WO2024088265A1

WO2024088265A1 - Conglomerate reservoir segmentation and clustering method and apparatus, storage medium, and processor

Info

Publication number: WO2024088265A1
Application number: PCT/CN2023/126270
Authority: WO
Inventors: 吕振虎; 王林生; 张羽鹏; 石善志; 董景峰; 王维和; 孔明炜; 刘进军; 陈小璐; 吴虎; 程福山
Original assignee: 中国石油天然气股份有限公司
Priority date: 2022-10-25
Filing date: 2023-10-24
Publication date: 2024-05-02
Also published as: CN117967295A

Abstract

Disclosed is a conglomerate reservoir segmentation and clustering method, comprising: acquiring mechanical specific energy of a horizontal well, geological parameter information of the horizontal well, and engineering parameter information of the horizontal well; using a clustering analysis algorithm to perform multi-dimensional data classification on data points of the horizontal well in depth; using a classification result and a preset horizontal segment length to segment the depth of the horizontal well; respectively using a proximity algorithm to calculate the comprehensive proximity of geological engineering parameters at each depth in each segment in the segmentation result, so as to obtain the position of a perforation cluster in the segment; and obtaining a conglomerate reservoir segmentation and clustering result according to the segmentation result and the position of the perforation cluster. The engineering sweet spot recognition accuracy can be improved, the balance of geological and engineering parameter distribution at perforation points in the segment is effectively improved, the construction difficulty is reduced, the occurrence probability of complex working conditions in construction is reduced, the construction fluid amount is conserved, the investment is saved, and the yield of oil and gas wells is increased. Also disclosed are an apparatus using the method, a storage medium, and a processor.

Description

Conglomerate reservoir segmentation and clustering method, device, storage medium and processor

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of Chinese patent application 202211312462.8 filed on October 25, 2022, the contents of which are incorporated herein by reference.

Technical Field

The present application relates to the technical field of oil and gas production enhancement, and in particular to a conglomerate reservoir segmentation and clustering method, a conglomerate reservoir segmentation and clustering device, a machine-readable storage medium and a processor.

Background technique

The Mahu Sag in the Junggar Basin is a large hydrocarbon-rich sag with multi-layer reservoirs. Its efficient development is of great significance to ensuring national energy security. Compared with conventional oil reservoirs, the conglomerate reservoirs in the Mahu Sag are affected by gravels, and the reservoir heterogeneity is stronger. They are also characterized by deep burial, poor physical properties, undeveloped natural fractures, and high closure pressure. Field practice shows that multi-cluster volume fracturing in horizontal well sections is a key means to reduce costs and improve efficiency in conglomerate reservoirs. By appropriately increasing the section length, increasing the number of clusters in the section, and reducing the number of single well construction sections, the cost of single well fracturing can be reduced by 15-20%. However, the conglomerate reservoir has strong heterogeneity, uneven distribution of gravel content and size, large lithology changes, and complex stress distribution. In the multi-cluster volume fracturing transformation in the horizontal well section, the balanced fracturing of each perforation cluster in the section cannot be effectively achieved, resulting in large differences in the amount of fluid and sand injected into each perforation cluster in the same transformation section. Some perforation clusters are over-transformed due to large amounts of fluid and sand injected, and some perforation clusters are insufficiently transformed due to small amounts of fluid and sand injected, which seriously affects the degree of reservoir production. In 2020, the multi-cluster fracturing technology in the promoted section of the Mahu gravel oil reservoir was used. The production profile test showed that more than 40% of the perforation clusters did not contribute to oil and gas flow. The downhole Eagle Eye test showed that only 2 to 3 clusters out of 6 clusters in a single section had the characteristics of fracturing sand erosion. Compared with 2019, the single well production decreased by 40 to 55%. Therefore, based on comprehensive data such as drilling and recording, the geological-engineering sweet spot collaborative optimization technology was carried out to optimize the segmented and clustered process. It is of great significance to achieve balanced fracturing of each cluster to increase production and reduce costs of single wells in gravel oil reservoirs.

Combined with reservoir characteristics, there are two main problems that need to be tackled urgently:

(1) Unlike conventional sandstone, the dominant fluid inflow clusters have a poor correlation with the horizontal minimum principal stress, and the main controlling factor of fracture initiation is unclear; the dominant fluid inflow clusters in conventional sandstone reservoirs are all low stress values. Downhole fiber optic monitoring shows that the dominant fluid inflow channels in conglomerate reservoirs are correlated with the minimum principal stress, but the correlation is not strong. A new engineering sweet spot identification method is urgently needed to provide a basis for segmentation and clustering.

(2) The current segmented clustering method has not yet organically integrated the geological and engineering sweet spot parameters, making it difficult to apply to conglomerate reservoirs. Conventional segmented clustering mainly characterizes the geological sweet spot and the engineering sweet spot, and comprehensively compares and selects the "double sweet spot" as the dominant perforation cluster. The geological sweet spot refers to the physical properties such as porosity, permeability, saturation and hydrocarbon content, and the engineering sweet spot mainly refers to the coupling position and stress. The engineering sweet spot that relies solely on stress cannot effectively characterize the fracture characteristics of each cluster. At the same time, the geological sweet spot and the engineering sweet spot are still separated, and they are not used as a unified organic whole for segmented clustering guidance.

Summary of the invention

The purpose of the embodiments of the present application is to provide a conglomerate reservoir segmentation and clustering method, a conglomerate reservoir segmentation and clustering device, a machine-readable storage medium and a processor.

