Modeling method and system for interlayer distribution characteristics based on three-dimensional probability volume constraints

Abstract

The present invention discloses a modeling method and system based on three-dimensional probability body constraints on interlayer distribution characteristics, and the method comprises the following steps: obtaining interlayer development probability characteristic information of a reservoir in a study area, and establishing a three-dimensional interlayer development probability body of the reservoir in the study area; obtaining interlayer plane scale data and interlayer section scale data of the reservoir in the study area; fitting the interlayer space scale relationship according to the interlayer plane scale data and the interlayer section scale data; obtaining variograms in various directions according to the interlayer plane scale data, the interlayer section scale data and the interlayer space scale relationship; establishing an interlayer model of the reservoir in the study area according to the variograms in various directions and the three-dimensional interlayer development probability body; thereby reducing the uncertainty of well-to-well prediction results in the model, establishing an interlayer model that is closer to the actual underground situation, and providing a priori models for understanding the oil and gas characteristics inside oil and gas reservoirs in the later stages of development.

Description

Technical Field

[0001] The invention relates to the technical field of reservoir description, and in particular to a modeling method and system based on three-dimensional probability body constraints on interlayer distribution characteristics. Background Art

[0002] Interlayers are low-permeability or non-permeable layers distributed in the reservoir and dividing the reservoir into multiple independent flowable units. They have a very important impact on the heterogeneity of the reservoir, so it is necessary to finely characterize and classify the interlayers. The purpose of interlayer modeling is to establish a geological model that can reflect different levels and types of interlayers, effectively reveal the three-dimensional distribution law of interlayers, and is of great significance to the adjustment of oilfield development.

[0003] The distribution characteristics of interlayers in different sedimentary environments vary greatly. Especially in the process of geological modeling and reservoir numerical simulation, it is necessary to distinguish different types of interlayers and simulate them separately to characterize the internal physical properties and seepage characteristics of different types of interlayers. In view of the above understanding, the traditional modeling method uses two-point geostatistical random simulation to predict the development characteristics of well interlayers, but the interlayers are randomly simulated between wells, which makes it difficult to characterize the understanding that the spatial distribution characteristics of different types of interlayers obtained in geological research are different. As a result, the established geological model is difficult to accurately characterize the real distribution of interlayers in the reservoir, and the reliability is poor. Summary of the invention

[0004] The present invention provides a modeling method and system based on three-dimensional probability body constraints on interlayer distribution characteristics, which can reduce the uncertainty of well-to-well prediction results in the model, establish an interlayer model that is closer to the actual underground situation, and provide a priori model for understanding the oil and gas characteristics inside oil and gas reservoirs in the later stage of development.

[0005] In a first aspect, a modeling method for interlayer distribution characteristics based on three-dimensional probability volume constraints is provided, comprising the following steps:

[0006] Obtain the interlayer development probability characteristic information of the reservoir in the study area, and establish a three-dimensional interlayer development probability body of the reservoir in the study area;

[0007] Obtaining interlayer plane scale data and interlayer cross-sectional scale data of the reservoir in the study area;

[0008] Fitting the spatial scale relationship of the interlayer according to the interlayer plane scale data and the interlayer cross-sectional scale data;

[0009] Obtaining the variogram in each direction according to the interlayer plane scale data, the interlayer cross-section scale data and the interlayer space scale relationship;

[0010] According to the variograms in various directions and the three-dimensional interlayer development probability body, an interlayer model of the reservoir in the study area is established.

[0011] According to the first aspect, in a first possible implementation manner of the first aspect, the step of “obtaining interlayer plane scale data of the reservoir in the study area” specifically includes the following steps:

[0012] Target modern deposits with similar sedimentary background to the reservoir in the study area were selected, and the length data of different types of interlayers parallel to the shoreline and the length data of different types of interlayers perpendicular to the shoreline in the target modern deposits were measured based on Google Earth software.

[0013] According to the first aspect, in a second possible implementation of the first aspect, the step of “obtaining interlayer profile scale data of the reservoir in the study area” specifically includes the following steps:

[0014] A target field outcrop with a similar sedimentary background to the reservoir in the study area is selected, and the vertical thickness data of different types of interlayers in the target field outcrop, as well as the length data of different types of interlayers perpendicular to the ancient shoreline are measured.

