Horizontal well reservoir interpretation method

Abstract

The invention provides a horizontal well reservoir interpretation method, and relates to the technical field of shale oil-gas field development, the method comprises the following steps: obtaining target sensitive parameters corresponding to a fracturing section of a target horizontal well, the target sensitive parameters comprising logging parameters and geological parameters; determining a lithology index value, an oiliness index value and a pore structure index value according to the target sensitive parameters and a preset target relation model; and inputting the lithology index value, the oiliness index value and the pore structure index value into a comprehensive interpretation model to obtain an interpretation result of the reservoir type of the fractured section of the target horizontal well. According to the method, the lithology index, the oiliness index and the pore structure index are calculated through the target relation model, and the characteristics of the reservoir can be reflected more comprehensively, so that the reservoir type of the horizontal well is explained through the comprehensive explanation model according to the multiple indexes, the precision of reservoir type explanation can be improved, and the accuracy of reservoir type explanation is improved. And accurate data support is provided for horizontal well fracturing segmentation and clustering.

Description

Technical Field

[0001] The present invention relates to the technical field of shale oil and gas field development, and in particular to a horizontal well reservoir interpretation method. Background Art

[0002] The interpretation of reservoir types in oil and gas wells is a key parameter for fracturing layer selection. Currently, commonly used fluid interpretation methods include single logging parameter crossplots, mathematical analysis methods such as Fisher discriminant analysis and gray cluster analysis, and nuclear magnetic resonance logging interpretation, among other methods. These methods are all based on single-disciplinary interpretation methods for well logging or mud logging, and are developed based on data from vertical or directional wells.

[0003] However, the logging mechanisms of horizontal wells differ significantly from those of vertical and directional wells, and the resistivity logging series also differ. Directly applying the interpretation methods for vertical or directional wells to interpret fluids in horizontal wells results in significant errors and fails to effectively improve the interpretation accuracy of horizontal wells. Summary of the Invention

[0004] The present invention provides a horizontal well reservoir interpretation method to improve the interpretation accuracy of reservoir types.

[0005] A horizontal well reservoir interpretation method, comprising: Obtaining target sensitive parameters corresponding to the fracturing section of the target horizontal well, wherein the target sensitive parameters include well logging parameters and geological parameters; Determine a lithology index value, an oil content index value, and a pore structure index value based on the target sensitive parameter and a preset target relationship model; wherein the lithology index value is used to characterize the lithology characteristics of the reservoir, the oil content index value is used to characterize the oil content characteristics of the reservoir, and the pore structure index value is used to characterize the pore structure characteristics of the reservoir; The lithology index value, the oil content index value, and the pore structure index value are input into a comprehensive interpretation model to obtain an interpretation result of the reservoir type of the fracturing section.

[0006] In some possible implementations, the target relationship model includes a lithology relationship model, an oil content relationship model, and a pore structure relationship model; the logging parameters include deep lateral resistivity, natural gamma, acoustic wave time difference curve, and full hydrocarbon logging curve; and the geological parameters include shale content, porosity, water porosity, oil saturation, and permeability; According to the target sensitive parameters and the preset target relationship model, the lithology index value, the oil content index value and the pore structure index value are determined, including: determining the lithology index value according to the deep lateral resistivity, the natural gamma, the acoustic wave transit time curve, the shale content, and the lithology relationship model; determining the oiliness index value according to the porosity, the water porosity, the oil saturation, the full hydrocarbon logging curve, and the oiliness relationship model; The pore structure index value is determined according to the acoustic wave time difference curve, the porosity, the permeability and a pore structure relationship model.

[0007] In some possible implementations, the lithologic relationship model is: , Among them, YXZS represents lithologic index; RT represents deep lateral resistivity; GR represents natural gamma ray; SH represents shale content; S AC-RT The envelope surface representing the acoustic transit time and the deep lateral resistivity is determined by the acoustic transit time curve and the deep lateral resistivity.

[0008] In some possible implementations, determining the oiliness index value according to the porosity, the water-porosity, the oil saturation, the full hydrocarbon logging curve, and the oiliness relationship model includes: determining a total hydrocarbon base value and a total hydrocarbon anomaly amplitude value according to the total hydrocarbon logging curve; The oiliness index value is determined based on the porosity, the natural gamma, the water porosity, the oil saturation, the total hydrocarbon base value, the total hydrocarbon anomaly amplitude value, and the oiliness relationship model.

[0009] In some possible implementations, the oil content relationship model is: , Among them, HYZS represents the oiliness index; φ represents the porosity; φ w Indicates water-containing porosity; C 异 Indicates the total hydrocarbon anomaly amplitude value, which is determined by the total hydrocarbon logging curve; C 基 Indicates the total hydrocarbon base value, which is determined by the total hydrocarbon logging curve; S o Indicates oil saturation.

[0010] In some possible implementations, the pore structure relationship model is: , Among them, KXJGZS represents the pore structure index; AC represents the acoustic time difference, which is determined by the acoustic time difference curve; AC min represents the minimum value of the acoustic time difference curve; φ represents porosity; K represents permeability.

[0011] In some possible embodiments, the comprehensive interpretation model is a three-dimensional map, and the three-dimensional coordinate system of the three-dimensional map corresponds to the lithology index, oil content index and pore structure index respectively, and different spatial regions of the three-dimensional map correspond to different reservoir types of horizontal wells.

