Construction method of horizontal well reservoir interpretation model

Abstract

The invention provides a construction method of a horizontal well reservoir interpretation model, and relates to the technical field of shale oil-gas field development, and the construction method comprises the following steps: obtaining a target oil testing result and a target parameter corresponding to a fractured section of a target horizontal well; performing correlation analysis on the target parameters according to the target oil testing result, and determining sensitive parameters sensitive to oil-water characteristics in the target parameters; constructing a target relation model according to the sensitive parameters; and establishing a comprehensive interpretation model for interpreting the reservoir type of the horizontal well. According to the method, the target relation model for calculating the lithology index, the oiliness index and the pore structure index is established, and the comprehensive interpretation model for interpreting the reservoir type of the horizontal well through the three indexes is established, so that the characteristics of multiple aspects such as lithology, oiliness and pore structure of the reservoir can be more comprehensively reflected; therefore, the precision of reservoir type interpretation is improved, and accurate data support can be provided for horizontal well fracturing staged clustering.

Description

Technical Field

[0001] The present disclosure relates to the technical field of oil and gas field development, and in particular to a method for constructing a horizontal well reservoir interpretation model. 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 disclosure provides a method for constructing a horizontal well reservoir interpretation model to improve the interpretation accuracy of reservoir types.

[0005] A method for constructing a horizontal well reservoir interpretation model, comprising: Obtaining target oil test results and target parameters corresponding to the fracturing stages of the target horizontal well, wherein the target parameters include well logging parameters and geological parameters, and the target oil test results include actual reservoir types corresponding to different fracturing stages; performing a correlation analysis on the target parameters according to the target oil test results to determine sensitive parameters among the target parameters that are sensitive to oil-water characteristics; According to the sensitive parameters, a target relationship model is constructed, wherein the target relationship model is used to determine the lithology index, oil content index and pore structure index of the reservoir, wherein the lithology index is used to characterize the lithology characteristics of the reservoir, the oil content index is used to characterize the oil content characteristics of the reservoir, and the pore structure index is used to characterize the pore structure characteristics of the reservoir; A comprehensive interpretation model for interpreting the reservoir type of the horizontal well is established, wherein the comprehensive interpretation model is used to obtain an interpretation result of the reservoir type of the fracturing section based on the lithology index, the oil content index and the pore structure index.

[0006] In some possible implementations, the target relationship model includes a lithology relationship model for calculating a lithology index, an oiliness relationship model for calculating an oiliness index, and a pore structure relationship model for calculating a pore structure index; sensitive parameters among the logging parameters include deep lateral resistivity, natural gamma, acoustic travel time curves, and total hydrocarbon logging curves; and sensitive parameters among the geological parameters include shale content, porosity, water porosity, oil saturation, and permeability; constructing a target relationship model based on the sensitive parameters includes: constructing the lithologic relationship model according to the deep lateral resistivity, the natural gamma ray, the acoustic wave transit time curve, and the shale content; constructing the oil-bearing relationship model according to the porosity, the water-porosity, the oil saturation and the full hydrocarbon logging curve; The pore structure relationship model is constructed according to the acoustic wave time difference curve, the porosity and the permeability.

[0007] In some possible implementations, the lithologic relationship model is:

[0008] 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.

[0009] In some possible implementations, the oil content relationship model is:

[0010] 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.

[0011] In some possible implementations, the pore structure relationship model is:

[0012] 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.

[0013] In some possible implementations, obtaining a target oil test result corresponding to a fracturing stage of a target horizontal well includes: Obtaining the water production contribution rate and oil production contribution rate of the fracturing section of the target horizontal well by using a fracturing tracing technology; According to the set horizontal well oil test standard and the water production contribution rate and oil production contribution rate of the fracturing section of the target horizontal well, the target oil test result corresponding to the fracturing section of the target horizontal well is determined.

[0014] In some possible implementations, before determining a target oil test result corresponding to the fracturing stage of the target horizontal well based on a set horizontal well oil test standard and the water production contribution rate and oil production contribution rate of the fracturing stage of the target horizontal well, the method further includes: The horizontal well testing standard is established based on the water production contribution rate and oil production contribution rate corresponding to different reservoir types in the vertical well testing standard.