In order to achieve the above-mentioned object, the present application provides a conglomerate reservoir segmentation and clustering method in a first aspect, comprising:

Obtaining horizontal well mechanical specific energy, horizontal well geological parameter information and horizontal well engineering parameter information;

According to the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well, a cluster analysis algorithm is used to perform multi-dimensional data classification on the data points of the horizontal well at depth to obtain a classification result;

Using the classification result and the preset horizontal segment length, the depth of the horizontal well is segmented to obtain a segmentation result;

The closeness algorithm is used to calculate the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result, and the perforation cluster position in the segment is obtained according to the comprehensive closeness of the geological engineering parameters at each depth;

The conglomerate reservoir segmentation and clustering results are obtained according to the segmentation results and the perforation cluster positions.

In the embodiment of the present application, the obtaining of the horizontal well mechanical specific energy includes:

Acquiring basic parameter information, wherein the basic parameter information at least includes drilling data and drilling tool assembly parameters;

According to the basic parameter information, the horizontal well mechanical specific energy is calculated using the horizontal well friction model.

In the embodiment of the present application, the horizontal well mechanical specific energy is calculated using the horizontal well friction model according to the basic parameter information, including:

Substituting the basic parameter information into the modified mechanical specific energy calculation formula in the horizontal well friction model to obtain the horizontal well mechanical specific energy;

The modified mechanical specific energy calculation formula is:

Among them, E is the mechanical specific energy of the horizontal well MPa, P is the drilling pressure MPa, D _b is the drill bit diameter mm, e is the natural logarithm, _ak is the well inclination angle rad, μ _well is the drill string friction coefficient, υ is the drilling speed m/h, q is the displacement per revolution of the drill bit, which is a structural parameter and is only related to the linear shape and geometric dimensions of the stator and rotor, L/r, Δp _p is the nozzle pressure drop of the screw drill bit MPa, n is the turntable speed r/min, μ _bit is the drill bit friction coefficient, K _N is the speed flow ratio of the power drill, r/L, and Q is the total flow, L/s.

In the embodiment of the present application, according to the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well, a cluster analysis algorithm is used to perform multidimensional data classification on the data points of the horizontal well at depth to obtain classification results, including:

S1: preprocessing the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information to obtain preprocessed data, wherein the preprocessed data includes a plurality of samples, each sample including the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information;

S2: randomly selecting K centers from the data points of the horizontal well at depth;

S3: Define loss function;

S4: Set the number of iterations;

S5: Evaluate the distance between each sample and the cluster center, and assign each sample to the cluster to which the nearest center belongs. If the loss condition is not met or the iteration stop condition is not reached, recalculate the cluster center point of each category;

S6: Repeat S5 until the loss function converges to obtain the classification result.

In the embodiment of the present application, the depth of the horizontal well is segmented by using the classification result and the preset horizontal segment length to obtain the segmentation result, including:

Obtain an initial segmentation point, and start from the initial segmentation point, at the depth of the horizontal well according to the preset horizontal segment The long segmentation is performed to obtain the initial segmentation result;

According to the classification results of each data point in each segment in the initial segmentation result, the data points with the same classification results in the segment are assigned the same label, and different classification results correspond to different labels;

Counting the number of data points corresponding to each label in each segment in the initial segmentation result;

The classification result corresponding to the label with the largest number of data points is used as the feature value of the majority point of the segment within the segment length range;

The segmentation result is obtained according to the feature values of the majority of points in each segment within the segment length range.

In the embodiment of the present application, the segmentation result is obtained according to the feature values of the majority of points in each segment within the segment length range, including:

Determine whether the majority of point feature values of each segment within the segment length range belong to one category in the classification result. If so, use the initial segmentation result as the segmentation result; if not, adjust the preset horizontal segment length and/or adjust the initial segmentation point, and re-segment to obtain the segmentation result.

In the embodiment of the present application, the closeness algorithm is used to calculate the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result, and the perforation cluster position in the segment is obtained according to the comprehensive closeness of the geological engineering parameters at each depth, including:

According to the mechanical specific energy, geological parameter information and engineering parameter information of horizontal wells in each section, fuzzy matter-element is constructed;

Based on the fuzzy value in the fuzzy matter-element, the optimal membership of the fuzzy value of each evaluation index is calculated according to the optimal membership principle;

According to the optimal membership of the fuzzy values of the evaluation indicators, a difference square composite fuzzy matter-element is constructed;

Determine the weight of each feature;

Substituting the square difference composite fuzzy matter-element and the characteristic weights into the closeness calculation formula, the comprehensive closeness of the geological engineering parameters at each depth in each section is obtained;

The perforation cluster positions within the section are obtained according to the comprehensive closeness of the geological engineering parameters at each depth.

A second aspect of the present application provides a conglomerate reservoir segmentation and clustering device, the conglomerate reservoir segmentation and clustering device comprising:

An information acquisition module is used to obtain horizontal well mechanical specific energy, horizontal well geological parameter information and horizontal well engineering parameter information;

A classification module, used to perform multi-dimensional data classification on the data points of the horizontal well at depth by using a cluster analysis algorithm according to the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well, to obtain a classification result;

A segmentation module, used to segment the depth of the horizontal well by using the classification result and a preset horizontal segment length to obtain a segmentation result;

A clustering module, for respectively calculating the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result by using a closeness algorithm, and obtaining the perforation cluster position in the segment according to the comprehensive closeness of the geological engineering parameters at each depth;

The result output module is used to obtain the conglomerate reservoir segmentation and clustering results according to the segmentation results and the perforation cluster positions.