[0012] In some possible embodiments, the reservoir types of the horizontal well include oil layer, poor oil layer, oil-water layer, oil-water layer, water layer, and dry layer. The oil testing standards for the reservoir types of the horizontal well are pre-determined by the vertical well oil testing standards. The oil testing standards include: The oil production contribution rate of the oil layer is greater than 8%, and the water production contribution rate of the oil layer is less than 10%; The oil production contribution rate of the poor oil layer is 5%-8%, and the water production contribution rate of the poor oil layer is less than 10%; The oil production contribution rate of the oil-water layer is 1%-5%, and the water production contribution rate of the oil-water layer is less than 10%; The oil production contribution rate of the oil-bearing water layer is 0%-1%, and the water production contribution rate of the oil-bearing water layer is less than 10%; The oil production contribution rate of the water layer is 0%, and the water production contribution rate of the water layer is greater than 10%; The oil production contribution rate and water production contribution rate of the dry layer are both 0%.

[0013] In some possible implementations, after inputting the lithology index value, the oil content index value, and the pore structure index value into a comprehensive interpretation model to obtain an interpretation result of the reservoir type of the fracturing section, the method further includes: The multiple fracturing sections in the target horizontal well are segmented and clustered according to interpretation results of reservoir types corresponding to the multiple fracturing sections.

[0014] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects: The present invention determines the lithologic index, oiliness index, and pore structure index values ​​for the target horizontal well's fracturing section using target sensitive parameters and a preset target relationship model. These values ​​are then input into a comprehensive interpretation model to obtain an interpretation of the reservoir type for the fracturing section. Calculating the lithologic index, oiliness index, and pore structure index using the target relationship model can more comprehensively reflect reservoir characteristics. The comprehensive interpretation model then interprets the reservoir type of the horizontal well based on multiple indices, improving the accuracy of reservoir type interpretation and providing accurate data support for horizontal well fracturing segmentation and clustering.

[0015] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] Figure 1 The figure is a flow chart of a horizontal well reservoir interpretation method according to an exemplary embodiment.

[0018] Figure 2 It is a schematic diagram of a three-dimensional chart according to an exemplary embodiment. DETAILED DESCRIPTION

[0019] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0020] As mentioned in the background, the reservoir type interpretation methods used in related technologies are all based on data from vertical or directional wells. However, the logging mechanisms of horizontal wells differ significantly from those of vertical and directional wells, and the resistivity logging series also differ.

[0021] A vertical well is designed with the borehole axis aligned with a plumb line, meaning the trajectory from the wellhead to the bottom remains roughly vertical. This well type is suitable for areas with stable formations and relatively concentrated oil and gas reservoirs.

[0022] The wellbore axis of a directional well is no longer a vertical line, but an inclined trajectory designed according to a specific target. This type of well is mainly used for oil and gas reservoirs that are difficult to develop with vertical wells, such as those in faults, fractures, or complex formations.

[0023] Horizontal wells are a special type of directional well, where the wellbore remains essentially horizontal after entering the target formation. Horizontal wells maximize the length of the wellbore within the oil and gas formation, thereby improving recovery efficiency. This well type is particularly suitable for developing thin-bedded, fractured, or low-permeability reservoirs.

[0024] The single parameter cross plot method is mainly used for conventional high-porosity and high-permeability reservoirs, while shale oil reservoirs are unconventional low-porosity and low-permeability reservoirs with poor permeability and widespread oil-water coexistence, resulting in poor practicality of the cross plot.

[0025] Fisher discriminant analysis uses prior knowledge to classify unknown samples. Its primary goal is to find an optimal projection direction that maximizes the separation of sample projections from different categories, thereby completing linear classification. Fisher discriminant analysis has a complex computational model, requiring extensive data preparation and function development. It is prone to multiple solutions and suffers from slow computational speed.

[0026] The grey cluster analysis method is based on the generation of a whitening function of grey numbers. The whitening functions of clusters for different clustering indices are grouped into n grey classes, thereby determining the grey class to which the clustered objects belong. This method requires strong mathematical computing skills and is computationally difficult. The calculation model is not easily applied by on-site production personnel, and its applicability is relatively weak.

[0027] Nuclear magnetic resonance (NMR) logging data provides multiple parameters, including effective porosity, pore size distribution, pore diameter, pore connectivity, and time constant. These parameters can be used to assess rock pore structure characteristics, such as porosity, pore distribution, and pore connectivity, and thus to determine fluid storage and flow conditions. However, NMR logging data is limited across oil and gas fields, and interpretation is expensive, making it difficult to use and generalize.

[0028] Reference Figure 1 , Figure 1 FIG. 1 is a flow chart showing a method for interpreting a horizontal well reservoir according to an exemplary embodiment. Figure 1 As shown, the horizontal well reservoir interpretation method includes the following steps.

