, bullet flow, block flow, annular flow, fine-bundle annular flow and mist flow. According to the second method, the flow pattern can be classified into a dispersed flow, a batch flow, a separated flow. Generally, a first type of division method is mostly adopted for a gas-liquid two-phase flow pattern based on phenomenon description, and a second type of division method is mostly adopted for a gas-liquid two-phase flow based on flow mechanism analysis.
The flow pattern diagram is one of important methods for identifying and judging the flow pattern, and is a static identification method. The real-time flow pattern identification method can be divided into two types according to the working principle, namely, the method directly determines the flow pattern according to a two-phase flow image, such as a visual method, a high-speed camera method, a contact probe method, a ray attenuation method, a capacitance tomography method, a process tomography method and the like; and secondly, indirectly processing and analyzing fluctuation signals reflecting two-phase flow characteristics to extract flow pattern characteristics, and further identifying the flow pattern, such as a frequency domain processing method, flow pattern identification based on a differential pressure fluctuation theory, flow pattern identification based on a network and the like.
The research method for the two-phase flow pattern of the porous medium is similar to the pipeline flow, firstly, the flow pattern is observed by adopting a visual experiment method and the transformation limit among various flow patterns is determined, and secondly, the flow pattern transformation mechanism is analyzed by adopting a method for establishing a theoretical model and the flow pattern transformation criterion is determined.
The research on gas-liquid two-phase flow mostly focuses on pipeline flow, but the size of the pipeline in the porous medium is different, and the structure is complex, so that the method aiming at the pipeline flow cannot be directly applied to the porous medium. At present, a research method for gas-liquid two-phase flow in a porous medium mostly fills spherical particles in a pipe, gas is injected into the pipe after the pipe is saturated with liquid, and the flow rate of the gas is gradually increased to observe the change of a flow pattern. The method cannot reflect the real rock pore structure, and the used particles have large volume and large porosity and pore size, and cannot be directly used for deep gas condensation experiments. The method is characterized in that gas is injected from the injection end of an experimental model, different gas-liquid two-phase flow patterns are obtained by controlling the gas flow, the condensate gas is influenced by pressure reduction in the flowing process, and condensate oil is separated out from the interior, so that the method is greatly different from the existing research method.
Disclosure of Invention
In order to solve at least one of the above technical problems, some aspects of the present invention provide a method and an apparatus for determining a multiphase and multi-fluid state of a deep condensate gas reservoir, which have high calculation efficiency and realize accurate determination of the multiphase and multi-fluid state of the condensate gas reservoir.
In one aspect, a method for discriminating multiple phases and multiple flow states of a deep condensate gas reservoir is provided, where the method includes:
acquiring seepage images of a required number of condensate gas in the seepage experiment process;
processing the obtained seepage image to obtain the numerical ranges of various shape parameters corresponding to each condensate flow state; the shape parameters comprise roundness, slenderness, contact ratio, area ratio, curvature and Euler number;
according to the numerical ranges of various shape parameters corresponding to each condensate flow state, the numerical range of the dimensionless times corresponding to each condensate flow state is obtained through the following formula, the corresponding relation between each condensate flow state and the numerical range of the dimensionless times is established,
Figure BDA0002845368330000021
wherein W is the dimensionless number, p is the ratio of the actual pressure to the dew point pressure, a is the area ratio, c is the roundness, L is the slenderness, s is the tortuosity, E is the Euler number, and t is the contact ratio;
obtaining a value of the dimensionless times corresponding to the condensate flow state according to the newly obtained seepage image; and the number of the first and second groups,
and determining the condensate oil flow state in the newly acquired seepage image according to the corresponding relation between the dimensionless number range and each condensate oil flow state.
In at least one embodiment of the present invention, the condensate flow regime includes a suspended flow, a wall flow, an interface flow, a stream flow, a slug flow, and a continuous flow.
In at least one embodiment of the present invention, the processing of the obtained seepage image to obtain the value range of various shape parameters corresponding to each condensate flow state includes:
graying and binarizing the seepage image to obtain a binary image with a condensate oil area being black and other areas being white;
calculating the values of various shape parameters of the geometric figure formed by the condensate oil area; and the number of the first and second groups,
and processing a data set formed by the values of various shape parameters corresponding to each condensate flow state to obtain the numerical range of various shape parameters corresponding to each condensate flow state.
In at least one embodiment of the invention, the calculation of the values of the various shape parameters of the geometry formed by the condensate region comprises:
calculating the roundness using formula (1), the slenderness using formula (2), the contact ratio using formula (3), the area ratio using formula (4), the tortuosity using formula (5), and the euler number using formula (6);
Figure BDA0002845368330000031