[0015] In some possible implementations, the reservoir types in the horizontal well oil test standard include oil layer, poor oil layer, oil-water layer, oil-water layer, water layer and dry layer; 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%.

[0016] 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, the oil content index and the pore structure index respectively, and different spatial regions of the three-dimensional map correspond to different reservoir types of horizontal wells.

[0017] In some possible implementations, after establishing a comprehensive interpretation model for interpreting the reservoir type of the horizontal well, the method further includes: Obtain actual oil test results and sensitive parameter data of the fracturing section of the sample horizontal well; Determining a sample lithology index, a sample oil content index, and a sample pore structure index of the sample horizontal well according to the sensitive parameter data of the fracturing section of the sample horizontal well and the target relationship model; Inputting the sample lithology index, the sample oil content index, and the sample pore structure index into the three-dimensional chart to obtain the test results corresponding to the sample horizontal well; The three-dimensional chart is optimized and verified based on the actual oil test results and the test oil test results.

[0018] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects: The present invention obtains target oil test results and target parameters corresponding to the fracturing section of the target horizontal well, the target parameters include logging parameters and geological parameters, and the target oil test results include actual reservoir types corresponding to different fracturing sections; based on the target oil test results, correlation analysis is performed on the target parameters to determine sensitive parameters in the target parameters that are sensitive to oil-water characteristics; based on the sensitive parameters, a target relationship model is constructed, and the target relationship model is used to determine the lithologic index, oil content index and pore structure index of the reservoir, the lithologic index is used to characterize the lithologic characteristics of the reservoir, the oil content index is used to characterize the oil content characteristics of the reservoir, and the pore structure index is used to characterize the pore structure characteristics of the reservoir; a comprehensive interpretation model is established for interpreting the reservoir type of the horizontal well, and the comprehensive interpretation model is used to obtain an interpretation result of the reservoir type of the fracturing section based on the lithologic index, oil content index and pore structure index. In this way, by establishing a target relationship model for calculating the lithology index, oil content index, and pore structure index, and a comprehensive interpretation model for interpreting the reservoir type of horizontal wells through the three indices, it is possible to more comprehensively reflect the various characteristics of the reservoir, such as lithology, oil content, and pore structure, thereby improving the accuracy of reservoir type interpretation and providing accurate data support for horizontal well fracturing segmentation and clustering.

[0019] 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 disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0021] Figure 1 The present invention is a flowchart of a method for constructing a horizontal well reservoir interpretation model according to an exemplary embodiment.

[0022] Figure 2 It is a schematic diagram of a three-dimensional chart according to an exemplary embodiment. DETAILED DESCRIPTION

[0023] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical 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 disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 1 FIG. 1 is a flow chart showing a method for constructing a horizontal well reservoir interpretation model according to an exemplary embodiment. Figure 1 As shown, the method for constructing the horizontal well reservoir interpretation model includes the following steps.

[0033] In step S201, target oil testing results and target parameters corresponding to the fracturing stages of the target horizontal well are obtained. The target parameters include well logging parameters and geological parameters. The target oil testing results include actual reservoir types corresponding to different fracturing stages.

[0034] In step S202, a correlation analysis is performed on the target parameters according to the target oil test results to determine sensitive parameters of the target parameters that are sensitive to oil-water characteristics.

[0035] In step S203, a target relationship model is constructed based on the sensitive parameters. The target relationship model is used to determine the lithologic index, oil content index and pore structure index of the reservoir. The lithologic index is used to characterize the lithologic characteristics of the reservoir, the oil content index is used to characterize the oil content characteristics of the reservoir, and the pore structure index is used to characterize the pore structure characteristics of the reservoir.

[0036] In step S204, a comprehensive interpretation model for interpreting the reservoir type of the horizontal well is established, wherein the comprehensive interpretation model is used to obtain an interpretation result of the reservoir type of the fracturing section based on the lithology index, the oil content index and the pore structure index.

