Conditionalization method and system for sedimentary microfacies model

Abstract

The invention provides a sedimentary microfacies model conditioning method and system. The method comprises the following steps: acquiring well drilling position information of a research area, depth information of well point condition data, a sedimentary microfacies type, a river channel position and logging data; according to the difference of sedimentary microfacies types of different depths at well points of a research area, calculating the dentation rate of a natural gamma logging curve, and converting the dentation rate of the logging curve into a distance attribute value; performing condition simulation based on the well point sedimentary microfacies type to obtain a sedimentary microfacies model, and establishing a first distance parameter field; establishing a second distance parameter field through the Kriging method according to the distance attribute value at the well point of the research area; unconditioned well points in the research area are obtained, the first distance parameter field is corrected through the second distance parameter field, and all corrected well points meet the hard data requirement; and updating the sedimentary microfacies model according to the conditioned result of the well point. Through the scheme, the well point data conditional efficiency is improved, and the matching degree of the model and the well point data is improved.

Description

Technical Field

[0001] The invention belongs to the field of oil reservoir description, and in particular relates to a sedimentary microfacies model conditioning method and system. Background Art

[0002] Reservoir geological modeling is the core research content of reservoir description. Target-based simulation is a common reservoir modeling method. It defines the geometric shape parameters of the target and the quantitative geological relationship between each parameter. It generates the target body at one time during the modeling process. It is more intuitive and can integrate prior geological experience such as outcrop data and important geological knowledge into the model.

[0003] However, the target-based modeling method has a common problem, that is, it is difficult to meet the requirements of conditioning when establishing a fine reservoir model, especially under dense well network conditions. In order to improve the matching degree between the three-dimensional geological model and the well data, and thus improve the accuracy of the sedimentary microfacies model, a large number of studies have been conducted on the conditioning problem based on the target model: Shmaryan and Deutsch (1999) proposed to condition by directly placing the target when establishing the one-dimensional river centerline; Hauge and Holden (2007) combined the Metropolis-Hastings algorithm to improve the target-based model; Zhang Chunlei et al. (2003) achieved the purpose of matching well observation data by adjusting the upstream and downstream positions of the river channel and designing tributaries.

[0004] The above improved methods require a long calculation time and a slow conditioning rate when there is a large amount of conditional data. It is difficult to handle different types of targets, and sometimes it is even impossible to converge within a reasonable time. With the gradual deepening of the development of the study area, the density of the well network continues to increase, and the commonly used Fluvsim algorithm also has shortcomings. The Fluvsim algorithm itself has a certain conditioning ability. It can use the simulated annealing method and apply data such as global phase ratio to move, add, delete, and correct the well spacing of the river channel, so that the simulation results can integrate a variety of information and be faithful to the well data. However, when the well spacing is small, the iteration rate of the simulated annealing method is reduced, and the conditioning time is longer. At the same time, in order to ensure the complete river channel morphology, the satisfaction of the Fluvsim method with the well observation data will also decrease. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a sedimentary microfacies model conditioning method and system for solving the problems of reduced iteration rate and long conditioning time in the traditional simulated annealing method based on the Fluvsim algorithm.

[0006] In a first aspect of an embodiment of the present invention, a method for conditioning a sedimentary microfacies model is provided, comprising:

[0007] Obtaining the location information of wells in the study area and the depth information of well point condition data, sedimentary microfacies types, river channel locations and well logging data;

[0008] According to the differences in sedimentary microfacies types at different depths at the well points in the study area, the toothing rate of the natural gamma logging curve in the logging data is calculated, and the toothing rate of the logging curve is converted into a distance attribute value;

[0009] A sedimentary microfacies model is obtained by conditional simulation based on the sedimentary microfacies type of the well point, and a first distance parameter field is established according to the sedimentary microfacies model;

[0010] According to the distance attribute values ​​at the well points in the study area, the second distance parameter field is established by Kriging method;

[0011] Obtain unconditioned well points in the study area, and modify the first distance parameter field through the second distance parameter field so that all modified well points meet the hard data requirements;

[0012] The sedimentary microfacies model is updated based on the conditioning results of all well points.