[0029] In step S201, target sensitive parameters corresponding to the fracturing section of the target horizontal well are obtained, wherein the target sensitive parameters include well logging parameters and geological parameters; In step S202, a lithology index value, an oiliness index value, and a pore structure index value are determined based on the target sensitive parameters and a preset target relationship model; wherein the lithology index value is used to characterize the lithology characteristics of the reservoir, the oiliness index value is used to characterize the oiliness characteristics of the reservoir, and the pore structure index value is used to characterize the pore structure characteristics of the reservoir; In step S203, the lithology index value, the oil content index value, and the pore structure index value are input into a comprehensive interpretation model to obtain an interpretation result of the reservoir type of the fracturing section.

[0030] For example, the target horizontal well is a horizontal well undergoing reservoir interpretation. Reservoir interpretation involves analyzing and evaluating the rock properties, fluid properties, and distribution of oil and gas reservoirs. The reservoir type interpretation results can provide a basis for oil and gas reservoir development and data support for optimal fracturing stage and cluster selection.

[0031] For example, in horizontal wells, to increase oil and gas production, the reservoir surrounding the wellbore is often fractured. A fractured zone is a series of independent fractured areas in the horizontal well that are separated by hydraulic fracturing. Each fractured zone may contain one or more fracture clusters.

[0032] For example, target sensitive parameters are key parameters that significantly impact reservoir interpretation results. These include well logging parameters and geological parameters. These parameters are selected from well logging parameters and geological parameters in well logging data to identify those parameters that are more sensitive to oil-water characteristics.

[0033] For example, the target relationship model refers to a mathematical model for calculating the lithologic index, oil content index, and pore structure index. The target relationship model can determine the lithologic index, oil content index, and pore structure index values ​​based on the target sensitive parameters. The target relationship model can be pre-fitted based on the sensitive parameters of different rock types and continuously verified and optimized.

[0034] For example, well logging parameters are reservoir physical property parameters obtained through well logging and mud logging techniques, reflecting the physical and fluid properties of the reservoir, such as resistivity, natural gamma, acoustic transit time, and total hydrocarbon logging curves. Geological parameters are parameters reflecting the geological characteristics of the reservoir, such as rock type, porosity, water porosity, oil saturation, formation pressure, and shale content.

[0035] For example, the lithology index quantifies the reservoir rock type and its physical properties and can be used to characterize the reservoir's permeability and stability. Rock types can include sandstone, mudstone, clastic rock, and so on. The oiliness index characterizes the abundance of hydrocarbons in the reservoir and can be used to characterize the distribution and saturation of reservoir fluids, such as oil, gas, and water. The pore structure index can be used to characterize reservoir permeability and fluid mobility, among other things.

[0036] Exemplarily, the comprehensive interpretation model is a model that integrates lithologic index, oil content index, and pore structure index, and is used to interpret reservoir types by integrating the three indices, and to classify or predict the reservoir type of the fracturing section.

[0037] For example, the comprehensive interpretation model can adopt a mathematical model or a neural network model; or a three-dimensional intersection map can be formed according to the lithology index, oil content index and pore structure index to describe different reservoir types based on different areas in the three-dimensional intersection map.

[0038] For example, sensitive parameters corresponding to the fracturing section of the target horizontal well can be obtained in advance. For example, the acquired data can be pre-processed, including data cleaning and standardization, based on drilling data, well logging data, fracturing data, oil testing data, and production profile data. Then, the sensitive parameters corresponding to the fracturing section of the target horizontal well can be determined through data correlation analysis.

[0039] For example, by analyzing the sensitivity of logging and well parameters and geological parameters to reservoir type interpretation, key target sensitive parameters are determined. These sensitive parameters can effectively reflect the lithology, oil content, and pore structure characteristics of the reservoir.

[0040] For example, based on the target sensitive parameters, the lithologic index, oil content index, pore structure index, and target relationship model that are pre-established can be used to determine the lithologic index, oil content index, and pore structure index that can characterize the reservoir characteristics.

[0041] For example, the calculated lithology index, oil content index, and pore structure index are input into a comprehensive interpretation model, which analyzes and processes the various indices to obtain an interpretation result of the reservoir type in the target horizontal well fracturing section.

[0042] For example, by comprehensively considering multiple parameters such as lithologic index, oil content index, and pore structure index, reservoir characteristics can be more comprehensively reflected, thereby improving the accuracy of reservoir type interpretation. Furthermore, accurate reservoir type interpretation results can help optimize horizontal well development plans. Based on the distribution of different fluid types, the appropriate segmentation and clustering of fracturing zones can be determined, thereby improving oil and gas recovery and reducing development costs.

[0043] The present invention determines the lithologic index, oiliness index, and pore structure index values ​​for the target horizontal well's fracturing section using target sensitive parameters and a preset target relationship model. These values ​​are then input into a comprehensive interpretation model to obtain an interpretation of the reservoir type for the fracturing section. Calculating the lithologic index, oiliness index, and pore structure index using the target relationship model can more comprehensively reflect reservoir characteristics. The comprehensive interpretation model then interprets the reservoir type of the horizontal well based on multiple indices, improving the accuracy of reservoir type interpretation and providing accurate data support for horizontal well fracturing segmentation and clustering.

