CN112200689B - Method and device for determining potential dispersity of oil reservoir seepage field - Google Patents
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Abstract
The application discloses a method and a device for determining potential dispersity of an oil reservoir seepage field, and belongs to the technical field of oil extraction engineering. The method comprises the following steps: determining residual potential distribution diagrams of the target oil reservoir at different times; identifying potential patches in each remaining potential profile and determining a number of potential patches in each remaining potential profile; determining the area and perimeter of each potential patch in each remaining potential profile; determining an average plaque area index, a plaque density index and an average shape index corresponding to each residual potential distribution map; determining weights corresponding to the average plaque area index, the plaque density index and the average shape index based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map; and determining potential dispersity of the oil reservoir seepage field of the target oil reservoir at different times. By adopting the method, the technical problem that a method for quantitatively characterizing the potential dispersity of the oil reservoir seepage field is lacked in the related technology can be effectively solved.
Description
Technical Field
The application relates to the technical field of oil extraction engineering, in particular to a method and a device for determining potential dispersity of an oil reservoir seepage field.
Background
Petroleum is an important strategic resource of the country and is an important proposition for the national economy development. Therefore, efficient recovery of the reservoir is important.
When the oil deposit is mined, reference is sometimes required to be made to the potential dispersity of the seepage field of the oil deposit, which is used for characterizing the concentration degree of the residual oil distribution in the oil deposit, wherein the seepage field is the sum of objective cognition and description results of the existence mode and change characteristics of the porous medium of the oil deposit and the fluid contained in the porous medium of the oil deposit from the aspects of the object and energy. The potential dispersity of the seepage field is low, so that the distribution of the residual oil is concentrated, and the oil reservoir is more suitable for exploitation; and if the potential dispersity of the seepage field is high, the residual oil is distributed more dispersedly, and the oil reservoir is not suitable for exploitation.
In the process of implementing the present application, the inventors found that the related art has at least the following problems:
at present, a method for quantitatively characterizing potential dispersity of an oil reservoir seepage field is lacking in the related technology, so that a certain influence is generated on oil reservoir exploitation.
Disclosure of Invention
In order to solve the technical problems in the related art, the embodiment of the application provides a method and a device for determining potential dispersity of an oil reservoir seepage field. The technical scheme of the method and the device for determining the potential dispersity of the oil reservoir seepage field is as follows:
In a first aspect, a method of determining a potential dispersion of a reservoir seepage field is provided, the method comprising:
determining a residual potential distribution map of the target oil reservoir at different times based on results of numerical simulation of the target oil reservoir;
identifying potential patches in each remaining potential profile and determining a number of potential patches in each remaining potential profile, wherein the potential patches are patches formed by areas in the remaining potential profile in which remaining oil is concentrated;
determining the area and perimeter of each potential patch in each remaining potential profile;
determining an average plaque area index, a plaque density index and an average shape index corresponding to each residual potential distribution map based on the number of potential plaques in each residual potential distribution map and the area and the perimeter of each potential plaque in each residual potential distribution map;
determining weights corresponding to the average plaque area index, the plaque density index and the average shape index based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map;
and determining the potential dispersity of the seepage field of the target oil reservoir at different times based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map and each weight.
Optionally, the identifying potential patches in each remaining potential profile and determining the number of potential patches in each remaining potential profile includes:
potential patches in each remaining potential profile are identified based on an eight neighborhood boundary tracking algorithm, and the number of potential patches in each remaining potential profile is determined.
Optionally, after identifying the potential patches in each remaining potential distribution map, the method further includes:
the identified potential patches are labeled in each remaining potential profile.
Optionally, the determining the area and the perimeter of each potential patch in each remaining potential profile includes:
based on the regionoprops function, the area and perimeter of each potential patch in each remaining potential profile is determined.
Optionally, the determining, based on the number of potential patches in each remaining potential distribution map and the area and circumference of each potential patch in each remaining potential distribution map, the average patch area index, the patch density index and the average shape index corresponding to each remaining potential distribution map includes:
determining a total area of potential patches in each remaining potential profile based on the area of each potential patch in each remaining potential profile;
Based on the number of potential patches in each remaining potential profile and the total area of potential patches in each remaining potential profile, by the formulaDetermining an average plaque area index corresponding to each residual potential distribution map by the formula +.>Determining plaque density indexes corresponding to each residual potential distribution map, wherein MPS represents an average plaque area index, A represents the total area of potential plaques, N represents the number of potential plaques, and PD represents a plaque density index;
determining a total perimeter of potential patches in each remaining potential profile based on the perimeter of each potential patch in each remaining potential profile;
based on the total area of potential patches in each remaining potential profile and the total perimeter of potential patches in each remaining potential profile, by the formulaAnd determining an average shape index corresponding to each residual potential distribution map, wherein MSI represents the average shape index, and E represents the total circumference of the potential plaque.
Optionally, the determining the weights corresponding to the average plaque area index, the plaque density index and the average shape index based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map includes:
And determining weights corresponding to the average plaque area index, the plaque density index and the average shape index through an entropy weight method based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map.
Optionally, after determining the potential dispersion degree of the seepage field of the target oil reservoir at different times, the method further comprises:
and performing curve fitting based on the potential dispersion degree of the seepage field of the target oil reservoir at different times to obtain a relation function of the time and the potential dispersion degree of the seepage field, and displaying a curve corresponding to the relation function.