[0013] In a second aspect of an embodiment of the present invention, a system for conditioning a sedimentary microfacies model is provided, comprising:

[0014] Acquisition module, used to obtain the drilling location information of the study area and the depth information of the well point condition data, sedimentary microfacies type, river channel location and logging data;

[0015] The conversion module is used to calculate the tooth rate of the natural gamma logging curve in the logging data according to the differences in sedimentary microfacies types at different depths at the well points in the study area, and convert the tooth rate of the logging curve into a distance attribute value;

[0016] A first construction module is used to perform conditional simulation based on the well point sedimentary microfacies type to obtain a sedimentary microfacies model, and to establish a first distance parameter field according to the sedimentary microfacies model;

[0017] The second construction module is used to establish a second distance parameter field through the Kriging method according to the distance attribute values ​​at the well points in the study area;

[0018] A correction module is used to obtain unconditioned well points in the study area and correct the first distance parameter field through the second distance parameter field so that all the corrected well points meet the hard data requirements;

[0019] The update module is used to update the sedimentary microfacies model based on the conditioning results of all well points.

[0020] In a third aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect of the embodiment of the present invention when executing the computer program.

[0021] In a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method provided in the first aspect of the embodiment of the present invention are implemented.

[0022] In the embodiment of the present invention, a sedimentary microfacies model is established by conditional simulation based on the Fluvsim method, and a distance parameter model is established by using the Kriging method, and then the simulated sedimentary microfacies model is corrected, thereby not only solving the problems of reduced iteration rate and long conditioning time existing in the traditional simulated annealing method, but also effectively improving the efficiency of model conditioning and improving the matching degree between the model and the well point data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic flow chart of a sedimentary microfacies model conditioning method provided by one embodiment of the present invention;

[0025] Figure 2 A schematic diagram of the well locations and sedimentary microfacies distribution in the study area provided by one embodiment of the present invention;

[0026] Figure 3 A schematic diagram of a tooth in a sand body of a well channel in a study area provided by one embodiment of the present invention;

[0027] Figure 4 A schematic diagram of a sedimentary microfacies model for simulating well data conditions in a study area provided by an embodiment of the present invention;

[0028] Figure 5 A schematic diagram of a first distance parameter field provided by an embodiment of the present invention;

[0029] Figure 6 A schematic diagram of a second distance parameter field provided by an embodiment of the present invention;

[0030] Figure 7 A schematic diagram of a modified distance parameter field provided by an embodiment of the present invention;

[0031] Figure 8 A schematic diagram of an updated sedimentary microfacies model provided by one embodiment of the present invention;

[0032] Fig. 9 A schematic diagram of the structure of a system for conditioning a sedimentary microfacies model provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] It should be understood that the term "including" and other similar expressions in the specification or claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, such as a process, method, system, or device including a series of steps or units is not limited to the listed steps or units. In addition, "first" and "second" are used to distinguish different objects, not to describe a specific order.

[0035] See also Figure 1 , a schematic flow chart of a sedimentary microfacies model conditioning method provided by an embodiment of the present invention includes:

[0036] S101, obtaining the drilling location information of the study area and the depth information of the well point condition data, sedimentary microfacies type, river channel location and logging data;

[0037] Based on the location of the study area, the number of wells, the location of the wells, the depth information of the drilling conditions, the sedimentary microfacies type at the drilling location, the river channel location (including the centerline location) and the logging curve are obtained. The well locations and sedimentary microfacies types of the study area are as follows: Figure 2 shown.

[0038] For example, the study area is 3km×3km in plane. During the modeling process, the plane study area is divided into 100×100 grid cells, and the size of a single grid is 50m×50m. There are 36 wells in the work area, with an average well spacing of 600m. There are two types of sedimentary microfacies in the study area, river channel and inter-channel sedimentary microfacies. The river channel location and well location in the study area are as follows: Figure 2The target-based Fluvsim method was used for simulation, with the average river channel width set to 1500m, the average thickness set to 7m, the average wavelength set to 200m, the average amplitude set to 300m, and the average azimuth of the river channel set to 20°. The river channel scale parameters of the study area obtained from the previous geological understanding can be used as known geological data.

