Multi-information constraint reservoir low-frequency model establishment method and device

Abstract

The invention discloses a multi-information constraint reservoir low-frequency model establishment method and device. The method comprises the steps that fault interpretation data and horizon interpretation data are combined, and a target stratum structure framework model is established; aiming at each phase belt of the target layer, screening out an attribute of which the correlation with the elastic parameter meets a set condition, and establishing a regression equation between the two; in a first area in which the well density cannot reach a set density threshold value, based on the distribution data of the attributes, establishing a very low frequency model of the elastic parameters by using a corresponding regression equation in a split-phase belt manner; in a second area with the well density reaching a set density threshold value, building a low-frequency model of the elastic parameters through well interpolation by taking construction of a framework model as control and taking the actually measured curve of the elastic parameters as a basis; and integrating the very-low-frequency model and the low-frequency model to establish a unified low-frequency model. The method is a step-by-step multi-information constraint low-frequency model establishment method, and is particularly suitable for carbonate rock research areas under the condition of irregular well patterns on the sea.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration and development technology, and in particular to a method and apparatus for establishing a low-frequency reservoir model with multiple information constraints. Background Technology

[0002] Seismic inversion technology, which originated in the 1970s, is a primary technique for quantitative oil and gas reservoir prediction. Based on convolutional models, seismic inversion has a rigorous geophysical theoretical foundation and is capable of quantitative reservoir prediction. The establishment of low-frequency models is a crucial step in deterministic seismic inversion, reflecting the spatial distribution patterns of reservoirs and improving lateral resolution and prediction accuracy. Developing a reasonable low-frequency model based on geological conditions requires a comprehensive analysis of geological, seismic, and well logging information to arrive at a scientific solution.

[0003] The establishment of conventional low-frequency models mainly utilizes well logging and structural interpretation data. First, stratigraphic data is interpolated to create a structural framework. Then, within this framework, well logging curves are interpolated to obtain the final low-frequency model. However, for carbonate oil and gas reservoirs with few or no wells or irregular well networks, the strong heterogeneity and complex lithological variations of the reservoirs make it difficult to obtain reliable low-frequency information by relying solely on well interpolation or well interpolation under simple attribute constraints. This ultimately leads to very low accuracy in seismic inversion and reservoir prediction results. Summary of the Invention

[0004] To enrich the process routes and increase the selection space, this invention provides a method and apparatus for establishing a low-frequency reservoir model with multiple information constraints. This model can establish a reliable low-frequency model for reservoir inversion, and is especially suitable for carbonate rock research areas under irregular well network conditions at sea.

[0005] In a first aspect, embodiments of the present invention provide a method for establishing a low-frequency reservoir model with multiple information constraints, comprising:

[0006] By combining fault interpretation data and stratigraphic interpretation data of the study area, a structural framework model of the target layer is established;

[0007] For each phase zone of the target layer, through multi-attribute well-seismic joint analysis, attributes whose correlation with elastic parameters meets the set conditions are screened out, and regression equations between the elastic parameters and the attributes are established.

[0008] In the first region where the well density in the target layer does not reach the set density threshold, based on the distribution data of the attribute, and with the structural framework model as the control, the very low frequency model of the elastic parameter is established by using the corresponding regression equation in the phase zone.

[0009] In a second area in the target layer where the well density reaches a set density threshold, a low-frequency model of the elastic parameter is established by well interpolation, with the tectonic framework model as control and the measured curve of the elastic parameter as basis;

[0010] The low-frequency model is established by comprehensively integrating the very low-frequency model and the low-frequency model.

[0011] In a second aspect, an embodiment of the present application provides a device for establishing a reservoir low-frequency model under multi-information constraints, comprising:

[0012] A tectonic framework model establishing module is configured to establish a tectonic framework model of a target layer by combining fault interpretation data and horizon interpretation data of a study area;

[0013] An elastic parameter and attribute regression equation establishing module is configured to, for each facies belt of the target layer, screen out an attribute whose correlation with an elastic parameter meets a set condition by multi-attribute well-seismic joint analysis, and establish a regression equation between the elastic parameter and the attribute;

[0014] A very low-frequency model establishing module is configured to, in a first area in the target layer where the well density does not reach a set density threshold, establish a very low-frequency model of the elastic parameter by facies belt and using a corresponding regression equation, with the tectonic framework model as control and based on distribution data of the attribute;

[0015] A low-frequency model establishing module is configured to, in a second area in the target layer where the well density reaches a set density threshold, establish a low-frequency model of the elastic parameter by well interpolation, with the tectonic framework model as control and the measured curve of the elastic parameter as basis;

[0016] A model fusion module is configured to establish a low-frequency model by comprehensively integrating the very low-frequency model and the low-frequency model.

[0017] In a third aspect, an embodiment of the present application provides a computer storage medium, which stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the above-mentioned method for establishing a reservoir low-frequency model under multi-information constraints.

[0018] In a fourth aspect, an embodiment of the present application provides a server, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned method for establishing a reservoir low-frequency model under multi-information constraints when executing the program.

[0019] The above-mentioned technical solution provided by the embodiments of the present application has at least the following beneficial effects:

[0020] The method for establishing the multi-information constrained reservoir low-frequency model provided by the embodiment of the present application comprises the following steps: building a structural framework model according to structural interpretation results; optimizing an attribute having the best correlation with the elastic parameters of the target facies belt characteristics through multi-attribute well-seismic joint analysis, and fitting a regression equation of the two; establishing a very low-frequency model of a well-free / low-well area based on the structural framework model, taking different facies belt ranges as boundaries and taking the regression equation as a basis; and finally, fusing the very low-frequency model and a low-frequency model of a multi-well area well interpolation to obtain a final objective three-dimensional low-frequency model. The method is a step-by-step multi-information constrained low-frequency model establishing method, is strong in pertinence, objective in means and scientific in process, and has important practical significance in the reservoir prediction work of the carbonate rock oil and gas reservoir under the condition of the irregular well pattern on the sea.

[0021] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and attained by means of the instrumentalities particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0022] The technical solutions of the present application are described in further detail below by means of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0023] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate embodiments of the present application and explain the present application together with the written description, and do not limit the present application. In the drawings:

[0024] Figure 1 The flow chart of the method for establishing the multi-information constrained reservoir low-frequency model in the embodiment one of the present application;

[0025] Figure 2 The flow chart of the facies belt identification technology in the embodiment one of the present application;

[0026] Figure 3 The flow chart of the method for establishing the multi-information constrained carbonate rock reservoir low-frequency model in the embodiment two of the present application;

[0027] Figure 4 The example diagram of the seismic interpretation profile in the embodiment two of the present application;

[0028] Figure 5 The diagram of the structural framework model established in the embodiment two of the present application;

[0029] Figure 6a The seismic interval velocity diagram of the BVE100 segment in the embodiment two of the present application;

[0030] Figure 6b The well logging curve diagram in the embodiment two of the present application;

[0031] Figure 6c Figure 7 is a seismic interval velocity variation rate map of the BVE100 section in the second embodiment of the present application;

[0032] Figure 6d Figure 8 is a seismic attribute slice map of the BVE100 section in the second embodiment of the present application;

[0033] Figure 7a Figure 9 is a P-S velocity ratio versus seismic velocity crossplot in the second embodiment of the present application;

[0034] Figure 7b Figure 10 is a P-S velocity ratio versus structural height crossplot in the second embodiment of the present application;

[0035] Figure 7c Figure 11 is a top surface structural map of the BVE100 section in the second embodiment of the present application;

[0036] Figure 7d Figure 12 is a P-S velocity ratio low frequency trend map of the BVE100 section in the second embodiment of the present application;

[0037] Figure 8a Figure 13 is a scarp-isolated platform depositional model;