[0037] For example, target horizontal wells are samples used to construct reservoir interpretation models. Reservoir interpretation involves analyzing and evaluating the rock properties, fluid properties, and distribution of oil and gas reservoirs. Reservoir type interpretation results can provide a basis for oil and gas reservoir development and data support for optimal fracturing stage and cluster selection.

[0038] For example, in horizontal wells, to increase oil and gas production, the reservoir surrounding the wellbore is often fractured. A fracture stage is a series of independent fracture zones in a horizontal well that are separated by hydraulic fracturing. Each fracture stage may contain one or more fracture clusters.

[0039] For example, target parameters are various parameters used to construct and calibrate reservoir interpretation models, including well logging parameters and geological parameters. Sensitive parameters are those selected from well logging parameters and geological parameters in the well logging data to be more sensitive to oil-water characteristics. Sensitive parameters include both well logging parameters and geological parameters.

[0040] For example, the target relationship model refers to a mathematical model for calculating the lithologic index, oil content index, and pore structure index. The lithologic index is used to characterize the lithologic characteristics of the reservoir, the oil content index is used to characterize the oil content of the reservoir, and the pore structure index is used to characterize the pore structure of the reservoir. The target relationship model can determine the lithologic index, oil content index, and pore structure index using the sensitive parameters. The target relationship model can be obtained by fitting and continuously verifying and optimizing the sensitive parameters of different rock types.

[0041] 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.

[0042] For example, the lithologic 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.

[0043] 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.

[0044] 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.

[0045] For example, sensitive parameters corresponding to the fracturing stage of the target horizontal well can be pre-determined. For example, the acquired data can be pre-processed based on drilling data, well logging data, fracturing data, oil testing data, and production profile data, including data cleaning and standardization.

[0046] 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.

[0047] 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.

[0048] For example, correlation analysis of target parameters can be performed using target oil testing results. This can identify sensitive parameters of well logging and geological parameters that are sensitive to oil-water characteristics. These sensitive parameters can effectively reflect the reservoir's lithology, oil content, and pore structure. Based on these sensitive parameters, a target relationship model can be established. This target relationship model can be used to calculate lithology indices, oil content indices, and pore structure indices that characterize the reservoir.

[0049] For example, a comprehensive interpretation model can be established based on the target relationship model. The comprehensive interpretation model can be used to interpret the lithology index, oil content index, and pore structure index calculated based on the target relationship model. By comprehensively analyzing and processing each index, an interpretation result of the reservoir type of the fracturing stage is obtained.

[0050] For example, by comprehensively considering multiple parameters and indicators, a reservoir interpretation model suitable for the target horizontal well can be systematically constructed. This model determines sensitive parameters through correlation analysis, calculates lithologic indices, oil content indices, and pore structure indices using the target relationship model, and ultimately, uses the integrated interpretation model to determine the reservoir type. This systematic modeling process improves the accuracy and reliability of reservoir interpretation.

[0051] The present invention obtains target oil test results and target parameters corresponding to the fracturing section of the target horizontal well, the target parameters include logging parameters and geological parameters, and the target oil test results include actual reservoir types corresponding to different fracturing sections; based on the target oil test results, correlation analysis is performed on the target parameters to determine sensitive parameters in the target parameters that are sensitive to oil-water characteristics; based on the sensitive parameters, a target relationship model is constructed, and the target relationship model is used to determine the lithologic index, oil content index and pore structure index of the reservoir, the lithologic index is used to characterize the lithologic characteristics of the reservoir, the oil content index is used to characterize the oil content characteristics of the reservoir, and the pore structure index is used to characterize the pore structure characteristics of the reservoir; a comprehensive interpretation model is established for interpreting the reservoir type of the horizontal well, and the comprehensive interpretation model is used to obtain an interpretation result of the reservoir type of the fracturing section based on the lithologic index, oil content index and pore structure index. In this way, by establishing a target relationship model for calculating the lithology index, oil content index, and pore structure index, and a comprehensive interpretation model for interpreting the reservoir type of horizontal wells through the three indices, it is possible to more comprehensively reflect the various characteristics of the reservoir, such as lithology, oil content, and pore structure, thereby improving the accuracy of reservoir type interpretation and providing accurate data support for horizontal well fracturing segmentation and clustering.

