Modeling method and device for tight gas reservoir river sandstone geology

Abstract

The invention discloses a tight gas reservoir river sandstone geology modeling method and device, and the method comprises the steps: firstly building a seismic interpretation speed model through logging data and seismic data, and converting a seismic interpretation result and inversion data from a time domain to a depth domain; the method comprises the steps that firstly, a stratigraphic structure model of a river channel sand body is established on the basis of accurate stratigraphic and sand layer structure data, then under the control of the structure model, lithofacies are subjected to phase conversion through depth domain sensitive seismic data to obtain a seismic inversion lithofacies body, and then lithofacies simulation is carried out on the basis of ground lithofacies division data; attribute values of well point calculation are used as basic data in the attribute model, seismic inversion attribute data are used as trends to carry out coKriging simulation calculation, and porosity, permeability and saturation models under different phase types are established. The model constructed by the method can well guide well position deployment, and meanwhile, a high-quality geologic model is provided for gas reservoir numerical simulation.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional geological modeling, and more specifically to a modeling method and device for channel sandstone geology in tight gas reservoirs. Background Art

[0002] Reservoir geological modeling is a very important task in aspects such as oil and gas field exploration and development and fine description of oil and gas reservoirs. According to the working stage and type, it mainly includes structural modeling (horizons, faults, etc.), facies modeling (lithofacies, sedimentary facies, etc.) and property modeling (porosity, permeability, saturation), etc. In actual work, there are generally two ways to model channels: ① Only model the reservoir, that is, all outside the channel is set as empty grids, and the model boundary directly adopts a complex channel boundary. Its advantages are fewer grid numbers, intuitive model and easy to understand, and simple reserve fitting process. The disadvantage is that the scalability of the model is small. Once low-quality reservoirs are found outside the channel in the later stage, it is difficult to expand the model; ② The way of jointly modeling the reservoir and non-reservoir, that is, jointly model the non-reservoir between channels. Its advantages are simple boundary, strong model scalability, and the ability to modify the whole model in a certain attribute way. The disadvantage is more grid numbers and larger calculation amount.

[0003] For the channel sand bodies in tight gas reservoirs, the lithofacies modeling adopts a method similar to other lithofacies types: the structural modeling is bounded by the top and bottom of the formation where the channel sand is located. There are two ways to depict the channels in the model: ① It is embodied in the form mainly based on facies modeling, that is, the formation section contains facies such as channel sand and inter-channel mudstone, and the distribution of channel sand is reflected by facies modeling (both deterministic and stochastic modeling methods are available); ② It is embodied in the form mainly based on structural modeling, that is, the characterization of the sand body adopts the description results of the top and bottom surfaces of the sand body structure of the channel sand body, and then deterministic modeling or stochastic modeling is carried out on the restricted part of the channel sand body. The former is suitable for areas with relatively low accuracy of channel description, and often focuses more on the trend of sand body distribution, etc.; the latter is suitable for areas with relatively high accuracy of channel description, and pays attention to the specific range of sand body distribution, the thickness of the sand body and the guiding role of the model for on-site deployment.

[0004] For example, the invention patent with the publication number of CN116579047A discloses a fine carving method for the spatial distribution of channels and sand bodies in tight gas reservoirs based on the integration of human-computer interaction and seismic attributes. In this patent, due to the problem that the establishment of the structural model does not consider the control of the horizontal well trajectory passing through the model, the established model cannot well reflect the true geological situation of the designed well. Summary of the Invention

[0005] In view of the problems and deficiencies existing in the current modeling process of channel sandstones in tight gas reservoirs, the present invention proposes a modeling method and device for the geology of channel sandstones in tight gas reservoirs, which uses high-quality seismic data to accurately depict the boundaries and longitudinal thicknesses of channel sand bodies, and based on this, accurately characterizes the spatial distribution characteristics of sand bodies and their physical properties, guides well location deployment, and at the same time provides a high-quality geological model for gas reservoir numerical simulation.

