CN117967295A

CN117967295A - Conglomerate reservoir segment clustering method, device, storage medium and processor

Info

Publication number: CN117967295A
Application number: CN202211312462.8A
Authority: CN
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: Petrochina Co Ltd
Current assignee: Petrochina Co Ltd
Priority date: 2022-10-25
Filing date: 2022-10-25
Publication date: 2024-05-03
Also published as: WO2024088265A1

Abstract

The embodiment of the application provides a method, a device, a storage medium and a processor for sectioning and clustering a conglomerate reservoir, belonging to the technical field of petroleum and natural gas yield increase. The application obtains 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; carrying out multidimensional data classification on data points of the horizontal well in depth by adopting a cluster analysis algorithm; segmenting the depth of the horizontal well by using the classification result and the preset horizontal segment length; calculating the comprehensive closeness of geological engineering parameters at each depth in each segment in the segmentation result by adopting a closeness algorithm, so as to obtain the position of a perforation cluster in the segment; and obtaining the segmentation clustering result of the conglomerate reservoir according to the segmentation result and the perforation cluster position. The method improves the identification precision of engineering desserts, effectively improves the balance of geology and engineering parameter distribution at perforation points in a section, reduces construction difficulty, reduces the occurrence probability of complex working conditions in construction, saves construction liquid amount, saves investment and improves the yield of oil and gas wells.

Description

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

Technical Field

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

Background

The concave of the Leucon basin and the Leucon lake is a large multi-layer hidden hydrocarbon-rich concave, and the efficient development of the concave is of great significance to the national energy safety. Compared with the conventional oil reservoir, the MAlake concave conglomerate oil reservoir is affected by gravels, the reservoir heterogeneity is stronger, and the method has the characteristics of deep burial, poor physical property, natural crack non-development, large closing pressure and the like, on-site practice shows that multi-cluster volume fracturing in a horizontal well section is a key means for improving the effect of reducing the cost of the conglomerate oil reservoir, the number of clusters in the section is increased by properly enlarging the length of the section, the number of single-well construction sections is reduced, the single-well fracturing cost can be reduced by 15-20%, but the heterogeneity of the conglomerate reservoir is stronger, the gravel content and size distribution are uneven, the lithology change is large, the stress distribution is complex, in the multi-cluster volume fracturing transformation in the horizontal well section, the balanced cracking of each perforation cluster in the section cannot be effectively realized, the difference of feed liquid sand of each perforation cluster in the same transformation section is large, part of perforation clusters are transformed due to the large quantity of feed liquid and sand, and part of perforation clusters are transformed due to the insufficient feed liquid and sand quantity, and the utilization degree of the reservoir is seriously affected. The multi-cluster fracturing process in the popularization section of the MAlake conglomerate oil reservoir in 2020 has the advantages that more than 40% of perforation clusters do not contribute to oil flow, the underground eagle eye test has the characteristic that only 2-3 clusters have fractured sand erosion under 6 clusters in a single section, and compared with 2019, the single well yield is reduced by 40-55%, so that comprehensive data such as drilling, recording and testing are comprehensively utilized, the geological-engineering dessert collaborative optimization technology is developed, the segmented clustering process is optimized, and the balanced cracking of each cluster is realized, so that the method has important significance for single well production reduction of the conglomerate oil reservoir.

In combination with reservoir characteristics, there are two major issues that need to be addressed:

(1) Unlike conventional sandstone, the dominant liquid inlet cluster has poor correlation with the horizontal minimum principal stress, and the main control factor of crack initiation is not clear; the dominant liquid inlet clusters in the conventional sandstone reservoir are all low in stress values, and underground optical fiber monitoring shows that the dominant liquid inlet channels in the conglomerate reservoir have correlation with the minimum main stress, but the correlation is not strong, so that a new engineering dessert identification method is needed, and a basis is provided for segmentation and clustering.

(2) The conventional sectional clustering method is difficult to apply to a conglomerate reservoir because geological desserts and engineering desserts are characterized, and the comprehensive comparison is preferably carried out to obtain 'double desserts' as dominant perforation clusters, wherein the geological desserts are physical properties such as holes, seepage, saturation and the like and oil-gas-containing property, the engineering desserts are mainly coupling positions and stresses, the engineering desserts purely relying on the stresses cannot effectively characterize the cracking characteristics of each cluster, meanwhile, the geological desserts and the engineering desserts still have the cracking characteristics, and the geological desserts and the engineering desserts are not used as a unified organic whole to conduct sectional clustering guidance.