[0052] In some possible implementations, the target relationship model includes a lithology relationship model for calculating a lithology index, an oiliness relationship model for calculating an oiliness index, and a pore structure relationship model for calculating a pore structure index; sensitive parameters among the logging parameters include deep lateral resistivity, natural gamma, acoustic travel time curves, and total hydrocarbon logging curves; and sensitive parameters among the geological parameters include shale content, porosity, water porosity, oil saturation, and permeability; constructing a target relationship model based on the sensitive parameters includes: constructing the lithologic relationship model according to the deep lateral resistivity, the natural gamma ray, the acoustic wave transit time curve, and the shale content; constructing the oil-bearing relationship model according to the porosity, the water-porosity, the oil saturation and the full hydrocarbon logging curve; The pore structure relationship model is constructed according to the acoustic wave time difference curve, the porosity and the permeability.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] For example, a lithologic relationship model is established based on sensitive parameters such as deep lateral resistivity, natural gamma, acoustic time difference curve and mud content that can reflect lithologic characteristics; an oil content relationship model is established based on sensitive parameters such as porosity, water porosity, oil saturation and total hydrocarbon logging curve that can reflect oil content characteristics; and a pore structure relationship model is established based on sensitive parameters such as acoustic time difference curve, porosity and permeability that can reflect pore structure characteristics.

[0065] In this way, by using static production data such as logging parameters and geological parameters, lithologic relationship models, oil content relationship models, and pore structure relationship models are established to more comprehensively reflect the characteristics of different aspects of the reservoir, thereby improving the accuracy of reservoir type interpretation, making the reservoir interpretation method more suitable for the characteristics of horizontal well reservoirs, and improving the scientific nature and reliability of the interpretation results.

[0066] In some possible implementations, the lithologic relationship model is:

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] In this way, based on the above lithologic relationship model, the lithologic index can be obtained through deep lateral resistivity, natural gamma, sonic time difference curve and mud content.

[0074] In some possible implementations, the oil content relationship model is:

[0075] 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 %.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] In this way, based on the above oiliness relationship model, the oiliness index can be obtained through porosity, natural gamma, water porosity, oil saturation, total hydrocarbon base value and total hydrocarbon anomaly amplitude value.

[0080] In some possible implementations, the pore structure relationship model is:

[0081] 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.

[0082] In this way, the oil content index can be obtained based on the above pore structure relationship model through the acoustic wave time difference curve, porosity and permeability.

[0083] In some possible implementations, obtaining a target oil test result corresponding to a fracturing stage of a target horizontal well includes: Obtaining the water production contribution rate and oil production contribution rate of the fracturing section of the target horizontal well by using a fracturing tracing technology; According to the set horizontal well oil test standard and the water production contribution rate and oil production contribution rate of the fracturing section of the target horizontal well, the target oil test result corresponding to the fracturing section of the target horizontal well is determined.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] In some possible implementations, before determining a target oil test result corresponding to the fracturing stage of the target horizontal well based on a set horizontal well oil test standard and the water production contribution rate and oil production contribution rate of the fracturing stage of the target horizontal well, the method further includes: The horizontal well testing standard is established based on the water production contribution rate and oil production contribution rate corresponding to different reservoir types in the vertical well testing standard.

[0090] For example, the reservoir type of a horizontal well refers to the different types of fluids present in the reservoir of the horizontal well, and the classification criteria can be predetermined based on the vertical well oil testing standards, wherein the vertical well oil testing can provide more accurate production capacity and fluid property data, including oil production, water content, etc.

[0091] For example, the reservoir types in the vertical well testing standard can be, based on the oil production contribution rate, sequentially including oil layer, poor oil layer, oil-water layer, oil-water layer, water layer, and dry layer. Correspondingly, the reservoir types in the horizontal well testing standard can also be, based on the oil production contribution rate, sequentially including oil layer, poor oil layer, oil-water layer, oil-water layer, water layer, and dry layer.

[0092] