Reservoir heterogeneity evaluation method, device and storage medium based on horizontal well

Abstract

The application provides a reservoir heterogeneity evaluation method and device based on a horizontal well and a storage medium, and the evaluation method comprises the following steps: different reservoir types are divided; a well logging identification chart of different reservoir types is established; reservoir type distribution of a vertical well and the horizontal well is determined; a permeability heterogeneity parameter of a single well point in different vertical well layers is calculated, meanwhile, a plurality of horizontal well sections are divided according to a predetermined well depth interval, and the permeability heterogeneity parameter of each horizontal well section is calculated; a longitudinal heterogeneity distribution map and a transverse heterogeneity distribution map of each reservoir type are determined; for each reservoir type, the transverse heterogeneity distribution map is calibrated on the longitudinal heterogeneity distribution map in a segmented projection manner, and a heterogeneity plane distribution chart of each reservoir type is obtained. The application describes the reservoir plane heterogeneity by using the plane projection and the variation coefficient grading evaluation of the reservoir heterogeneity parameter of the horizontal section of the horizontal well, and the operation is strong.

Description

Technical Field

[0001] This invention relates to the field of reservoir evaluation in oil and gas field exploration and development. Specifically, it relates to a reservoir heterogeneity evaluation method based on horizontal wells, a computer device for implementing the reservoir heterogeneity evaluation method, and a computer-readable storage medium storing a computer program for implementing the reservoir heterogeneity evaluation method. Background Technology

[0002] Reservoir heterogeneity refers to the uneven variation of internal reservoir properties (porosity, permeability, pore structure, etc.) caused by the influence of sedimentary environment, diagenesis, and tectonic activity during sedimentation and burial. It manifests as heterogeneity in both rock composition and pore space, and is a common characteristic of reservoirs. Currently, the main methods for studying reservoir heterogeneity include geological genetic analysis, well logging interpretation, and production dynamics analysis. Among these, geological genetic analysis starts with the geological origins of reservoir heterogeneity, revealing the main influencing factors and characterizing the strength of heterogeneity. Specifically, it studies the controlling factors of reservoir heterogeneity through tectonic, sedimentary, and diagenetic factors. Geological genetic analysis provides a profound understanding of the causes of reservoir heterogeneity, but lacks quantitative data analysis. Well logging interpretation primarily studies reservoir heterogeneity by calculating relevant coefficients of variation, surge coefficients, and ranges using well logging permeability, quantitatively identifying each sublayer to characterize the degree of heterogeneity development. Its drawback is that the results depend on the reliability of the well logging permeability model. Production dynamic analysis provides an effective approach to studying static reservoir heterogeneity from a dynamic perspective; various production dynamic information indirectly reflects the strength of reservoir heterogeneity development. While production dynamic data itself cannot directly characterize reservoir heterogeneity, it can verify reservoir heterogeneity such as interlayers identified by other methods. During the exploration phase, studying reservoir heterogeneity is of significant guiding importance for clarifying oil and gas migration, accumulation, and reservoir distribution patterns. During oil and gas field development, as water cut increases, reservoir heterogeneity plays a crucial guiding role in predicting remaining oil distribution and selecting injection wells. Therefore, it is necessary to establish corresponding reservoir heterogeneity evaluation methods to address the differences in reservoir characteristics at different stages of oil and gas exploration and development.

[0003] Conventional heterogeneity assessment methods are mainly based on core data from vertical wells, and lack effective methods for assessing reservoir heterogeneity variations between vertical wells. Published patents and related literature provide relatively detailed descriptions of heterogeneity assessment methods. For example, a patent document published on November 25, 2015, entitled "A Method for Studying the Heterogeneity of Bioclastic Limestone Reservoirs" (publication number CN105092820A), describes a method for studying the heterogeneity of bioclastic limestone reservoirs, including the following steps: classifying the sedimentary microfacies of bioclastic limestone reservoirs; establishing standard distribution ranges for porosity and permeability parameters of different microfacies; drawing regional sedimentary facies maps and examining the relationship between productivity and sedimentary facies distribution at different well locations; establishing the correspondence between major diagenetic types and lithology through diagenetic type analysis; studying pore structure and classifying reservoirs, and based on the reservoir classification results, giving a classification level of pore diagenetic degree for different reservoir types; calculating the pseudo-variance coefficient, pseudo-breakthrough coefficient, and pseudo-range of bioclastic limestone reservoirs, and giving the numerical ranges of heterogeneity parameters at each level, etc.

[0004] The patent document published on June 22, 2018, entitled "A Method for Characterizing Reservoir Heterogeneity under Regular Development Well Networks of Horizontal Wells" (publication number CN105651676A), describes a method for characterizing reservoir heterogeneity under regular development well networks of horizontal wells. The method mainly includes the following steps: establishing a structural model of the study area to construct a formation model of the target layer; simulating a lithology model based on the formation model established in step (2); establishing a three-dimensional model of reservoir permeability in the sandstone within the lithology model established in step (3); establishing a virtual vertical well at the wellhead of each horizontal well; extracting formation layer data and permeability curves of each virtual vertical well; calculating the permeability heterogeneity parameters of the target layer of each well using the permeability curves of the actual drilled vertical wells and the permeability curves of the virtual vertical wells obtained in step (6); and drawing plane contour maps of the permeability heterogeneity parameters to characterize the heterogeneity characteristics of the reservoir.

[0005] A patent document published on April 30, 2019, entitled "Comprehensive Evaluation Method for Intra-Layer Heterogeneity of Oilfield Reservoirs" (publication number CN108843312A), describes a comprehensive evaluation method for intra-layer heterogeneity based on a high-order neural network during oilfield development. Its core is to introduce dynamic parameters of water injection data from oilfield development and production, based on static geological parameters such as Vk, Tk, Jk, Pk, and Dk commonly used by scholars both domestically and internationally, as evaluation indicators for intra-layer heterogeneity. Furthermore, it employs a high-order neural network method to specifically learn the weights of each of the above intra-layer heterogeneity parameters, effectively improving the accuracy and timeliness of the comprehensive evaluation of intra-layer heterogeneity during oilfield development.

[0006] A patent document published on August 3, 2021, entitled "A Method for Evaluating the Vertical Heterogeneity of Reservoirs Based on the Seepage Control Equation" (publication number CN112016212A), describes a method for evaluating the vertical heterogeneity of reservoirs based on the seepage control equation. This method first uses numerical simulation technology to obtain a sample dataset of the reservoir. Then, it establishes a feature extractor based on the reservoir's dynamic data, sets multiple feature extractors to extract features from the dynamic data, generates a new dynamic dataset, and uses the new dynamic dataset and static data to construct a training and testing set for deep learning. A new loss function is defined based on physical constraints, and this function is used to train the weights and thresholds of the deep learning model, thereby generating an optimal deep learning prediction model for vertical permeability. The testing set is used to test the prediction accuracy of the deep learning model. Based on the prediction results of the deep learning model, the range and coefficient of variation are defined to characterize the heterogeneity of the reservoir.

[0007] A patent document published on July 13, 2021, entitled "A Method for Evaluating Reservoir Heterogeneity" (publication number CN113107463A), describes a method for evaluating reservoir heterogeneity. This method involves selecting multiple sets of measurement points between and within each stratum of the reservoir to measure permeability. The permeability is used to obtain the coefficient of variation, advance coefficient, and grade difference between and within each stratum. These are compared with the average coefficient of variation, average advance coefficient, and average grade difference between and within each stratum, and the average coefficient of variation, average advance coefficient, and average grade difference within each stratum, respectively. Furthermore, the rhythmic distribution of vertical particle size distribution within each stratum is considered to obtain the heterogeneity level between and within each stratum.

[0008] A patent document published on March 1, 2022, entitled "A Method for Characterizing the Heterogeneity of Extremely Heterogeneous Oil and Gas Reservoirs" (publication number CN110242291A), describes a method for characterizing the heterogeneity of extremely heterogeneous oil and gas reservoirs, including: permeability slippage index analysis; normal distribution processing of permeability data; and calculation of a comprehensive heterogeneity index. The method for characterizing the heterogeneity of extremely heterogeneous oil and gas reservoirs provided by this invention proposes a permeability slippage index method for quantitative characterization of extremely heterogeneous reservoirs, overcoming the problems of large and irregular permeability variations within small layers of single wells in the study area. It utilizes a normal distribution processing method for permeability data, optimizes multiple parameters, and uses an entropy weight mathematical algorithm to calculate a comprehensive heterogeneity index, thereby quantitatively describing the interlayer, intralayer, and planar heterogeneity of the reservoir.

[0009] The above methods propose a method for evaluating reservoir heterogeneity by combining different macroscopic and microscopic, production dynamic and static data in vertical wells, but none of them propose how to use information from the horizontal section of horizontal wells to reflect changes in reservoir planar heterogeneity. Summary of the Invention

[0010] The purpose of this invention is to address at least one of the aforementioned shortcomings of the prior art. For example, one objective of this invention is to provide a method for evaluating reservoir planar heterogeneity that can comprehensively consider the variations in heterogeneity parameters of both vertical and horizontal well sections.

[0011] To achieve the above objectives, this invention provides a method for evaluating reservoir heterogeneity based on horizontal wells. This method includes the following steps: identifying the rock composition of different lithofacies based on rock structure to classify different reservoir types; establishing logging identification charts for different reservoir types by statistically analyzing the logging response characteristics of different reservoir types; identifying the reservoir types of vertical and horizontal wells based on the logging identification charts to clarify the distribution of reservoir types in vertical and horizontal wells; dividing vertical wells into different vertical well layers according to different reservoir types, and calculating the permeability heterogeneity parameters of a single well point within different vertical well layers. Simultaneously, multiple horizontal well sections are divided according to predetermined well depth intervals, and the permeability heterogeneity parameters of each horizontal well section are calculated. The permeability heterogeneity parameters include the permeability variation coefficient, the surge coefficient, and the range. Based on the permeability heterogeneity parameters within the different vertical well layers, a vertical heterogeneity distribution map for each reservoir type is determined. Based on the permeability heterogeneity parameters of each horizontal well section, a horizontal heterogeneity distribution map for each reservoir type is determined. For each reservoir type, the horizontal heterogeneity distribution map is projected onto the vertical heterogeneity distribution map in a segmented manner to obtain a planar distribution map of the heterogeneity for each reservoir type.