wherein C is roundness, S is the area of the condensate region, and C is the perimeter of the condensate region; l is slenderness, LeThe distance traveled from end to end along the shape of the condensate region; t is the contact ratio, toThe length of the condensate region in contact with the rock wall surface; a is an area ratio, StIs the sum of the areas of condensate regions; s is tortuosity, LsThe straight line distance between the head and the tail of the condensate oil area is shown; e is the Euler number, PBNumber of pixels of condensate region, PWIs the number of pixels of the rock portion completely surrounded by the condensate region.
In at least one embodiment of the present invention, the condensate gas seepage experiment is performed using a visualized glass pore model;
and etching a pore structure identical to the pore structure image on a glass sheet by using an etching method to prepare the glass pore model.
On the other hand, a deep condensate gas reservoir multiphase and multi-fluid discrimination method is also provided, and the discrimination method comprises the following steps:
acquiring seepage images of a required number of condensate gas in the seepage experiment process;
processing the obtained seepage image to obtain the numerical ranges of various shape parameters corresponding to each condensate flow state; the condensate flow regime comprises a suspended flow, a wall flow, an interface flow, a stream flow, a slug flow and a continuous flow; the shape parameters comprise roundness, slenderness, contact ratio, area ratio, curvature and Euler number;
according to the numerical ranges of various shape parameters corresponding to each condensate flow state, the numerical range of the dimensionless times corresponding to each condensate flow state is obtained through the following formula, the corresponding relation between each condensate flow state and the numerical range of the dimensionless times is established,
Figure BDA0002845368330000041
wherein W is the dimensionless number, p is the ratio of the actual pressure to the dew point pressure, a is the area ratio, c is the roundness, L is the slenderness, s is the tortuosity, E is the Euler number, and t is the contact ratio;
obtaining the numerical range of the characteristic parameter corresponding to each condensate flow state according to the numerical range of various shape parameters corresponding to each condensate flow state, and establishing the corresponding relation between each condensate flow state and the numerical range of the characteristic parameter; the characteristic parameters are as follows: for a condensate flow regime, the range of values is stable and is closest to the shape parameters of the physical form of the condensate flow regime;
obtaining a value of the dimensionless times and a value of the characteristic parameter corresponding to the condensate flow state according to the newly obtained seepage image; and the number of the first and second groups,
and determining the condensate flow state in the newly acquired seepage image by combining the corresponding relation between the numerical range of the dimensionless times and each condensate flow state and the corresponding relation between the numerical range of the characteristic parameters and each condensate flow state.
In another aspect, a deep condensate gas reservoir multiphase and multi-fluid discrimination apparatus is provided, where the apparatus includes a processor and a memory, where the memory stores computer program instructions adapted to be executed by the processor, and the computer program instructions are executed by the processor to perform the steps in the deep condensate gas reservoir multiphase and multi-fluid discrimination method according to any one of the above embodiments.
The deep condensate gas reservoir multiphase and multi-flow state discrimination method utilizes the numerical ranges of various shape parameters corresponding to each condensate oil flow state obtained by identifying the seepage images, forms the corresponding relation with each condensate oil flow state by introducing the numerical range of the dimensionless times, and discriminates different condensate oil forms by the numerical values of the dimensionless times obtained by subsequent calculation; or combining the corresponding relation between the numerical range of the non-factor times and the flow state of each condensate oil and the corresponding relation between the characteristic parameters and the flow state of each condensate oil, and comprehensively judging different condensate oil forms through the numerical values of the non-factor times and the numerical values of the characteristic parameters obtained through subsequent calculation. The method has high calculation efficiency, and realizes accurate judgment of the condensate gas reservoir multiphase and multi-flow state.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the principles of the invention.
FIG. 1 is a schematic flow chart of a deep condensate reservoir multiphase multi-phase multi-fluid discrimination method according to an embodiment;
FIG. 2 is a schematic flow chart of a deep condensate reservoir multiphase multi-phase fluid discrimination method according to another embodiment;
FIG. 3 is a schematic view of a seepage image of a condensate flow regime of a suspended flow;
FIG. 4 is a schematic view of a seepage image of the wall flow condensate flow regime;
FIG. 5 is a schematic view of a seepage image of the interface flow condensate flow regime;
FIG. 6 is a schematic view of a seepage image of a stream-like condensate flow regime;
FIG. 7 is a schematic view of a seepage image of a slug flow condensate flow regime;
FIG. 8 is a schematic view of a percolation image of a continuous flow condensate flow regime;
fig. 9 is a schematic structural diagram of an exemplary deep condensate gas reservoir multiphase and multi-phase discrimination apparatus according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limitations of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that, the step numbers in the text are only for convenience of explanation of the specific embodiments, and do not serve to limit the execution sequence of the steps.
The method provided by some embodiments of the present invention can be executed by a relevant processor, and the following description will take the processor as an example of an execution subject. The execution subject can be adjusted according to the specific case, such as a server, an electronic device, a computer, and the like.