Disclosure of Invention

It is an object of embodiments of the present application to provide a conglomerate reservoir segment clustering method, a conglomerate reservoir segment clustering device, a machine-readable storage medium, and a processor.

To achieve the above object, a first aspect of the present application provides a method for sectioning and clustering a conglomerate reservoir, including:

Acquiring the mechanical specific energy of the horizontal well, geological parameter information of the horizontal well and engineering parameter information of the horizontal well;

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, carrying out multidimensional data classification on data points of the horizontal well in depth by adopting a cluster analysis algorithm to obtain classification results;

Segmenting the depth of the horizontal well by utilizing the classification result and the preset horizontal segment length to obtain a segmentation result;

Calculating the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result by adopting a closeness algorithm, and obtaining the position of the perforation cluster in the segment according to the comprehensive closeness of the geological engineering parameters at each depth;

and obtaining the conglomerate reservoir segmentation clustering result according to the segmentation result and the perforation cluster position.

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

Acquiring basic parameter information, wherein the basic parameter information at least comprises drilling data and drilling tool combination parameters;

And calculating the mechanical specific energy of the horizontal well by adopting a friction resistance model of the horizontal well according to the basic parameter information.

In the embodiment of the application, the calculating the mechanical specific energy of the horizontal well by adopting the friction model of the horizontal well according to the basic parameter information comprises the following steps:

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

The calculation formula of the modified mechanical specific energy is as follows:

Wherein E is the mechanical specific energy MPa of a horizontal well, P is the weight-on-bit MPa, D _b is the diameter of a drill bit mm, E is the natural logarithm, a _k is the well inclination angle rad, mu _well is the friction coefficient of a drill string, upsilon is the drilling speed m/h, Q is the displacement of the drill tool per revolution, and is a structural parameter only related to the linearity and the geometric dimension of a stator and a rotor, L/r and delta P _p are the nozzle pressure drop MPa of the screw drill, n is the revolution r/min of a turntable, mu _bit is the friction coefficient of the drill bit, K _N is the rotational speed flow ratio of the power drill, r/L and Q are the total flow and L/s.

In the embodiment of the present application, the classifying the data points of the horizontal well in 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 includes:

S1: preprocessing the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information to obtain preprocessing data, wherein the preprocessing data comprises a plurality of samples, and each sample comprises 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 data points of the horizontal well in depth;

S3: defining a loss function;

S4: setting iteration times;

S5: evaluating the distance from each sample to the clustering center, distributing each sample to the cluster to which the center closest to the sample belongs, and if the loss condition is not met or the iteration stop condition is not met, recalculating the clustering center point of each category;

S6: and S5, repeating the step S until the loss function converges to obtain a classification result.

In the embodiment of the present application, the segmenting the depth of the horizontal well by using the classification result and the preset horizontal segment length to obtain a segmentation result includes:

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

Respectively distributing the data points with the same classification result in each section to the same label according to the classification result of each data point in each section in the initial segmentation result, wherein the labels corresponding to different classification results are different;

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

taking a classification result corresponding to the label with the largest data point number as a plurality of point characteristic values of the segment in a segment length range;

and obtaining a segmentation result according to the characteristic values of a plurality of points of each segment in the segment length range.

In the embodiment of the application, the segmentation result according to the multi-point characteristic value of each segment in the segment length range comprises the following steps:

Judging whether the characteristic values of a plurality of points of each segment in the segment length range are one of the classification results, and if yes, taking the initial segmentation result as a segmentation result; if not, adjusting the preset horizontal segment length or/and adjusting the initial segmentation point, and re-segmenting to obtain a segmentation result.

In the embodiment of the present application, the calculating, by using a proximity algorithm, the comprehensive proximity 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 proximity of the geological engineering parameters at each depth includes:

Constructing fuzzy matter elements 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 in each section;

calculating the membership degree of each evaluation index fuzzy magnitude according to the membership degree principle based on the fuzzy magnitude in the fuzzy primitive;

constructing a difference square composite fuzzy element according to the suboptimal membership degree of the fuzzy magnitude of each evaluation index;

Determining the weight of each feature;

substituting the difference square composite fuzzy matter element and the characteristic weights into a closeness calculation formula to obtain comprehensive closeness of geological engineering parameters at each depth in each section;

and obtaining the position of the perforation cluster in the section according to the comprehensive closeness of the geological engineering parameters at each depth.