[0012] In an exemplary embodiment of the reservoir heterogeneity evaluation method based on horizontal wells of the present invention, determining the lateral heterogeneity distribution map of each reservoir type based on the permeability heterogeneity parameters of each horizontal well section may include: further determining the range of heterogeneity strength using a heterogeneity grading index based on the distribution range of the permeability variation coefficient of a certain reservoir type in the horizontal section of the horizontal well, wherein the formula for the heterogeneity grading index is: ,in, A VK It is a heterogeneous grading index, dimensionless; V kmax This represents the maximum coefficient of variation within a predetermined depth interval for a horizontal well. V kmin This represents the minimum coefficient of variation within a predetermined well depth interval. V Ki No. i The coefficient of variation for each sampling point is dimensionless.

[0013] In an exemplary embodiment of the reservoir heterogeneity evaluation method based on horizontal wells of the present invention, the measured depth MD intervals traversed by the horizontal well in such reservoirs can be screened based on the measured depth MD value when the horizontal well encounters the top of a certain reservoir type, and the coefficient of variation curves located within this interval can be retained.

[0014] In an exemplary embodiment of the reservoir heterogeneity evaluation method based on horizontal wells of the present invention, the heterogeneity in the work area can be divided into a first heterogeneous region, a second heterogeneous region, and a third heterogeneous region in the heterogeneity planar distribution chart, and marked with different colors. The heterogeneity in the first heterogeneous region is stronger than that in the second heterogeneous region, and the heterogeneity in the second heterogeneous region is stronger than that in the third heterogeneous region.

[0015] In an exemplary embodiment of the reservoir heterogeneity evaluation method based on horizontal wells of the present invention, the predetermined well depth interval can be 10m to 40m.

[0016] In an exemplary embodiment of the reservoir heterogeneity evaluation method based on horizontal wells of the present invention, the permeability variation coefficient within a predetermined well depth interval on the horizontal well section can be calculated by the following formula: , in, V k is the permeability variation coefficient within a predetermined well depth interval on a horizontal well section, dimensionless; K i For the first horizontal well section i Permeability of each sampling point, ×10 -3 μm 2 ; n This represents the number of sampling points within the horizontal well section. The thickness-weighted average permeability within the horizontal well section, multiplied by 10. -3 μm 2 .

[0017] In an exemplary embodiment of the reservoir heterogeneity evaluation method based on horizontal wells of the present invention, the inrush coefficient within a predetermined depth interval on the horizontal well section can be calculated by the following formula: ,in, T K , is the advance coefficient within a predetermined depth interval on a horizontal well section, dimensionless; K max The maximum permeability within a predetermined depth interval of a horizontal well, multiplied by 10. -3 μm 2 ; The thickness-weighted average permeability within the horizontal well section, multiplied by 10. -3 μm 2 .

[0018] In an exemplary embodiment of the reservoir heterogeneity evaluation method based on horizontal wells of the present invention, the range within a predetermined well depth interval on the horizontal well section can be calculated by the following formula: ,in, J K The range within a predetermined depth interval on a horizontal well section is dimensionless. K max The maximum permeability within a predetermined depth interval of a horizontal well, multiplied by 10. -3 μm 2 ; K min The minimum permeability within a predetermined depth interval of a horizontal well, multiplied by 10. -3 μm 2 .

[0019] In another aspect, the present invention provides a computer device comprising: a processor; and a memory storing a computer program, wherein when the computer program is executed by the processor, the reservoir heterogeneity evaluation method described above is implemented.

[0020] In another aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the reservoir heterogeneity evaluation method as described above.

[0021] Compared with the prior art, the beneficial effects of the present invention include at least one of the following: (1) The planar heterogeneity of the reservoir was characterized by the plane projection of the reservoir heterogeneity parameters in the horizontal section of the horizontal well and the graded evaluation of the coefficient of variation, which is highly operable; (2) The optimal horizontal segment interval was determined to calculate the heterogeneous parameters. This interval can be directly used for the evaluation of similar reservoir planar heterogeneous distribution. Attached Figure Description

[0022] The above and other objects and / or features of the present invention will become clearer from the following description taken in conjunction with the accompanying drawings, in which: Figure 1 A schematic flowchart of a reservoir heterogeneity evaluation method according to an exemplary embodiment of the present invention is shown.

[0023] Figure 2 A schematic diagram of the logging response characteristics of an exemplary embodiment of the present invention is shown.

[0024] Figure 3A This illustrates a GR-DT logging identification chart for a specific reservoir type, as shown in an exemplary embodiment of the present invention. Figure 3BThis illustrates a GR-LLD logging identification chart for a specific reservoir type, as shown in an exemplary embodiment of the present invention. Figure 3C This illustration shows a DT-LLD logging identification chart for a specific reservoir type, representing an exemplary embodiment of the present invention.

[0025] Figure 4 A diagram illustrating the reservoir type interpretation results for a vertical well according to an exemplary embodiment of the present invention is shown.

[0026] Figure 5 A diagram illustrating the reservoir type interpretation results for a horizontal well section according to an exemplary embodiment of the present invention is shown.

[0027] Figure 6A A graph showing the variation coefficient of different sub-layers in a vertical well, according to an exemplary embodiment of the present invention, is shown. Figure 6B A graph showing the variation of the penetration coefficient in different sub-layers of a vertical well, according to an exemplary embodiment of the present invention, is shown. Figure 6C A range variation diagram of different sub-layers in a vertical well is shown as an exemplary embodiment of the present invention.

[0028] Figure 7A A graph showing the coefficient of variation calculation for a horizontal well A1-3-2H according to an exemplary embodiment of the present invention is illustrated. Figure 7B A diagram showing the calculation of the inrush coefficient for a horizontal well A1-3-2H, an exemplary embodiment of the present invention, is illustrated. Figure 7C A range calculation diagram for a horizontal well A1-3-2H, according to an exemplary embodiment of the present invention, is shown.

[0029] Figure 8A A schematic diagram illustrating a horizontal well encountering the top of a reservoir type in an exemplary embodiment of the present invention is shown. Figure 8B A schematic diagram illustrating the process for determining the coefficient of variation of a horizontal well section in a reservoir type, according to an exemplary embodiment of the present invention, is shown.

[0030] Figure 9 The diagram shows the coefficient of variation distribution of all horizontal wells in the Kh2-2 sublayer of the AD1 well area, according to an exemplary embodiment of the present invention.

[0031] Figure 10 A plot showing the strength of heterogeneity in horizontal segments is provided as an exemplary embodiment of the present invention.

[0032] Figure 11 This diagram illustrates the coarsening and calibration of heterogeneous planar distribution within the work area, taking the Kh2-3 layer in the AD4 well area as an example, as an exemplary embodiment of the present invention.

[0033] Figure 12 This diagram illustrates a verification of the heterogeneous planar distribution of the Kh2-2 layer in the AD1 well area, as an example of an exemplary embodiment of the present invention.

[0034] Figure 13 A schematic diagram of the structure of a computer device is shown, illustrating another exemplary embodiment of the present invention.

[0035] Explanation of reference numerals in the attached figures: 20-Computer equipment, 201-Memory, 202-Processor. Detailed Implementation

[0036] In the following sections, the reservoir heterogeneity evaluation method, apparatus, and storage medium based on horizontal wells of the present invention will be described in detail with reference to exemplary embodiments.

[0037] It should be noted that "first," "second," "third," etc., are merely for the convenience of description and distinction, and should not be interpreted as indicating or implying relative importance.

[0038] The inventors discovered through research that existing technologies all employ a vertically segmented approach for evaluating reservoir heterogeneity in vertical wells, but none have addressed how to utilize information from the horizontal sections of horizontal wells to reflect changes in reservoir planar heterogeneity. However, compared to vertical wells, horizontal wells contain information about spatial variations in the reservoir between well points, providing direct data for understanding reservoir planar heterogeneity.

[0039] To address the problems of existing technologies, this invention provides a method for evaluating reservoir planar heterogeneity using horizontal well section parameters via planar projection. This technique uses oil and gas core samples as the object, employing a combination of sedimentary and diagenetic methods to classify reservoir types. Vertical wells are used to clarify the vertical heterogeneity variation characteristics of a specific reservoir type, while horizontal well sections are used to clarify the lateral heterogeneity variation of a specific reservoir type, thus establishing a planar heterogeneity evaluation method. Compared to conventional methods that only utilize vertical wells to characterize heterogeneity, the method proposed in this invention, which projects horizontal well sections onto a plane, can more clearly evaluate reservoir planar heterogeneity.

[0040] To achieve the above objectives, the present invention provides a method for evaluating reservoir heterogeneity based on horizontal wells.

[0041] In an exemplary embodiment of the present invention, a method for evaluating reservoir heterogeneity based on horizontal wells includes the following steps: Step S1: Determine the rock composition of different rock facies based on the rock structure, thereby classifying different reservoir types.

[0042] Step S2: By statistically analyzing the logging response characteristics of different reservoir types, establish logging identification charts for different reservoir types, and identify the reservoir types of vertical and horizontal wells based on the logging identification charts to clarify the reservoir type distribution of vertical and horizontal wells.

[0043] Step S3: Divide the vertical well into different vertical well layers according to different reservoir types, and calculate the permeability heterogeneity parameters of a single well point in different vertical well layers. Simultaneously, divide the well into multiple horizontal well sections according to predetermined well depth intervals, and calculate the permeability heterogeneity parameters of each horizontal well section. The permeability heterogeneity parameters include the permeability variation coefficient, the surge coefficient, and the range.

[0044] For example, the predetermined well depth interval can be 10m to 40m. Preferably, a predetermined well depth interval of 40m best reflects the heterogeneous variation characteristics of the horizontal reservoir.

[0045] The coefficient of variation of permeability within a predetermined depth interval in a horizontal well section can be calculated by the following formula: .

[0046] in, V k is the permeability variation coefficient within a predetermined well depth interval on a horizontal well section, dimensionless; K i For the first horizontal well section i Permeability of each sampling point, ×10 -3 μm 2 ; n This represents the number of sampling points within the horizontal well section. The thickness-weighted average permeability within the horizontal well section, multiplied by 10. -3 μm 2 .

[0047] The breakthrough coefficient within a predetermined depth interval on a horizontal well section can be calculated using the following formula: .

[0048] in, T K , is the advance coefficient within a predetermined depth interval on a horizontal well section, dimensionless; K max The maximum permeability within a predetermined depth interval of a horizontal well, multiplied by 10. -3 μm 2 ; The thickness-weighted average permeability within the horizontal well section, multiplied by 10. -3 μm 2 .

[0049] The range within a predetermined depth interval on a horizontal well section can be calculated using the following formula: .

[0050] in, J K The range within a predetermined depth interval on a horizontal well section is dimensionless. K maxThe maximum permeability within a predetermined depth interval of a horizontal well, multiplied by 10. -3 μm 2 ; K min The minimum permeability within a predetermined depth interval of a horizontal well, multiplied by 10. -3 μm 2 .

[0051] Step S4: Based on the permeability heterogeneity parameters within the different vertical well layers, determine the vertical heterogeneity distribution map for each reservoir type; based on the permeability heterogeneity parameters of each horizontal well section, determine the horizontal heterogeneity distribution map for each reservoir type.

[0052] Specifically, the strength of heterogeneity can be further determined using a heterogeneity grading index based on the distribution range of the permeability variation coefficient of a specific reservoir type within the horizontal section of a horizontal well. The formula for the heterogeneity grading index is: .

[0053] in, A VK It is a heterogeneous grading index, dimensionless; V kmax This represents the maximum coefficient of variation within a predetermined depth interval for a horizontal well. V kmin This represents the minimum coefficient of variation within a predetermined well depth interval. V Ki No. i The coefficient of variation for each sampling point is dimensionless.

[0054] Step S5: For each reservoir type, the lateral heterogeneity distribution map is plotted on the longitudinal heterogeneity distribution map in a segmented projection manner to obtain a heterogeneity planar distribution map of each reservoir type.

[0055] In the heterogeneity planar distribution map, the heterogeneity within the work area can be divided into a first heterogeneous region, a second heterogeneous region, and a third heterogeneous region, and marked with different colors. The heterogeneity in the first heterogeneous region is stronger than that in the second heterogeneous region, and the heterogeneity in the second heterogeneous region is stronger than that in the third heterogeneous region.

[0056] In this embodiment, the measured depth MD range of the horizontal well traversing the reservoir type can be screened based on the measured depth MD value when the horizontal well encounters the top of a certain reservoir type, and the coefficient of variation curve within this range can be retained.

[0057] The reservoir heterogeneity evaluation method according to the present invention can be programmed into a computer program and the corresponding program code or instructions can be stored in a computer-readable storage medium. When the program code or instructions are executed by a processor, the processor performs the above-described reservoir heterogeneity evaluation method. The processor and memory can be included in a computer device.

[0058] An exemplary embodiment of another aspect of the present invention also provides a computer-readable storage medium storing a computer program. The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the reservoir heterogeneity evaluation method according to the present invention. The computer-readable recording medium is any data storage device capable of storing data readable by a computer system. Examples of computer-readable recording media include: read-only memory, random access memory, read-only optical disk, magnetic tape, floppy disk, optical data storage device, and carrier waves (such as data transmission via the Internet through wired or wireless transmission paths).

[0059] An exemplary embodiment of another aspect of the present invention also provides a computer device. The computer device includes a processor and a memory. The memory stores a computer program. The computer program is executed by the processor, causing the processor to perform the computer program of the reservoir heterogeneity evaluation method according to the present invention.

[0060] To better understand the exemplary embodiments of the present invention described above, further explanation is provided below in conjunction with the accompanying drawings and specific examples.

[0061] Example 1 A method for evaluating reservoir heterogeneity based on horizontal wells includes the following steps: Step 1: Classify the lithofacies based on the rock structure and composition. Clarify the rock composition of different lithofacies from the differences in rock microstructure, classify reservoir types, and clarify the degree of heterogeneity of each type of reservoir.

[0062] For example, by observing the core and thin sections of cored wells, the lithofacies can be classified by comprehensively considering the differences in sedimentary characteristics, dissolution and cementation, and the physical properties of each lithofacies can be analyzed to classify reservoir types and clarify the degree of heterogeneity of each type of reservoir.

[0063] Step 2: Based on the reservoir type classification results of the core wells, establish logging response characteristics for different reservoir types, and use these characteristics to interpret the reservoir type of the target intervals in vertical and horizontal wells.

[0064] For example, a cross-plot of sensitive logging values ​​for reservoir types can be created to establish a logging interpretation chart for reservoir types. Based on this chart, logging identification of reservoir types in vertical and horizontal wells can be performed to clarify the reservoir types in the vertical and horizontal sections of vertical wells.

[0065] Step 3: Using existing permeability data, calculate the permeability variation coefficient, surge coefficient, and range for the entire vertical well section for each reservoir type, and take the average value of each section to obtain the permeability heterogeneity parameters for a specific reservoir type at a single well point. Simultaneously, select different statistical well depth intervals (i.e., predetermined well depth intervals) on the horizontal well section, and calculate the permeability variation coefficient, surge coefficient, and range for each interval to obtain the permeability heterogeneity parameters for each section of the horizontal well.

[0066] For example, the statistical well depth interval can be 10m, 20m and 40m. After comparison, it was determined that the 40m interval can truly reflect the heterogeneous variation characteristics of the horizontal section reservoir.

[0067] Generally speaking, the larger the above three parameters (i.e., coefficient of variation, coefficient of variation, and range), the stronger the heterogeneity of a certain lithofacies type in the sampling level segment; conversely, the weaker the heterogeneity.

[0068] Step 4: Based on the calculation results of the heterogeneous permeability parameters of vertical and horizontal wells, the heterogeneous permeability parameters of a certain reservoir type at each well point and between wells on the plane are segmented and projected, and the heterogeneity changes of a certain reservoir type on the plane are comprehensively analyzed.

[0069] For example, the coefficient of variation of a certain lithofacies type in the horizontal section of a horizontal well is divided into 5 levels according to its statistical proportion, and different colors are used to distinguish the magnitude of the coefficient of variation in the horizontal well trajectory segment for projection. Combined with the heterogeneous parameters at the well point of the vertical well, the projection results of the heterogeneous parameters in the horizontal section of the horizontal well are used to obtain a planar heterogeneous parameter distribution map of a certain lithofacies type.

[0070] Example 2 This example is applied to the Al-Ahdab oil field in Iraq to characterize reservoir planar heterogeneity, thereby addressing the impact of heterogeneity on crude oil distribution and water injection efficiency.

[0071] like Figure 1 As shown, the implementation process of a reservoir heterogeneity evaluation method based on horizontal wells is as follows.

[0072] Step 1: Classify rock facies types based on rock structure and composition. Clarify the rock composition of different rock facies based on the differences in rock microstructure and classify reservoir types.

[0073] Based on thin section observation, lithofacies types were classified according to grain composition and grain type. As shown in Table 1, eight lithofacies types were identified: argillaceous bioclastic marl, planktonic foraminiferous marl, laminated bioclastic marl, intraclastic bioclastic marl, patchy green algal marl, bioclastic green algal intraclastic marl, bioclastic intraclastic marl, and intraclastic grain limestone. Due to significant differences in pore type and pore structure among the different lithofacies types, these eight lithofacies types were further classified into eight reservoir types.

[0074] In addition, the Kh1 and Kh2 layers of a single well can be divided into sub-layers according to the reservoir type, which are divided into a total of 8 sub-layers, each corresponding to a certain type of reservoir.

[0075] Table 1. Classification of Lithofacies Types

[0076] Step 2: Based on the reservoir type classification results of the core wells, establish logging response characteristics for different reservoir types, and use these characteristics to interpret the reservoir type of the target intervals in vertical and horizontal wells.

[0077] By analyzing the logging response characteristics of different reservoir types (such as...) Figure 2 As shown), logging identification charts for different reservoir types were established (e.g. Figure 3A , Figure 3B and Figure 3C (As shown). Among them, Figure 3A , Figure 3B and Figure 3C In this context, GR represents natural gamma, DT represents acoustic transit time, and LLD represents deep lateral resistivity.

[0078] Using logging data from a single well, reservoir type identification charts are used to interpret the reservoir types of vertical and horizontal wells (e.g., Figure 4 and Figure 5 As shown in the figure, the distribution of the main reservoir types is clearly defined.

[0079] in, Figure 4 The vertical axis represents MD (depth of measurement, in meters); the horizontal axes represent CAL (well diameter, in inches), GR (natural gamma, API), ILD (deep induced resistivity, Ω•m), ILM (medium induced resistivity, Ω•m), MSFL (microsphere focused resistivity, Ω•m), NPHI (neutron porosity, v / v), and RHOB (density, g / cm³). 3 DT (sonic transit time, in μs / ft), POR (logging porosity, in v / v), CPHI sft Av (core average porosity, in v / v), CPHI sft (core average porosity, in v / v), Perm (logging permeability, in ×10⁻¹⁰) -3 μm 2 ), CKH sft Av (i.e., average permeability of the core, in units of ×10), -3 μm 2 ), CKH sft (i.e., core permeability, unit is ×10) -3 μm 2), Heated (i.e., heat), XRMI_DYN (i.e., imaging logging), Layer is the logging sub-layer, and Litho Zone is the lithofacies type. Figure 5 The vertical axis represents altitude depth (TVD), in meters (m); the horizontal axis represents horizontal displacement, in meters (m).