[0044] In some possible implementations, the target relationship model includes a lithology relationship model, an oil content relationship model, and a pore structure relationship model; the logging parameters include deep lateral resistivity, natural gamma, acoustic wave time difference curve, and full hydrocarbon logging curve; and the geological parameters include shale content, porosity, water porosity, oil saturation, and permeability; According to the target sensitive parameters and the preset target relationship model, the lithology index value, the oil content index value and the pore structure index value are determined, including: determining the lithology index value according to the deep lateral resistivity, the natural gamma, the acoustic wave transit time curve, the shale content, and the lithology relationship model; determining the oiliness index value according to the porosity, the water porosity, the oil saturation, the full hydrocarbon logging curve, and the oiliness relationship model; The pore structure index value is determined according to the acoustic wave time difference curve, the porosity, the permeability and a pore structure relationship model.

[0045] For example, a lithologic relationship model, an oil-bearing relationship model, and a pore structure relationship model can be pre-established. Specifically, the lithologic relationship model is established based on sensitive parameters such as deep lateral resistivity, natural gamma, acoustic transit time curves, and mud content that can reflect lithologic characteristics; the oil-bearing relationship model is established based on sensitive parameters such as porosity, water-porosity, oil saturation, and total hydrocarbon logging curves that can reflect oil-bearing characteristics; and the pore structure relationship model is established based on sensitive parameters such as acoustic transit time curves, porosity, and permeability that can reflect pore structure characteristics.

[0046] For example, deep lateral resistivity is a logging parameter. Deep lateral resistivity adopts the principle of focused current and constrains the current path of the main electrode through the shielding electrode to achieve vertical detection of formation resistivity. The measurement results reflect the vertical changes in the electrical characteristics of the rock formation. Deep lateral resistivity is related to the properties of the formation fluid.

[0047] For example, natural gamma rays can be obtained through natural gamma logging. Natural gamma logging is a method of identifying rock types by measuring the intensity of gamma rays emitted by natural radioactivity in the formation. It can be used to determine lithology and sedimentary environment. Different rocks contain different amounts of radioactive elements, so the intensity of natural gamma rays varies. For example, rocks with a higher argillaceous content typically exhibit stronger natural gamma ray intensities.

[0048] For example, acoustic transit time curves (ETTs) can be obtained through transit time logging. These curves reflect the elastic properties of rock and are closely related to porosity, lithology, and compaction. The transit time refers to the time difference between the emission and reception of an acoustic wave. TETs can help determine formation porosity, analyze rock permeability, and assist in determining rock hardness. TET curves vary in shape for different formations, and variations in these curves can reveal rock structure and provide a basis for oil and gas reservoir assessment. Formations with high porosity or well-developed fractures typically exhibit larger TETs.

[0049] For example, shale content refers to the percentage of shale (clay-like material) in a formation's total volume and is used to assess formation properties or material quality. Shale content can be measured using techniques such as natural gamma ray logging, acoustic transit time logging, and neutron-density intersection analysis.

[0050] For example, porosity refers to the ratio of the sum of the volumes of all pore spaces in a rock sample to the volume of the rock sample itself. This is called the total porosity of the rock. A greater total porosity in a reservoir indicates greater pore space within the rock. Porosity can be used to reflect a reservoir's storage capacity.

[0051] For example, water porosity is the ratio of the volume of water-filled pores in a rock or soil to the total volume, and can be used to indicate the fraction of the available pore space in the rock or soil occupied by water. Water porosity is related to the water content of the reservoir.

[0052] For example, oil saturation is the ratio of the oil volume in the effective pore space of a reservoir to the effective pore volume of the rock. It can be used to indicate the proportion of oil contained in the effective pore space of a rock or soil. Oil saturation can be used to reflect the oil content of a reservoir. It can be obtained using Archie's equation combined with resistivity data inversion.

[0053] For example, the total hydrocarbon logging curve is a curve of total hydrocarbon gas concentration detected during the logging process. The total hydrocarbon logging parameters can directly reflect the oil and gas abundance of the formation and are key parameters for dynamic evaluation of oil content.

[0054] For example, permeability is a parameter used to characterize the ability of a fluid to flow in a porous medium, and can be estimated by methods such as nuclear magnetic resonance or a core calibration model, wherein the core calibration model can be the Kozeny-Carman formula.

[0055] Exemplarily, the logging parameters and geological parameters of the horizontal well fracturing section are obtained, wherein the logging parameters include deep lateral resistivity, natural gamma, sonic time difference curve, and full hydrocarbon logging curve; and the geological parameters include mud content, porosity, water porosity, oil saturation, and permeability.

[0056] For example, the logging parameters and geological parameters are matched with three relationship models. The lithology index is calculated based on deep lateral resistivity, natural gamma ray, acoustic transit time curve, shale content, and the first relationship model; the oil content index is calculated based on porosity, natural gamma ray, water porosity, oil saturation, total hydrocarbon logging curve, and the second relationship model; and the pore structure index is calculated based on acoustic transit time curve, porosity, permeability, and the third relationship model.

[0057] In this way, by calculating the lithologic index, oil content index and pore structure index, the characteristics of different aspects of the reservoir can be more comprehensively reflected, the accuracy of reservoir type interpretation can be improved, the reservoir interpretation method can be made more suitable for the characteristics of horizontal well reservoirs, and the scientificity and reliability of the interpretation results can be improved.