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

the information acquisition module is used for acquiring 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 classification module is used for classifying the multidimensional data of the data points of the horizontal well in depth by adopting 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;

The segmentation module is used for segmenting the depth of the horizontal well by utilizing the classification result and the preset horizontal segment length to obtain a segmentation result;

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

And the result output module is used for obtaining the conglomerate reservoir segmentation clustering result according to the segmentation result and the perforation cluster position.

A third aspect of the application provides a processor configured to perform the above-described conglomerate reservoir segmentation clustering method.

A fourth aspect of the application provides a machine-readable storage medium having instructions stored thereon, which when executed by a processor, cause the processor to be configured to perform the above-described conglomerate reservoir segment clustering method.

According to the technical scheme, accuracy of identification of the desserts of the sandstone reservoir engineering can be greatly improved through the mechanical specific energy of the horizontal well, classification and segmentation are carried out through a clustering analysis algorithm, similarity of geology and engineering parameters in a horizontal well section can be guaranteed, meanwhile, a closeness algorithm is adopted to calculate closeness, and then perforation cluster positions are obtained, so that a method for segmenting and clustering the sandstone reservoir based on collaborative optimization of the desserts of the geological engineering is formed, balanced transformation of multiple clusters in a volume fracturing section of the horizontal well of the conglomerate reservoir is guided, balance of geological and engineering parameter distribution at perforation points in the section is effectively improved, balanced cracking of each cluster in the fracturing process of multiple clusters in the section is facilitated, construction difficulty is reduced, occurrence probability of complex working conditions in construction is reduced, construction liquid amount is saved, investment is saved, and oil and gas well yield is improved. The method is convenient to calculate and simple to operate.

Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.

Drawings

The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:

FIG. 1 schematically illustrates an application environment of a conglomerate reservoir segment clustering method according to an embodiment of the present application;

FIG. 2 schematically illustrates a flow diagram of a method for conglomerate reservoir segment clustering in accordance with an embodiment of the present application;

FIG. 3 schematically illustrates a flow diagram of one implementation of a conglomerate reservoir segment clustering method according to an embodiment of the present application;

FIG. 4 schematically illustrates a statistical graph of fiber monitoring dominant feed channel versus mechanical specific energy in accordance with an embodiment of the present application;

FIG. 5 schematically illustrates a schematic diagram of a proximity clustering principle according to an embodiment of the present application;

FIG. 6 schematically illustrates a distribution of geological engineering parameters at a perforation in accordance with an embodiment of the present application;

FIG. 7 schematically illustrates a construction graph according to an embodiment of the application;

FIG. 8 schematically illustrates a statistical diagram of construction results according to an embodiment of the present application;

FIG. 9 schematically illustrates a mechanical specific energy distribution diagram according to an embodiment of the application;

FIG. 10 schematically illustrates a cluster analysis result graph according to an embodiment of the application;

FIG. 11 schematically illustrates a map of the results of a map of the degree of localization at depth near cluster 1 in a single segment, according to an embodiment of the present application;

FIG. 12 schematically illustrates a segment clustering optimization result graph in accordance with an embodiment of the present application;

FIG. 13 schematically illustrates a block diagram of a conglomerate reservoir segment clustering device in accordance with an embodiment of the present application;

fig. 14 schematically shows an internal structural view of a computer device according to an embodiment of the present application.

Description of the reference numerals

102-Terminal; 104-a server; 410-an information acquisition module; 420-classification module; 430-segmentation module; 440-clustering module; 450-a result output module; a01-a processor; a02-a network interface; a03-an internal memory; a04-a display screen; a05-an input device; a06—a nonvolatile storage medium; b01-operating system; b02-computer program.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the detailed description described herein is merely for illustrating and explaining the embodiments of the present application, and is not intended to limit the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It should be noted that, if directional indications (such as up, down, left, right, front, and rear … …) are included in the embodiments of the present application, the directional indications are merely used to explain the relative positional relationship, movement conditions, etc. between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.

In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.

The method for sectioning and clustering the conglomerate reservoir can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The server 104 obtains 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 from the terminal 102; then, 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, carrying out multidimensional data classification on data points of the horizontal well in depth by adopting a cluster analysis algorithm to obtain classification results; then segmenting the depth of the horizontal well by utilizing the classification result and the preset horizontal segment length to obtain a segmentation result; calculating the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result by adopting a closeness algorithm, and obtaining the position of the perforation cluster in the segment according to the comprehensive closeness of the geological engineering parameters at each depth; and obtaining the conglomerate reservoir segmentation clustering result according to the segmentation result and the perforation cluster position. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 104 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.

Fig. 2 schematically shows a flow diagram of a conglomerate reservoir segment clustering method according to an embodiment of the present application, and fig. 3 schematically shows a flow diagram of an implementation of a conglomerate reservoir segment clustering method according to an embodiment of the present application. As shown in fig. 1, in an embodiment of the present application, a method for clustering conglomerate reservoirs is provided, and this embodiment is mainly applied to the terminal 102 (or the server 104) in fig. 1 to illustrate the method, and includes the following steps:

Step 210: acquiring the mechanical specific energy of the horizontal well, geological parameter information of the horizontal well and engineering parameter information of the horizontal well; the geological parameter information of the horizontal well comprises logging data, gravel linear density, oil content index and the like, and can be obtained according to the working condition of the actual horizontal well. The horizontal well engineering parameter information comprises information such as horizontal well coupling positions, stress and the like, and can be obtained according to the working conditions of an actual horizontal well. The mechanical specific energy of the horizontal well can be calculated according to a mechanical specific energy model, or can be obtained after the mechanical specific energy is corrected according to actual conditions. The mechanical specific energy characterizes the mechanical energy consumed by drilling single-bit-volume rock, combines the characteristics of rock mechanics, rock compressibility and the like, and can be used for identifying engineering desserts, so that the engineering dessert identification precision is higher. By acquiring 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 two aspects of geology and engineering, and the later stage segmentation clustering calculation is facilitated.

Because the drilling speed is increased by adopting the screw drilling tool in the normal horizontal drilling process, the influence of friction resistance and screw drilling tool parameters in horizontal well drilling on the mechanical specific energy is considered, and the obtained mechanical specific energy of the horizontal well can be corrected by adopting the following steps:

Firstly, basic parameter information is acquired, wherein the basic parameter information at least comprises drilling data and drilling tool combination parameters; the drilling data includes weight on bit, torque, rate of drilling, etc., and the drilling tool assembly parameters include screw drilling tool parameters. The basic parameter information can be obtained according to actual working conditions.

And then, according to the basic parameter information, calculating by adopting a horizontal well friction model to obtain the mechanical specific energy of the horizontal well. The horizontal well friction model is a horizontal well mechanical specific energy correction model which is established by considering the friction in the drilling of the horizontal well and the influence of screw drilling tool parameters on the mechanical specific energy, and can correct the mechanical specific energy.

The calculating to obtain the mechanical specific energy of the horizontal well can be to substitute the basic parameter information into a corrected mechanical specific energy calculation formula in the friction drag model of the horizontal well to obtain the mechanical specific energy of the horizontal well; the calculation formula of the modified mechanical specific energy is as follows:

In some embodiments, in order to facilitate the calculation of the corrected mechanical specific energy calculation formula, the horizontal well friction model may also perform correction processing on the drilling data, and then substitute corrected drilling data and drilling tool combination parameters into the corrected mechanical specific energy calculation formula to obtain the horizontal well mechanical specific energy.

Referring to fig. 4, fig. 4 schematically illustrates a statistical graph of the fiber monitoring dominant feed channel versus mechanical specific energy according to an embodiment of the present application. By acquiring the mechanical specific energy of the horizontal well and utilizing the method for identifying the engineering dessert by using the mechanical specific energy, the identification precision of the engineering dessert can be improved.

Step 220: 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, carrying out multidimensional data classification on data points of the horizontal well in depth by adopting a cluster analysis algorithm to obtain classification results; in this embodiment, the horizontal well geological parameter information may be one or more of logging data, gravel linear density, oil content index, etc., and the horizontal well engineering parameter information may be one or more of horizontal well collar position, stress, etc., which may be specifically set according to actual needs. The horizontal well may be comprised of a plurality of data points in depth, e.g., a point 100 meters deep, a point 300 meters deep. The various data points described above may be described in a plurality of dimensions, such as: each data point has a corresponding mechanical specific energy, logging data, and gravel line density.

It should be noted that, the multi-dimensional data classification may be data classification with a corresponding number of dimensions according to the mechanical specific energy of the horizontal well, the geological parameter information of the horizontal well, and the data number of the engineering parameter information of the horizontal well. Such as: and (3) classifying the data points of the horizontal well in depth by adopting a cluster analysis algorithm according to the mechanical specific energy, the well logging data and the gravel linear density. And (3) carrying out two-dimensional data classification on the data points of the horizontal well in depth by adopting a cluster analysis algorithm according to the mechanical specific energy and the oil content index.