[0006] In order to achieve the above-mentioned invention purposes, the technical solution of the present invention is as follows:

[0007] A modeling method for the geology of channel sandstones in tight gas reservoirs, comprising the following steps:

[0008] Step 1. Seismic interpretation velocity model: Establish a seismic interpretation velocity model based on well logging data and seismic data, and use the seismic interpretation velocity model to convert seismic interpretation results and inversion data from the time domain to the depth domain;

[0009] Step 2. Extended structural surfaces and framework models for the top and bottom of sandstones: Use the seismic fault and horizon interpretation results and single-well sub-layer division results after conversion by the accurate velocity model, and combine horizontal well trajectory data and sand layer data to establish extended structural surfaces and framework models for the top and bottom of sandstones;

[0010] Step 3. Facies model: According to the lithology single-well interpretation results, conduct geostatistical data analysis of facies data for each framework unit, and establish a channel sand body lithofacies model under the constraints of the extended structural surfaces and framework models for the top and bottom of sandstones;

[0011] Step 4. Property model: Under the control of the channel sand body lithofacies model, based on single-well well logging interpretation results, and using the seismic inversion data results after conversion by the seismic interpretation velocity model as co-Kriging simulation variables, establish porosity, permeability, and saturation models for different facies types respectively;

[0012] Step 5. Model update: Use the obtained facies model and property model as the trend constraint data volume for updating the model, perform update and coarsening on the newly added well data while keeping the original coarsened data unchanged, and on this basis, use the sequential indicator simulation algorithm and sequential Gaussian simulation algorithm to update the facies model and property model.

[0013] Preferably, in the present invention, the sand layer data includes the structural interpretation of the top and bottom horizons of the formation where the channel sand body is located and the structural interpretation data of the top and bottom horizons of the sand layer.

[0014] Preferably, in the present invention, the well data includes well logging curves.

[0015] Preferably, in the present invention, the logging curves include lithofacies curves, porosity curves, permeability logging curves, and water saturation logging curves. The water saturation logging curve can also be replaced with the converted gas saturation logging interpretation curve data.

[0016] A modeling device for the geology of channel sandstone in a tight gas reservoir, which is used to implement the above-mentioned modeling method for the geology of channel sandstone in a tight gas reservoir, includes:

[0017] A seismic interpretation velocity model establishment module, which establishes a seismic interpretation velocity model based on logging data and seismic data, and uses the seismic interpretation velocity model to convert seismic interpretation results and inversion data from the time domain to the depth domain;

[0018] A sandstone top and bottom extended structural surface and framework model establishment module, which uses the seismic fault, bedding surface interpretation results and single-well sub-layer division results after conversion by the accurate velocity model, combines the horizontal well trajectory data and sand layer data, and establishes a sandstone top and bottom extended structural surface and framework model;

[0019] A facies model establishment module, which conducts geostatistical data analysis of facies data for each simulation unit according to the lithology single-well interpretation results, and establishes a channel sand body lithofacies model under the constraint of the sandstone top and bottom extended structural surface and framework model;

[0020] An attribute model establishment module, under the control of the channel sand body lithofacies model, based on the single-well logging interpretation results, and using the seismic inversion data results converted by the seismic interpretation velocity model as co-Kriging simulation variables, respectively establishes porosity, permeability, and saturation models under different facies types.

[0021] A computer device, including a memory, a processor, and a computer program stored on the memory and executable in the processor. When the processor executes the computer program, it implements the steps of the above-mentioned modeling method for the geology of channel sandstone in a tight gas reservoir.

[0022] A computer-readable storage medium, which stores a computer program. When the computer program is executed in a computer processor, it implements the steps of the above-mentioned modeling method for the geology of channel sandstone in a tight gas reservoir.

[0023] The beneficial effects of the present invention:

[0024] 1. For the model constructed by the method of the present invention, a large number of horizontal well trajectories in the study area match correctly with the top and bottom of the formation and sand layer where they are located, and the established model can well reflect the true geological situation of the designed wells. Further, the established model can be used to better evaluate the geological conditions, drillability, etc. of newly deployed wells, and realize well location deployment, etc.

[0025] 2. The phase model and property model established by the present invention can, on the one hand, maintain the trend of seismic data, and on the other hand, make the well points consistent with the well data, and finally can establish a relatively accurate model representing the reservoir.