Optionally, after determining the potential dispersion degree of the seepage field of the target oil reservoir at different times, the method further comprises:
based on the potential dispersion degree of the seepage field of the target oil reservoir at different times, marking the potential dispersion degree of the seepage field corresponding to different times in a graph representing the corresponding relation between the time and the potential dispersion degree of the seepage field.
In a second aspect, there is provided an apparatus for determining a potential dispersion of a reservoir seepage field, the apparatus comprising:
the image determining module is used for determining residual potential distribution diagrams of the target oil reservoir at different times based on the result of numerical simulation of the target oil reservoir;
The identification module is used for identifying potential plaques in each residual potential distribution map and determining the number of potential plaques in each residual potential distribution map, wherein the potential plaques are plaques formed by areas in the residual potential distribution map, in which residual oil is concentrated;
a size determination module for determining an area and a perimeter of each potential patch in each remaining potential profile;
the index determining module is used for determining an average plaque area index, a plaque density index and an average shape index corresponding to each residual potential distribution map based on the number of potential plaques in each residual potential distribution map and the area and the perimeter of each potential plaque in each residual potential distribution map;
the weight determining module is used for determining weights corresponding to the average plaque area index, the plaque density index and the average shape index based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map;
and the seepage field potential dispersity determination module is used for determining the seepage field potential dispersity of the target oil reservoir at different times based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map and each weight.
Optionally, the identification module is configured to:
potential patches in each remaining potential profile are identified based on an eight neighborhood boundary tracking algorithm, and the number of potential patches in each remaining potential profile is determined.
Optionally, the identification module is further configured to:
the identified potential patches are labeled in each remaining potential profile.
Optionally, the size determining module is configured to:
based on the regionoprops function, the area and perimeter of each potential patch in each remaining potential profile is determined.
Optionally, the index determining module is configured to:
determining a total area of potential patches in each remaining potential profile based on the area of each potential patch in each remaining potential profile;
based on the number of potential patches in each remaining potential profile and the total area of potential patches in each remaining potential profile, by the formulaDetermining an average plaque area index corresponding to each residual potential distribution map by the formula +.>Determining plaque density indexes corresponding to each residual potential distribution map, wherein MPS represents an average plaque area index, A represents the total area of potential plaques, N represents the number of potential plaques, and PD represents a plaque density index;
Determining a total perimeter of potential patches in each remaining potential profile based on the perimeter of each potential patch in each remaining potential profile;
based on the total area of potential patches in each remaining potential profile and the total perimeter of potential patches in each remaining potential profile, by the formulaAnd determining an average shape index corresponding to each residual potential distribution map, wherein MSI represents the average shape index, and E represents the total circumference of the potential plaque.
Optionally, the weight determining module is configured to:
and determining weights corresponding to the average plaque area index, the plaque density index and the average shape index through an entropy weight method based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map.
Optionally, the device further includes a display module, configured to:
and performing curve fitting based on the potential dispersion degree of the seepage field of the target oil reservoir at different times to obtain a relation function of the time and the potential dispersion degree of the seepage field, and displaying a curve corresponding to the relation function.
Optionally, the device further includes a display module, configured to:
based on the potential dispersion degree of the seepage field of the target oil reservoir at different times, marking the potential dispersion degree of the seepage field corresponding to the target oil reservoir at different times in a graph representing the corresponding relationship between time and potential dispersion degree of the seepage field.
In a third aspect, there is provided a terminal comprising a processor and a memory having stored therein at least one instruction loaded and executed by the processor to implement the method of determining the dispersion of reservoir seepage field potential as described in the first aspect.
In a fourth aspect, there is provided a computer readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of determining a reservoir seepage field potential dispersion as described in the first aspect.
The beneficial effects that technical scheme that this application embodiment provided include at least:
according to the method provided by the embodiment of the application, the residual potential distribution map of the target oil reservoir at different times is determined based on the result of numerical simulation of the target oil reservoir; identifying potential patches in each remaining potential profile and determining a number of potential patches in each remaining potential profile; determining the area and perimeter of each potential patch in each remaining potential profile; determining an average plaque area index, a plaque density index and an average shape index corresponding to each residual potential distribution map based on the number of potential plaques in each residual potential distribution map and the area and the perimeter of each potential plaque in each residual potential distribution map; determining weights corresponding to the average plaque area index, the plaque density index and the average shape index based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map; and determining the potential dispersity of the seepage field of the target oil reservoir at different times based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map and each weight. The method provided by the embodiment of the application provides a method for quantitatively characterizing the potential dispersity of the seepage field of the oil reservoir, thereby being beneficial to exploitation of the oil reservoir.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for determining reservoir seepage field potential dispersion provided by an embodiment of the present application;
FIG. 2 is a schematic structural diagram of an apparatus for determining potential dispersion of a reservoir seepage field according to an embodiment of the present application;
fig. 3 is a block diagram of a terminal according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a residual potential profile of a target reservoir provided in an embodiment of the present application;
FIG. 5 is a schematic diagram of a residual potential profile of a target reservoir provided in an embodiment of the present application;
FIG. 6 is a schematic diagram of a residual potential profile of a target reservoir provided in an embodiment of the present application;
FIG. 7 is a schematic diagram of a residual potential profile of a target reservoir provided in an embodiment of the present application;
FIG. 8 is a schematic diagram of a potential dispersion of a seepage field of a target reservoir over time according to an embodiment of the present application;
FIG. 9 is a schematic diagram of the potential dispersion of a seepage field of a target reservoir over time according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The embodiment of the application provides a method for determining potential dispersity of an oil reservoir seepage field, which can be realized by computer equipment. The computer device can be a mobile terminal such as a mobile phone, a tablet computer, a notebook computer and the like, or a fixed terminal such as a desktop computer and the like.
The method provided by the embodiment of the application can be applied to the technical field of oil extraction engineering, and is particularly used for determining the potential dispersity of the seepage field of the oil reservoir. The specific method for determining the potential dispersity of the oil reservoir seepage field is that firstly, a residual potential distribution map of a target oil reservoir at different times is determined based on the result of numerical simulation of the target oil reservoir. Potential patches in each remaining potential profile are then identified and the number of potential patches in each remaining potential profile is determined. The area and perimeter of each potential patch in each remaining potential profile is then determined. Then, based on the number of potential patches in each remaining potential profile and the area and circumference of each potential patch in each remaining potential profile, an average patch area index, a patch density index, and an average shape index corresponding to each remaining potential profile are determined. Then, based on the average plaque area index, the plaque density index, and the average shape index corresponding to each residual potential distribution map, weights corresponding to the average plaque area index, the plaque density index, and the average shape index are determined. And finally, determining the potential dispersity of the seepage field of the target oil reservoir at different times based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map and each weight. After the potential dispersion degree of the seepage field of the target oil reservoir at different times is determined, the exploitation work of the target oil reservoir can be guided according to the obtained potential dispersion degree of each seepage field, such as determining the time of some important optimization measures for the target oil reservoir.
As shown in fig. 1, the process flow of the method may include the steps of:
in step 101, residual potential profiles for different times of the target reservoir are determined based on the results of the numerical simulation of the target reservoir.
The residual potential distribution map characterizes the exploitation potential of each region of the target oil reservoir, potential plaques are included in the residual potential distribution map, and the potential plaques are plaque potential plaques formed by regions with concentrated residual oil in the residual potential distribution map, namely plaques formed by regions with better exploitation potential, and the potential plaques can be also called residual oil plaques.
In practice, after a target oil reservoir is selected, data of the target oil reservoir is collected, and then numerical simulation is performed on the target oil reservoir. After data simulation is carried out on the target oil reservoir, a result file is generated, and then the result file is input into picture generation software, so that the picture generation software can generate residual potential distribution diagrams of the target oil reservoir at different times.
The residual potential distribution diagram of the target oil reservoir at different times can be shown IN fig. 4-7, and positions of the oil production well and the water injection well are also marked IN fig. 4-7, wherein the symbol of the oil production well is "p+ number", such as P1, and the symbol of the water injection well is "in+ number", such as IN1. From figures 4-7, which represent the left to late residual potential dispersion of the reservoir, it can be seen that the potential patches become smaller over time.
In step 102, potential patches in each remaining potential profile are identified and the number of potential patches in each remaining potential profile is determined.
The potential plaque is plaque formed in a region where residual oil is concentrated in the residual potential distribution map, namely plaque formed in a region with better mining potential in the residual potential distribution map, and the potential plaque can be also called residual oil plaque. Thus, the evaluation of potential dispersion of the reservoir vadose layer is that of the degree of potential plaque dispersion, i.e., the degree of residual oil plaque dispersion. The method for determining the potential dispersity of the oil reservoir seepage layer provided by the embodiment of the application can also be called as a method for determining the residual oil dispersity of the oil reservoir.
In implementation, each potential patch in the determined residual potential distribution map may be represented by a different color, and then the potential patches in the residual potential distribution map may be detected based on the pixel value, the potential patches in each residual potential distribution map are identified, the number of potential patches in each residual potential distribution map is determined, and the number of potential patches in each residual potential distribution map is recorded.
Alternatively, an eight-neighborhood boundary tracking algorithm may be used to identify potential patches and determine the number of potential patches, and the corresponding process of step 102 may identify potential patches in each remaining potential profile and determine the number of potential patches in each remaining potential profile based on the eight-neighborhood boundary tracking algorithm as described below.
Alternatively, the identified potential patches may be labeled, and the corresponding process may label the identified potential patches in each remaining potential profile as described below.
In practice, the identified potential patches may be marked with rectangular boxes, i.e., circumscribed rectangular boxes of each potential patch are marked in the remaining potential profile, and then the marked remaining potential profile is displayed.
In step 103, the area and perimeter of each potential patch in each remaining potential profile is determined.
In implementations, the area and perimeter of the potential patches in the residual potential profile can be determined using a regionoprops function, which is a function used to measure the image region properties, as a function in MATLAB software. The corresponding processing of step 103 may be as described below, determining the area and perimeter of each potential patch in each remaining potential profile based on the regionoprops function. The area and perimeter of each potential patch in each remaining potential profile is then recorded.
In step 104, an average plaque area index, plaque density index, and average shape index corresponding to each of the remaining potential profiles are determined based on the number of potential plaques in each of the remaining potential profiles and the area and perimeter of each of the potential plaques in each of the remaining potential profiles.