[0058] In some possible implementations, the lithologic relationship model is: , Wherein, YXZS represents lithologic index; RT represents deep lateral resistivity, in Ω·m; GR represents natural gamma ray, in API; SH represents shale content, in %; S AC-RT The envelope surface representing the acoustic transit time and the deep lateral resistivity is determined by the acoustic transit time curve and the deep lateral resistivity.

[0059] In well logging interpretation, the envelope of acoustic transit time and deep lateral resistivity refers to the distribution boundary of these two plots in a crossplot. It is used to define the numerical range of specific geological characteristics, such as lithology and fluid type. This envelope can be used to delineate the response regions of different reservoir types.

[0060] Among them, the envelope surface of acoustic wave time difference and deep lateral resistivity can be used to distinguish different lithologies such as sandstone, mudstone, carbonate rock, and for fluid type identification, such as dividing the response areas of oil layer, gas layer, water layer or dry layer.

[0061] For example, the acoustic transit time and deep lateral resistivity logging curves for the target well section can be obtained. Data preprocessing operations such as removing outliers and correcting for instrument response deviations are then performed. A distribution map of all data points is then plotted, with acoustic transit time as the horizontal axis and deep lateral resistivity as the vertical axis. Algorithms such as K-means and DBSCAN are then used to automatically partition the data into clusters, extract the cluster boundaries, and calculate the density contours of the data distribution. The boundaries of high-density areas are then selected as the envelope surface.

[0062] For example, an interactive crossplot tool may be provided based on well logging interpretation software (such as Techlog and Petrel), supporting manual drawing or automatic generation of envelope surfaces.

[0063] For example, the accuracy of the envelope can be verified using core data, oil test results, or imaging logging. The envelope boundary can be iteratively optimized based on new well data to adapt to regional geological changes.

[0064] In this way, based on the above lithologic relationship model, the lithologic index value can be obtained through deep lateral resistivity, natural gamma, sonic time difference curve and mud content.

[0065] In some possible implementations, determining the oiliness index value according to the porosity, the water-porosity, the oil saturation, the full hydrocarbon logging curve, and the oiliness relationship model includes: determining a total hydrocarbon base value and a total hydrocarbon anomaly amplitude value according to the total hydrocarbon logging curve; The oiliness index value is determined based on the porosity, the natural gamma, the water porosity, the oil saturation, the total hydrocarbon base value, the total hydrocarbon anomaly amplitude value, and the oiliness relationship model.

[0066] For example, in a full hydrocarbon logging curve, the full hydrocarbon logging data is stable within a certain range of values ​​and has a very small fluctuation range. This value is called the full hydrocarbon base value. When the real-time logging data rises to more than three times the full hydrocarbon base value, it is called a full hydrocarbon gas logging anomaly, which is manifested in the logging curve as an increase of more than three times the amplitude of the curve. Once an anomaly appears in the full hydrocarbon curve, it indicates an increase in the total amount of alkanes contained in the drilling fluid, which must be paid attention to by on-site logging and technical personnel to prevent leakage of oil and gas layers. The full hydrocarbon anomaly amplitude value is the difference between the abnormal peak value of the full hydrocarbon logging curve in the oil and gas layer section and the full hydrocarbon base value, indicating the relative content of oil and gas in the formation.

[0067] For example, a stable segment without oil or gas indication can be identified from the full hydrocarbon logging curve to determine the full hydrocarbon base value. The peak of the full hydrocarbon logging curve is found in the oil and gas layer segment, and the difference between the peak and the full hydrocarbon base value is calculated to obtain the full hydrocarbon anomaly amplitude value.

[0068] For example, the total hydrocarbon base value and total hydrocarbon anomaly amplitude are combined with parameters such as porosity, natural gamma, water porosity, and oil saturation to calculate the oiliness index using an oiliness relationship model. The total hydrocarbon base value and anomaly amplitude provide quantitative information on oil and gas content for the model, and together with other geological parameters, they more comprehensively and accurately reflect the oiliness characteristics of the reservoir.

[0069] In some possible implementations, the oil content relationship model is: , Among them, HYZS represents the oil content index; φ represents the porosity, the unit is %;φ w Indicates water-containing porosity, unit is %; C 异 Indicates the total hydrocarbon anomaly amplitude value, in %, determined by the total hydrocarbon logging curve; C 基 Indicates the total hydrocarbon base value, the unit is %, determined by the total hydrocarbon logging curve; S o Indicates oil saturation in %.

[0070] In this way, based on the above oiliness relationship model, the oiliness index value can be obtained through porosity, natural gamma, water porosity, oil saturation, total hydrocarbon base value and total hydrocarbon anomaly amplitude value.

[0071] In some possible implementations, the pore structure relationship model is: , Among them, KXJGZS represents the pore structure index; AC represents the acoustic time difference, the unit is μs / m, which is determined by the acoustic time difference curve; AC min It represents the minimum value of the acoustic time difference curve, and the unit is μs / m; φ represents the porosity, and the unit is %; K represents the permeability, and the unit is mD.

[0072] In this way, the oil content index value can be obtained based on the above pore structure relationship model through the acoustic wave time difference curve, porosity and permeability.

[0073] In some possible embodiments, the comprehensive interpretation model is a three-dimensional map, and the three-dimensional coordinate system of the three-dimensional map corresponds to the lithology index, oil content index and pore structure index respectively, and different spatial regions of the three-dimensional map correspond to different reservoir types of horizontal wells.

