WO2024088265A1 - 砾岩储层分段分簇方法、装置、存储介质及处理器 - Google Patents

砾岩储层分段分簇方法、装置、存储介质及处理器 Download PDF

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WO2024088265A1
WO2024088265A1 PCT/CN2023/126270 CN2023126270W WO2024088265A1 WO 2024088265 A1 WO2024088265 A1 WO 2024088265A1 CN 2023126270 W CN2023126270 W CN 2023126270W WO 2024088265 A1 WO2024088265 A1 WO 2024088265A1
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horizontal well
segmentation
segment
parameter information
depth
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French (fr)
Inventor
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
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Petrochina Co Ltd
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Petrochina Co Ltd
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/25—Methods for stimulating production
    • E21B43/26—Methods for stimulating production by forming crevices or fractures
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/09—Locating or determining the position of objects in boreholes or wells, e.g. the position of an extending arm; Identifying the free or blocked portions of pipes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches

Definitions

  • 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.
  • 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%.
  • the conglomerate reservoir has strong heterogeneity, uneven distribution of gravel content and size, large lithology changes, and complex stress distribution.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • the present application provides a conglomerate reservoir segmentation and clustering method in a first aspect, comprising:
  • 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
  • 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 obtaining of the horizontal well mechanical specific energy includes:
  • the basic parameter information at least includes drilling data and drilling tool assembly parameters
  • the horizontal well mechanical specific energy is calculated using the horizontal well friction model.
  • the horizontal well mechanical specific energy is calculated using the horizontal well friction model according to the basic parameter information, including:
  • the modified mechanical specific energy calculation formula is:
  • 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
  • ⁇ well is the drill string friction coefficient
  • ⁇ 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 ⁇ p p is the nozzle pressure drop of the screw drill bit MPa
  • n is the turntable speed r/min
  • ⁇ bit is the drill bit friction coefficient
  • K N is the speed flow ratio of the power drill, r/L
  • Q is the total flow, L/s.
  • 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:
  • 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;
  • 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:
  • the data points with the same classification results in the segment are assigned the same label, and different classification results correspond to different labels;
  • 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 segmentation result is obtained according to the feature values of the majority of points in each segment within the segment length range, including:
  • 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:
  • fuzzy matter-element is constructed
  • the optimal membership of the fuzzy value of each evaluation index is calculated according to the optimal membership principle
  • 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.
  • 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.
  • 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.
  • 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.
  • a conglomerate reservoir segmentation and clustering method provided in the present application can be applied in an application environment as shown in FIG1.
  • 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
  • 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 method of a conglomerate reservoir segmentation and clustering method according to 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.
  • 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.
  • 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.
  • 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:
  • 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
  • ⁇ well is the drill string friction coefficient
  • ⁇ 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 ⁇ p p is the nozzle pressure drop of the screw drill bit MPa
  • n is the turntable speed r/min
  • ⁇ bit is the drill bit friction coefficient
  • K N is the speed flow ratio of the power drill, r/L
  • Q is the total flow, L/s.
  • 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.
  • Figure 4 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.
  • 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.
  • 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
  • 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:
  • 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:
  • 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.
  • the segment length range is 50-100, which can set the horizontal segment length to 100.
  • 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.
  • the points of category N1 are assigned label K1
  • the points of category N2 are assigned label K2.
  • 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.
  • 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.
  • 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.
  • 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:
  • 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.
  • 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:
  • fuzzy matter-element is constructed according to the mechanical specific energy, geological parameter information and engineering parameter information of horizontal wells in each section.
  • the basic element of the thing can be represented by a triple (thing M, feature C, value x).
  • 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:
  • 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:
  • 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 .
  • the optimal membership fuzzy matter-element R' nm can be obtained by calculation:
  • 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 .
  • 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:
  • wj is the variance weight of Cj eigenvalue
  • ⁇ is the average value of Cj eigenvalue
  • n is the number of Cj eigenvalues
  • xi is the size of Cj eigenvalue.
  • 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, ⁇ j is the weight of each feature, and ⁇ ij is the difference square composite fuzzy matter-element.
  • the perforation cluster position in the section is obtained according to the comprehensive closeness of the geological engineering parameters at each depth.
  • 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 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.
  • Figure 9 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.
  • the characteristic values of the horizontal well section can be divided into 5 categories, K1-K5.
  • Figure 10 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.
  • the comprehensive closeness of the mechanical specific energy, oil content index, and gravel line density parameters at each depth in each section is calculated.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash random access memory
  • An embodiment of the present application provides a storage medium on which a program is stored.
  • the program is executed by a processor, the above-mentioned conglomerate reservoir segmentation and clustering method is implemented.
  • a computer device 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.
  • 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
  • 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.
  • 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.
  • 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.
  • the processor executes the program, the following steps are implemented:
  • 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
  • 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.
  • obtaining the mechanical specific energy of the horizontal well includes:
  • the basic parameter information at least includes drilling data and drilling tool assembly parameters
  • the horizontal well mechanical specific energy is calculated using the horizontal well friction model.
  • 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:
  • 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
  • ⁇ well is the drill string friction coefficient
  • ⁇ 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 ⁇ p p is the nozzle pressure drop of the screw drill bit MPa
  • n is the turntable speed r/min
  • ⁇ bit is the drill bit friction coefficient
  • K N is the speed flow ratio of the power drill bit
  • r/L is the total flow rate, L/s.