Wherein the average plaque area index represents the ratio of the total area of potential plaque to the total number of plaque, the plaque density index represents the number of potential plaque in unit area, and the average shape index represents the complexity of the shape of potential plaque.
In practice, the smaller the average plaque area index, the smaller the recoverable reserves of individual potential plaques, and the higher the potential dispersion of the percolation field under otherwise unchanged conditions. The larger the plaque density index, the larger the number of potential plaques, and the higher the potential dispersity of the seepage field under the condition of unchanged other conditions. The larger the average shape index, the more complex the shape of the potential plaque, and the higher the potential dispersion of the percolation field under other conditions.
When determining the average plaque area index and plaque density index corresponding to each residual potential distribution map, firstly, determining the total area of the potential plaques in each residual potential distribution map based on the area of each potential plaque in each residual potential distribution map, namely adding the areas of all the potential plaques in each residual potential distribution map to obtain the total area of the potential plaques. Then, based on the number of potential patches in each remaining potential profile and the total area of potential patches in each remaining potential profile, the method is formulated Determining an average plaque area index corresponding to each residual potential distribution map through a formulaAnd determining plaque density indexes corresponding to each residual potential distribution map.
Wherein MPS represents an average plaque area index, a represents the total area of potential plaque, N represents the number of potential plaque, and PD represents a plaque density index. The total area A of the potential plaque can be in units of m 2 The number N of potential plaques is in units, and the average plaque area index MPS can be in units of m 2 The plaque density index PD may be in units of one/m 2 。
In determining the average shape index corresponding to each residual potential profile, first, the average shape index is determined based on the average shape index of each residual potential profileThe perimeter of each potential patch is determined, and the total perimeter of the potential patches in each residual potential distribution map is determined, namely the perimeter of all the potential patches in each residual potential distribution map is added to obtain the total perimeter of the potential patches. Then, based on the total area of the potential patches in each remaining potential profile and the total perimeter of the potential patches in each remaining potential profile, the method is formulatedAnd determining an average shape index corresponding to each residual potential distribution map.
Where MSI represents the average shape index and E represents the total perimeter of the potential plaque. The units of the total perimeter E of the potential plaque may be m, the units of the average shape index MSI may be m/m, or understood as no units.
In step 105, weights corresponding to the average plaque area index, plaque density index, and average shape index are determined based on the average plaque area index, plaque density index, and average shape index corresponding to each residual potential distribution map.
The average plaque area index, the plaque density index and the weight corresponding to the average shape index represent the influence degree of each index on the potential dispersity of the seepage field.
In implementation, after three indexes of the residual potential distribution diagram are determined, weights of the three indexes are required to be determined, so that a unified characterization method for quantifying potential dispersion degree of the seepage field is formed. When determining the weights corresponding to the respective indexes, the weights of the respective indexes may be determined by the respective indexes themselves, or the weights of the respective indexes may be set based on actual experience.
Optionally, the weight corresponding to each index may be determined by an entropy weight method, and the corresponding processing in step 105 may be described below, based on the average plaque area index, the plaque density index, and the average shape index corresponding to each residual potential distribution map, and the weights corresponding to the average plaque area index, the plaque density index, and the average shape index may be determined by an entropy weight method.
The entropy weighting method is an objective weighting method and depends only on the discretization of the data. If the degree of variation of the index is smaller in the evaluation process, the information amount is smaller, the weight is smaller, and conversely, the weight is larger.
In practice, in order to avoid interference of human factors, the weight of each index may be determined according to the degree of variation of each index using an entropy weight method. And taking the whole of three indexes corresponding to each residual potential distribution map as one sample.
Because the measurement units of the indexes are not uniform, before the weights corresponding to the indexes are calculated by using the indexes, the standardization processing is needed first, and different algorithms are needed to be adopted for data standardization processing on the positive indexes and the negative indexes, wherein the positive indexes are indexes with larger index values and smaller potential dispersion degree of the seepage field, and the processing formulas of the positive indexes are as follows:
the negative index is an index with larger index value, smaller potential dispersion degree of the seepage field and larger potential dispersion degree of the seepage field, and the processing formula of the negative index is as follows:
in the two formulas, r ij The value of the j index of the normalized i sample, x ij The value of the j index of the i sample is given, and n is the total number of evaluation samples, i.e., the total number of residual potential distribution maps.
Then, the specific gravity of the jth index in the ith sample is calculated, and the calculation formula is as follows:
in the above formula, P ij Is the specific gravity of the jth index in the ith sample.
Calculating the entropy value e of the j-th index j The formula is as follows:
in the above formula, k=1/ln (n).
The entropy weight w of each index can be calculated according to the entropy value of each index j Thereby further obtaining the weight of each index, wherein the calculation formula of the weight is as follows:
in the above, w j For entropy weight of each index, S j Is the weight of each index.
After the weight of each index is determined, the potential dispersity of the seepage field of the target oil reservoir at different times is calculated, so that the change rule of the potential dispersity of the seepage field is quantified.
In step 106, the potential dispersion degree of the seepage field of the target oil reservoir at different times is determined based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map and the weights.
In implementation, multiplying the average plaque area index by the weight corresponding to the plaque area index, multiplying the plaque density index by the weight corresponding to the plaque density index, multiplying the average shape index by the weight corresponding to the plaque density index, and adding the obtained three products to obtain the potential dispersity of the seepage field. And (3) carrying out the treatment on the indexes of each residual potential distribution map to obtain the potential dispersion degree of the seepage field corresponding to each residual potential distribution map, namely the potential dispersion degree of the seepage field of the target oil reservoir at different times. The potential dispersion of the percolation field is not unity.
Alternatively, the time-dependent relationship of the potential dispersion of the seepage field of the target reservoir may be displayed in the form of an image, and the corresponding processing may be as follows, where the potential dispersion of the seepage field corresponding to different times is marked in a graph representing the time-potential dispersion correspondence relationship of the seepage field based on the potential dispersion of the seepage field of the target reservoir at different times.
In practice, as shown in fig. 8, the determined potential dispersions of the seepage field at different times of the target reservoir are marked in a graph characterizing the correspondence between time and potential dispersions of the seepage field. Alternatively, as shown in FIG. 8, the potential dispersion of the percolation field at different times may be marked in the form of a fork. In fig. 8, the vertical axis represents the potential dispersion of the seepage field of the target reservoir, and the horizontal axis represents time.
As can be seen from fig. 8, the change of the potential dispersion degree of the seepage field of the target oil reservoir is divided into four stages, namely a primary steady ascending stage, a rapid lifting stage, a fluctuation descending stage and a secondary steady ascending stage.
The reason why the potential dispersion degree of the seepage field steadily increases in the primary steady-state rising stage is mainly that the potential plaque area is reduced and the average shape index is increased, and the residual potential distribution diagram in the stage is shown in fig. 4.
The reason for the rapid increase in the potential dispersion of the percolation field during the rapid increase phase is mainly the increase in the number of potential plaques and the decrease in the potential plaque area, the remaining potential profile of this phase being shown in fig. 5.
The reason for the reduced potential dispersion of the percolation field during the wave-down phase is mainly the reduction of the potential plaque number, which is accompanied by a small increase in the potential plaque area during the descent, the remaining potential profile of this phase being shown in fig. 6.
The reason why the potential dispersion degree of the percolation field steadily increases in the secondary steady increase stage is mainly the decrease of the potential plaque area and the increase of the average shape index, and the remaining potential distribution diagram in this stage is shown in fig. 7.
Alternatively, the change relation of the potential dispersion degree of the seepage field of the target oil reservoir along with time can be displayed in the form of an image, corresponding processing can be performed as follows, curve fitting is performed based on the potential dispersion degree of the seepage field of the target oil reservoir at different times, a relation function of the time and the potential dispersion degree of the seepage field is obtained, and a curve corresponding to the relation function is displayed.
In implementation, curve fitting can be performed on the potential dispersion degree of the seepage field of the target oil reservoir at different times, and a curve corresponding to a relation function of the time and the potential dispersion degree of the seepage field can be displayed in the graph, as shown in fig. 9.
Based on the same technical concept, the embodiment of the present application further provides a device for determining the potential dispersion degree of the oil reservoir seepage field, where the device may be a terminal in the above embodiment, as shown in fig. 2, and the device includes:
an image determining module 201, configured to determine residual potential distribution maps of the target oil reservoir at different times based on results of numerical simulation of the target oil reservoir;
an identification module 202 for identifying potential patches in each remaining potential profile and determining the number of potential patches in each remaining potential profile;
a size determination module 203 for determining an area and a perimeter of each potential patch in each remaining potential profile;
the index determining module 204 is configured to determine an average plaque area index, a plaque density index, and an average shape index corresponding to each remaining potential distribution map based on the number of potential plaques in each remaining potential distribution map and the area and circumference of each potential plaque in each remaining potential distribution map;
the weight determining module 205 is configured to determine weights corresponding to the average plaque area index, the plaque density index, and the average shape index based on the average plaque area index, the plaque density index, and the average shape index corresponding to each residual potential distribution map;
The seepage field potential dispersity determination module 206 is configured to determine the seepage field potential dispersity of the target reservoir at different times based on the average plaque area index, the plaque density index, the average shape index, and the weights corresponding to each residual potential distribution map.
Optionally, the identification module 202 is configured to:
potential patches in each remaining potential profile are identified based on an eight neighborhood boundary tracking algorithm, and the number of potential patches in each remaining potential profile is determined.
Optionally, the identification module 202 is further configured to:
the identified potential patches are labeled in each remaining potential profile.
Optionally, the size determining module 203 is configured to:
based on the regionoprops function, the area and perimeter of each potential patch in each remaining potential profile is determined.
Optionally, the index determining module 204 is configured to:
determining a total area of potential patches in each remaining potential profile based on the area of each potential patch in each remaining potential profile;
based on the number of potential patches in each remaining potential profile and the total area of potential patches in each remaining potential profile, by the formulaDetermining an average plaque area index corresponding to each residual potential distribution map by the formula +. >Determining plaque density indexes corresponding to each residual potential distribution map, wherein MPS represents an average plaque area index, A represents the total area of potential plaques, N represents the number of potential plaques, and PD represents a plaque density index;
determining a total perimeter of potential patches in each remaining potential profile based on the perimeter of each potential patch in each remaining potential profile;
based on the total area of potential patches in each remaining potential profile and the total perimeter of potential patches in each remaining potential profile, by the formulaAnd determining an average shape index corresponding to each residual potential distribution map, wherein MSI represents the average shape index, and E represents the total circumference of the potential plaque.