[0074] For example, a 3D chart is a 3D visualization tool that uses the three axes of a 3D coordinate system to represent lithology, oil content, and pore structure. Different spatial regions correspond to different reservoir types. Reservoir types refer to the different types of fluids present in a reservoir, such as oil layers, gas layers, water layers, and oil-water layers.

[0075] like Figure 2 As shown in the figure, the lithologic index, oil content index, and pore structure index of different fracturing sections can be expressed in a three-dimensional coordinate system. The red fracturing section corresponds to the oil layer, the black fracturing section corresponds to the dry layer, the pink fracturing section corresponds to the poor oil layer, the light blue fracturing section corresponds to the oil-water layer, and the dark blue fracturing section corresponds to the oil-water layer.

[0076] By comprehensively considering three key factors—lithology, oil content, and pore structure—the 3D chart can more comprehensively reflect reservoir characteristics, thereby improving the accuracy of reservoir type interpretation. The 3D chart also provides an intuitive visualization tool, allowing for a direct display of the complex distribution of reservoir types. By observing the location and distribution of different regions on the 3D chart, one can quickly understand the fluid properties of the reservoir.

[0077] In some possible embodiments, the reservoir types of the horizontal well include oil layer, poor oil layer, oil-water layer, oil-water layer, water layer, and dry layer. The oil testing standards for the reservoir types of the horizontal well are pre-determined by the vertical well oil testing standards. The oil testing standards include: The oil production contribution rate of the oil layer is greater than 8%, and the water production contribution rate of the oil layer is less than 10%; The oil production contribution rate of the poor oil layer is 5%-8%, and the water production contribution rate of the poor oil layer is less than 10%; The oil production contribution rate of the oil-water layer is 1%-5%, and the water production contribution rate of the oil-water layer is less than 10%; The oil production contribution rate of the oil-bearing water layer is 0%-1%, and the water production contribution rate of the oil-bearing water layer is less than 10%; The oil production contribution rate of the water layer is 0%, and the water production contribution rate of the water layer is greater than 10%; The oil production contribution rate and water production contribution rate of the dry layer are both 0%.

[0078] For example, the different types and contents of fluids present in different reservoir types of a horizontal well can be used to predetermine the horizontal well oil testing standards based on the vertical well oil testing standards. The reservoir types of a horizontal well can be classified, in descending order based on oil production contribution, as oil layer, poor oil layer, oil-water layer, oil-water layer, water layer, and dry layer.

[0079] For example, fracture tracing technology can be used to determine the oil and water production contributions of horizontal wells. These are dynamic, post-fracture production data for horizontal wells, are subject to variability, and are expensive to obtain. Mud logging and geological parameters, on the other hand, are static data, less expensive, and more convenient for interpreting reservoir types.

[0080] A tracer is added to the fracturing fluid, and during post-fracturing flowback, intensive sampling is performed to monitor changes in tracer concentration (light intensity). The tracer's light intensity is proportional to the production volume, and its physicochemical properties are stable in the produced fluid. Utilizing its fixed excitation and emission spectra, the oil and production filtrate is placed in a cuvette with set excitation and emission wavelengths. During flowback, intensive sampling is performed to monitor changes in tracer concentration in the flowback fluid, thereby determining the production status and contribution of each layer.

[0081] For example, due to the temporal inconsistency and uncertainty of dynamic data, describing reservoir types using dynamic data may yield different results at different times. Reservoir type interpretation criteria can be established based on dynamic data, and a target relationship model based on static data can be used to determine reservoir type. The actual reservoir type determined based on the dynamic data corresponding to the reservoir can then be used to optimize the target relationship model.

[0082] For example, the reservoir types of horizontal wells can be divided in advance based on accurate dynamic data, i.e., oil production contribution rate and water production contribution rate. After establishing the target relationship model, the division results can be obtained through static data, i.e., well logging parameters and geological parameters, and the target relationship model. The dynamic data division results can be used as a standard and compared with the static data division results to verify the effectiveness and reliability of the target relationship model.

[0083] For example, vertical well testing can provide relatively accurate production capacity and fluid property data, including oil production, water content, etc. As shown in Table 1 below, by analyzing the vertical well testing results, quantitative standards for different reservoir types in horizontal wells can be established.

[0084] Table 1 Vertical well testing standards and shale oil horizontal well testing standards

[0085] Then, as shown in Table 2 below, the target oil test results of different fracturing stages of the horizontal well can be determined based on the established oil test standards for the fracturing stages of the horizontal well.