[0015] According to the second possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, the step of “fitting the interlayer scale spatial relationship according to the interlayer plane scale data and the interlayer cross-sectional scale data” specifically includes the following steps:

[0016] Fitting the length data of different types of interlayers in the target modern sediment parallel to the shoreline and the vertical thickness data of different types of interlayers in the target field outcrop to establish a first linear function between thickness and length;

[0017] The length data of different types of interlayers in the target modern sediments perpendicular to the shoreline, the length data of different types of interlayers in the target field outcrops perpendicular to the ancient shoreline, and the vertical thickness data of different types of interlayers in the target field outcrops are fitted to establish a second linear function between thickness and length.

[0018] According to the third possible implementation manner of the first aspect, in the fourth possible implementation manner of the first aspect, the step of “obtaining the variogram in each direction according to the interlayer plane scale data, the interlayer cross-sectional scale data and the interlayer spatial scale relationship” specifically includes the following steps:

[0019] According to the length data of different types of interlayers in the parallel shoreline direction in the modern sediments of the target and the first linear function, and in combination with the drilling spacing data in the study area, the variogram in the parallel shoreline direction is calculated using the PETREL modeling software;

[0020] According to the length data of different types of interlayers in the vertical shoreline direction in the modern sediments of the target and the second linear function, and combined with the drilling spacing data in the work area of ​​the study area, the variogram in the vertical shoreline direction is calculated using the PETREL modeling software;

[0021] According to the vertical thickness data of different types of interlayers in the target field outcrops, the lengths of different types of interlayers in the target field outcrops in the direction perpendicular to the ancient shoreline, and the second linear function, and the logging curve data obtained in the one-dimensional drilling in the study area, the vertical variation function is calculated using the PETREL modeling software.

[0022] According to the first aspect, in a fifth possible implementation of the first aspect, the step of “establishing a three-dimensional interlayer development probability body for the reservoir in the study area” specifically includes the following steps:

[0023] Utilizing PETREL modeling software, according to the interlayer development probability characteristic information, different types of three-dimensional interlayer development probability bodies corresponding to different types of interlayers in the reservoir of the study area are established.

[0024] According to the first aspect, in a sixth possible implementation of the first aspect, the step of “establishing an interlayer model of the reservoir in the study area according to the variograms in each direction and the three-dimensional interlayer development probability body” specifically includes the following steps:

[0025] In the PETREL modeling software, the probability bodies of different types of three-dimensional interlayer development are used as modeling constraints, and the interlayer model of the reservoir in the study area is established based on the sequential indicator simulation algorithm in two-point geostatistics and the variogram in each direction.

[0026] In a second aspect, a modeling system based on three-dimensional probability volume constraints and interlayer distribution characteristics is provided, comprising:

[0027] The three-dimensional probability body module is used to obtain the interlayer development probability characteristic information of the reservoir in the study area and establish the three-dimensional interlayer development probability body of the reservoir in the study area;

[0028] The interlayer scale data module is used to obtain the interlayer plane scale data and interlayer section scale data of the reservoir in the study area;

[0029] A fitting module, which is in communication connection with the interlayer scale data module, and is used for fitting the interlayer space scale relationship according to the interlayer plane scale data and the interlayer section scale data;

[0030] a variogram module, which is in communication with the interlayer scale data module and the fitting module, and is used to obtain variograms in various directions according to the interlayer plane scale data, the interlayer section scale data and the interlayer space scale relationship; and

[0031] The interlayer modeling module is in communication connection with the three-dimensional probability body module and the variogram module, and is used to establish an interlayer model of the reservoir in the study area according to the variograms in various directions and the three-dimensional interlayer development probability body.

[0032] In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the modeling method based on the three-dimensional probability volume constraint interlayer distribution characteristics as described above is implemented.

[0033] In a fourth aspect, an electronic device is provided, comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein when the processor runs the computer program, the modeling method based on the three-dimensional probabilistic volume constraint interlayer distribution characteristics as described above is implemented.