Optionally, the weight determining module 205 is configured to:
and determining weights corresponding to the average plaque area index, the plaque density index and the average shape index through an entropy weight method based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map.
Optionally, the apparatus further comprises a display module for:
and performing curve fitting based on the potential dispersion degree of the seepage field of the target oil reservoir at different times to obtain a relationship function of the potential dispersion degree of the seepage field and time, and displaying a curve corresponding to the relationship function.
Optionally, the apparatus further comprises a display module for:
based on the potential dispersion degree of the seepage field of the target oil reservoir at different times, marking the potential dispersion degree of the seepage field corresponding to the different times of the target oil reservoir in a graph representing the corresponding relation between the time and the potential dispersion degree of the seepage field.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
It should be noted that: the device for determining the potential dispersion degree of the oil reservoir seepage field provided in the above embodiment is only exemplified by the division of the above functional modules when determining the potential dispersion degree of the oil reservoir seepage field, and in practical application, the above functional distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the device for determining the potential dispersion degree of the oil reservoir seepage field provided in the above embodiment belongs to the same concept as the method embodiment for determining the potential dispersion degree of the oil reservoir seepage field, and the specific implementation process is detailed in the method embodiment and will not be described herein.
Fig. 3 is a block diagram of a terminal according to an embodiment of the present application. The terminal 300 may be a portable mobile terminal such as: smart phone, tablet computer. The terminal 300 may also be referred to by other names of user equipment, portable terminals, etc.
In general, the terminal 300 includes: a processor 301 and a memory 302.
Processor 301 may include one or more processing cores, such as a 4-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 301 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 301 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 301 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.
Memory 302 may include one or more computer-readable storage media, which may be tangible and non-transitory. Memory 302 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 302 is used to store at least one instruction for execution by processor 301 to implement the method of determining reservoir seepage field potential dispersion provided herein.
In some embodiments, the terminal 300 may further optionally include: a peripheral interface 303, and at least one peripheral. Specifically, the peripheral device includes: at least one of radio frequency circuitry 304, display 305, camera assembly 306, audio circuitry 307, positioning assembly 308, and power supply 309.
The peripheral interface 303 may be used to connect at least one Input/Output (I/O) related peripheral to the processor 301 and the memory 302. In some embodiments, processor 301, memory 302, and peripheral interface 303 are integrated on the same chip or circuit board; in some other embodiments, either or both of the processor 301, the memory 302, and the peripheral interface 303 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.
The Radio Frequency circuit 304 is configured to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuitry 304 communicates with a communication network and other communication devices via electromagnetic signals. The radio frequency circuit 304 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 304 includes: antenna systems, RF transceivers, one or more amplifiers, tuners, oscillators, digital signal processors, codec chipsets, subscriber identity module cards, and so forth. The radio frequency circuitry 304 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: the world wide web, metropolitan area networks, intranets, generation mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and/or WiFi (Wireless Fidelity ) networks. In some embodiments, the radio frequency circuitry 304 may also include NFC (Near Field Communication ) related circuitry, which is not limited in this application.
The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. The display screen 305 also has the ability to collect touch signals at or above the surface of the display screen 305. The touch signal may be input as a control signal to the processor 301 for processing. The display 305 is used to provide virtual buttons and/or virtual keyboards, also known as soft buttons and/or soft keyboards. In some embodiments, the display 305 may be one, providing a front panel of the terminal 300; in other embodiments, the display screen 305 may be at least two, respectively disposed on different surfaces of the terminal 300 or in a folded design; in still other embodiments, the display 305 may be a flexible display disposed on a curved surface or a folded surface of the terminal 300. Even more, the display screen 305 may be arranged in an irregular pattern other than rectangular, i.e., a shaped screen. The display 305 may be made of LCD (Liquid Crystal Display ), OLED (Organic Light-Emitting Diode) or other materials.
The camera assembly 306 is used to capture images or video. Optionally, the camera assembly 306 includes a front camera and a rear camera. In general, a front camera is used for realizing video call or self-photographing, and a rear camera is used for realizing photographing of pictures or videos. In some embodiments, the number of the rear cameras is at least two, and the rear cameras are any one of a main camera, a depth camera and a wide-angle camera, so as to realize fusion of the main camera and the depth camera to realize a background blurring function, and fusion of the main camera and the wide-angle camera to realize a panoramic shooting function and a Virtual Reality (VR) shooting function. In some embodiments, camera assembly 306 may also include a flash. The flash lamp can be a single-color temperature flash lamp or a double-color temperature flash lamp. The dual-color temperature flash lamp refers to a combination of a warm light flash lamp and a cold light flash lamp, and can be used for light compensation under different color temperatures.
Audio circuitry 307 is used to provide an audio interface between the user and terminal 300. The audio circuit 307 may include a microphone and a speaker. The microphone is used for collecting sound waves of users and environments, converting the sound waves into electric signals, and inputting the electric signals to the processor 301 for processing, or inputting the electric signals to the radio frequency circuit 304 for voice communication. For the purpose of stereo acquisition or noise reduction, a plurality of microphones may be respectively disposed at different portions of the terminal 300. The microphone may also be an array microphone or an omni-directional pickup microphone. The speaker is used to convert electrical signals from the processor 301 or the radio frequency circuit 304 into sound waves. The speaker may be a conventional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only the electric signal can be converted into a sound wave audible to humans, but also the electric signal can be converted into a sound wave inaudible to humans for ranging and other purposes. In some embodiments, the audio circuit 307 may also include a headphone jack.