  • 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:
  • 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;
  • 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:
  • 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;
  • 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.
  • obtaining the segmentation result according to the feature values of the majority of points in each segment within the segment length range includes:
  • 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:
  • fuzzy matter-element is constructed
  • the optimal membership of the fuzzy value of each evaluation index is calculated according to the optimal membership principle
  • the perforation cluster positions within the section are obtained according to the comprehensive closeness of the geological engineering parameters at each depth.
  • 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.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • 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.
  • a computing device includes one or more processors (CPU), input/output interfaces, network interfaces, and memory.
  • processors CPU
  • input/output interfaces network interfaces
  • memory volatile and non-volatile 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.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • 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.
  • computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

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Abstract

公开了一种砾岩储层分段分簇方法，通过获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；采用聚类分析算法对水平井在深度上的数据点进行多维数据分类；利用分类结果和预置的水平段长，对水平井的深度进行分段；分别采用贴近度算法计算分段结果中各段内各个深度处的地质工程参数的综合贴近度，进而得到该段内的射孔簇位置；根据分段结果、射孔簇位置得到砾岩储层分段分簇结果；能够提高工程甜点识别精度，有效提高段内射孔点处的地质、工程参数分布的均衡性，降低施工难度，减少施工中复杂工况的发生概率，节省施工液量，节约投资，提高油气井产量；还公开了使用该方法的装置、存储介质及处理器。

Description

砾岩储层分段分簇方法、装置、存储介质及处理器
相关申请的交叉引用
本申请要求2022年10月25日提交的中国专利申请202211312462.8的权益，该申请的内容通过引用被合并于本文。
技术领域
本申请涉及石油天然气增产技术领域，具体涉及一种砾岩储层分段分簇方法、一种砾岩储层分段分簇装置、一种机器可读存储介质及一种处理器。
背景技术
准噶尔盆地玛湖凹陷是一个多层系成藏的大型富烃凹陷，其高效开发对保障国家能源安全具有重要意义。与常规油藏相比，玛湖凹陷砾岩油藏受砾石影响，储层非均质性更强，且具有埋藏深、物性差、天然裂缝不发育、闭合压力大等特征，现场实践表明水平井段内多簇体积压裂是砾岩油藏降本提效的关键手段，通过适当放大段长，增加段内簇数，减少单井施工段数，单井压裂费用可以下降15～20％，但砾岩储层非均质性较强，砾石含量及大小分布不均、岩性变化大、应力分布复杂，在水平井段内多簇体积压裂改造中，未能有效实现段内各射孔簇的均衡起裂，导致同一改造段内各射孔簇进液砂量差异较大，部分射孔簇因进液、砂量大过度改造，部分射孔簇因进液、砂量小改造不充分，严重影响储层动用程度。2020年玛湖砾岩油藏推广段内多簇压裂工艺，产液剖面测试得到40％以上的射孔簇未贡献油气流，井下鹰眼测试得到单段6簇下只有2～3簇有被压裂砂冲蚀特征，与2019年相比单井产量下降40～55％，为此，综合钻、录测等综合资料，开展地质-工程甜点协同优化技术，优化分段分簇工艺，实现各簇均衡起裂对砾岩油藏单井提产降本具有重要意义。
结合储层特征，主要存在以下两点问题亟需攻关：
(1)与常规砂岩不同，优势进液簇与水平最小主应力相关性较差，裂缝起裂主控因素不明确；常规砂岩储层中优势进液簇均为应力低值，井下光纤监测显示，砾岩油藏中优势进液通道与最小主应力存在相关性，但相关性不强，亟需新的工程甜点识别方法，为分段分簇提供依据。
(2)目前分段分簇方法尚未有机整合地质工程甜点参数，难以应用于砾岩储层，常规分段分簇主要通过刻画地质甜点与工程甜点，综合对比优选“双甜点”作为优势射孔簇，其中地质甜点为孔、渗、饱等物性及含油气性，工程甜点主要为接箍位置及应力，单纯依靠应力的工程甜点，无法有效刻画各簇起裂特性，同时地质甜点与工程甜点依然存在割裂性，未将其作为一个统一的有机整体进行分段分簇指导。