[0034] Compared with the prior art, the advantages of the present invention are as follows: by acquiring the plan scale data and profile scale data of the interlayers in the study area reservoir, a geological knowledge base is established, and the calculation of the variogram in different directions in two-point geostatistics is guided; based on the probability characteristic information of the interlayer development in the study area reservoir in the existing geological research, a three-dimensional probability body constrained geological modeling process is established to solve the randomness problem of predicting the interlayer type and position inside the interwell sand body during the modeling process, and the existing geological knowledge is inherited and applied to geostatistics and geological modeling, reducing the uncertainty of the well-to-well prediction results in the model, and establishing an interlayer model that is closer to the actual underground situation, so as to provide a priori model for understanding the oil and gas characteristics inside the oil and gas reservoir in the late development stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of an embodiment of a modeling method based on three-dimensional probability volume constraint interlayer distribution characteristics of the present invention;

[0036] Figure 2 It is the interlayer development probability characteristic information of the reservoir in the study area of ​​the present invention;

[0037] Figure 3 The present invention is a scale diagram of the interlayer cross section based on the measured interlayer of the target field outcrop;

[0038] Figure 4 It is a fitting relationship diagram of the length and vertical thickness of the interlayer in the direction parallel to the ancient shoreline, which is obtained by integrating modern sedimentation and field outcrop fitting in the present invention;

[0039] Figure 5 It is a fitting relationship diagram of the length and vertical thickness scale of the interlayer in the direction perpendicular to the ancient shoreline, which is a combination of modern sedimentation and field outcrop fitting in the present invention;

[0040] Figure 6a~Figure 6c is the calculation result of the variogram in each direction of the present invention;

[0041] Figure 7a~Figure 7b It is the probability body of development of different types of three-dimensional interlayers of the present invention;

[0042] Figure 8 It is the interlayer model of the reservoir in the study area of ​​the present invention. DETAILED DESCRIPTION

[0043] Reference will now be made in detail to specific embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Although the present invention will be described in conjunction with specific embodiments, it will be understood that it is not intended to limit the present invention to the embodiments described. On the contrary, it is intended to cover changes, modifications and equivalents included in the spirit and scope of the present invention as defined by the appended claims. It should be noted that the method steps described herein can all be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of the two.

[0044] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0045] Note: The example to be introduced below is only a specific example, and is not intended to limit the embodiments of the present invention to the following specific steps, values, conditions, data, sequences, etc. Those skilled in the art can apply the concept of the present invention to construct more embodiments not mentioned in this specification by reading this specification.

[0046] See also Figure 1 As shown, an embodiment of the present invention provides a modeling method based on three-dimensional probability volume constraint interlayer distribution characteristics, comprising the following steps:

[0047] S100, obtaining interlayer development probability characteristic information of the reservoir in the study area, and establishing a three-dimensional interlayer development probability body of the reservoir in the study area;

[0048] The probability characteristic information of the interlayer development in the reservoir of the study area is as follows: Based on the previous geological knowledge obtained, there are two different types of interlayers in the study area: calcareous interlayers and muddy interlayers. In terms of space, the interlayer development characteristics of "nearshore calcareous and offshore muddy" around the ancient coastline are generally shown, and the depositional characteristics of syngenetic calcareous interlayers are easily developed at the pinch-out line of the sand body. For details, see Figure 2 As shown, the information on interlayer types, interlayer development locations and development proportions at different locations in the study area can be obtained.

[0049] S200, obtaining interlayer plane scale data and interlayer cross-sectional scale data of the reservoir in the study area;

[0050] S300, fitting the interlayer space scale relationship according to the interlayer plane scale data and the interlayer cross-sectional scale data;

[0051] S400, obtaining the variogram in each direction according to the interlayer plane scale data, the interlayer cross-sectional scale data and the interlayer space scale relationship;

[0052] S500, establishing an interlayer model of the reservoir in the study area according to the variograms in various directions and the three-dimensional interlayer development probability body.

[0053] Specifically, in this embodiment, the interlayer serves as the internal configuration interface of the reservoir sand body, divides the sand body inside the oil and gas reservoir into several unconnected flow units, and plays an important role in blocking the oil and gas migration and bottom water coning inside the oil and gas reservoir. For oil and gas reservoirs in the late stage of development, the type, scale and distribution characteristics of the interlayer play a controlling role in the distribution of the remaining oil and gas.

[0054] Therefore, the present invention establishes a geological knowledge base by acquiring the plan scale data and profile scale data of the interlayers in the study area reservoir, and guides the calculation of the variogram in different directions in two-point geostatistics; based on the probability characteristic information of the interlayer development in the study area reservoir in the existing geological research, a three-dimensional probability body constrained geological modeling process is established to solve the randomness problem of predicting the interlayer type and position inside the interwell sand body during the modeling process, and the existing geological knowledge is inherited and applied to geostatistics and geological modeling, reducing the uncertainty of the well-to-well prediction results in the model, and establishing an interlayer model that is closer to the actual underground situation, so as to provide a priori model for understanding the oil and gas characteristics inside the oil and gas reservoir in the late development stage.

[0055] Preferably, in another embodiment of the present application, the step of "S200, obtaining interlayer plane scale data of the reservoir in the study area" specifically includes the following steps:

[0056] Target modern deposits with similar sedimentary background to the reservoir in the study area were selected, and the length data of different types of interlayers parallel to the shoreline and the length data of different types of interlayers perpendicular to the shoreline in the target modern deposits were measured based on Google Earth software.

[0057] Specifically, in this embodiment, since the study area is an underground reservoir of an oil and gas field, the plane and vertical scale data of the interlayers are obtained by measuring modern wave-controlled shore phases with similar reservoir characteristics and similar sedimentary environments to the study area reservoirs on a global scale, as well as surveys of well-exposed outcrops in the field.

[0058] Among them, similar modern wave-controlled coastal facies refers to modern sedimentary systems that are similar to the Donghe sandstone section of the underground reservoir in the study area in terms of sedimentary background, hydrodynamic conditions, scale of beach, dam and interlayer development, as well as representative of similar deposits worldwide. Therefore, similar target modern deposits were selected as the objects of investigation and research.

[0059] Based on Google Earth software, similar target modern sediments with similar sedimentary background to the target layer in the study area were measured to obtain the plane scale data of interlayers in the target modern sediments, which is equivalent to the plane scale data of interlayers in the reservoir in the study area.

[0060] The sedimentary facies type studied in the present invention is the wave-controlled coastal facies, which can be further divided into two types: calcareous interlayers and muddy interlayers. Based on similar modern sedimentary measurements, the length range of interlayers parallel to the coastline and perpendicular to the coastline is quantitatively characterized, and a quantitative knowledge base is established. Table 1 shows a knowledge base established based on the plane scale of interlayers measured based on the target modern sedimentary measurements. It is believed that the length of the calcareous interlayers in the study area parallel to the coastline is between 400-2000m, and the length perpendicular to the coastline is between 230-850m. The length of the muddy interlayers parallel to the ancient coastline is between 530-2500m, and the length perpendicular to the ancient coastline is between 280-600m.

[0061] Table 1

[0062]

[0063] Preferably, in another embodiment of the present application, the step of "S200, obtaining interlayer profile scale data of the reservoir in the study area" specifically includes the following steps:

[0064] A target field outcrop with a similar sedimentary background to the reservoir in the study area is selected, and the vertical thickness data of different types of interlayers in the target field outcrop, as well as the length data of different types of interlayers perpendicular to the ancient shoreline are measured.

[0065] Specifically, in this embodiment, a database of intercalation profiles of the reservoir in the study area is obtained by digitally measuring field outcrops with similar sedimentary backgrounds. In this embodiment, field outcrop research refers to measuring paleostrata that are similar to the wave-controlled shore phase and sedimentary environment of the Donghe sandstone section in the study area, have similar reservoir characteristics, and are exposed on the surface due to geological action. Figure 3 As shown, this is a scale diagram of the interlayer profile based on the measured interlayer profile of the target field outcrop.

[0066] Based on the field outcrop measurement of the target, the vertical thickness of the interlayer and the length range of the vertical paleoshoreline are quantitatively characterized, and a quantitative knowledge base is established. Table 2 shows the knowledge base of the interlayer profile scale based on the field outcrop measurement. It is believed that the vertical thickness of the calcareous interlayer in the study area is between 0.13-0.8m, and the length in the direction perpendicular to the paleoshoreline is between 250-780m. The vertical thickness of the mud interlayer is between 0.11-0.86m, and the length in the direction perpendicular to the paleoshoreline is between 300-630m.

[0067] Table 2

[0068]

[0069] The explanation of shoreline and ancient shoreline is as follows: When expressing the direction of measurement data in modern sediments, you can directly say "parallel to the shoreline direction". Modern sediments photographed by Google Earth software are current and modern. The ancient shoreline can describe the outcrops in the field and the buried reservoirs, which are all several years ago, hundreds of millions of years ago, and the reservoirs are added with "ancient".