[0086] Table 2 Oil test results determined according to horizontal well oil test standards Number of segments Water production contribution rate Oil production contribution rate Oil test results 1 5.1 4.9 Oil and water in the same layer 2 3 1.6 Oil-bearing water layer 3 7.3 6.2 Poor oil layer 4 1.9 1.7 Oil-bearing water layer 5 9.6 4.6 Oil and water in the same layer 6 2.1 3.2 Oil and water in the same layer 7 2.8 6.6 Poor oil layer 8 6.7 4.4 Oil and water in the same layer 9 1.2 2.4 Oil and water in the same layer 10 1.7 2.2 Oil and water in the same layer 11 4.2 5.1 Poor oil layer 12 2.6 5.6 Poor oil layer 13 1.6 3.9 Oil and water in the same layer 14 7.1 2.2 Oil and water in the same layer 15 1.6 4.1 Oil and water in the same layer 16 2.7 4.8 Oil and water in the same layer 17 3.2 2.5 Oil and water in the same layer 18 6.0 2.2 Oil and water in the same layer 19 6.1 2.6 Oil and water in the same layer 20 3.5 3.5 Oil and water in the same layer 21 2.5 4.9 Oil and water in the same layer 22 7.7 6.2 Poor oil layer 23 4.8 4.5 Oil and water in the same layer 24 3.6 6.4 Poor oil layer 25 1.5 3.8 Oil and water in the same layer Then, based on the target oil test results, correlation analysis can be performed on the target parameters to determine sensitive parameters within the target parameters that are sensitive to oil-water characteristics. A target relationship model can be constructed based on the sensitive parameters. During the construction of the target relationship model, the target relationship model can be continuously verified and optimized through correlation analysis of a large number of target oil test results and target parameters until the final target relationship model is obtained.

[0087] In this way, reservoir types can be divided according to the oil testing standards established by the dynamic parameters of horizontal wells, which provides a clear quantitative standard for the interpretation of reservoir types based on static parameters of horizontal wells, ensures the consistency and comparability of the interpretation results, and reduces the errors caused by subjective judgment.

[0088] In some possible implementations, after inputting the lithology index value, the oil content index value, and the pore structure index value into a comprehensive interpretation model to obtain an interpretation result of the reservoir type of the fracturing section, the method further includes: The multiple fracturing sections in the target horizontal well are segmented and clustered according to interpretation results of reservoir types corresponding to the multiple fracturing sections.

[0089] For example, the interpretation result is a judgment result on the reservoir type obtained by comprehensively analyzing the reservoir parameters such as lithologic index, oil content index, and pore structure index. Reservoir types include oil layer, poor oil layer, oil-water layer, oil-water layer, water layer, and dry layer.

[0090] For example, in the fracturing operation of a horizontal well, the multiple fracturing sections of the horizontal well are further divided into different sections and clusters based on the interpretation results of the reservoir fluid type, so as to perform fracturing transformation more accurately and improve oil and gas recovery.

[0091] For example, a comprehensive interpretation model can be used to interpret the reservoir fluid type for each fracturing section based on lithologic indices, oil content indices, and pore structure indices. Based on the reservoir fluid type interpretation for each fracturing section, decisions can be made on how to segment and cluster these sections. For example, sections interpreted as oil or poor oil layers can be prioritized for fracturing to increase oil and gas production. Sections interpreted as oil-water layers or oil-water layers can be optimized by adjusting fracturing parameters based on the oil-water ratio. For sections interpreted as water or dry layers, fracturing operations can be reduced or avoided to save costs and mitigate unnecessary risks. Based on these decisions, actual segmentation and clustering operations are performed on multiple fracturing sections in a horizontal well, providing guidance for subsequent fracturing operations.

[0092] In this way, by segmenting and clustering according to the interpretation results of reservoir fluid types and rationally allocating fracturing resources, it is possible to ensure that fracturing operations are concentrated in the most promising oil and gas layers, improve fracturing efficiency, avoid unnecessary transformation of invalid or inefficient layers, and improve resource utilization efficiency.

[0093] The present invention establishes for the first time a multi-parameter fluid interpretation method integrating well logging and mud logging for shale oil horizontal wells, and for the first time establishes a comprehensive splitting coefficient of static production data and static geological parameters, thus realizing a detailed interpretation of the reservoir fluid in shale oil horizontal wells.

[0094] In one example, for the Nth section of a horizontal section of a shale oil well, the reservoir interpretation model of the present invention was used to obtain the logging and mud recording data of the fracturing section cluster of the horizontal well, calculate the lithologic index, oil content index and pore structure index of the section cluster, determine the reservoir type through the three-dimensional intersection diagram, and determine the actual reservoir type based on the oil production contribution rate and water production contribution rate of the horizontal well. The final determination showed that the reservoir type interpretation conformity of the three-dimensional intersection diagram was 81.25%, which was 12.4% higher than the interpretation conformity of the two-dimensional parameter interpretation method of 73%.

[0095] This method, unlike many other fluid interpretation methods, is the first to consider post-fracture production dynamics data from clustered horizontal well sections. It effectively combines static geological data with post-fracture production dynamics data and fully incorporates the characteristics of unconventional lithologic reservoirs: lithology controls pore structure, which in turn controls fluid occurrence. Compared to crossplot methods, this method offers improved accuracy; compared to mathematical analysis, it is simpler and easier to implement; and compared to nuclear magnetic resonance logging, it offers lower costs and greater ease of field application.

[0096] Furthermore, the word "exemplary" is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as advantageous over other aspects or designs. Rather, the use of the word exemplary is intended to present concepts in a concrete manner. As used herein, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X applies to A or B" is intended to mean any of the natural inclusive permutations. That is, if X applies to A; X applies to B; or X applies to both A and B, then "X applies to A or B" satisfies any of the aforementioned instances. Furthermore, the articles "a" and "an," as used in this application and the appended claims, are generally understood to mean "one or more," unless otherwise specified or clear from the context to refer to the singular form.

[0097] Likewise, although the present invention has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. The present invention includes all such modifications and variations and is limited only by the scope of the claims. With particular regard to the various functions performed by the components (e.g., elements, resources, etc.) described above, unless otherwise indicated, terms used to describe such components are intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if not structurally equivalent to the disclosed structure. Furthermore, although particular features of the present invention may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms "include," "have," "have," "have," or variations thereof are used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term "comprising."

[0098] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the appended claims.

[0099] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A horizontal well reservoir interpretation method, characterized in that: include: Obtaining target sensitive parameters corresponding to the fracturing section of the target horizontal well, wherein the target sensitive parameters include well logging parameters and geological parameters; Determine a lithology index value, an oil content index value, and a pore structure index value based on the target sensitive parameter and a preset target relationship model; wherein the lithology index value is used to characterize the lithology characteristics of the reservoir, the oil content index value is used to characterize the oil content characteristics of the reservoir, and the pore structure index value is used to characterize the pore structure characteristics of the reservoir; The lithology index value, the oil content index value, and the pore structure index value are input into a comprehensive interpretation model to obtain an interpretation result of the reservoir type of the fracturing section.

2. The method according to claim 1, characterized in that The target relationship model includes a lithology relationship model, an oil content relationship model, and a pore structure relationship model; the logging parameters include deep lateral resistivity, natural gamma, acoustic wave time difference curve, and full hydrocarbon logging curve; and the geological parameters include shale content, porosity, water porosity, oil saturation, and permeability; According to the target sensitive parameters and the preset target relationship model, the lithology index value, the oil content index value and the pore structure index value are determined, including: determining the lithology index value according to the deep lateral resistivity, the natural gamma, the acoustic wave transit time curve, the shale content, and the lithology relationship model; determining the oiliness index value according to the porosity, the water porosity, the oil saturation, the full hydrocarbon logging curve, and the oiliness relationship model; The pore structure index value is determined according to the acoustic wave transit time curve, the porosity, the permeability and a pore structure relationship model.

3. The method according to claim 2, characterized in that The lithologic relationship model is: ， Among them, YXZS represents lithologic index; RT represents deep lateral resistivity; GR represents natural gamma; SH represents shale content; S AC-RT The envelope surface representing the acoustic transit time and the deep lateral resistivity is determined by the acoustic transit time curve and the deep lateral resistivity.

4. The method according to claim 2, characterized in that Determining the oiliness index value according to the porosity, the water-containing porosity, the oil saturation, the full hydrocarbon logging curve, and the oiliness relationship model includes: determining a total hydrocarbon base value and a total hydrocarbon anomaly amplitude value according to the total hydrocarbon logging curve; The oiliness index value is determined according to the porosity, the natural gamma, the water porosity, the oil saturation, the total hydrocarbon base value, the total hydrocarbon anomaly amplitude value and the oiliness relationship model.

5. The method according to claim 4, characterized in that The oil content relationship model is: ， Among them, HYZS represents the oiliness index; φ represents the porosity; φ w Indicates water-containing porosity; C 异 Indicates the total hydrocarbon anomaly amplitude value, which is determined by the total hydrocarbon logging curve; C 基 Indicates the total hydrocarbon base value, which is determined by the total hydrocarbon logging curve; S o Indicates oil saturation.

6. The method according to claim 2, characterized in that The pore structure relationship model is: ， Among them, KXJGZS represents the pore structure index; AC represents the acoustic time difference, which is determined by the acoustic time difference curve; AC min represents the minimum value of the acoustic time difference curve; φ represents porosity; K represents permeability.

7. The method according to any one of claims 1 to 6, characterized in that The comprehensive interpretation model is a three-dimensional map, and the three-dimensional coordinate system of the three-dimensional map corresponds to the lithology index, oil content index and pore structure index respectively. Different spatial areas of the three-dimensional map correspond to different reservoir types of horizontal wells.

8. The method according to any one of claims 1 to 6, characterized in that The reservoir types of the horizontal well include oil layer, poor oil layer, oil-water layer, oil-water layer, water layer and dry layer. The oil testing standards of the reservoir types of the horizontal well are pre-determined by the vertical well oil testing standards. The oil testing standards include: The oil production contribution rate of the oil layer is greater than 8%, and the water production contribution rate of the oil layer is less than 10%; The oil production contribution rate of the poor oil layer is 5%-8%, and the water production contribution rate of the poor oil layer is less than 10%; The oil production contribution rate of the oil-water layer is 1%-5%, and the water production contribution rate of the oil-water layer is less than 10%; The oil production contribution rate of the oil-bearing water layer is 0%-1%, and the water production contribution rate of the oil-bearing water layer is less than 10%; The oil production contribution rate of the water layer is 0%, and the water production contribution rate of the water layer is greater than 10%; The oil production contribution rate and water production contribution rate of the dry layer are both 0%.

9. The method according to any one of claims 1 to 6, characterized in that After inputting the lithology index value, the oil content index value, and the pore structure index value into a comprehensive interpretation model to obtain an interpretation result of the reservoir type of the fracturing section, the method further includes: The multiple fracturing sections in the target horizontal well are segmented and clustered according to interpretation results of reservoir types corresponding to the multiple fracturing sections.

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