发明内容
本申请实施例的目的是提供一种砾岩储层分段分簇方法、一种砾岩储层分段分簇装置、一种机器可读存储介质及一种处理器。
为了实现上述目的，本申请第一方面提供一种砾岩储层分段分簇方法，包括：
获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；
根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果；
利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果；
分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置；
根据所述分段结果、所述射孔簇位置得到砾岩储层分段分簇结果。
在本申请实施例中，所述获取水平井机械比能，包括：
获取基础参数信息，所述基础参数信息至少包括钻井数据、钻具组合参数；
根据所述基础参数信息，采用水平井摩阻模型计算得到水平井机械比能。
在本申请实施例中，所述根据所述基础参数信息，采用水平井摩阻模型计算得到水平井机械比能，包括：
将所述基础参数信息代入到所述水平井摩阻模型中的修正机械比能计算公式中，得到水平井机械比能；
所述修正机械比能计算公式为：
其中，E为水平井机械比能MPa，P为钻压MPa，Db为钻头直径mm，e为自然对数，ak为井斜角rad，μwell为钻柱摩擦系数，υ为钻速m/h，q为钻具每转排量，是一个结构参数，仅与定子和转子的线型和几何尺寸有关，L/r，Δpp为螺杆钻具喷嘴压降MPa，n为转盘转数r/min，μbit为钻头摩擦系数，KN为动力钻具的转速流量比，r/L，Q为总流量，L/s。
在本申请实施例中，所述根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果，包括：
S1：对所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息进行预处理，得到预处理数据，所述预处理数据包含多个样本，各个样本包括水平井机械比能、水平井地质参数信息和水平井工程参数信息；
S2：在所述水平井在深度上的数据点中随机选取K个中心；
S3：定义损失函数；
S4：设置迭代次数；
S5：评估每个所述样本到聚类中心的距离，并将每一个样本分配到距离最近的中心所属的簇中，若未满足损失条件或未达到迭代停止条件，则重新计算每一个类别的聚类中心点；
S6：重复S5至损失函数收敛，得到分类结果。
在本申请实施例中，所述利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果，包括：
获取初始分段点，并从所述初始分段点开始，在所述水平井的深度上按照预置的水平段 长进行分段，得到初始分段结果；
分别根据所述初始分段结果中各段中各个数据点的分类结果，将该段中分类结果相同的所述数据点分配相同的标签，不同的分类结果对应的标签不同；
统计所述初始分段结果中各段中各个标签对应的数据点数量；
将所述数据点数量最多的标签对应的分类结果作为该段在段长范围内的多数点特征值；
根据各段在段长范围内的多数点特征值得到分段结果。
在本申请实施例中，所述根据各段在段长范围内的多数点特征值得到分段结果，包括：
判断所述各段在段长范围内的多数点特征值是否为所述分类结果中的一类，若是，则将所述初始分段结果作为分段结果；若否，则调整所述预置的水平段长或/和调整所述初始分段点，并重新进行分段，得到分段结果。
在本申请实施例中，所述分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置，包括：
根据各段中水平井机械比能、水平井地质参数信息和水平井工程参数信息，构建模糊物元；
基于模糊物元中的模糊量值，根据从优隶属度原则计算各评价指标模糊量值的从优隶属度；
根据所述各评价指标模糊量值的从优隶属度，构建差平方复合模糊物元；
确定各特征权重；
将差平方复合模糊物元与所述各特征权重代入到贴近度计算公式中，得到各段内各个深度处的地质工程参数的综合贴近度；
根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置。
本申请第二方面提供一种砾岩储层分段分簇装置，所述砾岩储层分段分簇装置包括：
信息获取模块，用于获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；
分类模块，用于根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果；
分段模块，用于利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果；
分簇模块，用于分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置；
结果输出模块，用于根据所述分段结果、所述射孔簇位置得到砾岩储层分段分簇结果。
本申请第三方面提供一种处理器，被配置成执行上述的砾岩储层分段分簇方法。
本申请第四方面提供一种机器可读存储介质，该机器可读存储介质上存储有指令，该指令在被处理器执行时使得所述处理器被配置成执行上述的砾岩储层分段分簇方法。
通过上述技术方案，通过水平井机械比能可以大幅提高砂砾岩储层工程甜点识别的准确性，采用聚类分析算法进行分类并进行分段，可保证水平井段内地质、工程参数相似，同时采用贴近度算法计算贴近度，进而得到射孔簇位置，形成基于地质工程甜点协同优化的砂砾 岩储层分段分簇方法，指导砾岩油藏水平井体积压裂段内多簇均衡改造，从而有效提高段内射孔点处的地质、工程参数分布的均衡性，有利于段内多簇压裂过程中各簇均衡起裂，降低施工难度，减少施工中复杂工况的发生概率，节省施工液量，节约投资，提高油气井产量。该方法计算便捷、操作简单。
本申请实施例的其它特征和优点将在随后的具体实施方式部分予以详细说明。
附图说明
附图是用来提供对本申请实施例的进一步理解，并且构成说明书的一部分，与下面的具体实施方式一起用于解释本申请实施例，但并不构成对本申请实施例的限制。在附图中：
图1示意性示出了根据本申请实施例的一种砾岩储层分段分簇方法的应用环境示意图；
图2示意性示出了根据本申请实施例的一种砾岩储层分段分簇方法的流程示意图；