The multi-dimensional data classification is performed on the data points of the horizontal well in depth by adopting a cluster analysis algorithm, wherein the data points are classified by analyzing from multiple dimensions. 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 can be imported into a cluster analysis model to obtain the mechanical specific energy of the horizontal well, wherein the cluster analysis algorithm can be a kmeans algorithm which is a typical distance-based clustering algorithm, and the similarity is larger as the distance is considered to be closer to the two objects, the distance is adopted as an evaluation index of the similarity. The kernel of the kmeans algorithm is to divide a given data set into K classes according to the number of clusters, and then determine whether the clustering result satisfies a loop stop condition through loop iteration. The method specifically comprises the following steps:

Step S1: preprocessing the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information to obtain preprocessing data, wherein the preprocessing data comprises a plurality of samples, and each sample comprises the horizontal well mechanical specific energy, the horizontal well geological parameter information and the horizontal well engineering parameter information; the preprocessing comprises the steps of normalizing the data, filtering abnormal points and the like, so that the later classification is facilitated.

Step S2: randomly selecting K centers from the data points of the horizontal well in depth, wherein the K centers are respectively marked as

Step S3: defining a loss function; in a multidimensional space formed by all geological engineering parameters, K clusters (Cluster) are searched for in an iterative manner to enable a loss function corresponding to a geological engineering parameter clustering result to be minimum, wherein the loss function describes the degree of compactness among clustering center points, and the smaller the value of the loss function is, the higher the similarity among samples in the clusters is, namely the better the clustering effect is. Typically the loss function is defined as: Wherein J (c, u) is the sum of squares of errors of the samples from the center point of the cluster to which each sample belongs, x _i is the ith sample, c _i is the cluster to which x _i belongs,/> M is the center point corresponding to the cluster, M is the total number of samples.

Step S4: setting the iteration times as t; the setting can be specifically performed according to actual conditions.

Step S5: evaluating the distance from each sample to the clustering center, distributing each sample to the cluster to which the center closest to the sample belongs, and if the loss condition is not met or the iteration stop condition is not met, recalculating the clustering center point of each category; wherein, evaluating the distance of each sample to the cluster center and assigning each sample x _i to the cluster to which the closest center belongs can be calculated using the following formula: where k is the center point of each category,/> For each sample the distance to the cluster center. If the loss condition is not met or the iteration stop condition is not met, the center point k of each category is recalculated, and the following formula can be adopted for calculation:

Wherein/> Is the centroid position after t+1 iterations.

Step S6: and (5) repeating the step S5 until the loss function converges to obtain a classification result.

The kmeans algorithm is fast and can quickly classify data points of the horizontal well in depth.

Step 230: segmenting the depth of the horizontal well by utilizing the classification result and the preset horizontal segment length to obtain a segmentation result; the segmentation is to divide the depth of the horizontal well into a plurality of sections, and specifically comprises the following steps:

Firstly, an initial segmentation point is obtained, segmentation is carried out on the depth of the horizontal well from the initial segmentation point according to a preset horizontal segment length, and an initial segmentation result is obtained; the initial segmentation point refers to a point for starting segmentation, can be selected empirically or in actual condition, and can generally start from point A, namely the bottom of the horizontal well. The preset horizontal segment length may be a segment length range determined in combination with a pre-development experience, and then the maximum horizontal segment length is selected according to the set segment length range. For example, the segment length may range from 50-100, which may set the horizontal segment length to 100.

Then, respectively distributing the data points with the same classification result in each section to the same label according to the classification result of each data point in each section in the initial segmentation result, wherein the labels corresponding to different classification results are different; in this embodiment, each segment of the initial segmentation result includes a plurality of data points, each data point has a classification result in the step 220, and then each classification result may be assigned a label, and different classes may be assigned different labels. For example, a point of category N1 is assigned a label of K1, and a point of category N2 is assigned a label of K2.

Then, counting the number of data points corresponding to each label in each segment in the initial segmentation result; multiple categories may appear in each segment, each category including multiple data points, and the number of data points corresponding to each tag may be counted.