[0026] 3. The present invention conducts reserve calculation on the basis of a geological model, guides the demonstration of evaluation wells and development wells based on the plane, profile and three-dimensional analysis of the model, and can carry out work such as reservoir heterogeneity evaluation based on the geological model during the later production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The foregoing and following specific descriptions of the present invention will become clearer when read in conjunction with the following drawings, in which:

[0028] Figure 1 is the flowchart of the method of the present invention;

[0029] Figure 2 is the structural composition diagram of the device of the present invention;

[0030] Figure 3 is the spatial relationship of common horizons in the structural model of the horizontal well area of channel sandstone in a tight gas reservoir;

[0031] Figure 4 is a schematic diagram of the layer-sand layer relationship of the top and bottom extension structural surfaces of sandstone in the structural model;

[0032] Figure 5 is the location of the jq511 platform and the top and bottom structure diagrams of the J 2 s 2 1 sub-member in Example 2 of the present invention;

[0033] Figure 6 is the J in the jq511 platform in Example 2 of the present invention 2 s 2 1 top and bottom structure and thickness diagrams of the No. 6 sand group of sandstone in the sub-member;

[0034] Figure 7 is the J in the jq511 platform in Example 2 of the present invention 2 s 2 1 top and bottom structure and thickness diagrams of the sandstone extending to the well area of the platform of the No. 6 sand group in the sub-member;

[0035] Figure 8 is the cross-section of the structural framework model of jq511-6-H1 and jq511-6-H3 passing through the jq511 platform in Example 2 of the present invention;

[0036] Figure 9For the inversion of Vp / Vs profiles, lithofacies, and lithofacies model profiles of jq511-6-H1 and jq511-6-H3 across the jq511 platform in Embodiment 2 of the present invention;

[0037] Figure 10 For the inversion of porosity profiles and porosity model profiles of jq511-6-H1 and jq511-6-H3 across the jq511 platform in Embodiment 2 of the present invention;

[0038] Figure 11 For the three-dimensional models of the tectonic framework, sand bodies, and porosity properties of the jq511 platform in Embodiment 2 of the present invention. Detailed implementation manners

[0039] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will further illustrate the technical solutions for achieving the invention purpose of the present invention through several specific embodiments. It should be noted that the technical solutions claimed by the present invention include but are not limited to the following embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] The following will further illustrate the technical solutions for achieving the invention purpose of the present invention through specific embodiments. It should be noted that the technical solutions claimed by the present invention include but are not limited to the following embodiments.

[0041] Embodiment 1

[0042] The particularity and difficulties of geological modeling of channel sand bodies in tight sandstone gas reservoirs mainly lie in: ① In the structural modeling stage, a large number of horizontal well trajectories need to be processed, making the structural plane coordinate with the well trajectories, and the accuracy requirements for the structural plane are extremely high; ② In terms of facies modeling and property modeling, taking the Sichuan Basin as an example, the tight gas reservoirs are generally buried relatively shallow, so the quality of seismic data is generally high and the reservoir prediction accuracy is high. When using seismic data to constrain geological modeling, there are relatively high requirements for the degree of conformity between facies modeling, property modeling, and the trend of the original seismic data. Therefore, the present invention mainly focuses on the structural modeling in the geological modeling of channel sand bodies in tight sandstone gas reservoirs, and at the same time proposes an effective method suitable for facies modeling and property modeling of such reservoirs.

[0043] In actual work, there are generally two ways to model river channels: ① Only model the reservoir, that is, set all areas outside the river channels as empty grids, and directly adopt complex river channel boundaries for the model boundaries. Its advantages are fewer grid numbers, intuitive and easy-to-understand model, and simple reserve fitting process. The disadvantage is that the scalability of the model is small. Once low-quality reservoirs are found outside the river channels in the later stage, it is difficult to expand the model; ② The way of jointly modeling the reservoir and non-reservoir, that is, jointly model the non-reservoir between river channels. Its advantages are simple boundaries, strong model scalability, and the ability to modify the entire model in the form of a certain attribute. The disadvantage is more grid numbers and larger computational volume. Based on the nature of the comprehensive research area, the present invention discusses based on the latter.