Aiming at the defects of the gas-liquid two-phase flow pattern research of the existing condensate gas reservoir, the embodiments of the invention provide a deep condensate gas reservoir multiphase and multi-flow pattern distinguishing method and device, so as to improve the calculation efficiency and realize the accurate distinguishing of the condensate gas reservoir multiphase and multi-flow pattern. The method can be suitable for experimental research of condensate gas reservoir gas injection development technology.
On one hand, refer to a schematic flow chart of a deep condensate gas reservoir multiphase and multi-fluid discrimination method in an embodiment shown in fig. 1; a deep condensate gas reservoir multiphase and multi-fluid discrimination method comprises the following steps:
and acquiring seepage images in the required number of condensate gas seepage experiment processes. The step is to shoot and collect seepage experiment images at different moments by using an image collecting device such as a high-speed camera in the process of a condensate gas seepage experiment. It will be appreciated that the experimental setup must be visualized to facilitate observation of the state of seepage therein. In the process of changing the initial state to the final state of the whole seepage experiment, relevant experimental images of the whole process are continuously acquired so that the acquired experimental image set can reflect each different seepage state, and a plurality of acquired experimental images are stored for subsequent processing.
And processing the obtained seepage image to obtain the numerical range of various shape parameters corresponding to each condensate flow state. The shape parameters include roundness, slenderness, contact ratio, area ratio, tortuosity, and euler number. In this step, the existing image processing algorithm may be used for processing, and the relevant information in the image is extracted for subsequent processing. The specific meanings and the acquisition methods of the various shape parameters will be described in detail later in the relevant steps.
According to the numerical ranges of various shape parameters corresponding to each condensate flow state, the numerical range of the dimensionless times corresponding to each condensate flow state is obtained through the following formula, the corresponding relation between each condensate flow state and the numerical range of the dimensionless times is established,
Figure BDA0002845368330000061
wherein W is the dimensionless number, p is the ratio of the actual pressure to the dew point pressure, a is the area ratio, c is the roundness, L is the slenderness, s is the tortuosity, E is the Euler number, and t is the contact ratio. In this step, in processing each experimental image reflecting different seepage states, a set of values of the shape parameters corresponding to the state can be obtained, after all the experimental images are processed, a set formed by values of all the shape parameters corresponding to the seepage state of each experimental image can be obtained, and a value change range of each shape parameter corresponding to each seepage state can be obtained by using a statistical method, so that value ranges of various shape parameters can be obtained. The range of values for the same shape parameter will vary for different seepage conditions. Similarly, the value of each shape parameter is substituted into the formula for calculating the non-dimensional number W to obtain a set formed by the values of all the non-dimensional numbers W, and the value range of the non-dimensional number W corresponding to each seepage state can be obtained by using a statistical method. And establishing a corresponding relation between each condensate flow state and the dimensionless number of numerical value range, and storing the corresponding relation as a database.
And obtaining a value of the dimensionless times corresponding to the condensate flow state according to the newly obtained seepage image. In this step, the newly acquired seepage image may be obtained through an experimental method, or may be a real core seepage image obtained in an underground storage environment. The method for obtaining the dimensionless number according to the seepage image can be the same as the method, or different image processing algorithms can be adopted to obtain corresponding shape parameter values, and then the value of the dimensionless number W is obtained according to a calculation formula of the dimensionless number W.
And determining the condensate oil flow state in the newly acquired seepage image according to the corresponding relation between the dimensionless number range and each condensate oil flow state. And substituting the value of the non-factor times W obtained in the last step into a database of the corresponding relation between the flow state of each condensate oil and the numerical range of the non-factor times for judgment, and judging the corresponding seepage state in which numerical range.
In the above-disclosed determination method, the non-factor value W is introduced, and the correspondence between the seepage state and the numerical range of the non-factor value W is established by the data of the experimental image, and then the determination is performed by calculating the obtained non-factor value W. That is, the different flow states are identified by image recognition, and the shape parameters and dimensionless number W values of each flow state are calculated. The calculation process is simple, the calculation efficiency is high, and the condensate gas reservoir multiphase and multi-flow state can be accurately judged.
Furthermore, with the increase of the condensate output in the experimental process, the condensate flow state comprises a suspension flow, a wall flow, an interface flow, a stream flow, a slug flow and a continuous flow.
In some embodiments, the step of processing the obtained seepage image to obtain the numerical ranges of the various shape parameters corresponding to each condensate flow state may employ the following image processing algorithm, which specifically includes:
graying and binarization processing are performed on the seepage image to obtain a binary image with a condensate oil area being black and other areas being white, and the effects of fig. 3 to 8 can be seen.