[0039] S102, calculating the toothing rate of the natural gamma logging curve in the logging data according to the difference in sedimentary microfacies types at different depths at the well points in the study area, and converting the toothing rate of the logging curve into a distance attribute value;

[0040] The river channel generally shows the characteristic of low natural gamma curve value. When the hydrodynamics change or the river channel overlaps, the natural gamma curve value will change suddenly, showing a "tooth" feature. "Teeth" can represent the fine-grained sedimentation phenomenon in a set of stable coarse-grained sediments in terms of lithology, and also represent the probability and frequency of changes in sedimentary materials. By referring to the core description, calibrating the core and logging curves, and clarifying the range of natural gamma values ​​corresponding to each lithofacies, we can identify the change in grain size and then identify the "teeth".

[0041] For example, Figure 3 As shown in the figure, taking Well W1 as an example, a thick river channel sand body is developed at a burial depth of 2719-2733m. The sediment grain size becomes finer from bottom to top. The lower part is a set of coarse sandstone deposits, the upper part is medium sandstone, and there is a set of thin-layered fine-grained siltstone deposits in the middle and upper parts of the river channel. After calibration, the natural gamma logging value of coarse sandstone is below 40API, and the natural gamma logging value of medium sandstone ranges from 40 to 60API. Therefore, when the natural gamma logging value is greater than 60API, it is considered to show tooth characteristics on the logging curve. For example, in Well W1, the natural gamma logging value of the siltstone section is between 60 and 80, so the section is considered to be toothed.

[0042] The toothing rate reflects the changes in hydrodynamic conditions and lithological grain size, and its size is characterized by calculating three parameters of the tooth, including the tooth variation amplitude, tooth distribution density and frequency.

[0043] Among them, the tooth ratio is calculated according to the formula:

[0044] G tooth =a×E tooth +b×D tooth +c×F tooth ;

[0045]

[0046] D tooth =H tooth / H;

[0047] Ftooth =N tooth / H;

[0048] In the formula, G tooth is the toothing rate; E tooth is the average amplitude of the teeth; D tooth is the distribution density of the teeth, m / m; F tooth is the distribution frequency of the teeth, pieces / m; a, b, c are the weight coefficients of the average amplitude, distribution density and distribution frequency of the teeth respectively; is the average natural gamma value of all teeth in the sand body of this section of the river channel; GR min is the minimum value of the natural gamma logging curve in the sand body of this section of the river channel, API; H tooth is the cumulative thickness of all teeth in the sand body of this section of the river channel, m; H is the thickness of the sand body of this section of the river channel, m; N tooth is the number of teeth in the sand body of this section of the river channel.

[0049] Optionally, the reservoir in the study area is divided into different layers according to the depth of the well points in the study area, and the toothing rates of the natural gamma logging curves corresponding to the different layers are calculated.

[0050] It can be understood that the average amplitude of the teeth varies between 0 and 1. The size of the average amplitude is related to the lithological grain size. Generally, the finer the grain size, the larger the amplitude. The distribution density of the teeth is also between 0 and 1. The larger the distribution density of the teeth, the more unstable the material supply capacity and the greater the change in the lithological grain size. On the contrary, it means that the material source is sufficient and stable. The distribution frequency of the teeth is related to the stability of the hydrodynamics. When the distribution frequency decreases, it means that the hydrodynamic conditions are more stable and the changes in the lithology are relatively small. The distribution frequency of the teeth is usually small, but sometimes it may be greater than 1. In this case, it needs to be normalized to ensure that it is distributed in the range of 0 to 1. In the formula for calculating the toothing rate, the three weight coefficients are mainly determined based on the geological characteristics of the study area. Generally, when the hydrodynamics and material supply change, the average amplitude of the teeth will change first, followed by the change in the density of the teeth, and finally the change in the distribution frequency of the teeth. According to the existing geological research results in the study area, the weight coefficients of the three parameters of the average amplitude, distribution density and distribution frequency of the teeth can be assigned as 0.55, 0.3 and 0.15 respectively.