[0044] For the river channel sand bodies in tight gas reservoirs, the lithofacies modeling adopts a method similar to other lithofacies types: the structural modeling is bounded by the top and bottom of the formation where the river channel sand is located. There are two ways to depict the river channels in the model: ① It is embodied in the form mainly based on facies modeling (taking lithofacies as an example in the present invention), that is, the formation section contains facies such as river channel sand and inter-channel mudstone, and the distribution of river channel sand is reflected in the way of facies modeling (both deterministic and stochastic modeling methods are applicable); ② It is embodied in the form mainly based on structural modeling. That is, the characterization of the sand body adopts the description results of the top and bottom surfaces of the sand body structure of the river channel sand body, and then deterministic modeling or stochastic modeling is carried out on the restricted part of the river channel sand body. The former is suitable for areas with relatively low accuracy of river channel description, and often focuses more on the trend of sand body distribution, etc.; the latter is suitable for areas with relatively high accuracy of river channel description, and pays attention to the specific range of sand body distribution, the thickness of the sand body, and the guiding role of the model for on-site deployment. The present invention adopts the first method to implement, that is, to implement it in the way of facies modeling, so as to ensure that the top and bottom surfaces of the sand body are only the most probable manifestation forms of the sand body data, reflecting a trend, rather than seeking that the sandstone top and bottom structural surfaces must exactly coincide with the sandstone lithofacies data, which is more in line with the practices and realities of engineering on-site applications.

[0045] The embodiment of the present invention provides a method and device for modeling the geology of river channel sandstone in tight gas reservoirs, specifically a method and device for comprehensively establishing a geology model of river channel sandstone in tight gas reservoirs based on rich geological basic data, seismic interpretation results and well logging interpretation results during the rolling evaluation stage in the exploration and development process. This method is applicable but not limited to the geology modeling of river channel sandstone in tight gas reservoirs found in places such as the Sichuan Basin. The particularity and difficulty of river channel sand bodies in tight sandstone gas reservoirs are that: river channel sand bodies are generally narrow and mostly strip-shaped on the plane. Therefore, for such geological models to guide well drilling deployment, the accuracy requirements for predicting the distribution range of river channels on the plane and the thickness of river channels longitudinally are extremely high. The characteristics of the present invention lie in applying high-quality seismic data to accurately depict the boundaries and longitudinal thicknesses of river channel sand bodies, and based on this, accurately characterize the spatial distribution characteristics of sand bodies and their physical properties, guide well location deployment, and at the same time provide a high-quality geological model for gas reservoir numerical simulation.

[0046] The technical solution adopted by the present invention is as follows: By applying the velocity trend analysis of the velocity spectrum formed in the seismic interpretation stage and combining with the wellbore synthetic record velocity data, a seismic interpretation velocity model is established. The seismic interpretation results and inversion data (Vp / Vs, porosity, permeability, saturation, etc.) in the time domain are converted into those in the depth domain, so that a large number of horizontal well trajectories in the study area are correctly matched with the top and bottom of the formation and sand layer where they are located, as well as the top and bottom relationships of the sand layers. Based on the accurate formation and sand layer structure data, a formation structure model of the channel sand body is established; under the control of the structure model framework, the lithofacies is converted by using depth-domain sensitive seismic data (such as the P-wave to S-wave velocity ratio) to obtain the seismic inversion lithofacies body, and then lithofacies simulation is carried out based on the wellbore lithofacies division data. Taking the porosity model as an example in the attribute model, there is generally a corresponding seismic inversion attribute body, but its resolution and the number of wells participating in the calculation often need to be further enhanced in the geological model. Based on the attribute values calculated at the well points as the basic data, the seismic inversion attribute data is used as the trend to perform co-kriging simulation calculation. The realization process of the tight gas reservoir channel sandstone is as follows:

[0047] (1) Establishment and domain conversion of the velocity model considering the main formation framework

[0048] In this step, well logging data and seismic data are used to establish a seismic interpretation velocity model, and then through this model, the seismic interpretation results and inversion data are converted from the time domain to the depth domain. For a small error in the depth domain, the multiple iteration method can be used to gradually approach it. The final seismic interpretation results and inversion data in the depth domain should achieve the following final effect: The geological stratification is coordinated with the seismic horizons, especially the corresponding relationship between the well trajectory and the sand body is correct (in some cases, there are stacked sand bodies, and it is necessary to determine the specific sand body drilled by the well from well logging, mud logging and production data and correctly represent it in the model).