图3示意性示出了根据本申请实施例的一种砾岩储层分段分簇方法的一种实施方式的流程示意图；
图4示意性示出了根据本申请实施例的光纤监测优势进液通道与机械比能关系统计图；
图5示意性示出了根据本申请实施例的贴近度法分簇原理示意图；
图6示意性示出了根据本申请实施例的射孔处地质工程参数分布情况图；
图7示意性示出了根据本申请实施例的施工曲线图；
图8示意性示出了根据本申请实施例的施工结果统计图；
图9示意性示出了根据本申请实施例的机械比能分布图；
图10示意性示出了根据本申请实施例的聚类分析结果图；
图11示意性示出了根据本申请实施例的单段内第1簇位置附近深度处贴进度计算结果图；
图12示意性示出了根据本申请实施例的分段分簇优化结果图；
图13示意性示出了根据本申请实施例的一种砾岩储层分段分簇装置的结构框图；
图14示意性示出了根据本申请实施例的计算机设备的内部结构图。
附图标记说明
102-终端；104-服务器；410-信息获取模块；420-分类模块；430-分段模块；440-分簇
模块；450-结果输出模块；A01-处理器；A02-网络接口；A03-内存储器；A04-显示屏；A05-输入装置；A06-非易失性存储介质；B01-操作系统；B02-计算机程序。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，应当理解的是，此处所描述的具体实施方式仅用于说明和解释本申请实施例，并不用于限制本申请实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本申请保护的范围。
需要说明，若本申请实施例中有涉及方向性指示(诸如上、下、左、右、前、后……)，则该方向性指示仅用于解释在某一特定姿态(如附图所示)下各部件之间的相对位置关系、运动情况等，如果该特定姿态发生改变时，则该方向性指示也相应地随之改变。另外，若本申请实施例中有涉及“第一”、“第二”等的描述，则该“第一”、“第二”等的描述仅用于描述目的，而不能理解为指示或暗示其相对重要性或者隐含指明所指示的技术 特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。另外，各个实施例之间的技术方案可以相互结合，但是必须是以本领域普通技术人员能够实现为基础，当技术方案的结合出现相互矛盾或无法实现时应当认为这种技术方案的结合不存在，也不在本申请要求的保护范围之内。
本申请提供的一种砾岩储层分段分簇方法，可以应用于如图1所示的应用环境中。其中，终端102通过网络与服务器104通过网络进行通信。服务器104通过从终端102中获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；然后根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果；再利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果；分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置；根据所述分段结果、所述射孔簇位置得到砾岩储层分段分簇结果。其中，终端102可以但不限于是各种个人计算机、笔记本电脑、智能手机、平板电脑和便携式可穿戴设备，服务器104可以用独立的服务器或者是多个服务器组成的服务器集群来实现。
图2示意性示出了根据本申请实施例的一种砾岩储层分段分簇方法的流程示意图，图3示意性示出了根据本申请实施例的一种砾岩储层分段分簇方法的一种实施方式的流程示意图。如图1所示，在本申请一实施例中，提供了一种砾岩储层分段分簇方法，本实施例主要以该方法应用于上述图1中的终端102(或服务器104)来举例说明，包括以下步骤：
步骤210：获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；上述水平井地质参数信息包括测井数据、砾石线密度、含油性指数等，可以根据实际水平井的工况得到。上述水平井工程参数信息包括水平井接箍位置、应力等信息，可以根据实际水平井的工况得到。上述水平井机械比能可以是根据机械比能模型计算得到，还可以是根据实际情况对机械比能修正后得到。其中，机械比能表征了钻开单位体积岩石所消耗的机械能，机械比能综合了岩石力学、岩石可压性等特性，利用机械比能可以识别工程甜点，使工程甜点识别精度更高。通过获取水平井机械比能、水平井地质参数信息和水平井工程参数信息，可以从地质与工程两个方面得到水平井的信息，便于后期分段分簇计算。
由于通常钻进水平段过程中会采用螺杆钻具加快钻进速度，故需考虑在水平井钻井中的摩阻及螺杆钻具参数对机械比能的影响，上述获取水平井机械比能可以采用以下步骤修正得到：
首先，获取基础参数信息，所述基础参数信息至少包括钻井数据、钻具组合参数；所述钻井数据包括钻压、扭矩、钻速等，钻具组合参数包括螺杆钻具参数。上述基础参数信息可以根据实际工况获取得到。
然后，根据所述基础参数信息，采用水平井摩阻模型计算得到水平井机械比能。上述水平井摩阻模型是考虑在水平井钻井中的摩阻及螺杆钻具参数对机械比能的影响建立的水平井机械比能修正模型，可以对机械比能进行修正。
其中，上述计算得到水平井机械比能可以是将所述基础参数信息代入到所述水平井摩阻模型中的修正机械比能计算公式中，得到水平井机械比能；所述修正机械比能计算公式为：
其中，E为水平井机械比能MPa，P为钻压MPa，Db为钻头直径mm，e为自然对数，ak为井斜角rad，μwell为钻柱摩擦系数，υ为钻速m/h，q为钻具每转排量，是一个结构参数，仅与定子和转子的线型和几何尺寸有关，L/r，Δpp为螺杆钻具喷嘴压降MPa，n为转盘转数r/min，μbit为钻头摩擦系数，KN为动力钻具的转速流量比，r/L，Q为总流量，L/s。
在一些实施例中，为了方便上述修正机械比能计算公式进行计算，上述水平井摩阻模型还可以先对所述钻井数据进行矫正处理，然后将矫正后的钻井数据、钻具组合参数代入到上述修正机械比能计算公式中，得到水平井机械比能。
请参看图4，图4示意性示出了根据本申请实施例的光纤监测优势进液通道与机械比能关系统计图。通过获取水平井机械比能，利用机械比能识别工程甜点的方法，可以提高工程甜点识别精度。