The values of the various shape parameters of the geometry formed by the condensate region are calculated. This step is to be noted that the same seepage image may include a plurality of condensate regions, and different condensate regions may be continuous or dispersed, because in the experimental image acquired at the same time, some condensate regions may be in one seepage state such as a stream, and condensate regions in another region may be in another seepage state such as a slug.
And processing a data set formed by the values of various shape parameters corresponding to each condensate flow state to obtain the numerical range of various shape parameters corresponding to each condensate flow state. The numerical ranges of the various shape parameters corresponding to each condensate flow regime can be found from the corresponding data sets by using statistical methods. Because the shape parameters of the condensate oil areas with different shapes are also in different numerical value ranges, the corresponding relation between the shape parameters and different forms of the condensate oil can be established.
Further, the step of calculating the values of the various shape parameters of the geometry formed by the condensate region may comprise:
calculating the roundness using formula (1), the slenderness using formula (2), the contact ratio using formula (3), the area ratio using formula (4), the tortuosity using formula (5), and the euler number using formula (6);
Figure BDA0002845368330000081
wherein C is roundness, S is the area of the condensate region, and C is the perimeter of the condensate region; l is slenderness, LeThe distance traveled from end to end along the shape of the condensate region; t is the contact ratio, toThe length of the condensate region in contact with the rock wall surface; a is an area ratio, StIs the sum of the areas of condensate regions; s is tortuosity, LsThe straight line distance between the head and the tail of the condensate oil area is shown; e is the Euler number, PBNumber of pixels of condensate region, PWIs the number of pixels of the rock portion completely surrounded by the condensate region.
The above six formulas are definitions of six shape parameters, i.e., roundness, slenderness, contact ratio, area ratio, tortuosity, and euler number, respectively. In particular, the method comprises the following steps of,
roundness c:
Figure BDA0002845368330000091
in the formula: s is the area of the condensate region and C is the perimeter of the condensate region.
The slenderness L:
Figure BDA0002845368330000092
in the formula: l iseIs the distance it takes along the condensate region shape from its head to tail (e.g., for the shape "S", LeA curved path length from one end to the other end thereof).
Contact ratio t:
Figure BDA0002845368330000093
in the formula: t is toThe length of the condensate region in contact with the rock wall.
Area ratio a:
Figure BDA0002845368330000094
in the formula: stIs the sum of the condensate zone areas.
Curvature s:
Figure BDA0002845368330000095
in the formula: l issIs the linear distance between the head and tail of the condensate region (e.g., for the shape of "S", LsAs the linear distance between its two ends).
Euler number E:
Figure BDA0002845368330000096
in the formula: pBIs the number of pixels of the communicating body (communicating body, i.e. condensate region), PWIs the number of pixels of the hole (the hole is the portion of the rock completely surrounded by the condensate region).
Wherein special case explanation needs to be made for the shape parameters of part of forms: the Euler number of the external forms except the interface flow and the continuous flow is 0; l of continuous floweIs the path from one end point to the end point farthest therefrom.
In this step, the values of the parameters of the condensate region, i.e., the area of the condensate region, the perimeter of the condensate region, the distance from the head to the tail of the condensate region along the shape of the condensate region, the length of the condensate region in contact with the rock wall surface, the total condensate region area, the linear distance between the head and the tail of the condensate region, the number of pixels of the condensate region, and the number of pixels of the rock portion completely surrounded by the condensate region, can be obtained by an image recognition algorithm, and then each shape parameter can be calculated by substituting these parameters into the definition formula of each shape parameter.
In some embodiments, the condensate gas infiltration experiment is performed using a visualized glass pore model in order to obtain more accurate infiltration state and dimensionless number of value ranges at the beginning.
The manufacturing process of the glass pore model comprises the steps of scanning the pore structure of a real core slice to obtain a pore structure image, taking the obtained pore structure image as a master mask, and etching a pore structure identical to the pore structure image on a glass slice by using an etching method. Compared with the existing experimental model, the glass pore model has more real simulation effect. In addition, a condensate gas seepage experiment is carried out by utilizing the glass pore model, so that the phase change rule of the condensate gas in the seepage process can be better obtained, and the flow state of the condensate gas can be more accurately divided.
On the other hand, refer to a schematic flow chart of a deep condensate gas reservoir multiphase and multi-fluid discrimination method according to another embodiment shown in fig. 2; a deep condensate gas reservoir multiphase and multi-fluid discrimination method comprises the following steps:
and acquiring seepage images in the required number of condensate gas seepage experiment processes.
Processing the obtained seepage image to obtain the numerical ranges of various shape parameters corresponding to each condensate flow state; the condensate flow regime comprises a suspended flow, a wall flow, an interface flow, a stream flow, a slug flow and a continuous flow; the shape parameters include roundness, slenderness, contact ratio, area ratio, tortuosity, and euler number.