The location component 308 is used to locate the current geographic location of the terminal 300 to enable navigation or LBS (Location Based Service, location-based services). The positioning component 308 may be a positioning component based on the United states GPS (Global Positioning System ), the Beidou system of China, or the Galileo system of Russia.
The power supply 309 is used to power the various components in the terminal 300. The power source 309 may be alternating current, direct current, disposable or rechargeable. When the power source 309 comprises a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge technology.
In some embodiments, the terminal 300 further includes one or more sensors 310. The one or more sensors 310 include, but are not limited to: acceleration sensor 311, gyroscope sensor 312, pressure sensor 313, fingerprint sensor 314, optical sensor 315, and proximity sensor 316.
The acceleration sensor 311 can detect the magnitudes of accelerations on three coordinate axes of the coordinate system established with the terminal 300. For example, the acceleration sensor 311 may be used to detect components of gravitational acceleration on three coordinate axes. The processor 301 may control the display screen 305 to display a user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 311. The acceleration sensor 311 may also be used for the acquisition of motion data of a game or a user.
The gyro sensor 312 may detect the body direction and the rotation angle of the terminal 300, and the gyro sensor 312 may collect the 3D motion of the user to the terminal 300 in cooperation with the acceleration sensor 311. The processor 301 may implement the following functions according to the data collected by the gyro sensor 312: motion sensing (e.g., changing UI according to a tilting operation by a user), image stabilization at shooting, game control, and inertial navigation.
The pressure sensor 313 may be disposed at a side frame of the terminal 300 and/or at a lower layer of the display 305. When the pressure sensor 313 is provided at the side frame of the terminal 300, a grip signal of the terminal 300 by a user may be detected, and left-right hand recognition or shortcut operation may be performed according to the grip signal. When the pressure sensor 313 is disposed at the lower layer of the display screen 305, control of the operability control on the UI interface can be achieved according to the pressure operation of the user on the display screen 305. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.
The fingerprint sensor 314 is used to collect a fingerprint of a user to identify the identity of the user based on the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the user is authorized by the processor 301 to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying for and changing settings, etc. The fingerprint sensor 314 may be provided on the front, back or side of the terminal 300. When a physical key or a manufacturer Logo is provided on the terminal 300, the fingerprint sensor 314 may be integrated with the physical key or the manufacturer Logo.
The optical sensor 315 is used to collect the ambient light intensity. In one embodiment, processor 301 may control the display brightness of display screen 305 based on the intensity of ambient light collected by optical sensor 315. Specifically, when the intensity of the ambient light is high, the display brightness of the display screen 305 is turned up; when the ambient light intensity is low, the display brightness of the display screen 305 is turned down. In another embodiment, the processor 301 may also dynamically adjust the shooting parameters of the camera assembly 306 according to the ambient light intensity collected by the optical sensor 315.
A proximity sensor 316, also referred to as a distance sensor, is typically disposed on the front face of the terminal 300. The proximity sensor 316 is used to collect the distance between the user and the front of the terminal 300. In one embodiment, when the proximity sensor 316 detects a gradual decrease in the distance between the user and the front of the terminal 300, the processor 301 controls the display 305 to switch from the bright screen state to the off screen state; when the proximity sensor 316 detects that the distance between the user and the front surface of the terminal 300 gradually increases, the processor 301 controls the display screen 305 to switch from the off-screen state to the on-screen state.
Those skilled in the art will appreciate that the structure shown in fig. 3 is not limiting and that more or fewer components than shown may be included or certain components may be combined or a different arrangement of components may be employed.
In an exemplary embodiment, a computer readable storage medium is also provided, in which at least one instruction is stored, the at least one instruction being loaded and executed by a processor to implement the method of determining reservoir seepage field potential dispersion in the above embodiment. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (11)
1. A method of determining a potential dispersion of a reservoir seepage field, the method comprising:
determining a residual potential distribution map of the target oil reservoir at different times based on results of numerical simulation of the target oil reservoir;
Identifying potential patches in each remaining potential profile and determining a number of potential patches in each remaining potential profile, wherein the potential patches are patches formed by areas in the remaining potential profile in which remaining oil is concentrated;
determining the area and perimeter of each potential patch in each remaining potential profile;
determining an average plaque area index, a plaque density index and an average shape index corresponding to each residual potential distribution map based on the number of potential plaques in each residual potential distribution map and the area and the perimeter of each potential plaque in each residual potential distribution map;
determining weights corresponding to the average plaque area index, the plaque density index and the average shape index based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map;
and determining the potential dispersity of the seepage field of the target oil reservoir at different times based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map and each weight.
2. The method of claim 1, wherein the identifying potential patches in each remaining potential profile and determining the number of potential patches in each remaining potential profile comprises:
Potential patches in each remaining potential profile are identified based on an eight neighborhood boundary tracking algorithm, and the number of potential patches in each remaining potential profile is determined.
3. The method of claim 1, wherein after identifying potential patches in each remaining potential profile, further comprising:
the identified potential patches are labeled in each remaining potential profile.