[0049] During the modeling process, the horizontal well trajectory is an objectively existing object, and the formation horizon is also an objectively existing object. However, during the establishment of the geological model, the horizontal well trajectory is deterministic and basically there will be no uncertainty problems under the premise of accurate data collection, while the formation horizon is mainly completed through seismic interpretation and well point correction, and then through interpolation or simulation in the model. A great deal of uncertainty often occurs in this series of processes for the formation horizon. Referring to the attached drawings of the specification Figure 3 as shown, the actual drilling situation is as Figure 3 shown in a. The well trajectory normally enters the target sand body, but during the representation process of the geological model, situations such as a, b, c, and d will occur. Obviously, Figure 3Among b, 3c, and 3d, due to problems such as geological stratification, deviation in seismic interpretation horizon identification, and migration (stacking) imaging velocity in the seismic processing stage, which lead to deformities in the seismic data itself, and velocity in the seismic interpretation stage caused by time-depth domain calibration, etc., the well trajectory is inconsistent with the stratigraphic horizons and does not conform to the actual situation. For cases with excessive errors, it is usually necessary to carefully search and analyze the reasons for the deviation. Here, only the phenomenon of a small depth deviation between the common well trajectory and the seismic horizons (the depth error can be considered within 30 m) is discussed, which is usually caused by the velocity in the seismic interpretation stage due to time-depth domain calibration. The solution to this problem is completed through the established high-precision seismic interpretation velocity model. In the present invention, the method in the invention patent with the publication number CN114563816A can be used to establish the seismic interpretation velocity model.

[0050] (2) Establishment of the sandstone top and bottom extended structural plane and framework model

[0051] During the geological modeling process in the development stage, the main significance of including the top and bottom surfaces of sandstone in the model as constraint horizons is that: during the stage of carrying out fine reservoir research, it is often necessary to extract the overall properties of the sandstone section or along-layer slice data. If the top and bottom structures of the sandstone section are not considered, it is difficult to extract a plan view that can more objectively express reservoir parameters.

[0052] Based on the structural interpretation of the top and bottom surfaces of the strata where the channel sand bodies are located and the structural interpretation of the top and bottom surfaces of the sand layers, combined with the horizontal well trajectory data and the seismic fault and horizon interpretation results and single-well sub-layer division results obtained after conversion using the aforementioned velocity model, establish the sandstone top and bottom structural plane extension and framework model. For the top and bottom surfaces of the sand layers, their stability and continuity are often not as good as those of mudstone. Therefore, it is difficult to continuously track the top and bottom surfaces of the sand layers. The acquisition method is usually to obtain preliminary data points through methods such as seismic attribute extraction and horizon carving, and then, combined with geological understanding, delineate the channel boundaries. Within the boundaries, manual tracking areas that have not been reached can be filled by methods such as smooth interpolation, and thus the top and bottom structural surfaces of the sand layers are obtained.

[0053] Consider the sand layer data in the stratigraphic framework and extend it to the entire study area. For the areas outside the sand layers, they are all mudstone. The extended part has no actual stratigraphic significance but only to enhance the later expandability of the model, as shown in the attached Figure 4 description. Figure 4 In Figures a and 4b, the solid lines represent the stratigraphic boundaries where the sand layers are located, the numbers ① - ⑥ corresponding to the layers represent the plane of the longitudinal division scheme of the model, and the double-dotted lines represent the sandstone top and bottom extended structural planes).

[0054] (3) Facies model

[0055] Based on the single-well lithologic interpretation results, geostatistical data analysis of facies data (sedimentary facies or / and lithofacies) is carried out for each of the above framework units, and a lithofacies model of channel sand bodies is established under the constraint of the sand body framework model.

[0056] Through correlation analysis, a set of sensitive facies probability data volumes are selected, such as seismic predicted P-wave impedance, P-S wave velocity ratio and other data volumes, and they are used as probability volumes to participate in the constraint. The realization of this simulation process mainly involves the sequential indicator simulation algorithm in geostatistical algorithms. Its basis is the co-located cokriging algorithm (which is a simplified form of the cokriging algorithm). Its general form is:

[0057]

[0058] In the formula, x 0 is the prediction point; the random function Z 1 (x) is the prediction variable; Z 2 (x) is the constraint second variable, usually a seismic parameter; Z cok (x 0 ) is the co-located cokriging estimate of the random function Z 1 (x) at the prediction point x 0 ; the weighting coefficients λ i and μ are the undetermined coefficients in the cokriging equations. For the study of the channel sandstone facies model in tight gas reservoirs, Z 1 (x) is usually lithofacies data; Z 2 (x) is lithofacies seismic inversion data. In practice, it can be a discrete facies variable data volume obtained by seismic inversion, or a discrete variable obtained by truncating sensitive continuous seismic inversion data.

[0059] Of course, in the process of sequential indicator simulation calculation of discrete variables, it also involves the indicator transformation of variables and obtaining the facies probability value at the estimated point. The complex equations are omitted here. Its algorithm form is extended to the sequential indicator simulation method with trend (SISTR) or the sequential indicator co-located co-simulation (SICoSim) method. It introduces the second variable (such as seismic attributes) into the sequential indicator simulation algorithm (SIS) by replacing indicator kriging with co-located indicator cokriging.

[0060] For stochastic modeling, the results usually have large uncertainties. In areas with high-quality seismic constraint data, attention should be paid to ensuring the consistency of the trends of seismic prediction data and the original constraint data. To approach this goal, the randomness can be reduced through multiple realizations of facies simulation.

[0061] (4) Attribute model

[0062] Attributes such as porosity belong to continuous variables. In the present invention, the simulation of continuous variables takes the porosity parameter as an example.

[0063] For tight gas reservoir channel sandstone, the characteristics of modeling are as follows: the representation results of the model need to be basically consistent with the prediction results of high-precision seismic data, and ensure consistency with the calculation results of the facies model in step (3) and compliance with the well logging interpretation results.

[0064] The common geostatistical algorithms involved in this step are mainly sequential Gaussian simulation algorithms, and their theoretical basis is still the co-kriging algorithm module or the ordinary kriging algorithm module. The formula of the co-kriging algorithm is shown in Equation ②, and the basic theory of the ordinary kriging algorithm is briefly described as follows:

[0065] Let Z(x) be a random function, and its value at each point changes with the point variable x. Given the prior model of this function, that is, its covariance C(h), and the prediction point x 0 The estimation solution of the random function at this point is expressed as: the weighted coefficient λ i and the random function values Z(x i (i = 1, 2,..., n) at the given point x i ) are multiplied and then summed to obtain the weighted average value:

[0066]

[0067] The universal kriging equations are derived under the following assumptions: the random function Z(x) is the sum of an unknown polynomial trend term and a remainder term with zero error expectation. Taking the two-dimensional linear trend case as an example, the coordinates of point x have two components - x, y, and the linear trend equation of the random function Z(x) is written as T(x) = T(x, y) = ax + by + c. Ordinary kriging is a simplified form of universal kriging. At this time, the trend is an unknown constant, that is, T(x, y) = c.

[0068] Common attribute modeling constraint methods include trend surface and trend body constraints. When using trend body constraints, the co-kriging algorithm module is mostly used, that is, the case with the theoretical basis of ②. For a more detailed algorithm description, please refer to relevant geostatistics textbooks and will not be elaborated here.

[0069] (5) Model update

[0070] In the rolling exploration and development stage of tight gas reservoir channel sandstone, the production rhythm is usually relatively fast, involving a large amount of model update work. The method we adopt here is: using the obtained facies model and attribute model as the trend constraint data volume for updating the model, performing update coarsening on the newly added well data, such as well logging curve data such as lithofacies curves and porosity curves, while keeping the original coarsened data unchanged. On this basis, the sequential indicator simulation algorithm and the sequential Gaussian simulation algorithm are used to update the lithofacies model and the porosity model.

[0071] In the present invention, depending on the on-site situation and the requirements for model accuracy, generally, the two types of updates for the facies model and the property model are more frequent. If other results are significantly different from the actual drilling, updates are also required. For example, when updating the structural model, deterministic algorithms, Kriging, or inverse distance squared methods, etc. are used according to the structural data of the newly added wells to correct the residual distribution and thus modify the structure.

[0072] Furthermore, based on the same inventive concept, an apparatus for modeling the geology of channel sandstones in tight gas reservoirs is also provided in the embodiments of the present invention. The apparatus is used to implement the steps of the above-described modeling method as described in the following embodiments. As used hereinafter, the term "unit" or "module" may be a combination of software and / or hardware that can achieve a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated. Referring to the accompanying Figure 2 drawings, specifically, the apparatus may include: a seismic interpretation velocity model establishment module 201, a sandstone top and bottom extended structural surface and framework model establishment module 202, a facies model establishment module 203, and a property model establishment module. The following will specifically describe this structure.

[0073] The seismic interpretation velocity model establishment module 201 establishes a seismic interpretation velocity model based on well logging data and seismic data, and uses the seismic interpretation velocity model to convert the seismic interpretation results and inversion data from the time domain to the depth domain;

[0074] The sandstone top and bottom extended structural surface and framework model establishment module 202 establishes a sandstone top and bottom extended structural surface and framework model by using the seismic fault, horizon interpretation results and single-well sub-layer division results after conversion by the accurate velocity model, in combination with horizontal well trajectory data and sand layer data;

[0075] The facies model establishment module 203 performs geostatistical data analysis on the facies data of each simulation unit according to the well logging interpretation results of lithology, and establishes a channel sand body lithofacies model under the constraint of the sandstone top and bottom extended structural surface and framework model;

[0076] The property model establishment module 204, under the control of the channel sand body lithofacies model, based on the well logging interpretation results of single wells, and using the seismic inversion data results after conversion by the seismic interpretation velocity model as co-Kriging simulation variables, respectively establishes porosity, permeability, and saturation models for different facies types.

[0077] It should be noted that the systems, devices, models or units described in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. For the convenience of description, in this specification, when describing the above devices, various units are described separately according to their functions. Of course, when implementing the present invention, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0078] In addition, in this specification, adjectives such as first and second can only be used to distinguish one element or action, and do not necessarily imply or suggest any actual such relationship or order.

[0079] Furthermore, an embodiment of the present invention also provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable in the processor. When the processor executes the computer program, the steps of the above-mentioned modeling method are implemented.

[0080] Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed in a computer processor, the steps of the above-mentioned modeling method are implemented.

[0081] Embodiment 2

[0082] With the help of a self-developed seismic velocity analysis module and the internationally common commercial geological modeling software Petrel2018, a typical work area in the Sichuan Basin was selected for modeling to verify the effect of the present invention. The work area is a well group model.

[0083] As Figure 5 shown, the jq511 platform is located in the northern part of the JQ gas field work area in central Sichuan, with an area of 198 km 2 . The overall structural form is a monocline structure plunging in the east-southeast - north-northwest direction, and the local micro-structure is a small nose-like structure, and faults are basically not developed. The well data in this example is discussed based on one pilot well and four horizontal wells on the platform. The platform location and the study interval J 2 s 2 1 The top and bottom structure maps of the J Figure 6 sub-member are as

[0084] shown. The sandstone top and bottom structure data is obtained by applying the sand body perspective method, and then the sandstone thickness data is generated as Figure 7 shown. On this basis, the layer extension calculation method is used to extend it to the entire study work area as Figure 8 shown. The cross-section of the structural framework model established accordingly is as Figure 7 shown.

[0085] On this basis, by applying the method of the present invention, under the constraint of high-precision seismic data information, a lithofacies model and a porosity model of the study area are established. The cross-sectional view is as shown in Figure 9 and Figure 10 . It can be seen from the figure that on the one hand, the modeling result maintains the trend of the seismic data, and on the other hand, the well points also conform to the given well data. As shown in Figure 10 , at the three positions indicated by the dashed arrows, the relationship and difference between the seismic inversion data and the model data are well shown. It is not difficult to see that the seismic data clearly reflects the trend of the sand body distribution. However, for the well point details, it cannot be completely guaranteed to conform to them. Through geological modeling work, the trend of the sand body and physical property distribution is more perfectly and accurately characterized. The finally established three-dimensional structural framework model, sand body model and porosity attribute model are as shown in Figure 11 .

[0086] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Any simple modification and equivalent change made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A modeling method for the geology of channel sandstone in tight gas reservoirs, characterized in that, it includes the following steps: Step 1. Seismic interpretation velocity model. Establish a seismic interpretation velocity model based on well logging data and seismic data, and use the seismic interpretation velocity model to convert seismic interpretation results and inversion data from the time domain to the depth domain; Step 2. Sandstone top and bottom extended structural surface and framework model. Use the seismic fault, surface interpretation results and single-well sub-layer division results after conversion by the accurate velocity model, combined with horizontal well trajectory data and sand layer data, to establish a sandstone top and bottom extended structural surface and framework model; Step 3. Facies model. According to the single-well lithology interpretation results, conduct geostatistical data analysis of facies data for each framework unit, and establish a channel sand body lithofacies model under the constraint of the sandstone top and bottom extended structural surface and framework model; Step 4. Attribute model. Under the control of the channel sand body lithofacies model, based on the single-well well logging interpretation results, and using the seismic inversion data results after conversion by the seismic interpretation velocity model as co-Kriging simulation variables, establish porosity, permeability and saturation models for different facies types respectively.

2. A modeling method for the geology of channel sandstone in tight gas reservoirs according to claim 1, characterized in that, it further includes: Step 5. Model update. Use the obtained facies model and attribute model as the trend constraint data volume for updating the model, perform update coarsening on the newly added well data while keeping the original coarsened data unchanged, and on this basis, use the sequential indicator simulation algorithm and sequential Gaussian simulation algorithm to update the facies model and attribute model.

3. A modeling method for the geology of channel sandstone in tight gas reservoirs according to claim 1, characterized in that, the sand layer data includes the structural interpretation of the top and bottom surfaces of the formation where the channel sand body is located and the structural interpretation data of the top and bottom surfaces of the sand layer.

4. A modeling method for the geology of channel sandstone in tight gas reservoirs according to claim 1, characterized in that, the well data includes well logging curves.

5. A modeling method for the geology of channel sandstone in tight gas reservoirs according to claim 4, characterized in that, the well logging curves include lithofacies curves, porosity curves, permeability well logging curves and saturation well logging curves.

6. A modeling device for the geology of channel sandstone in tight gas reservoirs, characterized in that, the device is used to implement the modeling method described in any one of claims 1-3 above, and includes: A seismic interpretation velocity model establishment module, which establishes a seismic interpretation velocity model based on well logging data and seismic data, and uses the seismic interpretation velocity model to convert seismic interpretation results and inversion data from the time domain to the depth domain; A sandstone top and bottom structural surface extension and framework model establishment module, which uses the seismic fault, surface interpretation results and single-well sub-layer division results after conversion by the accurate velocity model, combined with horizontal well trajectory data and sand layer data, to establish a sandstone top and bottom structural surface extension and framework model; A facies model establishment module, which conducts geostatistical data analysis of facies data for each simulation unit according to the single-well lithology interpretation results, and establishes a channel sand body lithofacies model under the constraint of the sandstone top and bottom structural surface extension and framework model; The attribute model establishment module, under the control of the river channel sand body lithofacies model, based on the single well logging interpretation results, and using the seismic inversion data results converted from the seismic interpretation velocity model as the co-Kriging simulation variable, respectively establishes the porosity, permeability and saturation models under different reservoir types.

7. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable in the processor, characterized in that, when the processor executes the computer program, the method steps described in any one of claims 1-5 above are implemented.

8. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed in a computer processor, the method steps described in any one of claims 1-5 above are implemented.

Images