步骤220：根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果；在本实施例中，所述水平井地质参数信息可以是测井数据、砾石线密度、含油性指数等数据中的一个或是多个数据，所述水平井工程参数信息可以是水平井接箍位置、应力等数据中的一个或是多个数据，具体可以根据实际需要设置。所述水平井在深度上可以是由多个数据点构成，比如，深度为100米的点、深度为300米的点。上述各个数据点可以是通过多个维度进行描述，比如：各个数据点有对应的机械比能、测井数据、砾石线密度。
需要说明的是，上述进行多维数据分类可以是根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息的数据个数采用相应数量维度的数据分类。比如：根据机械比能、测井数据、砾石线密度，采用聚类分析算法对所述水平井在深度上的数据点进行三维数据分类。根据机械比能、含油性指数，采用聚类分析算法对所述水平井在深度上的数据点进行二维数据分类。
上述采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，是从多个维度分析，对数据点进行分类。可以是将所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息导入到聚类分析模型中得到，其中，上述聚类分析算法可以是kmeans算法，kmeans算法是典型的基于距离的聚类算法，采用距离作为相似性的评价指标，即认为两个对象的距离越近，其相似度就越大。kmeans算法的核心在于依据簇的数量将给定的数据集划分成K类，然后通过循环迭代来确定聚类结果是否满足循环停止条件。具体包括以下步骤：
步骤S1：对所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息进行预处理，得到预处理数据，所述预处理数据包含多个样本，各个样本包括水平井机械比能、水平井地质参数信息和水平井工程参数信息；上述预处理包括对数据进行标准化、过滤异常点等处理，从而便于后期进行分类。
步骤S2：在所述水平井在深度上的数据点中随机选取K个中心，比如K个中心分别记为
步骤S3：定义损失函数；在全部地质工程参数构成的多维空间中，通过迭代寻找K个簇(Cluster)使得地质工程参数聚类结果对应的损失函数最小，其中损失函数描述的是聚类中心点之间的紧密程度，损失函数的值越小则表示簇内样本之间的相似度越高，也就是聚类效果越好。通常损失函数定义为：其中，J(c,u)为各个样本距离所属簇的中心点的误差平方和，xi为第i个样本，ci为xi所属的簇，为簇对应的中心点，M—样本总数。
步骤S4：设置迭代次数为t；具体可以根据实际情况进行设置。
步骤S5：评估每个所述样本到聚类中心的距离，并将每一个样本分配到距离最近的中心所属的簇中，若未满足损失条件或未达到迭代停止条件，则重新计算每一个类别的聚类中心点；其中，评估每个样本到聚类中心的距离并将每一个样本xi分配到距离最近的中心所属的簇中可以采用以下公式计算：其中，k为每一个类别的中心点，为每个样本到聚类中心的距离。若未满足损失条件或未达到迭代停止条件，则重新计算每一个类别的中心点k，可以采用以下公式计算：
其中，为t+1轮迭代后的质心位置。
步骤S6：重复步骤S5至损失函数收敛，得到分类结果。
kmeans算法速度很快，可以快速对水平井在深度上的数据点进行分类。
步骤230：利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果；上述进行分段是将所述水平井的深度分为多段，具体包括以下步骤：
首先，获取初始分段点，并从所述初始分段点开始，在所述水平井的深度上按照预置的水平段长进行分段，得到初始分段结果；上述初始分段点是指开始分段的点，可以根据经验或是实际情况选定，一般可以从A点，即水平井的底部开始。上述预置的水平段长可以是结合前期开发经验确定的段长范围，然后根据设定的段长范围选取最大水平段长。比如，段长范围为50-100，这可以设置水平段长为100。
然后，分别根据所述初始分段结果中各段中各个数据点的分类结果，将该段中分类结果相同的所述数据点分配相同的标签，不同的分类结果对应的标签不同；在本实施例中，所述初始分段结果中每一段中包含多个数据点，每个数据点在上述步骤步骤220中都有一个分类结果，然后可以将每个分类结果分配一个标签，不同的类别分配不同的标签。比如，类别为N1的点分配标签为K1，类别为N2的点分配标签为K2。
然后，统计所述初始分段结果中各段中各个标签对应的数据点数量；每一段中可能出现多种类别，每个类别包括有多个数据点，可以分别统计各个标签对应的数据点的数量。
然后，将所述数据点数量最多的标签对应的分类结果作为该段在段长范围内的多数点特征值；将上述各个标签对应的分类结果作为该段中的特征值，比如上述例子中，该段中的特征值包括K1，K2。然后将对于数据点数量最多的标签作为该段在段长范围内的多数点特征值，比如，在上述例子中，K1对应的数据点由5个，K2对应的数据点由2个，则得到该段在段长范围内的多数点特征值为K1。
最后，根据各段在段长范围内的多数点特征值得到分段结果。由于在上述分段过程中可能存在数据点均匀分布的情况，则没有多数点特征值，说明该分段是无效的，即若多数点特征值是属于上述分类结果中的一类，则说明分段有效，反之，则需要进行调整，具体可以通过以下步骤判断：
判断所述各段在段长范围内的多数点特征值是否为所述分类结果中的一类，若是，则将所述初始分段结果作为分段结果；若否，则调整所述预置的水平段长或/和调整所述初始分段点，并重新进行分段，得到分段结果。
需要说明的是，上述重新分段可以是调整所述预置的水平段长，比如减小水平段长重新判断直至水平段长减小为最小值；也可以是调整所述初始分段点，比如将初始分段点向后移动1m；还是可以先调整预置的水平段长，再调整所述初始分段点。
例如：从A点开始判断该段长范围内是否存在多数点的特征值属于聚类分析结果中的某一类，若是可将该段长分为一类，若不是则减小段长重新判断直至段长减小为最小值，若依旧不能划分段则将起点向后移动1m，重复上述判断流程，直至将全水平段的段分完。
步骤240：分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置；请参看图5，图5示意性示出了根据本申请实施例的贴近度法分簇原理示意图。具体包括以下步骤：
首先，根据各段中水平井机械比能、水平井地质参数信息和水平井工程参数信息，构建模糊物元；对于给定的事物可以用三元组(事物M、特征C、量值x)来代表事物的基本元，如果该事物的量值具有模糊性的话，则可称为模糊物元，记作：如果事物M有n个特征C1,C2,…,Cn与这些特征相对应的模糊量值为x1,x2…，xn，则称Rn为n维模糊物元；若有m个事物用其共同的n个特征C1,C2,…,Cn及其相应的模糊量值x1,x2,…,xn来描述，那么称Rnm为m个事物的n维模糊复合物元，表示为：