[0051] Optionally, the toothing rate of the well point is linearly converted to obtain a distance attribute value, and the distance attribute value is judged to determine that the reservoir with a distance attribute value less than 1 is a channel microfacies, and the reservoir with a distance attribute value greater than 1 is determined as an inter-channel microfacies.

[0052] The channel characterization method using the toothing ratio has significantly improved the accuracy of the main channel boundary, and the subtle changes in the channel are clearer and the quantitative description ability is stronger. Therefore, to determine the distance attribute, it is necessary to first calculate the toothing ratio of each well in the study area, and then obtain the distance attribute through linear conversion. Assuming that the toothing ratio is the standard for distinguishing between channels and non-channels, the channel sand body is identified by defining the toothing ratio value for each point P in the grid: For point G(P), when G tooth ≤r, then it is a river channel. When G tooth >r, it is not a river channel.

[0053] For the tooth rate value in the study area, x n For many well points, 0 can be used to represent the river channel and 1 can be used to represent the non-river channel. Then the toothing rate of the logging curve can be converted into a distance attribute value to determine the river channel microfacies and inter-channel microfacies.

[0054] For example, Table 1 shows the toothing rate of well points in the study area, and the reservoir in the study area is divided into two layers, h1 and h2.

[0055]

[0056] Table 1

[0057] Table 2 shows the distance attribute value data after conversion of the well point tooth rate in the study area.

[0058]

[0059] Table 2

[0060] S103, performing conditional simulation based on the well point sedimentary microfacies type to obtain a sedimentary microfacies model, and establishing a first distance parameter field according to the sedimentary microfacies model;

[0061] The sedimentary microfacies model is a three-dimensional spatial representation and simulation of tiny sedimentary units with unique lithology, paleontology and sedimentary structural characteristics in the sedimentary environment. It is usually constructed based on geological data, logging data, core analysis and other means to describe the heterogeneity of reservoirs and predict the distribution of resources such as oil and gas. Sedimentary microfacies is a subdivision of sedimentary facies. It is a more specific sedimentary unit further divided on the basis of sedimentary facies. For example, river facies can be further divided into river channel microfacies, beach microfacies, floodplain microfacies, etc. Sedimentary microfacies can reflect the changes and local characteristics of the sedimentary environment in a more detailed manner, which is of great significance for studying the physical properties of reservoirs and the enrichment laws of oil and gas.

[0062] Specifically, the well-point sedimentary microfacies were conditionally simulated using the Fluvsim algorithm, and the microfacies between distributary channels were used as the background phase to obtain the sedimentary microfacies model.

[0063] The sedimentary microfacies data of the well point are obtained, and the sedimentary microfacies model established by combining the Fluvsim algorithm condition simulation is used. The center line of the river channel is obtained based on the spatial distribution characteristics of the river channel microfacies in the sedimentary microfacies model, and the distance body from the center line of the river channel is established based on the center line of the river channel to obtain the corresponding first distance parameter field.

[0064] In some embodiments, Figure 4 This is a schematic diagram of the sedimentary microfacies model simulated based on the well data conditions in the study area. Figure 5 The schematic diagram of the corresponding first distance parameter field established based on conditional simulation. The conditional simulation was performed using the extracted microfacies type data of 36 wells. The simulation parameters remained unchanged and the sedimentary microfacies model was established. In the case of 36 wells, multiple simulations were performed to achieve that the river channel well points could not be fully conditioned, and the compliance rate at the well points was 80.6%. The sedimentary microfacies model based on conditional simulation established the first distance parameter field corresponding to the river channel in the model. The distance field is related to the river channel position in the microfacies model. Under the condition of dense well network, the Fluvsim algorithm is difficult to fully condition. At some well point locations, the distance parameter field data is inconsistent with the distance parameter data obtained from the natural gamma logging curve of the well point.

[0065] S104, establishing a second distance parameter field by using the Kriging method according to the distance attribute values ​​at the well points in the study area;

[0066] The Kriging method is a regression algorithm for spatial modeling and prediction (interpolation) of random processes / random fields based on covariance functions, which can provide optimal linear unbiased estimates in specific random processes. The Kriging method can form a hybrid algorithm with other models, and when the form of the covariance function is equivalent and the modeling object is a stationary Gaussian process, the output of ordinary Kriging is the same as the mean and confidence interval output by Gaussian process regression under normal likelihood, and has a stable prediction effect. As a typical geostatistical algorithm, the Kriging method is widely used in spatial interpolation in geological engineering, numerical models of mineral deposits, and geographic information system engineering. The implementation process of the Kriging interpolation method involves considering the randomness and correlation of reservoir parameters, and giving the best linear unbiased interpolation under the condition of satisfying the minimum interpolation variance, while providing the interpolation variance, so that the reliability of the interpolation is clear at a glance. The advantage of the Kriging method is that it takes into account the spatial correlation properties of the description object, making the interpolation result more scientific and closer to the actual situation, and can provide the interpolation error, i.e., the Kriging variance, to evaluate the reliability of the interpolation.

[0067] In some embodiments, the second distance parameter field established by the Kriging method based on the well data is shown in FIG. Figure 6As shown. For the distance parameter value of the natural gamma tooth transformation at the well point, the Kriging method is used to simulate the distance parameter field, and the search ellipsoid is set. The main range direction of the search ellipsoid is set to 90°, the main range is set to 400m, the secondary range is set to 200m, and the vertical range is set to 7m, thereby establishing a model body that meets the distance parameter value at the well point.

[0068] S105, obtaining unconditioned well points in the study area, and correcting the first distance parameter field by using the second distance parameter field, so that all the corrected well points meet the hard data requirements;

[0069] By correcting the first distance parameter field with the second distance parameter field, conditional processing of unconditioned well points can be achieved.

[0070] Wherein, if the distance attribute value of the well point in the first distance parameter field is greater than 1 and less than 1 in the second distance parameter field, the distance attribute value in the second distance parameter field is assigned to the first distance parameter field;

[0071] If the distance attribute value in the first distance parameter field is less than 1 and in the second distance parameter field is greater than 1, the distance attribute value in the second distance parameter field is assigned to the first distance parameter field;

[0072] If the distance attribute values ​​in the first distance parameter field and the second distance parameter field are both greater than 1 or both less than 1, the distance attribute value in the first distance parameter field is kept unchanged.

[0073] In this embodiment, by determining the logical relationship between the distance parameters in the first distance parameter field and the second distance parameter field, the first distance field is corrected to achieve the update of each well point grid unit.

[0074] It is understandable that when updating the first distance parameter field, there may be well data that cannot be conditioned with any river channel target. This situation usually occurs when a river channel well is surrounded by non-river channel wells. When this happens, it is necessary to appropriately reduce the distance value of the river channel well or increase the range of the search ellipse (that is, increase the probability that the river channel well is located in the river channel target) according to the actual situation. However, this cannot guarantee that all well data can be conditioned. If the distance value of the river channel well point is set to 0 (located at the center line of the river channel) and it still cannot be conditioned, then relax the standard for stopping the conditioning of the well point. After updating the first distance field, it is truncated with a truncation value of 1, such as Figure 7 As shown in the figure, 1 is used as the dividing point between the channel and the non-channel, and the part with a distance value less than 1 is converted into the channel, and the part with a distance value greater than 1 is converted into the non-channel, thereby establishing a conditional sedimentary microfacies model. Compared with the results obtained by conditional simulation, after the conditional operation, all well points meet the hard data requirements.

[0075] S106. Update the sedimentary microfacies model based on the conditioning results of all well points.

[0076] Among them, when the distance attribute value of the well point in the first distance parameter field is less than 1, the well point grid unit is assigned to the river channel microfacies in the sedimentary microfacies model, otherwise it is assigned to the inter-channel microfacies.

[0077] The distance field is used to condition the sedimentary microfacies model. Based on the updating principle of the well point simulation grid by the conditioning method, when the attribute value of a simulation grid in the distance model is less than 1, the well point grid is assigned to the river phase in the sedimentary microfacies model, otherwise it is assigned to the inter-channel phase. The sedimentary microfacies model after conditioning is as follows: Figure 8 shown.

[0078] Among them, the updated sedimentary microfacies model can be verified based on the river channel location.

[0079] In this embodiment, based on the random simulation based on the Fluvsim method, it is proposed to use the distance attribute parameter to condition the target model, use the Kriging method to establish a distance parameter model, and improve the simulated sedimentary microfacies model by correcting the first distance field, thereby not only improving the conditioning efficiency of the sedimentary microfacies model, but also improving the matching degree between the model and the well point data.

[0080] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0081] Fig. 9 A schematic diagram of a system for conditioning a sedimentary microfacies model provided in an embodiment of the present invention, the system comprising:

[0082] Acquisition module 910, for acquiring drilling location information of the study area and depth information of well point condition data, sedimentary microfacies type, river channel location and logging data;

[0083] The conversion module 920 is used to calculate the tooth rate of the natural gamma logging curve in the logging data according to the difference of sedimentary microfacies types at different depths at the well points in the study area, and convert the tooth rate of the logging curve into a distance attribute value;

[0084] Among them, according to the depth information of the well points in the study area, the reservoir in the study area is divided into different layers, and the toothing rate of the natural gamma logging curve corresponding to different layers is calculated.

[0085] The converting of the logging curve toothing rate into a distance attribute value comprises:

[0086] The toothing rate of the well point is linearly converted to obtain the distance attribute value, and the distance attribute value is judged. The reservoir with a distance attribute value less than 1 is judged as the channel microfacies, and the reservoir with a distance attribute value greater than 1 is judged as the inter-channel microfacies.

[0087] The first construction module 930 is used to perform conditional simulation based on the well point sedimentary microfacies type to obtain a sedimentary microfacies model, and establish a first distance parameter field according to the sedimentary microfacies model;

[0088] Optionally, the conditional simulation based on the well point sedimentary microfacies type to obtain the sedimentary microfacies model includes:

[0089] The well-point sedimentary microfacies were conditionally simulated using the Fluvsim algorithm, and the microfacies between distributary channels were used as the background phase to obtain the sedimentary microfacies model.

[0090] The second construction module 940 is used to establish a second distance parameter field by Kriging method according to the distance attribute values ​​at the well points in the study area;

[0091] A correction module 950 is used to obtain unconditioned well points in the study area, and correct the first distance parameter field by using the second distance parameter field so that all the corrected well points meet the hard data requirements;

[0092] Specifically, the correcting the first distance parameter field by using the second distance parameter field includes:

[0093] If the distance attribute value of the well point in the first distance parameter field is greater than 1 and less than 1 in the second distance parameter field, the distance attribute value in the second distance parameter field is assigned to the first distance parameter field;

[0094] If the distance attribute value in the first distance parameter field is less than 1 and in the second distance parameter field is greater than 1, the distance attribute value in the second distance parameter field is assigned to the first distance parameter field;

[0095] If the distance attribute values ​​in the first distance parameter field and the second distance parameter field are both greater than 1 or both less than 1, the distance attribute value in the first distance parameter field is kept unchanged.

[0096] The updating module 960 is used to update the sedimentary microfacies model according to the conditioning results of all well points.

[0097] Among them, when the distance attribute value of the well point in the first distance parameter field is less than 1, the well point grid is assigned to the river channel microfacies in the sedimentary microfacies model, otherwise it is assigned to the inter-channel microfacies.

[0098] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and modules can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0099] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it implements part or all of the processes in steps S101 to S106. The storage medium includes ROM / RAM, etc. In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For the part that is not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0100] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sedimentary microfacies model conditioning method, characterized in that: include: Obtaining the location information of the wells in the study area and the depth information of the well point condition data, sedimentary microfacies types, river channel locations and well logging data; According to the differences in sedimentary microfacies types at different depths at the well points in the study area, the toothing rate of the natural gamma logging curve in the logging data is calculated, and the toothing rate of the logging curve is converted into a distance attribute value; A sedimentary microfacies model is obtained by conditional simulation based on the sedimentary microfacies type of the well point, and a first distance parameter field is established according to the sedimentary microfacies model; According to the distance attribute values ​​at the well points in the study area, the second distance parameter field is established by Kriging method; Obtain unconditioned well points in the study area, and modify the first distance parameter field through the second distance parameter field so that all modified well points meet the hard data requirements; The sedimentary microfacies model is updated based on the conditioning results of all well points.

2. The method according to claim 1, characterized in that The calculation of the toothing rate of the natural gamma logging curve in the logging data according to the difference in sedimentary microfacies types at different depths at the well points in the study area includes: According to the depth information of the well points in the study area, the reservoir in the study area is divided into different layers, and the toothing rate of the natural gamma logging curve corresponding to different layers is calculated.

3. The method according to claim 1, characterized in that The converting of the logging curve tooth rate into a distance attribute value comprises: The toothing rate of the well point is linearly converted to obtain the distance attribute value, and the distance attribute value is judged. The reservoir with a distance attribute value less than 1 is judged as the channel microfacies, and the reservoir with a distance attribute value greater than 1 is judged as the inter-channel microfacies.

4. The method according to claim 1, characterized in that The sedimentary microfacies model obtained by conditional simulation based on the well point sedimentary microfacies type includes: The well-point sedimentary microfacies were conditionally simulated using the Fluvsim algorithm, and the microfacies between distributary channels were used as the background phase to obtain the sedimentary microfacies model.

5. The method according to claim 1, characterized in that The obtaining of unconditioned well points in the study area and the correction of the first distance parameter field by the second distance parameter field include: If the distance attribute value of the well point in the first distance parameter field is greater than 1 and less than 1 in the second distance parameter field, the distance attribute value in the second distance parameter field is assigned to the first distance parameter field; If the distance attribute value in the first distance parameter field is less than 1 and in the second distance parameter field is greater than 1, the distance attribute value in the second distance parameter field is assigned to the first distance parameter field; If the distance attribute values ​​in the first distance parameter field and the second distance parameter field are both greater than 1 or both less than 1, the distance attribute value in the first distance parameter field is kept unchanged.

6. The method according to claim 1, characterized in that The updating of the sedimentary microfacies model according to the conditioning results of all well points includes: When the distance attribute value of the well point in the first distance parameter field is less than 1, the well point grid unit is assigned to the channel microfacies in the sedimentary microfacies model, otherwise it is assigned to the inter-channel microfacies.

7. A system for conditioning a sedimentary microfacies model, characterized in that: include: Acquisition module, used to obtain the drilling location information of the study area and the depth information of the well point condition data, sedimentary microfacies type, river channel location and logging data; The conversion module is used to calculate the tooth rate of the natural gamma logging curve in the logging data according to the differences in sedimentary microfacies types at different depths at the well points in the study area, and convert the tooth rate of the logging curve into a distance attribute value; A first construction module is used to perform conditional simulation based on the well point sedimentary microfacies type to obtain a sedimentary microfacies model, and to establish a first distance parameter field according to the sedimentary microfacies model; The second construction module is used to establish a second distance parameter field through the Kriging method according to the distance attribute values ​​at the well points in the study area; A correction module is used to obtain unconditioned well points in the study area and correct the first distance parameter field through the second distance parameter field so that all the corrected well points meet the hard data requirements; The update module is used to update the sedimentary microfacies model based on the conditioning results of all well points.

8. The system according to claim 7, characterized in that The converting of the logging curve tooth rate into a distance attribute value comprises: The toothing rate of the well point is linearly converted to obtain the distance attribute value, and the distance attribute value is judged. The reservoir with a distance attribute value less than 1 is judged as the channel microfacies, and the reservoir with a distance attribute value greater than 1 is judged as the inter-channel microfacies.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a sedimentary microfacies model conditioning method as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the steps of a sedimentary microfacies model conditioning method as described in any one of claims 1 to 6 are implemented.

Images