[0070] Preferably, in another embodiment of the present application, the step of "S300, fitting the interlayer scale spatial relationship according to the interlayer plane scale data and the interlayer cross-sectional scale data" specifically includes the following steps:

[0071] Fitting the length data of different types of interlayers in the target modern sediment parallel to the shoreline and the vertical thickness data of different types of interlayers in the target field outcrop to establish a first linear function between thickness and length;

[0072] The length data of different types of interlayers in the target modern sediments perpendicular to the shoreline, the length data of different types of interlayers in the target field outcrops perpendicular to the ancient shoreline, and the vertical thickness data of different types of interlayers in the target field outcrops are fitted to establish a second linear function between thickness and length.

[0073] Specifically, in this embodiment, the length of the interlayer parallel to the shoreline measured in modern sediments is fitted with the thickness of the interlayer measured in the field outcrop, and the fitting relationship obtained is Y=2524X-22.02, and the correlation can reach 0.62. For details, see Figure 4As shown, it is a fitting relationship diagram of the length and vertical thickness scale of the interlayer parallel to the ancient shoreline based on the comprehensive modern sedimentation and field outcrop fitting.

[0074] The interlayer length perpendicular to the ancient shoreline measured in modern sediments and field outcrops was fitted with the interlayer thickness measured in field outcrops, and the fitting relationship was Y=807.37X+200.71. Figure 5 As shown, it is a fitting relationship diagram of the length and vertical thickness scale of the interlayer in the direction perpendicular to the ancient shoreline, which is a comprehensive modern sedimentation and field outcrop fitting.

[0075] Preferably, in another embodiment of the present application, the step of "S400, obtaining the variogram in each direction according to the interlayer plane scale data, the interlayer cross-sectional scale data and the interlayer space scale relationship" specifically includes the following steps:

[0076] According to the length data of different types of interlayers in the parallel shoreline direction in the modern sediments of the target and the first linear function, and in combination with the drilling spacing data in the study area, the variogram in the parallel shoreline direction is calculated using the PETREL modeling software;

[0077] According to the length data of different types of interlayers in the vertical shoreline direction in the modern sediments of the target and the second linear function, and combined with the drilling spacing data in the work area of ​​the study area, the variogram in the vertical shoreline direction is calculated using the PETREL modeling software;

[0078] According to the vertical thickness data of different types of interlayers in the target field outcrops, the lengths of different types of interlayers in the target field outcrops in the direction perpendicular to the ancient shoreline, and the second linear function, and the logging curve data obtained in the one-dimensional drilling in the study area, the vertical variation function is calculated using the PETREL modeling software.

[0079] Specifically, in this embodiment, the variogram refers to a mathematical method used to describe the spatial differences between data in a specific direction.

[0080] According to the data range of the lengths of different types of interlayers in the parallel shoreline direction in the target modern sediments, the first linear function between thickness and length, combined with the drilling spacing data in the study area, the main variable distance, that is, the parallel shoreline direction variation function, is calculated based on the PETREL modeling software.

[0081] According to the data range of different types of interlayer lengths in the vertical shoreline direction in the target modern sediments, the second linear function between thickness and length, combined with the drilling spacing data in the study area, the secondary range distance, that is, the vertical shoreline direction variation function is calculated based on the PETREL modeling software.

[0082] The calculation result of the variogram is used to reflect the spatial correlation of data in the random modeling process, to describe the spatial variation characteristics of the reservoir inside the reservoir, and to predict the distribution of reservoirs between wells.

[0083] Mainly based on the logging curves obtained from one-dimensional drilling in the study area, the vertical variation function is calculated to guide the length data of different types of interlayers in the target field outcrops perpendicular to the ancient shoreline, the vertical thickness data of different types of interlayers in the target field outcrops, and the second linear function between the thickness and the length, and a vertical variation function result is established that is consistent with the lateral and spatial distribution characteristics and conforms to the geological laws of the study area.

[0084] The calculation process of how to calculate the variance function in each direction in the PETREL modeling software is as follows: for example, combining the point pairs in all lag distances of the drilling data in the study area in the direction perpendicular to the coastline, calculating the average of the square differences of the point pairs within the lag distance, and obtaining a single value of the variance within the lag distance, and plotting the results on a variance-interval diagram, fitting the variance value through a mathematical equation, and establishing a variance function model in the direction perpendicular to the coastline.