然后，基于模糊物元中的模糊量值，根据从优隶属度原则计算各评价指标模糊量值的从 优隶属度；其中，根据指标的效用不同，从优隶属度可分为两种：
第一种，针对含油性指数等指标为越大越优型，采用以下式子表示：
第二种，针对机械比能、砾石线密度等指标为越小越优型，采用以下式子表示：
上述式子中，minxij、maxxij分别表示第i项指标在各事物中的最小值和最大值，即在Rnm中每一行的最小值和最大值。根据上述模糊物元公式，经计算可得从优隶属度模糊物元R'nm：
然后，根据所述各评价指标模糊量值的从优隶属度，构建差平方复合模糊物元；在构建差平方复合模糊物元之前，需先确定标准(最优)糊物元。标准模糊物元是从优隶属度模糊物元R′nm中每个指标的从优隶属度的最大值或最小值，用Ro来表示。在建立完标准模糊物元后，将从优隶属度模糊物元R′nm中每个指标中的各项与标准模糊物元Ro差的平方表示为：
Vij(i＝1,2,…,n；j＝1,2,…,m)，即Vij＝(uij-uoj)2，则可构建差平方复合模糊物元Rv，记作：
然后，确定各特征权重；由于段内各簇非均衡发育主要是因各簇起裂条件差异过大致使优势簇起裂后，段内压力值难以达到其他簇起裂要求，故段内各簇均衡发育的根本性需求为保证各射孔簇处特征相近。为满足此需求，本模型采用方差作为各特征的权重，可以表示为：
其中，wj为Cj特征值方差权重，μ为Cj特征值的平均值，n为Cj特征值的个数，xi为Cj特征值大小。
然后，将差平方复合模糊物元与所述各特征权重代入到贴近度计算公式中，得到各段内 各个深度处的地质工程参数的综合贴近度；上述贴近度计算公式为：其中，ωj为各特征权重，Δij为差平方复合模糊物元。
最后，根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置。在本实施例中，计算得到地质工程参数的贴近度后，选取贴近度值最低的位置作为射孔簇位置。
步骤250：根据所述分段结果、所述射孔簇位置得到砾岩储层分段分簇结果。上述计算得到分段结果和确定好射孔簇位置后，就可以得到砾岩储层分段分簇结果。请参看图6-图8，采用本发明进行分段分簇后，工程参数分布的更加均衡、施工难度更低、减少了施工中复杂工况的发生概率，节省施工液量。
下面以J-1井为例，举例说明分段分簇过程。
首先，将J-1井修正后的钻井参数带入到修正的机械比能计算公式中，计算得到水平段机械比能分布情况，请参看图9，图9示意性示出了根据本申请实施例的机械比能分布图；然后，综合机械比能、含油性指数、砾石线密度参数，利用聚类分析算法，对J-1井各深度点进行分类。其中，水平井段的各特征值可分为K1-K5，5个类别，请参看图10，图10示意性示出了根据本申请实施例的聚类分析结果图。然后，利用上述分类结果，结合前期开发经验限制结果段长范围为50-100m，对水平井进行分段，得到分段结果。然后，根据上述分得的各段，计算各段内每个深度处的机械比能、含油性指数、砾石线密度参数的综合贴近度，结合前期开发经验限制分簇结果为首段2簇其余段3簇，最小簇间距10m，选取贴进度值低的位置，即图11中①深度处作为本段内第1簇射孔簇位置。最后输出分段分簇结果，请参看图12，图12示意性示出了根据本申请实施例的分段分簇优化结果图。
上述实现过程中，通过获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果；然后利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果；然后分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置；最后根据所述分段结果、所述射孔簇位置得到砾岩储层分段分簇结果。通过水平井机械比能可以大幅提高砂砾岩储层工程甜点识别的准确性，采用聚类分析算法进行分类并进行分段，可保证水平井段内地质、工程参数相似，同时采用贴近度算法计算贴近度，进而得到射孔簇位置，形成基于地质工程甜点协同优化的砂砾岩储层分段分簇方法，指导砾岩油藏水平井体积压裂段内多簇均衡改造，从而有效提高段内射孔点处的地质、工程参数分布的均衡性，有利于段内多簇压裂过程中各簇均衡起裂，降低施工难度，减少施工中复杂工况的发生概率，节省施工液量，节约投资，提高油气井产量。该方法计算便捷、操作简单。
图2为一个实施例中砾岩储层分段分簇方法的流程示意图。应该理解的是，虽然图2的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图2中的至少一部分步骤可以包括多个子步骤或者多个阶 段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
在一个实施例中，如图13所示，图13示意性示出了根据本申请实施例的一种砾岩储层分段分簇装置的结构框图。提供了一种砾岩储层分段分簇装置，包括信息获取模块410、分类模块420、分段模块430、分簇模块440、结果输出模块450，其中：
信息获取模块410，用于获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；
分类模块420，用于根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果；
分段模块430，用于利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果；
分簇模块440，用于分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置；
结果输出模块450，用于根据所述分段结果、所述射孔簇位置得到砾岩储层分段分簇结果。
所述砾岩储层分段分簇装置包括处理器和存储器，上述信息获取模块410、分类模块420、分段模块430、分簇模块440、结果输出模块450等均作为程序单元存储在存储器中，由处理器执行存储在存储器中的上述程序模块中实现相应的功能。
处理器中包含内核，由内核去存储器中调取相应的程序单元。内核可以设置一个或以上，通过调整内核参数来实现对砾岩储层分段分簇方法。
存储器可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)，存储器包括至少一个存储芯片。
本申请实施例提供了一种存储介质，其上存储有程序，该程序被处理器执行时实现上述一种砾岩储层分段分簇方法。
在一个实施例中，提供了一种计算机设备，该计算机设备可以是终端，其内部结构图可以如图14所示。该计算机设备包括通过系统总线连接的处理器A01、网络接口A02、显示屏A04、输入装置A05和存储器(图中未示出)。其中，该计算机设备的处理器A01用于提供计算和控制能力。该计算机设备的存储器包括内存储器A03和非易失性存储介质A06。该非易失性存储介质A06存储有操作系统B01和计算机程序B02。该内存储器A03为非易失性存储介质A06中的操作系统B01和计算机程序B02的运行提供环境。该计算机设备的网络接口A02用于与外部的终端通过网络连接通信。该计算机程序被处理器A01执行时以实现一种砾岩储层分段分簇方法。该计算机设备的显示屏A04可以是液晶显示屏或者电子墨水显示屏，该计算机设备的输入装置A05可以是显示屏上覆盖的触摸层，也可以是计算机设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
本领域技术人员可以理解，图14中示出的结构，仅仅是与本申请方案相关的部分结构 的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，本申请提供的砾岩储层分段分簇装置可以实现为一种计算机程序的形式，计算机程序可在如图14所示的计算机设备上运行。计算机设备的存储器中可存储组成该砾岩储层分段分簇装置的各个程序模块，比如，图13所示的信息获取模块410、分类模块420、分段模块430、分簇模块440、结果输出模块450。各个程序模块构成的计算机程序使得处理器执行本说明书中描述的本申请各个实施例的砾岩储层分段分簇方法中的步骤。
图8所示的计算机设备可以通过如图13所示的砾岩储层分段分簇装置中的信息获取模块410执行步骤210。计算机设备可通过分类模块420执行步骤220，通过分段模块430执行步骤230，通过分簇模块440执行步骤240，通过结果输出模块450执行步骤250。
本申请实施例提供了一种设备，设备包括处理器、存储器及存储在存储器上并可在处理器上运行的程序，处理器执行程序时实现以下步骤：
获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；
根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果；
利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果；
分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置；
根据所述分段结果、所述射孔簇位置得到砾岩储层分段分簇结果。
在一个实施例中，所述获取水平井机械比能，包括：
获取基础参数信息，所述基础参数信息至少包括钻井数据、钻具组合参数；
根据所述基础参数信息，采用水平井摩阻模型计算得到水平井机械比能。
在一个实施例中，所述根据所述基础参数信息，采用水平井摩阻模型计算得到水平井机械比能，包括：
将所述基础参数信息代入到所述水平井摩阻模型中的修正机械比能计算公式中，得到水平井机械比能；
所述修正机械比能计算公式为：
其中，E为水平井机械比能MPa，P为钻压MPa，Db为钻头直径mm，e为自然对数，ak为井斜角rad，μwell为钻柱摩擦系数，υ为钻速m/h，q为钻具每转排量，是一个结构参数，仅与定子和转子的线型和几何尺寸有关，L/r，Δpp为螺杆钻具喷嘴压降MPa，n为转盘转数r/min，μbit为钻头摩擦系数，KN为动力钻具的转速流量比，r/L，Q为总流量，L/s。
在一个实施例中，所述根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得 到分类结果，包括：
S1：对所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息进行预处理，得到预处理数据，所述预处理数据包含多个样本，各个样本包括水平井机械比能、水平井地质参数信息和水平井工程参数信息；
S2：在所述水平井在深度上的数据点中随机选取K个中心；
S3：定义损失函数；
S4：设置迭代次数；
S5：评估每个所述样本到聚类中心的距离，并将每一个样本分配到距离最近的中心所属的簇中，若未满足损失条件或未达到迭代停止条件，则重新计算每一个类别的聚类中心点；
S6：重复S5至损失函数收敛，得到分类结果。
在一个实施例中，所述利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果，包括：
获取初始分段点，并从所述初始分段点开始，在所述水平井的深度上按照预置的水平段长进行分段，得到初始分段结果；
分别根据所述初始分段结果中各段中各个数据点的分类结果，将该段中分类结果相同的所述数据点分配相同的标签，不同的分类结果对应的标签不同；
统计所述初始分段结果中各段中各个标签对应的数据点数量；
将所述数据点数量最多的标签对应的分类结果作为该段在段长范围内的多数点特征值；
根据各段在段长范围内的多数点特征值得到分段结果。
在一个实施例中，所述根据各段在段长范围内的多数点特征值得到分段结果，包括：
判断所述各段在段长范围内的多数点特征值是否为所述分类结果中的一类，若是，则将所述初始分段结果作为分段结果；若否，则调整所述预置的水平段长或/和调整所述初始分段点，并重新进行分段，得到分段结果。
在一个实施例中，所述分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置，包括：
根据各段中水平井机械比能、水平井地质参数信息和水平井工程参数信息，构建模糊物元；
基于模糊物元中的模糊量值，根据从优隶属度原则计算各评价指标模糊量值的从优隶属度；
根据所述各评价指标模糊量值的从优隶属度，构建差平方复合模糊物元；
确定各特征权重；
将差平方复合模糊物元与所述各特征权重代入到贴近度计算公式中，得到各段内各个深度处的地质工程参数的综合贴近度；
根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形 式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