4. The method of claim 1, wherein said determining the area and perimeter of each potential patch in each remaining potential profile comprises:
based on the regionoprops function, the area and perimeter of each potential patch in each remaining potential profile is determined.
5. The method of claim 1, wherein determining the average plaque area index, plaque density index, and average shape index for each remaining potential profile based on the number of potential plaques in each remaining potential profile and the area and perimeter of each potential plaque in each remaining potential profile comprises:
determining a total area of potential patches in each remaining potential profile based on the area of each potential patch in each remaining potential profile;
Based on the number of potential patches in each remaining potential profile and the total area of potential patches in each remaining potential profile, by the formulaDetermining an average plaque area index corresponding to each residual potential distribution map through a formulaDetermining plaque density indexes corresponding to each residual potential distribution map, wherein MPS represents an average plaque area index, A represents the total area of potential plaques, N represents the number of potential plaques, and PD represents a plaque density index;
determining a total perimeter of potential patches in each remaining potential profile based on the perimeter of each potential patch in each remaining potential profile;
based on the total area of potential patches in each remaining potential profile and the total perimeter of potential patches in each remaining potential profile, by the formulaAnd determining an average shape index corresponding to each residual potential distribution map, wherein MSI represents the average shape index, and E represents the total circumference of the potential plaque.
6. The method of claim 1, wherein the determining the weights for the average plaque area index, the plaque density index, and the average shape index based on the average plaque area index, the plaque density index, and the average shape index for each residual potential profile comprises:
And determining weights corresponding to the average plaque area index, the plaque density index and the average shape index through an entropy weight method based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map.
7. The method of claim 1, wherein after determining the potential dispersion of the percolation field for different times of the target reservoir, further comprising:
and performing curve fitting based on the potential dispersion degree of the seepage field of the target oil reservoir at different times to obtain a relation function of the time and the potential dispersion degree of the seepage field, and displaying a curve corresponding to the relation function.
8. The method of claim 1, wherein after determining the potential dispersion of the percolation field for different times of the target reservoir, further comprising:
based on the potential dispersion degree of the seepage field of the target oil reservoir at different times, marking the potential dispersion degree of the seepage field corresponding to different times in a graph representing the corresponding relation between the time and the potential dispersion degree of the seepage field.
9. An apparatus for determining potential dispersion of a reservoir seepage field, the apparatus comprising:
the image determining module is used for determining residual potential distribution diagrams of the target oil reservoir at different times based on the result of numerical simulation of the target oil reservoir;
The identification module is used for identifying potential plaques in each residual potential distribution map and determining the number of potential plaques in each residual potential distribution map, wherein the potential plaques are plaques formed by areas in the residual potential distribution map, in which residual oil is concentrated;
a size determination module for determining an area and a perimeter of each potential patch in each remaining potential profile;
the index determining module is used for determining an average plaque area index, a plaque density index and an average shape index corresponding to each residual potential distribution map based on the number of potential plaques in each residual potential distribution map and the area and the perimeter of each potential plaque in each residual potential distribution map;
the weight determining module is used for determining weights corresponding to the average plaque area index, the plaque density index and the average shape index based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map;
and the seepage field potential dispersity determination module is used for determining the seepage field potential dispersity of the target oil reservoir at different times based on the average plaque area index, the plaque density index and the average shape index corresponding to each residual potential distribution map and each weight.
10. A terminal comprising a processor and a memory having stored therein at least one instruction that is loaded and executed by the processor to implement the method of determining a potential dispersion of a reservoir seepage field of any one of claims 1 to 8.
11. A computer readable storage medium having stored therein at least one instruction loaded and executed by a processor to implement the method of determining a potential dispersion of a reservoir seepage field of any one of claims 1 to 8.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2007108980A (en) * | 2007-03-12 | 2008-09-20 | Владимир Анатольевич Иванов (RU) | METHOD FOR DETERMINING SPATIAL DISTRIBUTION OF OIL-SATURATED AREAS IN WATERFILLED LAYERS |
| CN104712328A (en) * | 2014-12-31 | 2015-06-17 | 长江大学 | Method for rapidly evaluating producing condition of single flow unit in complex oil deposit |
| CN108182510A (en) * | 2017-12-11 | 2018-06-19 | 中国石油天然气股份有限公司 | production potential evaluation method and device |
| CN108868715A (en) * | 2018-06-02 | 2018-11-23 | 东北石油大学 | The method for determining matrix-high infiltration strip chemical flooding remaining oil distribution and migration rule with fractal theory |
-
2019
- 2019-07-08 CN CN201910610929.9A patent/CN112200689B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2007108980A (en) * | 2007-03-12 | 2008-09-20 | Владимир Анатольевич Иванов (RU) | METHOD FOR DETERMINING SPATIAL DISTRIBUTION OF OIL-SATURATED AREAS IN WATERFILLED LAYERS |
| CN104712328A (en) * | 2014-12-31 | 2015-06-17 | 长江大学 | Method for rapidly evaluating producing condition of single flow unit in complex oil deposit |
| CN108182510A (en) * | 2017-12-11 | 2018-06-19 | 中国石油天然气股份有限公司 | production potential evaluation method and device |
| CN108868715A (en) * | 2018-06-02 | 2018-11-23 | 东北石油大学 | The method for determining matrix-high infiltration strip chemical flooding remaining oil distribution and migration rule with fractal theory |
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