A third aspect of the present application provides a processor configured to execute the above-mentioned conglomerate reservoir segmentation and clustering method.

A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configure the processor to execute the above-mentioned conglomerate reservoir segmentation and clustering method.

Through the above technical scheme, the accuracy of identifying engineering sweet spots in sandy conglomerate reservoirs can be greatly improved through the mechanical specific energy of horizontal wells. The cluster analysis algorithm is used for classification and segmentation to ensure that the geological and engineering parameters in the horizontal well section are similar. At the same time, the proximity algorithm is used to calculate the proximity, and then the perforation cluster position is obtained to form a sandy conglomerate reservoir based on the collaborative optimization of geological and engineering sweet spots. The segmentation and clustering method of rock reservoirs guides the balanced transformation of multiple clusters in the volume fracturing section of horizontal wells in conglomerate reservoirs, thereby effectively improving the balance of geological and engineering parameter distribution at the perforation points in the section, which is conducive to the balanced fracturing of each cluster during the multi-cluster fracturing process in the section, reducing the difficulty of construction, reducing the probability of complex working conditions during construction, saving construction fluid, saving investment, and increasing the production of oil and gas wells. This method is convenient to calculate and simple to operate.

Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

FIG1 schematically shows an application environment diagram of a conglomerate reservoir segmentation and clustering method according to an embodiment of the present application;

FIG2 schematically shows a flow chart of a conglomerate reservoir segmentation and clustering method according to an embodiment of the present application;

FIG3 schematically shows a flow chart of an implementation of a conglomerate reservoir segmentation and clustering method according to an embodiment of the present application;

FIG4 schematically shows a statistical diagram of the relationship between the optical fiber monitoring advantage liquid inlet channel and the mechanical specific energy according to an embodiment of the present application;

FIG5 schematically shows a schematic diagram of clustering principle using the proximity method according to an embodiment of the present application;

FIG6 schematically shows a distribution diagram of geological engineering parameters at a perforation location according to an embodiment of the present application;

FIG7 schematically shows a construction curve diagram according to an embodiment of the present application;

FIG8 schematically shows a construction result statistical diagram according to an embodiment of the present application;

FIG9 schematically shows a mechanical specific energy distribution diagram according to an embodiment of the present application;

FIG10 schematically shows a cluster analysis result diagram according to an embodiment of the present application;

FIG11 schematically shows a diagram of calculation results of the pasting progress at a depth near the first cluster position in a single segment according to an embodiment of the present application;

FIG12 schematically shows a segmented clustering optimization result diagram according to an embodiment of the present application;

FIG13 schematically shows a structural block diagram of a conglomerate reservoir segmentation and clustering device according to an embodiment of the present application;

FIG14 schematically shows an internal structure diagram of a computer device according to an embodiment of the present application.

Description of Reference Numerals
102-terminal; 104-server; 410-information acquisition module; 420-classification module; 430-segmentation module; 440-clustering module; 450-result output module; A01-processor; A02-network interface; A03-internal memory; A04-display screen; A05-input device; A06-non-volatile storage medium; B01-operating system; B02-computer program.

Detailed ways

In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

It should be noted that if there are directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application, the directional indications are only used to explain the relative position relationship, movement, etc. between the components under a certain posture (as shown in the attached figure). If the specific posture changes, the directional indication will also change accordingly. In addition, if there are descriptions of "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the indicated technology. The number of features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments may be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in the field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

A conglomerate reservoir segmentation and clustering method provided in the present application can be applied in an application environment as shown in FIG1. Among them, the terminal 102 communicates with the server 104 through the network. The server 104 obtains the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information from the terminal 102; then, according to the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information, a clustering analysis algorithm is used to perform multi-dimensional data classification on the data points of the horizontal well at depth to obtain a classification result; then, using the classification result and the preset horizontal segment length, the depth of the horizontal well is segmented to obtain a segmentation result; respectively, the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result is calculated by the closeness algorithm, and the perforation cluster position in the segment is obtained according to the comprehensive closeness of the geological engineering parameters at each depth; and the conglomerate reservoir segmentation and clustering result is obtained according to the segmentation result and the perforation cluster position. The terminal 102 may be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

FIG2 schematically shows a flow chart of a conglomerate reservoir segmentation and clustering method according to an embodiment of the present application, and FIG3 schematically shows a flow chart of an implementation method of a conglomerate reservoir segmentation and clustering method according to an embodiment of the present application. As shown in FIG1 , in an embodiment of the present application, a conglomerate reservoir segmentation and clustering method is provided. This embodiment mainly uses the method applied to the terminal 102 (or server 104) in FIG1 as an example, and includes the following steps:

Step 210: Obtain the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well, and the engineering parameter information of the horizontal well; the above-mentioned geological parameter information of the horizontal well includes logging data, gravel line density, oil content index, etc., which can be obtained according to the actual working conditions of the horizontal well. The above-mentioned engineering parameter information of the horizontal well includes information such as the position and stress of the horizontal well coupling, which can be obtained according to the actual working conditions of the horizontal well. The above-mentioned mechanical specific energy of the horizontal well can be calculated based on the mechanical specific energy model, or it can be obtained after correcting the mechanical specific energy according to the actual situation. Among them, the mechanical specific energy characterizes the mechanical energy consumed by drilling a unit volume of rock. The mechanical specific energy integrates the characteristics of rock mechanics and rock compressibility. The mechanical specific energy can be used to identify the engineering sweet spot, so that the identification accuracy of the engineering sweet spot is higher. By obtaining the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well, and the engineering parameter information of the horizontal well, the information of the horizontal well can be obtained from both geological and engineering aspects, which is convenient for the later segmentation and clustering calculation.