存储器可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。存储器是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体，可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
以上仅为本申请的实施例而已，并不用于限制本申请。对于本领域技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在本申请的权利要求范围之内。

Claims (10)

  1. 一种砾岩储层分段分簇方法，其特征在于，所述砾岩储层分段分簇方法包括：
    获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；
    根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果；
    利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果；
    分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置；
    根据所述分段结果、所述射孔簇位置得到砾岩储层分段分簇结果。
  2. 根据权利要求1所述的方法，其特征在于，所述获取水平井机械比能，包括：
    获取基础参数信息，所述基础参数信息至少包括钻井数据、钻具组合参数；
    根据所述基础参数信息，采用水平井摩阻模型计算得到水平井机械比能。
  3. 根据权利要求2所述的方法，其特征在于，所述根据所述基础参数信息，采用水平井摩阻模型计算得到水平井机械比能，包括：
    将所述基础参数信息代入到所述水平井摩阻模型中的修正机械比能计算公式中，得到水平井机械比能；
    所述修正机械比能计算公式为：
    其中，E为水平井机械比能，P为钻压，Db为钻头直径，e为自然对数，ak为井斜角，μwell为钻柱摩擦系数，υ为钻速，q为钻具每转排量，Δpp为螺杆钻具喷嘴压降，n为转盘转数，μbit为钻头摩擦系数，KN为动力钻具的转速流量比，Q为总流量。
  4. 根据权利要求1所述的方法，其特征在于，所述根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果，包括：
    S1：对所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息进行预处理，得到预处理数据，所述预处理数据包含多个样本，各个样本包括水平井机械比能、水平井地质参数信息和水平井工程参数信息；
    S2：在所述水平井在深度上的数据点中随机选取K个中心；
    S3：定义损失函数；
    S4：设置迭代次数；
    S5：评估每个所述样本到聚类中心的距离，并将每一个样本分配到距离最近的中心所属的簇中，若未满足损失条件或未达到迭代停止条件，则重新计算每一个类别的聚类中心点；
    S6：重复S5至损失函数收敛，得到分类结果。
  5. 根据权利要求1所述的方法，其特征在于，所述利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果，包括：
    获取初始分段点，并从所述初始分段点开始，在所述水平井的深度上按照预置的水平段长进行分段，得到初始分段结果；
    分别根据所述初始分段结果中各段中各个数据点的分类结果，将该段中分类结果相同的所述数据点分配相同的标签，不同的分类结果对应的标签不同；
    统计所述初始分段结果中各段中各个标签对应的数据点数量；
    将所述数据点数量最多的标签对应的分类结果作为该段在段长范围内的多数点特征值；
    根据各段在段长范围内的多数点特征值得到分段结果。
  6. 根据权利要求5所述的方法，其特征在于，所述根据各段在段长范围内的多数点特征值得到分段结果，包括：
    判断所述各段在段长范围内的多数点特征值是否为所述分类结果中的一类，若是，则将所述初始分段结果作为分段结果；若否，则调整所述预置的水平段长或/和调整所述初始分段点，并重新进行分段，得到分段结果。
  7. 根据权利要求1所述的方法，其特征在于，所述分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置，包括：
    根据各段中水平井机械比能、水平井地质参数信息和水平井工程参数信息，构建模糊物元；
    基于模糊物元中的模糊量值，根据从优隶属度原则计算各评价指标模糊量值的从优隶属度；
    根据所述各评价指标模糊量值的从优隶属度，构建差平方复合模糊物元；
    确定各特征权重；
    将差平方复合模糊物元与所述各特征权重代入到贴近度计算公式中，得到各段内各个深度处的地质工程参数的综合贴近度；
    根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置。
  8. 一种砾岩储层分段分簇装置，其特征在于，所述砾岩储层分段分簇装置包括：
    信息获取模块，用于获取水平井机械比能、水平井地质参数信息和水平井工程参数信息；
    分类模块，用于根据所述水平井机械比能、所述水平井地质参数信息和所述水平井工程参数信息，采用聚类分析算法对所述水平井在深度上的数据点进行多维数据分类，得到分类结果；
    分段模块，用于利用所述分类结果和预置的水平段长，对所述水平井的深度进行分段，得到分段结果；
    分簇模块，用于分别采用贴近度算法计算所述分段结果中各段内各个深度处的地质工程参数的综合贴近度，并根据所述各个深度处的地质工程参数的综合贴近度得到该段内的射孔簇位置；
    结果输出模块，用于根据所述分段结果、所述射孔簇位置得到砾岩储层分段分簇结果。
  9. 一种处理器，其特征在于，被配置成执行根据权利要求1至7中任一项所述的砾岩储层分段分簇方法。
  10. 一种机器可读存储介质，该机器可读存储介质上存储有指令，其特征在于，该指令在被处理器执行时使得所述处理器被配置成执行根据权利要求1至7中任一项所述的砾岩储 层分段分簇方法。
PCT/CN2023/126270 2022-10-25 2023-10-24 砾岩储层分段分簇方法、装置、存储介质及处理器 Ceased WO2024088265A1 (zh)

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