According to the numerical ranges of various shape parameters corresponding to each condensate flow state, the numerical range of the dimensionless times corresponding to each condensate flow state is obtained through the following formula, the corresponding relation between each condensate flow state and the numerical range of the dimensionless times is established,
Figure BDA0002845368330000101
wherein W is the dimensionless number, p is the ratio of the actual pressure to the dew point pressure, a is the area ratio, c is the roundness, L is the slenderness, s is the tortuosity, E is the Euler number, and t is the contact ratio.
The above steps are the same as those of the first embodiment, and are not described again.
Obtaining the numerical range of the characteristic parameter corresponding to each condensate flow state according to the numerical range of various shape parameters corresponding to each condensate flow state, and establishing the corresponding relation between each condensate flow state and the numerical range of the characteristic parameter; the characteristic parameters are as follows: for a certain condensate flow regime, the range of values is stable and the shape parameter is closest to the physical form of the condensate flow regime (for example, for a droplet of condensate, a circular or near-circular form is generally assumed, whereas roundness describes the extent to which a shape is near a circle, and the greater the value, the closer the shape is to a circle, the closest the roundness is to the droplet of condensate form). The step is to establish a corresponding relationship between the flow state of each condensate and the numerical range of the characteristic parameter. According to the definition of the characteristic parameters, the characteristic parameters are one or more shape parameters selected from six shape parameters which can best express the characteristics of the seepage state. That is, each flow state has its own characteristic parameter, and the characteristic parameter of the droplet-shaped condensate is roundness, and the value thereof is close to 1; the characteristic parameter of the wall pasting flow and the slug flow is a contact ratio, and the numerical values of the wall pasting flow and the slug flow are respectively close to 0.5 and 1; the characteristic parameter of the interface flow is Euler number, and the value of the characteristic parameter is negative; characteristic parameters of the stream are slenderness (its value is close to 0.5) or tortuosity (its value is close to 1 and greater than 1); the characteristic parameter of continuous flow is the area ratio (its value size is close to 0.7).
And obtaining a value of the dimensionless times and a value of the characteristic parameter corresponding to the condensate flow state according to the newly obtained seepage image. By processing the experimental image of condensate gas (oil) flow, the shape parameters of various condensate oil flow states and the corresponding numerical value ranges thereof can be obtained, and the characteristic parameters of each flow state can be obtained through experimental statistics.
And determining the condensate flow state in the newly acquired seepage image by combining the corresponding relation between the numerical range of the dimensionless times and each condensate flow state and the corresponding relation between the numerical range of the characteristic parameters and each condensate flow state.
For some simple seepage forms, the characteristic parameters are single and have strong distinctiveness, and the corresponding seepage form can be judged by judging the characteristic parameters alone. For some seepage forms with complex shapes and less obvious characteristic parameter differences, judgment needs to be carried out by dimensionless times. For example, simple shapes such as the blob-like shape can be preliminarily identified by a single characteristic parameter, and the characteristic parameters such as the wall flow and the slug flow are similar, or the characteristic parameters such as the stream-like flow are not unique, and cannot be distinguished according to the characteristic parameters.
The second embodiment uses the feature parameters in combination with the dimensionless number W to identify various forms, and is more flexible and accurate in determination results than the first embodiment.
The following describes each condensate flow regime in detail with reference to the data obtained from a particular experiment.
Referring to the suspended flow shown in fig. 3, the condensate is in the form of small droplets. At this time, the actual pressure is slightly lower than the dew point pressure, the amount of condensate is small, and the condensate is dispersed in the gas phase in the form of droplets and carried by the gas phase. When the liquid drops contain the separated wax, the viscosity of the liquid drops is increased, and the liquid drops are trailing and move like tadpoles under the action of interfacial tension and self-viscous force. The roundness is 0.948-0.936, the slenderness is 0.335-0.444, the area ratio is 0.039-0.044, the bending degree is 1.010-1.100, and the contact ratio is 0.
Referring to the wall flow shown in fig. 4, the condensate collects on the walls of the channels to form an oil film of varying thickness. Under the condition of little or no bound water, precipitated condensate is spread on the surfaces of the pore channels and is gathered around the particles under the action of interfacial tension. The gas phase flows between the oil films and carries the oil films, the deformation of the oil films is gradually increased along with the continuous accumulation of the condensate oil, finally, a part of the condensate oil is separated from the oil films and carried away by the gas phase, then the condensate oil is accumulated again, deformed and carried away, and the cyclic reciprocating process is pulse flow. The roundness is 0.056 to 0.085, the slenderness is 0.467 to 0.476, the contact ratio is 0.431 to 0.475, the area ratio is 0.456 to 0.562, and the bending degree is 1.234 to 1.507.
Referring to the interface flow shown in fig. 5, the oil films on the walls of adjacent channels are interfacially merged. At this time, the amount of condensate is further increased, and oil films accumulated on the walls of adjacent channels are in contact with each other, and the interfaces are merged. When a large amount of condensate oil exists, a strong Jamin effect can be formed, and the calculation formula must be considered. Roundness of 0.149 to 0.864, contact ratio of 0.846 to 0.855, area ratio of 0.425 to 0.590, Euler number of-14.940 to-3.241.