Since screw drill bits are usually used to speed up the drilling process during horizontal drilling, it is necessary to consider the influence of friction and screw drill bit parameters on the mechanical specific energy during horizontal well drilling. The mechanical specific energy of the horizontal well can be corrected by the following steps:

First, basic parameter information is obtained, and the basic parameter information includes at least drilling data and drilling tool assembly parameters; the drilling data includes drilling pressure, torque, drilling speed, etc., and the drilling tool assembly parameters include screw drilling tool parameters. The above basic parameter information can be obtained according to actual working conditions.

Then, according to the basic parameter information, the horizontal well friction model is used to calculate the horizontal well mechanical specific energy. The horizontal well friction model is a horizontal well mechanical specific energy correction model established by considering the influence of friction and screw drill parameters on mechanical specific energy in horizontal well drilling, which can correct the mechanical specific energy.

The horizontal well mechanical specific energy obtained by the above calculation may be obtained by substituting the basic parameter information into the modified mechanical specific energy calculation formula in the horizontal well friction model to obtain the horizontal well mechanical specific energy; the modified mechanical specific energy calculation formula is:

In some embodiments, in order to facilitate the calculation of the above-mentioned modified mechanical specific energy calculation formula, the above-mentioned horizontal well friction model can also first correct the drilling data, and then substitute the corrected drilling data and drill bit combination parameters into the above-mentioned modified mechanical specific energy calculation formula to obtain the horizontal well mechanical specific energy.

Please refer to Figure 4, which schematically shows a statistical diagram of the relationship between the optical fiber monitoring superior inlet channel and the mechanical specific energy according to an embodiment of the present application. By obtaining the mechanical specific energy of the horizontal well and using the method of identifying the engineering sweet spot using the mechanical specific energy, the accuracy of identifying the engineering sweet spot can be improved.

Step 220: Based on the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well, a cluster analysis algorithm is used to perform multi-dimensional data classification on the data points of the horizontal well at depth to obtain a classification result; in this embodiment, the geological parameter information of the horizontal well can be one or more data such as logging data, gravel line density, oil content index, etc., and the engineering parameter information of the horizontal well can be one or more data such as horizontal well coupling position, stress, etc., which can be set according to actual needs. The horizontal well can be composed of multiple data points at depth, for example, a point with a depth of 100 meters and a point with a depth of 300 meters. The above-mentioned data points can be described by multiple dimensions, for example: each data point has a corresponding mechanical specific energy, logging data, and gravel line density.

It should be noted that the above multi-dimensional data classification can be data classification using corresponding quantitative dimensions according to the number of data of the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information. For example: based on the mechanical specific energy, well logging data and gravel line density, a cluster analysis algorithm is used to perform three-dimensional data classification on the data points of the horizontal well at depth. Based on the mechanical specific energy and oil content index, a cluster analysis algorithm is used to perform two-dimensional data classification on the data points of the horizontal well at depth.

The above-mentioned clustering analysis algorithm is used to perform multi-dimensional data classification on the data points of the horizontal well at depth, and the data points are classified from multiple dimensions. It can be obtained by importing the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well into the clustering analysis model, wherein the above-mentioned clustering analysis algorithm can be the kmeans algorithm, which is a typical distance-based clustering algorithm. The distance is used as the evaluation index of similarity, that is, it is believed that the closer the distance between two objects, the greater their similarity. The core of the kmeans algorithm is to divide the given data set into K categories according to the number of clusters, and then determine whether the clustering result meets the loop stop condition through loop iteration. Specifically, it includes the following steps:

Step S1: pre-process the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information to obtain pre-processed data, wherein the pre-processed data comprises a plurality of samples, each sample comprising the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information; the above-mentioned pre-processing comprises standardizing the data, filtering abnormal points and the like, so as to facilitate the subsequent classification.

Step S2: randomly select K centers from the data points of the horizontal well at depth, for example, the K centers are respectively recorded as

Step S3: Define the loss function; in the multidimensional space composed of all geotechnical parameters, iteratively search for K clusters (Cluster) to minimize the loss function corresponding to the clustering result of geotechnical parameters. The loss function describes the closeness between the cluster centers. The smaller the value of the loss function, the higher the similarity between the samples in the cluster, that is, the better the clustering effect. The loss function is usually defined as: Among them, J(c,u) is the sum of squared errors of each sample from the center point of the cluster to which it belongs, _xi is the i-th sample, _ci is the cluster to which _xi belongs, is the center point corresponding to the cluster, M is the total number of samples.

Step S4: Set the number of iterations to t; the number can be set specifically according to actual conditions.

Step S5: Evaluate the distance from each sample to the cluster center, and assign each sample to the cluster to which the nearest center belongs. If the loss condition is not met or the iteration stop condition is not reached, recalculate the cluster center point of each category; wherein, the distance from each sample to the cluster center is evaluated and each sample _xi is assigned to the cluster to which the nearest center belongs, which can be calculated using the following formula: Among them, k is the center point of each category, is the distance from each sample to the cluster center. If the loss condition is not met or the iteration stop condition is not reached, the center point k of each category is recalculated, which can be calculated using the following formula:

in, is the centroid position after t+1 rounds of iteration.

Step S6: Repeat step S5 until the loss function converges to obtain the classification result.

The kmeans algorithm is very fast and can quickly classify data points at depth for horizontal wells.

Step 230: using the classification result and the preset horizontal segment length, segmenting the depth of the horizontal well to obtain a segmentation result; the segmentation is to divide the depth of the horizontal well into multiple segments, specifically including the following steps:

First, obtain the initial segmentation point, and start from the initial segmentation point, segment the horizontal well according to the preset horizontal segment length at the depth of the horizontal well to obtain the initial segmentation result; the above-mentioned initial segmentation point refers to the point where segmentation starts, which can be selected based on experience or actual conditions, and can generally start from point A, that is, the bottom of the horizontal well. The above-mentioned preset horizontal segment length can be a segment length range determined in combination with previous development experience, and then the maximum horizontal segment length is selected according to the set segment length range. For example, the segment length range is 50-100, which can set the horizontal segment length to 100.

Then, according to the classification results of each data point in each segment of the initial segmentation result, the data points with the same classification results in the segment are assigned the same label, and different classification results correspond to different labels; in this embodiment, each segment of the initial segmentation result contains multiple data points, and each data point has a classification result in the above step 220, and then each classification result can be assigned a label, and different categories are assigned different labels. For example, the points of category N1 are assigned label K1, and the points of category N2 are assigned label K2.

Then, the number of data points corresponding to each label in each segment in the initial segmentation result is counted; there may be multiple categories in each segment, each category includes multiple data points, and the number of data points corresponding to each label can be counted separately.

Then, the classification result corresponding to the label with the largest number of data points is used as the feature value of the majority point of the segment within the segment length range; the classification results corresponding to the above labels are used as the feature value in the segment, for example, in the above example, the feature values in the segment include K1 and K2. Then, the label with the largest number of data points is used as the feature value of the majority point of the segment within the segment length range. For example, in the above example, the data points corresponding to K1 are 5, and the data points corresponding to K2 are 2, so the feature value of the majority point of the segment within the segment length range is K1.

Finally, the segmentation result is obtained according to the feature values of the majority points in each segment within the segment length. Since there may be a situation where the data points are evenly distributed in the above segmentation process, there is no feature value of the majority point, which means that the segmentation is invalid. That is, if the feature value of the majority point belongs to one of the above classification results, the segmentation is valid. Otherwise, it needs to be adjusted. The specific judgment can be made through the following steps:

It should be noted that the above-mentioned re-segmentation can be to adjust the preset horizontal segment length, such as reducing the horizontal segment length and re-judging until the horizontal segment length is reduced to the minimum value; it can also be to adjust the initial segmentation point, such as moving the initial segmentation point backward by 1m; or it can be to adjust the preset horizontal segment length first, and then adjust the initial segmentation point.

For example: starting from point A, determine whether the characteristic values of most points in the segment length range belong to a certain category in the cluster analysis results. If so, the segment length can be divided into one category. If not, reduce the segment length and re-judge until the segment length is reduced to the minimum value. If it still cannot be divided into segments, move the starting point back 1m and repeat the above judgment process until all horizontal segments are divided.

Step 240: respectively use the closeness algorithm to calculate the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result, and obtain the perforation cluster position in the segment according to the comprehensive closeness of the geological engineering parameters at each depth; please refer to Figure 5, which schematically shows a schematic diagram of the clustering principle of the closeness method according to an embodiment of the present application. Specifically, the following steps are included:

First, fuzzy matter-element is constructed according to the mechanical specific energy, geological parameter information and engineering parameter information of horizontal wells in each section. For a given thing, the basic element of the thing can be represented by a triple (thing M, feature C, value x). If the value of the thing is fuzzy, it can be called a fuzzy matter-element, which is recorded as: If an object M has n features C1, C2, ..., Cn and the fuzzy values corresponding to these features are x1, x2 ..., xn, then _Rn is called an n-dimensional fuzzy object element; if there are m objects described by their common n features C1, C2, ..., Cn and their corresponding fuzzy values x1, x2, ..., xn, then _Rnm is called an n-dimensional fuzzy composite element of m objects, expressed as:

Then, based on the fuzzy value in the fuzzy matter-element, the fuzzy value of each evaluation index is calculated according to the principle of optimal membership. Optimal membership; among them, according to the different utilities of indicators, the optimal membership can be divided into two types:

The first type is that the larger the oil content index and other indicators, the better, and the following formula is used to express it:

The second type is that the smaller the mechanical specific energy and gravel linear density, the better the performance, which is expressed by the following formula:

In the above formula, minx _ij and maxx _ij represent the minimum and maximum values of the ith index in each thing, that is, the minimum and maximum values of each row in R _nm . According to the above fuzzy matter-element formula, the optimal membership fuzzy matter-element R' _nm can be obtained by calculation:

Then, according to the superior membership of the fuzzy values of the evaluation indicators, a difference square composite fuzzy matter-element is constructed; before constructing the difference square composite fuzzy matter-element, the standard (optimal) fuzzy matter-element must be determined first. The standard fuzzy matter-element is the maximum or minimum value of the superior membership of each indicator in the superior membership fuzzy matter-element R′ _nm , represented by R _o . After the standard fuzzy matter-element is established, the square of the difference between each item in each indicator in the superior membership fuzzy matter-element R′ _nm and the standard fuzzy matter-element R _o is expressed as:

V _ij (i＝1,2,…,n；j＝1,2,…,m), namely V _ij ＝(u _ij -u _oj ) ² , then the difference square composite fuzzy matter-element R _v can be constructed, which is recorded as:

Then, the weight of each feature is determined; since the uneven development of each cluster in a segment is mainly due to the large differences in the fracturing conditions of each cluster, after the dominant cluster is fractured, the pressure value in the segment is difficult to meet the fracturing requirements of other clusters. Therefore, the fundamental requirement for the balanced development of each cluster in the segment is to ensure that the characteristics of each perforation cluster are similar. To meet this requirement, this model uses variance as the weight of each feature, which can be expressed as:

Among them, _wj is the variance weight of _Cj eigenvalue, μ is the average value of _Cj eigenvalue, n is the number of _Cj eigenvalues, and _xi is the size of _Cj eigenvalue.

Then, the difference square composite fuzzy matter-element and the weights of each feature are substituted into the closeness calculation formula to obtain The comprehensive closeness of geological engineering parameters at each depth; the above closeness calculation formula is: Among them, ω _j is the weight of each feature, and Δ _ij is the difference square composite fuzzy matter-element.

Finally, the perforation cluster position in the section is obtained according to the comprehensive closeness of the geological engineering parameters at each depth. In this embodiment, after the closeness of the geological engineering parameters is calculated, the position with the lowest closeness value is selected as the perforation cluster position.

Step 250: Obtain the segmentation and clustering results of the conglomerate reservoir according to the segmentation results and the perforation cluster positions. After the segmentation results are obtained by the above calculations and the perforation cluster positions are determined, the segmentation and clustering results of the conglomerate reservoir can be obtained. Please refer to Figures 6 to 8. After the segmentation and clustering are performed by the present invention, the distribution of engineering parameters is more balanced, the construction difficulty is lower, the probability of complex working conditions during construction is reduced, and the amount of construction fluid is saved.

The following takes the J-1 well as an example to illustrate the segmentation and clustering process.

First, the corrected drilling parameters of the J-1 well are brought into the corrected mechanical specific energy calculation formula to calculate the distribution of the mechanical specific energy of the horizontal section. Please refer to Figure 9, which schematically shows the distribution diagram of the mechanical specific energy according to the embodiment of the present application; then, the mechanical specific energy, oil content index, and gravel line density parameters are comprehensively considered, and the cluster analysis algorithm is used to classify the depth points of the J-1 well. Among them, the characteristic values of the horizontal well section can be divided into 5 categories, K1-K5. Please refer to Figure 10, which schematically shows the cluster analysis result diagram according to the embodiment of the present application. Then, using the above classification results, combined with the previous development experience to limit the result segment length range to 50-100m, the horizontal well is segmented to obtain the segmentation results. Then, according to the above-mentioned sections, the comprehensive closeness of the mechanical specific energy, oil content index, and gravel line density parameters at each depth in each section is calculated. Combined with the previous development experience, the clustering result is limited to 2 clusters in the first section and 3 clusters in the remaining sections, with a minimum cluster spacing of 10m. The position with a low closeness value, that is, the depth ① in Figure 11, is selected as the position of the first perforation cluster in this section. Finally, the segmented clustering result is output, please refer to Figure 12, which schematically shows the segmented clustering optimization result diagram according to an embodiment of the present application.

In the above implementation process, the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well are obtained; according to the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well, a cluster analysis algorithm is used to perform multi-dimensional data classification on the data points of the horizontal well at depth to obtain a classification result; then the depth of the horizontal well is segmented using the classification result and a preset horizontal segment length to obtain a segmentation result; then the proximity algorithm is used to calculate the comprehensive proximity of the geological engineering parameters at each depth in each segment in the segmentation result, and the perforation cluster position in the segment is obtained according to the comprehensive proximity of the geological engineering parameters at each depth; finally, the segmentation clustering result of the conglomerate reservoir is obtained according to the segmentation result and the perforation cluster position. The mechanical specific energy of horizontal wells can greatly improve the accuracy of identifying engineering sweet spots in sandy conglomerate reservoirs. Cluster analysis algorithms are used for classification and segmentation to ensure that the geological and engineering parameters in the horizontal well sections are similar. At the same time, the proximity algorithm is used to calculate the proximity, and then the perforation cluster position is obtained, forming a segmentation and clustering method for sandy conglomerate reservoirs based on the coordinated optimization of geological and engineering sweet spots, guiding the balanced transformation of multiple clusters in the volume fracturing section of horizontal wells in conglomerate reservoirs, thereby effectively improving the balance of the distribution of geological and engineering parameters at the perforation points in the section, which is conducive to the balanced fracturing of each cluster in the multi-cluster fracturing process in the section, reducing the construction difficulty, reducing the probability of complex working conditions during construction, saving construction fluid, saving investment, and increasing the production of oil and gas wells. This method is convenient to calculate and simple to operate.