[0080] Step 3: Calculate the permeability variation coefficient, surge coefficient, and range for the entire section of each reservoir type in vertical wells to obtain the permeability heterogeneity parameters for a single well point of a specific reservoir type. Also, calculate the permeability variation coefficient, surge coefficient, and range within each interval of horizontal wells to obtain the permeability heterogeneity parameters for each section of the horizontal well.

[0081] First, the permeability heterogeneity parameters for different reservoir types in vertical wells were calculated. Since the study area was divided into sub-layers based on reservoir type, the heterogeneity parameters for different sub-layers (such as...) were statistically analyzed. Figure 6A , Figure 6B and Figure 6C (As shown).

[0082] Then, the permeability heterogeneity of the horizontal well was calculated according to three interval division methods (10m, 20m, and 40m). Figure 7A , Figure 7B and Figure 7C (As shown). Since the 10m interval division scheme is too short, some intervals may contain outliers, interfering with the analysis of the overall heterogeneity trend. Therefore, the data interval should not be too small when evaluating the heterogeneity of horizontal well layers. This method uses a 40m interval for evaluation.

[0083] Step 4: Combining the heterogeneous parameters at the well points of the vertical wells, and using the projection results of the heterogeneous parameters of the horizontal section of the horizontal wells, obtain a planar heterogeneous parameter distribution map of a certain lithofacies type.

[0084] First, based on the MD value when a horizontal well encounters the top of a certain reservoir type (e.g.) Figure 8A As shown), the MD intervals traversed by horizontal wells in this type of reservoir are screened, and the coefficient of variation curves within these intervals are retained. Figure 8A The vertical axis represents altitude depth (TVD), in meters (m); the horizontal axis represents horizontal displacement, in meters (m).

[0085] like Figure 8B As shown, the coefficient of variation curves that do not traverse the horizontal section of the Kh2-2 sub-layer are removed, and the MD numerical coefficient of variation curves at the measurement depth greater than the top of the Kh2-2 sub-layer are retained.

[0086] Secondly, based on the distribution range of the permeability variation coefficient of a certain reservoir type in the horizontal section of a horizontal well (e.g. Figure 9 As shown), using the heterogeneity grading index (formula: The range of heterogeneity strength was determined by calculating the coefficient of variation, and different colors were used to mark the intervals of a certain reservoir type in the horizontal section of a single well (as shown in Table 2 and...). Figure 10 (As shown). Here, because Figure 9 The large number of intervals makes it difficult to calibrate the horizontal segments of the horizontal wells in subsequent projections. Therefore, the heterogeneity grading index is used to further determine the range of heterogeneity strength.

[0087] Table 2. Classification index of variation coefficient in AD4 well area (KH2-3 well section)

[0088] After projecting all the coefficient of variation curves of the corresponding well area onto the horizontal well trajectory segment, the heterogeneity within the work area is divided into three categories: strong, medium, and weak. Areas with relatively dense green and blue coefficient of variation plane calibration segments are defined as weak heterogeneity areas (i.e., the third heterogeneity area); areas with relatively dense red and purple coefficient of variation plane calibration segments are defined as strong heterogeneity areas (i.e., the first heterogeneity area); and areas with other colors changing rapidly and no specific color appearing densely are defined as medium heterogeneity areas (i.e., the second heterogeneity area). This process coarsens and calibrates the planar distribution of heterogeneity within the work area (e.g., ...). Figure 11 (As shown).

[0089] It should be noted that, considering the large difference between the range and the outlier coefficient, outliers have a significant impact on the maximum and minimum values, while the coefficient of variation is more stable. Therefore, this example uses the coefficient of variation to determine the range of heterogeneity.

[0090] The verification results for the heterogeneous planar distribution are as follows: By observing the actual presence of tracers, the strength of connectivity between injection wells and surrounding production wells can be determined, thereby verifying the heterogeneity present in the plane. For example... Figure 12 As shown, AD1-14-4H is a water injection well, while AD1-13-3H and AD1-13-6H are both production wells in the Kh2-2 layer. The heterogeneous planar distribution depicted using this technology is shown below. Figure 11 As shown, statistical analysis of the average tracer advancement speed reveals that the average tracer encounter time in wells with strong heterogeneity (AD1-13-3H) is 96 days, corresponding to a slow water cut rise, while in wells with weak heterogeneity (AD1-13-6H) the average encounter time is 29 days, corresponding to a rapid water cut rise. Therefore, it is concluded that the planar heterogeneity distribution characterized by horizontal well heterogeneity parameters has a good correlation with the water cut rise type and tracer encounter status, and the results are reliable.

[0091] Example 3 like Figure 13As shown, a computer device 20 includes a memory 201 and a processor 202. The memory 201 stores a computer program. When the computer program is executed by the processor, it implements the geological-guided strata identification and prediction method as described above.

[0092] In summary, this invention proposes a method for evaluating reservoir planar heterogeneity using horizontal well section parameter planar projection, which has the following advantages: ① The planar heterogeneity of the reservoir was characterized by using the planar projection of reservoir heterogeneity parameters in the horizontal section of the horizontal well and the graded evaluation of the coefficient of variation, which is highly operable.