Then, taking a classification result corresponding to the label with the largest data point number as a plurality of point characteristic values of the segment in a segment length range; the classification result corresponding to each tag is taken as the characteristic value in the segment, for example, in the above example, the characteristic value in the segment includes K1 and K2. Then, the label with the largest number of data points is taken as the characteristic value of the majority point of the segment in the segment length range, for example, in the above example, the characteristic value of the majority point of the segment in the segment length range is obtained as K1 when the number of data points corresponding to K1 is 5 and the number of data points corresponding to K2 is 2.

And finally, obtaining a segmentation result according to the multi-point characteristic values of each segment in the segment length range. Since there may be a case where data points are uniformly distributed in the segmentation process, there is no characteristic value of a plurality of points, which indicates that the segmentation is invalid, that is, if the characteristic value of a plurality of points belongs to one of the classification results, the segmentation is valid, otherwise, adjustment is needed, and the method specifically can be determined by the following steps:

It should be noted that, the re-segmentation may be to adjust the preset horizontal segment length, for example, reduce the horizontal segment length to be re-determined until the horizontal segment length is reduced to a minimum value; it is also possible to adjust the initial segmentation point, for example to move the initial segmentation point backwards by 1m; or the preset horizontal segment length can be adjusted first, and then the initial segmentation point can be adjusted.

For example: judging whether the characteristic values of a plurality of points exist in the segment length range from the point A belongs to a certain class in the clustering analysis result, if so, dividing the segment length into one class, if not, reducing the segment length again until the segment length is reduced to the minimum value, if still, moving the starting point backwards by 1m, and repeating the judging process until the segment of the full horizontal segment is divided.

Step 240: calculating the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmentation result by adopting a closeness algorithm, and obtaining the position of the perforation cluster in the segment according to the comprehensive closeness of the geological engineering parameters at each depth; referring to fig. 5, fig. 5 schematically illustrates a schematic diagram of a proximity clustering principle according to an embodiment of the present application. The method specifically comprises the following steps:

firstly, constructing fuzzy matter elements according to the mechanical specific energy of a horizontal well, geological parameter information of the horizontal well and engineering parameter information of the horizontal well in each section; for a given thing, the basic element of the thing can be represented by a triplet (thing M, feature C, magnitude x), which can be called a fuzzy primitive if the magnitude of the thing has ambiguity, and is denoted as: If the object M has n characteristics C1, C2, … and Cn, and the fuzzy magnitude corresponding to the characteristics is x1, x2 … and xn, R _n is called as an n-dimensional fuzzy element; if there are m things described by their common n features C1, C2, …, cn and their corresponding blur magnitudes x1, x2, …, xn, then R _nm is called an n-dimensional blur complex element of m things, expressed as:

then, based on the fuzzy magnitude value in the fuzzy primitive, calculating the membership degree of each evaluation index fuzzy magnitude value according to the membership degree principle; the preferred membership degree can be divided into two types according to different indexes:

First, for the greater and more optimal index such as oil index, the following formula is adopted:

second, the smaller and more preferable the indexes such as mechanical specific energy and gravel linear density are, the following expression is adopted:

In the above equation, min x _ij、max x_ij represents the minimum and maximum values of the i-th index in each thing, i.e., the minimum and maximum values of each line in R _nm, respectively. According to the fuzzy primitive formula, the membership degree fuzzy primitive R' _nm can be obtained through calculation:

Then, constructing a difference square composite fuzzy element according to the suboptimal membership degree of the fuzzy magnitude of each evaluation index; before the difference squared composite blur pixel is constructed, a standard (optimal) paste pixel is determined. The standard fuzzy primitive is the maximum value or minimum value of the membership degree of each index in the membership degree fuzzy primitive R' _nm, and is represented by R _o. After the standard fuzzy primitive is established, the square of the difference between each of the indices in the membership degree fuzzy primitive R' _nm and the standard fuzzy primitive R _o is expressed as:

V _ij (i=1, 2, …, n; j=1, 2, …, m), i.e. V _ij＝(u_ij-u_oj)², a difference-squared complex blur element R _v can be constructed, denoted as:

then, determining the weight of each feature; because unbalanced development of each cluster in the segment mainly causes that after the dominant cluster is cracked due to overlarge difference of cracking conditions of each cluster, the pressure value in the segment is difficult to reach the cracking requirement of other clusters, so the fundamental requirement of balanced development of each cluster in the segment is to ensure that the characteristics of the perforation clusters are similar. To meet this requirement, the present model uses variance as the weight of each feature, which can be expressed as:

Wherein w _j is the variance weight of the characteristic value of C _j, mu is the average value of the characteristic values of C _j, n is the number of the characteristic values of C _j, and x _i is the size of the characteristic value of C _j.