Referring to the stream shown in FIG. 6, when the temperature is low and the fluid contains solid components but can still flow, the condensate is a non-Newtonian fluid. At this time, if the amount of the condensate is large, the condensate flows in the channels in a stream. The roundness is 0.031-0.061, the slenderness is 0.453-0.472, the area ratio is 0.312-0.595, and the bending degree is 1.052-1.623.
Referring to the slug flow shown in fig. 7, the condensate is present in the bore in the form of a plug. When the condensate oil quantity is increased to a certain degree, the condensate oil is gathered in the pore channels under the action of capillary force and stays in the pore channels to form blockage, the slugs preferentially exist in the fine pore channels, and the large pore channels become main flowing spaces. Only when the pulse force is greater than the capillary force can a portion of the fine capillaries open. When the condensate gas protrudes from the axis of the pore, a layer of oil film with different thickness is left on the pore wall, the formed oil film creeps along the pore wall, and the originally blocked pore becomes a condensate gas flowing channel again. When the slug is broken through, the pores become the gas and liquid flowing channels again, and the condensate oil begins to gather and gradually evolves to the next slug. The roundness is 0.211-0.390, the slenderness is 0.271-0.354, the contact ratio is 0.891-0.938, the area ratio is 0.369-0.515, and the bending degree is 1.000-1.201.
Referring to the continuous flow shown in fig. 8, the condensate precipitates in large amounts to form a continuous phase. The condensate oil forms a larger whole, occupies a certain size of pore space and flows, and the proportion of other discontinuous flow states is reduced. The contact ratio is 0.285-0.338, the area ratio is 0.652-0.755, and the Euler number is 5.443-15.358.
The corresponding relationship obtained according to the experiment is as follows: the number of the suspension flow W ranges from 0.083 to 0.135; the W number of the wall pasting ranges from 0.166 to 0.284; the number of the interface flow W ranges from 0.279 to 0.335; the W number of the stream flow ranges from 0.360 to 0.389; the W number of the slug flow ranges from 0.378 to 0.402; the continuous flow W number ranges from 0.451 to 0.513.
The method is used for distinguishing different flow states occurring in the condensate gas seepage process by calculating the dimensionless times W, is summarized by experimental data, is simple in calculation method, and realizes the classification and distinguishing of the multiphase and multi-flow states of the condensate gas reservoir.
Some embodiments of the present invention further provide a deep condensate gas reservoir multiphase and multi-phase discrimination apparatus, referring to an exemplary structural schematic diagram of the deep condensate gas reservoir multiphase and multi-phase discrimination apparatus shown in fig. 9, where the apparatus includes a communication interface 1000, a memory 2000, and a processor 3000. The communication interface 1000 is used for communicating with an external device to perform data interactive transmission. The memory 2000 has stored therein a computer program that is executable on the processor 3000. The number of the memory 2000 and the processor 3000 may be one or more.
If the communication interface 1000, the memory 2000 and the processor 3000 are implemented independently, the communication interface 1000, the memory 2000 and the processor 3000 may be connected to each other through a bus to complete communication therebetween. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not represent only one bus or one type of bus.
Optionally, in a specific implementation, if the communication interface 1000, the memory 2000, and the processor 3000 are integrated on a chip, the communication interface 1000, the memory 2000, and the processor 3000 may complete communication with each other through an internal interface.
The processor is used for supporting the obtaining device to execute one or more steps in the deep condensate gas reservoir multiphase and multi-flow state discrimination method according to any one of the above embodiments. The processor may be a Central Processing Unit (CPU), or may be other general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory stores computer program instructions adapted to be executed by the processor, and the computer program instructions, when executed by the processor, perform one or more steps of the deep condensate reservoir multiphase and multi-flow-state discrimination method according to any one of the above embodiments.
The Memory may be a Read-Only Memory (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these. The memory may be self-contained and coupled to the processor via a communication bus. The memory may also be integral to the processor.
In the description herein, reference to the description of the terms "one embodiment/mode," "some embodiments/modes," "example," "specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to be the same embodiment/mode or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/aspects or examples and features of the various embodiments/aspects or examples described in this specification can be combined and combined by one skilled in the art without conflicting therewith.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. Meanwhile, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "connected" and "connected" should be interpreted broadly, for example, as being fixedly connected, detachably connected, or integrally connected; the connection can be mechanical connection or electrical connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
It will be understood by those skilled in the art that the foregoing embodiments are merely for clarity of description and are not intended to limit the scope of the invention. Other variations or modifications will occur to those skilled in the art based on the foregoing disclosure and are within the scope of the invention.