[0085] The variogram formula is as follows:

[0086]

[0087] Where: h: lag distance; N(h): number of data pairs; x i and i : a data pair; γ(h): variogram.

[0088] FIG. 6 is a parallel shoreline direction variation function ( Figure 6a ), vertical coastline direction variation function ( Figure 6b ) and vertical variogram ( Figure 6c ) calculation results. The variogram is a statistic that describes the spatial correlation of random fields and random processes and is defined as the variance of the difference between two points in space. The variogram is mainly used in the modeling of random fields and random processes.

[0089] Preferably, in another embodiment of the present application, the step of "S100, establishing a three-dimensional interlayer development probability body of the reservoir in the study area" specifically includes the following steps:

[0090] Utilizing PETREL modeling software, according to the interlayer development probability characteristic information, different types of three-dimensional interlayer development probability bodies corresponding to different types of interlayers in the reservoir of the study area are established.

[0091] Specifically, in this embodiment, FIG. 7 is a graph of the present invention based on different types of interlayer development probability feature information (such as Figure 2The three-dimensional interlayer development probability body is established to characterize the development probability and trend change characteristics of different interlayers in space, as shown in Figure 7.

[0092] Specifically, Figure 2 The distribution positions of different types of interlayers provided have a certain relationship with the pinch-out line and the ancient shoreline. Combined with the above geological knowledge, a corresponding three-dimensional interlayer development probability body is established for each interlayer model, such as Figure 7a It is a probabilistic body for the development of calcareous interlayers. Figure 7b It is a probability body for the development of muddy interlayers.

[0093] The closer to the ancient shoreline or pinch-out line, the more developed the calcareous interlayers are; the probability body of the distribution of the constrained calcareous interlayer model established is that the probability of developing calcareous interlayers is 1 at the position closest to the ancient shoreline (i.e. along the pinch-out line, near the shore); the probability of developing calcareous interlayers is 0 at the position farthest from the ancient shoreline (also known as the far shore). From the near shore to the far shore, the probability of developing calcareous interlayers gradually increases and gradually changes. For details, see Figure 7a shown.

[0094] Similarly, within the study area, the farther away from the ancient shoreline, the more developed the mud interlayers are. The 3D probability body constraining the distribution characteristics of mud interlayers is established. The probability of developing mud interlayers is 0 at the location closest to the ancient shoreline, and the probability of developing mud interlayers is 1 at the location farthest from the ancient shoreline. From nearshore to farshore, the probability of developing mud interlayers gradually decreases, which is exactly the opposite of the probability of developing calcareous interlayers. For details, see Figure 7b shown.

[0095] Furthermore, in this embodiment, the characterization of interlayer type and development location characteristics in the three-dimensional interlayer development probability body described in step 6 is established based on the interlayer development characteristics of the No. 3 layer of the Donghe sandstone section in the Hader 11 well area in the Tarim Basin. The typical example selected by the present invention and the method for establishing a probability body are suitable for the characterization of interlayers in similar sedimentary systems. However, the probability body established in other work areas should change with the interlayer development location characteristics obtained from geological knowledge.

[0096] Preferably, in another embodiment of the present application, the step of "S500, establishing an interlayer model of the reservoir in the study area according to various types of variograms and the three-dimensional interlayer development probability body" specifically includes the following steps:

[0097] In the PETREL modeling software, the probability bodies of different types of three-dimensional interlayer development are used as modeling constraints, and the interlayer model of the reservoir in the study area is established based on the sequential indicator simulation algorithm in two-point geostatistics and the variogram in each direction.

[0098] Specifically, in this embodiment, according to the guidance of the database of different types of interlayer scales, the variograms in different directions are calculated, and according to the three-dimensional interlayer development probability body constraint modeling, the sequential indicator simulation algorithm in two-point geostatistics is used to establish the interlayer model of the study area in the PETREL modeling software, such as Figure 8 As shown, it is the interlayer model of the reservoir in the study area of ​​the present invention.