Then substituting the difference square composite fuzzy matter element and the characteristic weights into a closeness calculation formula to obtain the comprehensive closeness of the geological engineering parameters at each depth in each section; the above formula for calculating the proximity degree is: Where ω _j is the feature weight and Δ _ij is the difference squared composite blur element.

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

Step 250: and obtaining the conglomerate reservoir segmentation clustering result according to the segmentation result and the perforation cluster position. And after the segmentation result is obtained and the perforation cluster position is determined, the segmentation clustering result of the conglomerate reservoir can be obtained. Referring to fig. 6-8, after the invention is adopted for sectioning and clustering, the engineering parameter distribution is more balanced, the construction difficulty is lower, the occurrence probability of complex working conditions in construction is reduced, and the construction liquid amount is saved.

The following illustrates a segment clustering process using a J-1 well as an example.

Firstly, the corrected drilling parameters of the J-1 well are brought into a corrected mechanical specific energy calculation formula, the mechanical specific energy distribution situation of a horizontal section is calculated, and referring to FIG. 9, FIG. 9 schematically shows the mechanical specific energy distribution diagram according to an embodiment of the application; and then, integrating mechanical specific energy, oil content index and gravel linear density parameters, and classifying each depth point of the J-1 well by using a cluster analysis algorithm. Wherein, each characteristic value of the horizontal well section can be divided into K1-K5 and 5 categories, please refer to FIG. 10, FIG. 10 schematically illustrates a cluster analysis result chart according to an embodiment of the present application. And then, utilizing the classification result, limiting the length range of the result section to be 50-100m by combining the early development experience, and segmenting the horizontal well to obtain a segmentation result. And then, according to the obtained sections, calculating the comprehensive closeness of the mechanical specific energy, the oil-content index and the gravel linear density parameters at each depth in each section, combining the clustering result of the earlier development experience limitation to form 3 clusters of the rest section of the first section 2 clusters, selecting the position with the low paste progress value, namely the position at the depth ① in fig. 11 as the position of the 1 st cluster perforation cluster in the section, wherein the minimum cluster distance is 10 m. Finally, the segmentation clustering result is output, please refer to fig. 12, fig. 12 schematically illustrates a segmentation clustering optimization result diagram according to an embodiment of the present application.

In the 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, carrying out multidimensional data classification on data points of the horizontal well in depth by adopting a cluster analysis algorithm to obtain classification results; then segmenting the depth of the horizontal well by utilizing the classification result and the preset horizontal segment length to obtain a segmentation result; then respectively adopting 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 position of the perforation cluster in the segment according to the comprehensive closeness of the geological engineering parameters at each depth; and finally obtaining a conglomerate reservoir segmentation clustering result according to the segmentation result and the perforation cluster position. The accuracy of the identification of the desserts of the conglomerate reservoir engineering can be greatly improved through the mechanical specific energy of the horizontal well, the classification and segmentation are carried out through a clustering analysis algorithm, the similarity of geology and engineering parameters in the horizontal well section can be guaranteed, meanwhile, the closeness is calculated through a closeness algorithm, and then the perforation cluster position is obtained, so that the conglomerate reservoir segmentation clustering method based on the collaborative optimization of the desserts of the geological engineering is formed, the balanced transformation of multiple clusters in the volume fracturing section of the conglomerate reservoir horizontal well is guided, the balance of the distribution of the geology and engineering parameters at the perforation points in the section is effectively improved, the balanced cracking of each cluster in the multi-cluster fracturing process in the section is facilitated, the construction difficulty is reduced, the occurrence probability of complex working conditions in construction is reduced, the construction liquid amount is saved, the investment is saved, and the yield of the oil and gas well is improved. The method is convenient to calculate and simple to operate.

FIG. 2 is a flow diagram of a method of zonal clustering of conglomerate reservoirs in one embodiment. It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.

In one embodiment, as shown in fig. 13, fig. 13 schematically illustrates a block diagram of a conglomerate reservoir segment clustering device according to an embodiment of the present application. There is provided a conglomerate reservoir segment clustering device, 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:

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

The classification module 420 is configured to classify the data points of the horizontal well in 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, so as to obtain a classification result;

The segmentation module 430 is configured to segment the depth of the horizontal well by using the classification result and a preset horizontal segment length, so as to obtain a segmentation result;

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

and the result output module 450 is used for obtaining the conglomerate reservoir segmentation clustering result according to the segmentation result and the perforation cluster position.

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

The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The core can be provided with one or more cores, and the method for sectionally clustering the conglomerate reservoir is realized by adjusting the parameters of the core.

The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.

The embodiment of the application provides a storage medium, wherein a program is stored on the storage medium, and the program is executed by a processor to realize the method for sectioning and clustering the conglomerate reservoir.

In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 14. The computer apparatus includes a processor a01, a network interface a02, a display screen a04, an input device a05, and a memory (not shown in the figure) which are connected through a system bus. Wherein the processor a01 of the computer device is adapted to provide computing and control capabilities. The memory of the computer device includes an internal memory a03 and a nonvolatile storage medium a06. The nonvolatile 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 nonvolatile storage medium a06. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program is executed by the processor a01 to implement a conglomerate reservoir segment clustering method. 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 may be a key, a track ball or a touch pad arranged on a casing of the computer device, or may be an external keyboard, a touch pad or a mouse.

It will be appreciated by those skilled in the art that the structure shown in fig. 14 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements are applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, the conglomerate reservoir segment clustering means provided by the present application may be implemented in the form of a computer program that is executable on a computer device as shown in fig. 14. The memory of the computer device may store various program modules constituting the conglomerate reservoir segmentation clustering means, 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 fig. 13. The computer program of each program module causes the processor to carry out the steps in the method for the segmentation and clustering of a conglomerate reservoir according to each embodiment of the present application as described in the present specification.

The computer apparatus shown in fig. 8 may perform step 210 by means of an information acquisition module 410 in a conglomerate reservoir segment clustering device as shown in fig. 13. The computer device may perform step 220 via classification module 420, step 230 via segmentation module 430, step 240 via clustering module 440, and step 250 via result output module 450.

The embodiment of the application provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor realizes the following steps when executing the program:

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

In one embodiment, the calculating the mechanical specific energy of the horizontal well by using the friction drag model of the horizontal well according to the basic parameter information includes:

In one embodiment, the classifying the data points of the horizontal well in depth 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 by adopting a cluster analysis algorithm to obtain classification results comprises:

S3: defining a loss function;

S4: setting iteration times;

In one embodiment, the segmenting the depth of the horizontal well by using the classification result and the preset horizontal segment length to obtain a segmentation result includes:

In one embodiment, the segmentation result based on the multiple point feature values of each segment in the segment length range comprises:

In one embodiment, the calculating the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmented 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 includes:

Determining the weight of each feature;

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

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

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

Computer-readable media include both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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 disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims

1. A method of conglomerate reservoir segment clustering, the method comprising:

2. The method of claim 1, wherein the obtaining a horizontal well mechanical specific energy comprises:

3. The method of claim 2, wherein calculating a horizontal well mechanical specific energy using a horizontal well friction model based on the base parameter information comprises:

Wherein E is the mechanical specific energy of the horizontal well, P is the bit pressure, D _b is the bit diameter, E is the natural logarithm, a _k is the well inclination angle, mu _well is the friction coefficient of the drill string, v is the drilling speed, Q is the displacement of the drilling tool per revolution, delta P _p is the nozzle pressure drop of the screw drilling tool, n is the revolution of the rotary table, mu _bit is the friction coefficient of the bit, K _N is the rotational speed flow ratio of the power drilling tool, and Q is the total flow.

4. The method of claim 1, wherein classifying the data points of the horizontal well in 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 classification results comprises:

S3: defining a loss function;

S4: setting iteration times;

5. The method of claim 1, wherein the segmenting the depth of the horizontal well using the classification result and a preset horizontal segment length to obtain a segmented result comprises:

6. The method of claim 5, wherein the segmenting results based on the multiple point feature values of each segment over the segment length range comprises:

7. The method of claim 1, wherein the calculating the comprehensive closeness of the geological engineering parameters at each depth in each segment in the segmented 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, comprises:

Determining the weight of each feature;

8. A conglomerate reservoir segment clustering device, characterized in that the conglomerate reservoir segment clustering device comprises:

9. A processor configured to perform the conglomerate reservoir segment clustering method of any one of claims 1 to 7.

10. A machine-readable storage medium having instructions stored thereon, which when executed by a processor, cause the processor to be configured to perform the conglomerate reservoir segment clustering method of any one of claims 1 to 7.