Claims (7)

1. A deep condensate gas reservoir multiphase and multi-fluid discrimination method is characterized by comprising the following steps:
acquiring seepage images of a required number of condensate gas in the seepage experiment process;
processing the obtained seepage image to obtain the numerical ranges of various shape parameters corresponding to each condensate flow state; the shape parameters comprise roundness, slenderness, contact ratio, area ratio, curvature and Euler number;
according to the numerical ranges of various shape parameters corresponding to each condensate flow state, the numerical range of the dimensionless times corresponding to each condensate flow state is obtained through the following formula, the corresponding relation between each condensate flow state and the numerical range of the dimensionless times is established,
Figure FDA0002845368320000011
wherein W is the dimensionless number, p is the ratio of the actual pressure to the dew point pressure, a is the area ratio, c is the roundness, L is the slenderness, s is the tortuosity, E is the Euler number, and t is the contact ratio;
obtaining a value of the dimensionless times corresponding to the condensate flow state according to the newly obtained seepage image; and the number of the first and second groups,
and determining the condensate oil flow state in the newly acquired seepage image according to the corresponding relation between the dimensionless number range and each condensate oil flow state.
2. The method of claim 1, wherein the condensate flow regimes include a suspended flow, a wall flow, an interface flow, a stream flow, a slug flow, and a continuous flow.
3. The method for multi-phase and multi-flow discrimination of a deep condensate gas reservoir as claimed in claim 2, wherein the processing of the obtained seepage image to obtain the numerical ranges of various shape parameters corresponding to each condensate flow state comprises:
graying and binarizing the seepage image to obtain a binary image with a condensate oil area being black and other areas being white;
calculating the values of various shape parameters of the geometric figure formed by the condensate oil area; and the number of the first and second groups,
and processing a data set formed by the values of various shape parameters corresponding to each condensate flow state to obtain the numerical range of various shape parameters corresponding to each condensate flow state.
4. The method for multiphase and multi-fluid discrimination of a deep condensate gas reservoir as claimed in claim 3, wherein the calculating of the values of various shape parameters of the geometric figure formed by the condensate region comprises:
calculating the roundness using formula (1), the slenderness using formula (2), the contact ratio using formula (3), the area ratio using formula (4), the tortuosity using formula (5), and the euler number using formula (6);
Figure FDA0002845368320000021
wherein c is roundness, and S is coagulationThe area of the condensate region, C being the perimeter of the condensate region; l is slenderness, LeThe distance traveled from end to end along the shape of the condensate region; t is the contact ratio, toThe length of the condensate region in contact with the rock wall surface; a is an area ratio, StIs the sum of the areas of condensate regions; s is tortuosity, LsThe straight line distance between the head and the tail of the condensate oil area is shown; e is the Euler number, PBNumber of pixels of condensate region, PWIs the number of pixels of the rock portion completely surrounded by the condensate region.
5. The method for multiphase and multi-fluid discrimination of a deep condensate gas reservoir as claimed in claim 1, wherein the condensate gas seepage experiment is performed by using a visual glass pore model;
and etching a pore structure identical to the pore structure image on a glass sheet by using an etching method to prepare the glass pore model.
6. A deep condensate gas reservoir multiphase and multi-fluid discrimination method is characterized by comprising the following steps:
acquiring seepage images of a required number of condensate gas in the seepage experiment process;
processing the obtained seepage image to obtain the numerical ranges of various shape parameters corresponding to each condensate flow state; the condensate flow regime comprises a suspended flow, a wall flow, an interface flow, a stream flow, a slug flow and a continuous flow; the shape parameters comprise roundness, slenderness, contact ratio, area ratio, curvature and Euler number;
according to the numerical ranges of various shape parameters corresponding to each condensate flow state, the numerical range of the dimensionless times corresponding to each condensate flow state is obtained through the following formula, the corresponding relation between each condensate flow state and the numerical range of the dimensionless times is established,
Figure FDA0002845368320000031
wherein W is the dimensionless number, p is the ratio of the actual pressure to the dew point pressure, a is the area ratio, c is the roundness, L is the slenderness, s is the tortuosity, E is the Euler number, and t is the contact ratio;
obtaining the numerical range of the characteristic parameter corresponding to each condensate flow state according to the numerical range of various shape parameters corresponding to each condensate flow state, and establishing the corresponding relation between each condensate flow state and the numerical range of the characteristic parameter; the characteristic parameters are as follows: for a condensate flow regime, the range of values is stable and is closest to the shape parameters of the physical form of the condensate flow regime;
obtaining a value of the dimensionless times and a value of the characteristic parameter corresponding to the condensate flow state according to the newly obtained seepage image; and the number of the first and second groups,
and determining the condensate flow state in the newly acquired seepage image by combining the corresponding relation between the numerical range of the dimensionless times and each condensate flow state and the corresponding relation between the numerical range of the characteristic parameters and each condensate flow state.
7. A deep condensate reservoir multiphase multithread discrimination apparatus comprising a processor and a memory, wherein the memory stores computer program instructions adapted to be executed by the processor, and the computer program instructions are executed by the processor to perform the steps of the multiphase multithread discrimination method according to claim 1 or 6.
CN202011507563.1A 2020-12-18 2020-12-18 Deep condensate gas reservoir multiphase and multi-flow state discrimination method and device Active CN112507638B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011507563.1A CN112507638B (en) 2020-12-18 2020-12-18 Deep condensate gas reservoir multiphase and multi-flow state discrimination method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011507563.1A CN112507638B (en) 2020-12-18 2020-12-18 Deep condensate gas reservoir multiphase and multi-flow state discrimination method and device