[0093] ② The optimal horizontal segment interval was determined to calculate the heterogeneity parameters. This interval can be directly used for the evaluation of similar reservoir planar heterogeneity distributions.

[0094] Although the present invention has been described above in conjunction with exemplary embodiments and accompanying drawings, those skilled in the art should understand that various modifications can be made to the above embodiments without departing from the spirit and scope of the claims.

Claims

1. A method for evaluating reservoir heterogeneity based on horizontal wells, characterized in that, The reservoir heterogeneity evaluation method includes the following steps: Based on the rock structure, the rock composition of different rock facies can be determined, thereby classifying different reservoir types; By statistically analyzing the logging response characteristics of different reservoir types, logging identification charts for different reservoir types were established. Based on the well logging identification chart, the reservoir types of vertical and horizontal wells are identified through well logging to clarify the distribution of reservoir types in vertical and horizontal wells; Vertical wells are divided into different vertical well layers according to different reservoir types. The permeability heterogeneity parameters of a single well point in different vertical well layers are calculated. At the same time, multiple horizontal well sections are divided according to predetermined well depth intervals, and the permeability heterogeneity parameters of each horizontal well section are calculated. The permeability heterogeneity parameters include the permeability variation coefficient, the breakthrough coefficient, and the range. Based on the permeability heterogeneity parameters within the different vertical well layers, a vertical heterogeneity distribution map for each reservoir type is determined; based on the permeability heterogeneity parameters of each horizontal well section, a horizontal heterogeneity distribution map for each reservoir type is determined. For each reservoir type, the lateral heterogeneity distribution map is plotted on the longitudinal heterogeneity distribution map in a segmented projection manner to obtain a planar distribution map of heterogeneity for each reservoir type. The determination of the lateral heterogeneity distribution map for each reservoir type based on the permeability heterogeneity parameters of each horizontal well section includes: Based on the distribution range of the permeability variation coefficient of a certain reservoir type in the horizontal section of a horizontal well, the heterogeneity grading index is used to further determine the range of heterogeneity strength. The formula for the heterogeneity grading index is as follows: ， in, A VK It is a heterogeneous grading index, dimensionless; V kmax This represents the maximum coefficient of variation within a predetermined depth interval for a horizontal well. V kmin This represents the minimum coefficient of variation within a predetermined well depth interval. V Ki For the first i The coefficient of variation for each sampling point is dimensionless.

2. The reservoir heterogeneity evaluation method based on horizontal wells according to claim 1, characterized in that, Based on the measured depth (MD) values ​​when a horizontal well encounters the top of a certain reservoir type, the measured depth (MD) intervals in which the horizontal well travels through this type of reservoir are selected, and the coefficient of variation curves within this interval are retained.

3. The reservoir heterogeneity evaluation method based on horizontal wells according to claim 1, characterized in that, In the heterogeneity planar distribution map, the heterogeneity within the work area is divided into a first heterogeneous region, a second heterogeneous region, and a third heterogeneous region, and marked with different colors. The heterogeneity in the first heterogeneous region is stronger than that in the second heterogeneous region, and the heterogeneity in the second heterogeneous region is stronger than that in the third heterogeneous region.

4. The reservoir heterogeneity evaluation method based on horizontal wells according to claim 1, characterized in that, The predetermined well depth interval is between 10m and 40m.

5. The reservoir heterogeneity evaluation method based on horizontal wells according to claim 1, characterized in that, The coefficient of variation of permeability within a predetermined depth interval in a horizontal well section is calculated by the following formula: ， in, V k is the permeability variation coefficient within a predetermined well depth interval on a horizontal well section, dimensionless; K i For the first horizontal well section i Permeability of each sampling point, ×10 -3 μm 2 ; n This represents the number of sampling points within the horizontal well section. The thickness-weighted average permeability within the horizontal well section, multiplied by 10. -3 μm 2 .

6. The reservoir heterogeneity evaluation method based on horizontal wells according to claim 1, characterized in that, The breakthrough coefficient within a predetermined depth interval on a horizontal well section is calculated by the following formula: ， in, T K , is the advance coefficient within a predetermined depth interval on a horizontal well section, dimensionless; K max The maximum permeability within a predetermined depth interval of the horizontal well, multiplied by 10. -3 μm 2 ; The thickness-weighted average permeability within the horizontal well section, multiplied by 10. -3 μm 2 .

7. The reservoir heterogeneity evaluation method based on horizontal wells according to claim 1, characterized in that, The range within a predetermined depth interval on a horizontal well section is calculated by the following formula: ， in, J K The range within a predetermined depth interval on a horizontal well section is dimensionless. K max The maximum permeability within a predetermined depth interval of the horizontal well, multiplied by 10. -3 μm 2 ; K min The minimum permeability within a predetermined depth interval of a horizontal well, multiplied by 10. -3 μm 2 .

8. A computer device, characterized in that, The computer device includes: processor; and The memory stores a computer program that, when executed by a processor, implements the reservoir heterogeneity evaluation method as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the reservoir heterogeneity evaluation method as described in any one of claims 1 to 7.

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