FIG2 is a flow chart of a conglomerate reservoir segmentation and clustering method in one embodiment. It should be understood that although the steps in the flow chart of FIG2 are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in FIG2 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily one by one, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

In one embodiment, as shown in FIG13 , FIG13 schematically shows a structural block diagram of a conglomerate reservoir segmentation and clustering device according to an embodiment of the present application. A conglomerate reservoir segmentation and clustering device is provided, comprising an information acquisition module 410, a classification module 420, a segmentation module 430, a clustering module 440, and a result output module 450, wherein:

Information acquisition module 410, used to acquire horizontal well mechanical specific energy, horizontal well geological parameter information and horizontal well engineering parameter information;

A classification module 420 is used to perform multi-dimensional data classification on the data points of the horizontal well at depth using a cluster analysis algorithm according to the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information to obtain a classification result;

A segmentation module 430 is used to segment the depth of the horizontal well by using the classification result and the preset horizontal segment length to obtain a segmentation result;

The clustering module 440 is used to calculate the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result by using a closeness algorithm, and obtain the perforation cluster position in the segment according to the comprehensive closeness of the geological engineering parameters at each depth;

The result output module 450 is used to obtain the conglomerate reservoir segmentation and clustering results according to the segmentation results and the perforation cluster positions.

The conglomerate reservoir segmentation and clustering device includes a processor and a memory. The information acquisition module 410, classification module 420, segmentation module 430, clustering module 440, result output module 450, etc. are all stored in the memory as program units, and the processor executes the program modules stored in the memory to implement corresponding functions.

The processor includes a kernel, and the kernel calls the corresponding program unit from the memory. One or more kernels can be set, and the conglomerate reservoir segmentation and clustering method is implemented by adjusting kernel parameters.

The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and/or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

An embodiment of the present application provides a storage medium on which a program is stored. When the program is executed by a processor, the above-mentioned conglomerate reservoir segmentation and clustering method is implemented.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG14. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown in the figure) connected via a system bus. The processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor A01, a conglomerate reservoir segmentation and clustering method is implemented. The display screen A04 of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device A05 of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

Those skilled in the art will appreciate that the structure shown in FIG. 14 is only a partial structure related to the present application. The block diagram does not constitute a limitation on the computer device to which the present application solution is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

In one embodiment, the conglomerate reservoir segmentation and clustering device provided in the present application may be implemented in the form of a computer program, and the computer program may be run on a computer device as shown in FIG14. The memory of the computer device may store various program modules constituting the conglomerate reservoir segmentation and clustering device, such as the information acquisition module 410, the classification module 420, the segmentation module 430, the clustering module 440, and the result output module 450 shown in FIG13. The computer program composed of various program modules enables the processor to execute the steps of the conglomerate reservoir segmentation and clustering method in various embodiments of the present application described in this specification.

The computer device shown in FIG8 can perform step 210 through the information acquisition module 410 in the conglomerate reservoir segmentation and clustering device shown in FIG13. The computer device can perform step 220 through the classification module 420, perform step 230 through the segmentation module 430, perform step 240 through the clustering module 440, and perform step 250 through the result output module 450.

The embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented:

In one embodiment, obtaining the mechanical specific energy of the horizontal well includes:

In one embodiment, the horizontal well mechanical specific energy is calculated using a horizontal well friction model according to the basic parameter information, including:

The modified mechanical specific energy calculation formula is:

Among them, E is the mechanical specific energy of the horizontal well MPa, P is the drilling pressure MPa, D _b is the drill bit diameter mm, e is the natural logarithm, _ak is the well inclination angle rad, μ _well is the drill string friction coefficient, υ is the drilling speed m/h, q is the displacement per revolution of the drill bit, which is a structural parameter and is only related to the linear shape and geometric dimensions of the stator and rotor, L/r, Δp _p is the nozzle pressure drop of the screw drill bit MPa, n is the turntable speed r/min, μ _bit is the drill bit friction coefficient, K _N is the speed flow ratio of the power drill bit, r/L, and Q is the total flow rate, L/s.

In one embodiment, according to the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well, a cluster analysis algorithm is used to perform multidimensional data classification on the data points of the horizontal well at depth, and the To the classification results, including:

S3: Define loss function;

S4: Set the number of iterations;

In one embodiment, the depth of the horizontal well is segmented by using the classification result and the preset horizontal segment length to obtain the segmentation result, including:

Obtaining an initial segmentation point, and starting from the initial segmentation point, segmenting at the depth of the horizontal well according to a preset horizontal segment length to obtain an initial segmentation result;

According to the classification results of each data point in each segment in the initial segmentation result, the data points with the same classification results in the segment are assigned the same label, and different labels are corresponding to different classification results;

In one embodiment, obtaining the segmentation result according to the feature values of the majority of points in each segment within the segment length range includes:

In one embodiment, the method of respectively using a closeness algorithm to calculate the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result, and obtaining the perforation cluster position in the segment according to the comprehensive closeness of the geological engineering parameters at each depth, includes:

According to the optimal membership degree of the fuzzy value of each evaluation index, a difference square composite fuzzy matter-element is constructed;

Determine the weight of each feature;

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. Mode.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

In a typical configuration, a computing device includes one or more processors (CPU), input/output interfaces, network interfaces, and memory.

Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and/or non-volatile memory in the form of read-only memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined in this article, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

A conglomerate reservoir segmentation and clustering method, characterized in that the conglomerate reservoir segmentation and clustering method comprises:

Obtaining horizontal well mechanical specific energy, horizontal well geological parameter information and horizontal well engineering parameter information;

According to the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well, a cluster analysis algorithm is used to perform multi-dimensional data classification on the data points of the horizontal well at depth to obtain a classification result;

Using the classification result and the preset horizontal segment length, the depth of the horizontal well is segmented to obtain a segmentation result;

The closeness algorithm is used to calculate the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result, and the perforation cluster position in the segment is obtained according to the comprehensive closeness of the geological engineering parameters at each depth;

The conglomerate reservoir segmentation and clustering results are obtained according to the segmentation results and the perforation cluster positions.
The method according to claim 1, characterized in that the obtaining of the horizontal well mechanical specific energy comprises:

Acquiring basic parameter information, wherein the basic parameter information at least includes drilling data and drilling tool assembly parameters;

According to the basic parameter information, the horizontal well mechanical specific energy is calculated using the horizontal well friction model.
The method according to claim 2 is characterized in that the step of calculating the horizontal well mechanical specific energy using a horizontal well friction model based on the basic parameter information comprises:

Substituting the basic parameter information into the modified mechanical specific energy calculation formula in the horizontal well friction model to obtain the horizontal well mechanical specific energy;

The modified mechanical specific energy calculation formula is:

Among them, E is the mechanical specific energy of the horizontal well, P is the drilling pressure, D b is the drill bit diameter, e is the natural logarithm, ak is the well inclination angle, μ well is the drill string friction coefficient, υ is the drilling speed, q is the displacement per revolution of the drill bit, Δpp is the nozzle pressure drop of the screw drill bit, n is the number of turntable revolutions, μ bit is the drill bit friction coefficient, K N is the speed flow ratio of the power drill bit, and Q is the total flow.
The method according to claim 1 is characterized in that, according to the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well, a cluster analysis algorithm is used to perform multidimensional data classification on the data points of the horizontal well at depth to obtain a classification result, including:

S1: preprocessing the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information to obtain preprocessed data, wherein the preprocessed data includes a plurality of samples, each sample including the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information;

S2: randomly selecting K centers from the data points of the horizontal well at depth;

S3: Define loss function;

S4: Set the number of iterations;

S5: Evaluate the distance between each sample and the cluster center, and assign each sample to the cluster to which the nearest center belongs. If the loss condition is not met or the iteration stop condition is not reached, recalculate the cluster center point of each category;

S6: Repeat S5 until the loss function converges to obtain the classification result.
The method according to claim 1 is characterized in that the depth of the horizontal well is segmented by using the classification result and the preset horizontal segment length to obtain the segmentation result, comprising:

Obtaining an initial segmentation point, and starting from the initial segmentation point, segmenting at the depth of the horizontal well according to a preset horizontal segment length to obtain an initial segmentation result;

According to the classification results of each data point in each segment in the initial segmentation result, the data points with the same classification results in the segment are assigned the same label, and different labels are corresponding to different classification results;

Counting the number of data points corresponding to each label in each segment in the initial segmentation result;

The classification result corresponding to the label with the largest number of data points is used as the feature value of the majority point of the segment within the segment length range;

The segmentation result is obtained according to the feature values of the majority of points in each segment within the segment length range.
The method according to claim 5, characterized in that the step of obtaining the segmentation result according to the feature values of the majority of points in each segment within the segment length range comprises:

Determine whether the majority of point feature values of each segment within the segment length range belong to one category in the classification result. If so, use the initial segmentation result as the segmentation result; if not, adjust the preset horizontal segment length and/or adjust the initial segmentation point, and re-segment to obtain the segmentation result.
The method according to claim 1 is characterized in that the step of respectively using a closeness algorithm to calculate the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result, and obtaining the perforation cluster position in the segment according to the comprehensive closeness of the geological engineering parameters at each depth, comprises:

According to the mechanical specific energy, geological parameter information and engineering parameter information of horizontal wells in each section, fuzzy matter-element is constructed;

Based on the fuzzy value in the fuzzy matter-element, the optimal membership of the fuzzy value of each evaluation index is calculated according to the optimal membership principle;

According to the optimal membership degree of the fuzzy value of each evaluation index, a difference square composite fuzzy matter-element is constructed;

Determine the weight of each feature;

Substituting the square difference composite fuzzy matter-element and the weights of each feature into the closeness calculation formula, the comprehensive closeness of the geological engineering parameters at each depth in each section is obtained;

The perforation cluster positions within the section are obtained according to the comprehensive closeness of the geological engineering parameters at each depth.
A conglomerate reservoir segmentation and clustering device, characterized in that the conglomerate reservoir segmentation and clustering device comprises:

An information acquisition module is used to obtain horizontal well mechanical specific energy, horizontal well geological parameter information and horizontal well engineering parameter information;

A classification module, used to perform multi-dimensional data classification on the data points of the horizontal well at depth by using a cluster analysis algorithm according to the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well and the engineering parameter information of the horizontal well, to obtain a classification result;

A segmentation module, used to segment the depth of the horizontal well by using the classification result and the preset horizontal segment length to obtain a segmentation result;

A clustering module, for respectively calculating the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result by using a closeness algorithm, and obtaining the perforation cluster position in the segment according to the comprehensive closeness of the geological engineering parameters at each depth;

The result output module is used to obtain the conglomerate reservoir segmentation and clustering results according to the segmentation results and the perforation cluster positions.
A processor, characterized in that it is configured to execute the conglomerate reservoir segmentation and clustering method according to any one of claims 1 to 7.
A machine-readable storage medium having instructions stored thereon, characterized in that when the instructions are executed by a processor, the processor is configured to execute the conglomerate reservoir according to any one of claims 1 to 7. Layer segmentation clustering method.