Publications (2)

Publication Number Publication Date
CN112507638A true CN112507638A (en) 2021-03-16
CN112507638B CN112507638B (en) 2021-08-06

Family

ID=74922525

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011507563.1A Active CN112507638B (en) 2020-12-18 2020-12-18 Deep condensate gas reservoir multiphase and multi-flow state discrimination method and device

Country Status (1)

Country Link
CN (1) CN112507638B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113822952A (en) * 2021-09-22 2021-12-21 西安石大派普特科技工程有限公司 Multiphase flow pattern discrimination method based on image processing
CN114510974A (en) * 2022-01-27 2022-05-17 西安交通大学 Intelligent identification method for gas-liquid two-phase flow pattern in porous medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1538168A (en) * 2003-10-21 2004-10-20 浙江大学 Oil-gas two-phase flow measuring method based on copacitance chromatorgraphy imaging system and its device
CN1635369A (en) * 2004-12-30 2005-07-06 山东大学 Fast on-line recognition method for flow pattern of gas-liquid two-phase flow
CN101140216A (en) * 2007-08-08 2008-03-12 东北电力大学 Gas-liquid two-phase flow type recognition method based on digital graphic processing technique
US20150276447A1 (en) * 2014-04-01 2015-10-01 Saudi Arabian Oil Company Flow regime identification of multiphase flows by face recognition bayesian classification
CN108304770A (en) * 2017-12-18 2018-07-20 中国计量大学 A method of the flow pattern of gas-liquid two-phase flow based on time frequency analysis algorithm combination deep learning theory
CN110276415A (en) * 2019-07-01 2019-09-24 山东浪潮人工智能研究院有限公司 A kind of petroleum industry multiphase flow pattern recognition methods based on deep learning

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1538168A (en) * 2003-10-21 2004-10-20 浙江大学 Oil-gas two-phase flow measuring method based on copacitance chromatorgraphy imaging system and its device
CN1635369A (en) * 2004-12-30 2005-07-06 山东大学 Fast on-line recognition method for flow pattern of gas-liquid two-phase flow
CN101140216A (en) * 2007-08-08 2008-03-12 东北电力大学 Gas-liquid two-phase flow type recognition method based on digital graphic processing technique
US20150276447A1 (en) * 2014-04-01 2015-10-01 Saudi Arabian Oil Company Flow regime identification of multiphase flows by face recognition bayesian classification
CN108304770A (en) * 2017-12-18 2018-07-20 中国计量大学 A method of the flow pattern of gas-liquid two-phase flow based on time frequency analysis algorithm combination deep learning theory
CN110276415A (en) * 2019-07-01 2019-09-24 山东浪潮人工智能研究院有限公司 A kind of petroleum industry multiphase flow pattern recognition methods based on deep learning

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
L.J.XU等: "Gas/liquid two-phase flow regime identification by ultrasonic tomography", 《FLOW MEASUREMENT AND INSTRUMENTATION》 *
施丽莲: "基于数字图像识别技术的气液两相流参数检测的研究", 《中国博士学位论文全文数据库信息科技辑》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113822952A (en) * 2021-09-22 2021-12-21 西安石大派普特科技工程有限公司 Multiphase flow pattern discrimination method based on image processing
CN114510974A (en) * 2022-01-27 2022-05-17 西安交通大学 Intelligent identification method for gas-liquid two-phase flow pattern in porous medium
CN114510974B (en) * 2022-01-27 2024-04-09 西安交通大学 Intelligent recognition method for gas-liquid two-phase flow pattern in porous medium

Also Published As

Publication number Publication date
CN112507638B (en) 2021-08-06

Similar Documents

Publication Publication Date Title
Rigaut An empirical formulation relating boundary lengths to resolution in specimens showing ‘non‐ideally fractal’dimensions
Matsui Automatic identification of flow regimes in vertical two-phase flow using differential pressure fluctuations
Jana et al. Flow regime identification of two-phase liquid–liquid upflow through vertical pipe
CN112507638B (en) Deep condensate gas reservoir multiphase and multi-flow state discrimination method and device
US10969321B2 (en) Method and system for determining contact angle of porous media
CN110618158B (en) Method for constructing capillary pressure curve of rock core by utilizing nuclear magnetic resonance information
CN110865011A (en) Method for calculating relative permeability of compact rock core based on digital imaging technology
CN107993261B (en) A kind of hole based on three-dimensional Core Scanning Image and pore throat recognition methods
CN112070774A (en) Neural network optimization method for shale digital core image segmentation
CN109685117A (en) The image measuring method of flow parameter in a kind of gas liquid particle three phase system
Laupsien et al. Image processing for bubble morphology characteristics in diluted bubble swarms
CN111625917A (en) Method and device for determining relative permeability curve of porous medium
CN111507986A (en) Method and device for determining internal fluctuation condition of pore
CN110018108B (en) Method and equipment for measuring pore diameter of rock pore
Grieve Spatial statistics for dialectology
CN116383573B (en) Condensate gas productivity evaluation method based on multi-region phase change mass transfer seepage coupling
CN115406806B (en) Metering device for two-phase seepage experiment of compact oil-gas reservoir
CN106247917A (en) Horizontal flow pattern of gas-liquid two-phase flow quantitatively judges method and device
CN113610095B (en) Method and processor for dividing occurrence forms of micro residual oil
CN111192317B (en) Method for acquiring saturation of gas-liquid displacement image in micron-scale planar pore network
CN109063733A (en) A kind of outlier detection method based on the two-parameter factor that peels off
CN112966365B (en) Method for evaluating reverse condensation injury of ultralow condensation gas reservoir
Lendhe et al. Machine learning approach to predict viscous fingering in Hele-Shaw cells
CN109993786B (en) Tortuosity acquisition method, device, equipment and storage medium
Fu et al. Study of bubble behavior in a gas–solid dense-phase fluidized bed based on deep learning

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