[0038] Figure 8b Figure 14 is a reservoir prediction profile before and after the low frequency model control in the second embodiment of the present application;

[0039] Figure 9 Figure 15 is a structural schematic diagram of a multi-information constrained reservoir low frequency model establishment device in the embodiment of the present application. DETAILED DESCRIPTION

[0040] Exemplary embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0041] It should be understood that the terms used in the present application are merely used to describe particular embodiments and are not intended to limit the present application. In addition, for numerical ranges in the present application, it should be understood that each intermediate value between the upper limit and the lower limit of the range is specifically disclosed. Each smaller range within any stated range or within any stated intermediate value and any other stated range or intermediate value is also included in the present application. The upper limit and the lower limit of these smaller ranges can be included or excluded independently.

[0042] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0043] This invention provides a method and apparatus for establishing a low-frequency reservoir model with multiple information constraints. The method is a step-by-step, multi-information-constrained low-frequency model establishment method, which is particularly suitable for carbonate rock research areas under irregular well network conditions at sea.

[0044] Example 1

[0045] Embodiment 1 of the present invention provides a method for establishing a low-frequency reservoir model with multiple information constraints, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0046] Step S11: Combine fault interpretation data and stratigraphic interpretation data of the study area to establish a structural framework model of the target layer.

[0047] A geological structural framework is the foundation for building a low-frequency model. First, based on the study area, seismic grids, stratigraphic interpretation data, and fault interpretation data are imported. Then, based on the fault interpretation data of the target layer in the study area, fault relationships are analyzed and edited to ensure reasonable intersection relationships, and a fault model is established. Next, by interpolating the stratigraphic interpretation data, bedding plane models are built layer by layer, and the fault models are used to cut the bedding planes in the bedding plane models, resulting in a structural framework model that reflects the structure of the target layer.

[0048] Step S12: For each phase zone of the target layer, through multi-attribute well-seismic joint analysis, the attributes whose correlation with the elastic parameters meets the set conditions are screened out, and the regression equation between the elastic parameters and the attributes is established.

[0049] The establishment of regression equations between elastic parameters and properties of different phase zones mainly provides a basis for the establishment of very low frequency models for the first region in the target layer where the well density does not reach the set density threshold, i.e., the region with no or few wells.

[0050] Although the well-free / well-scarce area is difficult to obtain the accurate medium-low frequency information of sensitive elastic parameters due to the lack of drilling and logging data, the very low frequency information sources are relatively more. And through the interactive verification of various methods and approaches, the estimated very low frequency trend can achieve high reliability. First, starting from the actual drilling wells in the adjacent area, the petrophysical relationship between different lithofacies / reservoir facies is analyzed from the aspects of geology and logging, providing conceptual model guidance and data trend support for well-free areas. The sensitive elastic parameters of certain facies have good correlation with seismic velocity; while the correlation between the sensitive elastic parameters of certain facies and seismic velocity is not good due to sedimentation, but they have good correlation with structure. According to this, the attributes with the best correlation between different facies and sensitive elastic parameters are selected, and then the regression equation between them is fitted. Based on the existing attributes, the very low frequency trend of sensitive elastic parameters is calculated through the regression equation. If further verification of its reliability is needed, the method of interactive verification of multiple parameters can be used to check it.

[0051] Step S13: In the first area where the well density in the target layer does not reach the set density threshold, based on the distribution data of the attributes, the very low frequency model of the elastic parameters is established in different facies using the corresponding regression equation with the control of the structural framework model.

[0052] The very low frequency (or trend) model of the first area, i.e. the well-free / well-scarce area, is established in different facies with the range of each facies as the boundary.

[0053] Step S14: In the second area where the well density in the target layer reaches the set density threshold, the low frequency model of the elastic parameters is established through well interpolation with the control of the structural framework model and the basis of the measured curve of the elastic parameters.

[0054] The logging curve is the most reliable low frequency information source, so the low frequency trend of the well-controlled area (the second area) is retained, and the low frequency model of the well-free / well-scarce area (the first area) is supplemented. First, the three-dimensional low frequency model of the elastic parameters is established through well interpolation with the control of the structural framework model established in step S11 and the basis of the measured elastic curve.

[0055] Step S15: The low frequency model is established by integrating the very low frequency model and the low frequency model.

[0056] For the area with high well control, the range of the facies it belongs to is taken as the boundary, and its own low frequency trend is retained. For the well-free / well-scarce area, the very low frequency (or trend) model of the respective facies is introduced with the range of the facies it belongs to as the boundary. Finally, the unified low frequency model is established by integrating the very low frequency model and the low frequency model through stratigraphic system and facies.

[0057] The method for establishing a multi-information constrained reservoir low-frequency model provided by the embodiment one of the present application comprises the following steps: a structural framework model is built according to a structural interpretation result; through multi-attribute well-seismic joint analysis, an attribute having the best correlation with the elastic parameters of the target facies belt is selected, and a regression equation of the two is fitted; a very low frequency model of a no-well / less-well area is established based on the structural framework model, with different facies belt ranges as boundaries and the regression equation as a basis; and finally, the very low frequency model and a low frequency model interpolated from wells in a multi-well area are fused to obtain a final objective three-dimensional low frequency model. The method is a step-by-step multi-information constrained low frequency model establishing method, which is strong in pertinence, objective in means and scientific in process, and has important practical significance in the reservoir prediction of carbonate reservoirs under the condition of irregular well patterns on the sea.

[0058] In some embodiments, the division of facies belts, i.e. facies belt distribution data of the target layer, can be determined in the following manner:

[0059] According to the selected seismic attribute, in combination with the logging data, the facies belt distribution data of the target layer is determined, and the selected seismic attribute at least includes a seismic velocity related attribute, such as a seismic velocity variation rate attribute.

[0060] A formation with simple lithological combination often shows a single linear relationship of the related attribute. Under this kind of geological condition, the range of different facies belts can often be determined through a certain attribute, or even directly as a spatial trend to constrain well interpolation to establish a low frequency model. However, for carbonate reservoirs, the lithology is complex, and sandstone, mudstone and limestone are often developed. The rock physics relationship of different lithological reservoirs is diversified, and the correlation and spatial trend of various attributes often show zonation. In addition, due to the lack of drilling information support in the no-well / less-well area, the implementation of facies belts can guide the range and trend of the related attributes. Therefore, the identification of the boundary of the overall facies belt is needed first. Seismic velocity is the preferred characteristic information reflecting the lithology or reservoir facies of the formation, and the boundary of lithology change often appears obvious change of velocity, which can better reflect this change with the aid of velocity variation rate and other attributes to indicate the boundary of facies belt. In addition, through the method of seismic sedimentology analysis, guided by rock physics rules and seismic reflection characteristics, the extraction of prestack and poststack attributes such as seismic intercept and gradient of different layers through stratigraphic slicing can also provide a good indication for the identification of facies belt boundary. Finally, through the joint method of seismic velocity and seismic attribute, the distribution range of different facies belts can be implemented.

[0061] In addition, it should be noted that for the physical property deviation or lithology combination although there is a certain change, but not enough to affect the geological conditions of reservoir classification, the identification of seismic velocity to lithofacies / reservoir facies boundary may not be implemented. At this time, according to the rock physics law, forward research of the earthquake is carried out, and the seismic reflection characteristics of the target facies zone and the prestack-poststack seismic attribute which has an indication to the sedimentary facies zone are further determined; combined with the distribution data of prestack-poststack seismic attribute and logging data, more accurate prestack-poststack attribute joint identification facies zone range is carried out, and the facies zone distribution data of the target layer is determined.

[0062] Referring to Figure 2 , a complete facies zone range identification technical flow chart is shown.

[0063] Embodiment two

[0064] Embodiment two of the present application provides a specific application of a multi-information constrained carbonate reservoir low-frequency model establishing method, and xx oilfield in xx basin is selected as an example. The xx oilfield is a larger deepwater oilfield, and the water depth is about 2100m. The main target layer is the lower Cretaceous microbial limestone and shell limestone. Regional sedimentary research shows that the xx oilfield belongs to carbonate platform sedimentary facies type. At present, drilling is mainly concentrated in the platform margin, platform and other facies zones, and there is almost no drilling in the platform outside area. Therefore, in the reservoir prediction work, the judgment and constraint of the platform outside reservoir have always lacked scientific and reasonable support.

[0065] Firstly, a structural framework model covering the range of the research area is built according to the structural interpretation results; then the correlation analysis of seismic velocity-seismic attribute and lithofacies / reservoir facies zone is carried out to determine the distribution range of different facies zones; through multi-attribute well-seismic joint analysis, the attribute with the best correlation with the characteristic attribute of the target facies zone is selected, and the regression equation of the two is fitted; then based on the structural framework model, controlled by the facies zone range, and according to the regression equation, a very low frequency three-dimensional model is established; finally, the very low frequency model and the low frequency model interpolated through the well are fused to obtain the final three-dimensional low frequency model. According to this idea, the corresponding technical route is shown in Figure 3 . The main steps of this embodiment are as follows:

[0066] 1. Build a structural framework model.

[0067] The main geological horizons in the research area are BVE100 top boundary, BVE300 top boundary, ITP100 top boundary and PIC top boundary. Figure 4 is a seismic interpretation profile schematic diagram. Through the seismic profile, it can be seen that the well-seismic consistency is good, the existing fault interpretation is reasonable, and the horizon tracking is accurate. Therefore, a geological framework model is built based on this set of structural interpretation results, as shown in Figure 5 .

[0068] 2. Determine the range of different facies zones.

[0069] Firstly, the distribution range of different facies zones was identified through seismic layer velocities. The seismic layer velocities in the BVE segment exhibit a characteristic of high velocities within the platform zone and low velocities outside the platform zone. Figure 6a Since the outer zone is located in a low-lying structural region, and considering the trends and amplitudes of seismic velocity changes, the influence of physical properties and gas content can be ruled out. Analysis of well logging and well logging curves revealed that an increase in clay content significantly reduces velocity. Figure 6b The figure shows, from left to right, natural gamma, clay content, velocity, and effective porosity. Comprehensive geological analysis indicates that the decrease in velocity can only be caused by a significant increase in clay content, indicating a marked change in lithology. Simultaneously, the increase in clay content also deteriorates reservoir properties, meaning it is unlikely to become a high-quality reservoir. Therefore, comprehensive analysis suggests that changes in seismic velocity can be used as one of the factors for facies zone identification. To make changes in seismic velocity more prominent, the velocity change rate is calculated to be more sensitive to the indication of facies transition location: that is, the velocity change is smaller in relatively homogeneous lithological areas within the platform, while the velocity change rate is more pronounced in areas with greater lithological changes outside the platform. Figure 6c Simultaneously, using seismic sedimentology methods, seismic intercepts and gradient properties were extracted through stratigraphic slices, which clearly indicated the presence of phasing transition boundaries, and the locations were largely consistent with the seismic velocity indications. Figure 7d Ultimately, the range of different lithofacies / reservoir facies zones was determined through mutual verification between the two.

[0070] In summary, by combining seismic layer velocity, seismic intercept, and gradient attributes from the seismic velocity-related properties, a reasonable identification of phase bands was achieved.

[0071] 3. Establish a very low frequency (trend) model for well-free areas.

[0072] Although seismic velocity can identify the subfacies of sedimentary facies, the petrological patterns between different facies zones exhibit different trends. The P-wave to S-wave velocity ratio of carbonate rocks in the platform margin and intraplatform zone is positively correlated with seismic velocity. Figure 7a However, in the high-muddy-content strata outside the platform, the P-wave / S-wave velocity ratio is negatively correlated with seismic velocity; therefore, seismic velocity cannot be directly applied to the establishment of a low-frequency model for the entire region. Through various well logging data and geological parameter surveys, the study found a significant correlation between the P-wave / S-wave velocity ratio and the geological structure. Figure 7b This is partly due to the gradual decrease in water depth from bottom to top in the target strata of the study area, resulting in a lower proportion of mud in the strata; and partly due to the higher mud content in the lower sedimentary layers during the deposition of carbonate platforms. These two factors combined lead to an increase in the P-wave and S-wave velocity ratio. Finally, a conversion formula was established by regressing the relationship between the P-wave and S-wave velocity ratio and the tectonic height. Then, using the previously identified facies zone as the boundary, a very low frequency (trend) model of the P-wave and S-wave velocity ratio was established for the stratified system.Figure 7c and Figure 7d ).

[0073] 4. Low frequency model fusion.

[0074] Firstly, based on the structural framework model, the measured P-S wave velocity ratio is taken as the data source, and the full-area well control P-S wave velocity ratio low frequency model is established by interpolation and extrapolation. For the platform margin and platform inner facies belt with high well control degree, no intervention is made, and the original low frequency trend is retained. For the platform outer belt without well or with few wells, the facies belt boundary is controlled, the very low frequency information generated in the previous step is fused layer by layer, and finally the low frequency model with reliable well area and controllable no-well area is obtained. Based on the low frequency model, the seismic inversion work is carried out, and more geological mode and more scientific and reasonable reservoir prediction results can be obtained. As shown in Fig. 1, it is a steep cliff isolated platform sedimentation mode diagram (from Jin Zhenqi, Carbonate Sedimentary Facies and Facies Mode, 2013), Figure 8a Figure 8b Fig. 1 is a steep cliff isolated platform sedimentation mode diagram, and Fig. 2 is a reservoir prediction profile before low frequency model constraint, a low frequency model profile and a reservoir prediction profile after low frequency model constraint from top to bottom, respectively. It can be seen that in the inversion result lacking low frequency information of no-well area, a large number of reservoirs are developed in the lower part of the structure, which is seriously contrary to the geological mode, and after the low frequency trend is corrected by the low frequency model established by the present application, the reservoir prediction result is more in line with the geological law.

[0075] Seven new wells are added in the research area subsequently, and blind well error analysis is carried out accordingly. According to statistics, the average relative error of the reservoir prediction thickness of each well main layer to the actual drilling thickness is 10%, and the prediction accuracy meets the exploration and development needs of the research area (Table 1).

[0076] Table 1 is a reservoir prediction thickness error statistical table of each layer of the new well.

[0077]

[0078] Based on the inventive concept of the present application, the present application further provides a reservoir low frequency model establishing device with multiple information constraints, and the structure of the device is shown in Fig. 9, which comprises: Figure 9

[0079] A structural framework model establishing module 91 is used for combining fault interpretation data and horizon interpretation data of the research area to establish a structural framework model of the target layer.

[0080] A regression equation establishing module 92 is used for screening attributes related to elastic parameters and meeting set conditions through multi-attribute well-seismic joint analysis for each facies belt of the target layer, and establishing a regression equation between the elastic parameters and the attributes.

[0081] ​​The very low frequency model establishing module 93 is configured to, in a first area in the target layer where the well density fails to reach a set density threshold, establish a very low frequency model of the elastic parameter in the facies belt based on the distribution data of the attribute and using the tectonic framework model as a control;

[0082] The low frequency model establishing module 94 is configured to, in a second area in the target layer where the well density reaches the set density threshold, establish a low frequency model of the elastic parameter by well interpolation based on the measured curve of the elastic parameter and using the tectonic framework model as a control;

[0083] The model fusion module 95 is configured to fuse the very low frequency model and the low frequency model to establish a low frequency model.

[0084] In some embodiments, the tectonic framework model establishing module 91 is configured to establish a tectonic framework model of the target layer by combining the fault interpretation data and the horizon interpretation data of the study area, and is configured to:

[0085] establish a fault model according to the fault interpretation data of the target layer of the study area; build a horizon model layer by layer by interpolation of the horizon interpretation data; and cut the horizons in the horizon model using the fault model to obtain the tectonic framework model of the target layer.

[0086] In some embodiments, the device further comprises a facies belt identification module 96 configured to determine the facies belt distribution data of the target layer by:

[0087] determining the facies belt distribution data of the target layer according to selected seismic attributes in combination with well logging data, wherein the selected seismic attributes include seismic velocity related attributes.

[0088] In some embodiments, the facies belt identification module 96 is further configured to determine the facies belt distribution data of the target layer by:

[0089] carrying out seismic forward research according to rock physics rules to determine the seismic reflection characteristics of the target facies belt and the pre-stack-post-stack seismic attributes that have an indicative effect on the sedimentary facies belt; and determining the facies belt distribution data of the target layer in combination with the distribution data of the pre-stack-post-stack seismic attributes and well logging data.

[0090] In some embodiments, the model fusion module 95 is configured to fuse the very low frequency model and the low frequency model to establish a low frequency model, and is configured to:

[0091] fuse the very low frequency model and the low frequency model by layer and facies belt to establish a low frequency model.

[0092] With regard to the apparatus in the above-described embodiments, in which the specific manner in which various means perform operations has been described in detail in the embodiments relating to the method, no detailed elaboration will be given here.

[0093] Based on the inventive concept of the present application, the embodiments of the present application further provide a computer storage medium, in which computer executable instructions are stored, and the computer executable instructions are executed by a processor to implement the method for establishing a low-frequency reservoir model with multiple information constraints.

[0094] Based on the inventive concept of the present application, the embodiments of the present application further provide a server, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method for establishing a low-frequency reservoir model with multiple information constraints when executing the program.

[0095] Unless specifically stated otherwise, terms such as processing, computing, calculating, determining, displaying, and the like, can refer to an action and / or process of one or more processing or computing systems, or similar devices, that manipulate and / or transform data represented as physical (e.g., electronic) quantities within the processing system's registers and / or memories into other data similarly represented as physical quantities within the processing system's memories, registers or other such information storage, transmission or display devices. Information and signals can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0096] It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example. Based upon design preferences, it should be understood that the specific order or hierarchy of steps in the processes can be re-arranged while remaining within the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and as such the order is merely an example and not intended to reflect an order in which the various steps can be actually performed.

[0097] In the above detailed description, various features are grouped together in single embodiments for the purpose of streamlining the disclosure. This disclosed approach is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are explicitly recited in each claim. Rather, as the claims below reflect, inventive subject matter can lie in fewer than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate preferred embodiment of the present application.

[0098] Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0099] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0100] For a software implementation, the techniques described in this application can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes can be stored in memory units and executed by processors. The memory unit can be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.

[0101] The above description includes one or more examples of the embodiments. Of course, not all possible combinations of components or method steps described above can be claimed as embodiments. One of ordinary skill in the art can recognize that modifications and variations of the described embodiments are possible and are within the scope of the present disclosure. It is therefore intended that the embodiments described herein be considered in all respects as only illustrative and not restrictive. Specifically, the description of the embodiments should be considered to be illustrative and not exhaustive, and should be considered in the light of the claims. Further, the use of the term "comprise" in the specification is to be construed to be analogous to the term "include". Further, the use of the term "or" in any claim is to be construed as "non-exclusive or". The terms "first", "second" are used only for descriptive purposes and are not to be construed as indicating or implying relative importance.

Claims

1. A method for establishing a multi-information constrained reservoir low-frequency model, characterized in that, The method comprises the following steps: establishing a structural framework model of the target layer by combining fault interpretation data and horizon interpretation data of a study area; for each facies belt of the target layer, screening out attributes related to elastic parameters that meet a set condition by multi-attribute well-seismic joint analysis, and establishing a regression equation between the elastic parameters and the attributes; in a first region of the target layer where well density does not reach a set density threshold, establishing a very low frequency model of the elastic parameters by using corresponding regression equations in different facies belts based on distribution data of the attributes and taking the structural framework model as a control; in a second region of the target layer where well density reaches the set density threshold, establishing a low frequency model of the elastic parameters by well interpolation based on measured curves of the elastic parameters and taking the structural framework model as a control; establishing a low frequency model by integrating the very low frequency model and the low frequency model.

2. The method of claim 1, wherein, The method comprises the following steps: establishing a fault model according to fault interpretation data of the target layer of the study area; building a layer model layer by layer by horizon interpretation data interpolation; cutting layers in the layer model by using the fault model to obtain a structural framework model of the target layer.

3. The method of claim 1, wherein, The method further comprises determining facies belt distribution data of the target layer in the following manner: determining the facies belt distribution data of the target layer according to selected seismic attributes and in combination with logging data, wherein the selected seismic attributes include seismic velocity-related attributes.

4. The method of claim 3, wherein, The target layer is a carbonate reservoir, and the selected seismic attributes further include seismic intercept and gradient.

5. The method of claim 1, wherein, The method further comprises determining facies belt distribution data of the target layer in the following manner: determining seismic reflection characteristics of the target facies belt and prestack-poststack seismic attributes that have an indicating effect on sedimentary facies belts according to rock physics laws and by developing seismic forward research; determining the facies belt distribution data of the target layer in combination with distribution data of the prestack-poststack seismic attributes and logging data.

6. The method of claim 1, wherein, The target layer is a carbonate reservoir, and the method of screening out attributes related to elastic parameters that meet a set condition comprises the following steps: screening out attributes related to a ratio of P-wave velocity to S-wave velocity as structural height.

7. The method according to any one of claims 1 to 6, characterized in that, The method of integrating the very low frequency model and the low frequency model to establish a low frequency model comprises the following steps: integrating the very low frequency model and the low frequency model by layer system and facies belt to establish a low frequency model.

8. A device for establishing a multi-information constrained reservoir low-frequency model, characterized in that, The method comprises the following steps: a structural framework model establishment module is configured to establish a structural framework model of a target layer by combining fault interpretation data and horizon interpretation data of a study area; an elastic parameter-attribute regression equation establishment module is configured to screen out attributes related to elastic parameters that meet a set condition by multi-attribute well-seismic joint analysis for each facies belt of the target layer, and establish a regression equation between the elastic parameters and the attributes; a very low frequency model establishment module is configured to establish a very low frequency model of the elastic parameters in a first region of the target layer where well density does not reach a set density threshold by using corresponding regression equations in different facies belts based on distribution data of the attributes and taking the structural framework model as a control; The low-frequency model establishing module is configured to, in a second region where the well density in the target layer reaches a set density threshold, establish a low-frequency model of the elastic parameter by well interpolation, with the tectonic framework model as a control and the measured curve of the elastic parameter as a basis; The model fusion module is configured to integrate the very low-frequency model and the low-frequency model to establish a low-frequency model.

9. A computer storage medium, characterized in that The computer storage medium stores computer executable instructions, and the computer executable instructions are executed by the processor to implement the multi-information constrained reservoir low-frequency model establishing method in any one of claims 1-7.

10. A server, characterized by The computer storage medium stores computer executable instructions, and the computer executable instructions are executed by the processor to implement the multi-information constrained reservoir low-frequency model establishing method in any one of claims 1-7. The computer storage medium stores computer executable instructions, and the computer executable instructions are executed by the processor to implement the multi-information constrained reservoir low-frequency model establishing method in any one of claims 1-7.

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