[0099] An embodiment of the present invention provides a modeling system based on three-dimensional probability volume constraints and interlayer distribution characteristics, comprising:

[0100] The three-dimensional probability body module is used to obtain the interlayer development probability characteristic information of the reservoir in the study area and establish the three-dimensional interlayer development probability body of the reservoir in the study area;

[0101] The interlayer scale data module is used to obtain the interlayer plane scale data and interlayer section scale data of the reservoir in the study area;

[0102] A fitting module, which is in communication connection with the interlayer scale data module, and is used for fitting the interlayer scale spatial relationship according to the interlayer plane scale data and the interlayer section scale data;

[0103] a variogram module, which is in communication with the interlayer scale data module and the fitting module, and is used to obtain variograms in various directions according to the interlayer plane scale data, the interlayer section scale data and the interlayer scale spatial relationship; and

[0104] The interlayer modeling module is in communication connection with the three-dimensional probability body module and the variogram module, and is used to establish an interlayer model of the reservoir in the study area according to the variograms in various directions and the three-dimensional interlayer development probability body.

[0105] In the traditional modeling method, the interlayer development characteristics of well intervals are predicted through random simulation of two-point geostatistics, but the interlayers are randomly simulated between wells, which makes it difficult to characterize the understanding that different types of interlayers obtained in geological research have different spatial distribution characteristics. As a result, the established geological model is difficult to accurately characterize the actual distribution of interlayers inside the reservoir, and the reliability is poor. The embodiment of the present invention provides a modeling system based on a three-dimensional probability body to constrain the distribution characteristics of interlayers. Through the reliable understanding obtained in the previous geological research, a three-dimensional interlayer development probability body is established to characterize the development probability of different interlayers in space to constrain geological modeling, so as to solve the problem of randomness of interlayer simulation in the sand body, and the existing geological understanding is inherited and applied to the modeling process. The model results accurately characterize the spatial distribution characteristics of interlayers in oil and gas reservoirs and the scale characteristics of different types of interlayers.

[0106] Specifically, this embodiment corresponds one-to-one to the above method embodiment, and the functions of each module have been described in detail in the corresponding method embodiment, so they will not be repeated here.

[0107] Based on the same inventive concept, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, all or part of the method steps of the above method are implemented.

[0108] The present invention implements all or part of the processes in the above method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of each of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0109] Based on the same inventive concept, an embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program running on the processor, and when the processor executes the computer program, all or part of the method steps in the above method are implemented.

[0110] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of a computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0111] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and an application required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, video data, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0112] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, servers or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), servers, and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0116] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A modeling method based on three-dimensional probability volume constraint interlayer distribution characteristics, characterized in that: The following steps are involved: Obtain the interlayer development probability characteristic information of the reservoir in the study area, and establish a three-dimensional interlayer development probability body of the reservoir in the study area; Obtaining interlayer plane scale data and interlayer cross-sectional scale data of the reservoir in the study area; Fitting the spatial scale relationship of the interlayer according to the interlayer plane scale data and the interlayer cross-sectional scale data; Obtaining the variogram in each direction according to the interlayer plane scale data, the interlayer cross-sectional scale data and the interlayer space scale relationship; According to the variograms in each direction and the three-dimensional interlayer development probability body, an interlayer model of the reservoir in the study area is established; Obtaining the interlayer plane scale data of the reservoir in the study area, including: selecting target modern sediments with similar sedimentary background to the reservoir in the study area, and measuring the length data of different types of interlayers in the target modern sediments parallel to the shoreline direction and the length data of different types of interlayers in the target modern sediments perpendicular to the shoreline direction based on Google Earth software; Obtaining interlayer profile scale data of the reservoir in the study area, including: selecting a target field outcrop with a similar sedimentary background to the reservoir in the study area, measuring vertical thickness data of different types of interlayers in the target field outcrop, and length data of different types of interlayers perpendicular to the paleoshoreline; The step of fitting the interlayer scale spatial relationship according to the interlayer plane scale data and the interlayer cross-sectional scale data comprises: Fitting the length data of different types of interlayers in the target modern sediment parallel to the shoreline and the vertical thickness data of different types of interlayers in the target field outcrop to establish a first linear function between thickness and length; The length data of different types of interlayers in the target modern sediments perpendicular to the shoreline, the length data of different types of interlayers in the target field outcrops perpendicular to the ancient shoreline, and the vertical thickness data of different types of interlayers in the target field outcrops are fitted to establish a second linear function between thickness and length.

2. The modeling method based on three-dimensional probability volume constraint interlayer distribution characteristics as claimed in claim 1 is characterized in that: The obtaining of the variogram in each direction according to the interlayer plane scale data, the interlayer cross-section scale data and the interlayer space scale relationship comprises: According to the length data of different types of interlayers in the parallel shoreline direction in the modern sediments of the target and the first linear function, and in combination with the drilling spacing data in the study area, the variogram in the parallel shoreline direction is calculated using the PETREL modeling software; According to the length data of different types of interlayers in the vertical shoreline direction in the modern sediments of the target and the second linear function, and combined with the drilling spacing data in the work area of ​​the study area, the variogram in the vertical shoreline direction is calculated using the PETREL modeling software; According to the vertical thickness data of different types of interlayers in the target field outcrops, the lengths of different types of interlayers in the target field outcrops in the direction perpendicular to the ancient shoreline, and the second linear function, and the logging curve data obtained in the one-dimensional drilling in the study area, the vertical variation function is calculated using the PETREL modeling software.

3. The modeling method based on three-dimensional probability volume constraint interlayer distribution characteristics as claimed in claim 1, characterized in that: The establishment of a three-dimensional interlayer development probability body of the reservoir in the study area includes: Utilizing PETREL modeling software, according to the interlayer development probability characteristic information, different types of three-dimensional interlayer development probability bodies corresponding to different types of interlayers in the reservoir of the study area are established.

4. The modeling method based on three-dimensional probability volume constraint interlayer distribution characteristics as claimed in claim 1, characterized in that: The interlayer model of the reservoir in the study area is established according to the variograms in each direction and the three-dimensional interlayer development probability body, including: In the PETREL modeling software, the probability bodies of different types of three-dimensional interlayer development are used as modeling constraints, and the interlayer model of the reservoir in the study area is established based on the sequential indicator simulation algorithm in two-point geostatistics and the variogram in each direction.

5. A modeling system based on three-dimensional probability volume constraint interlayer distribution characteristics, characterized in that: include: The three-dimensional probability body module is used to obtain the interlayer development probability characteristic information of the reservoir in the study area and establish the three-dimensional interlayer development probability body of the reservoir in the study area; The interlayer scale data module is used to obtain the interlayer plane scale data and interlayer section scale data of the reservoir in the study area; A fitting module, which is in communication connection with the interlayer scale data module, and is used for fitting the interlayer space scale relationship according to the interlayer plane scale data and the interlayer section scale data; a variogram module, which is in communication with the interlayer scale data module and the fitting module, and is used to obtain variograms in various directions according to the interlayer plane scale data, the interlayer section scale data and the interlayer space scale relationship; and An interlayer modeling module is connected to the three-dimensional probability body module and the variogram module, and is used to establish an interlayer model of the reservoir in the study area according to the variograms in each direction and the three-dimensional interlayer development probability body; Obtaining the interlayer plane scale data of the reservoir in the study area, including: selecting target modern sediments with similar sedimentary background to the reservoir in the study area, and measuring the length data of different types of interlayers in the target modern sediments parallel to the shoreline direction and the length data of different types of interlayers in the target modern sediments perpendicular to the shoreline direction based on Google Earth software; Obtaining interlayer profile scale data of the reservoir in the study area, including: selecting a target field outcrop with a similar sedimentary background to the reservoir in the study area, measuring vertical thickness data of different types of interlayers in the target field outcrop, and length data of different types of interlayers perpendicular to the paleoshoreline; The step of fitting the interlayer scale spatial relationship according to the interlayer plane scale data and the interlayer cross-sectional scale data comprises: Fitting the length data of different types of interlayers in the target modern sediment parallel to the shoreline and the vertical thickness data of different types of interlayers in the target field outcrop to establish a first linear function between thickness and length; The length data of different types of interlayers in the target modern sediments perpendicular to the shoreline, the length data of different types of interlayers in the target field outcrops perpendicular to the ancient shoreline, and the vertical thickness data of different types of interlayers in the target field outcrops are fitted to establish a second linear function between thickness and length.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the modeling method based on three-dimensional probabilistic volume constraints on interlayer distribution characteristics as claimed in any one of claims 1 to 4 is implemented.

7. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor runs the computer program, the modeling method based on three-dimensional probabilistic volume constraints on interlayer distribution characteristics as described in any one of claims 1 to 4 is implemented.

Images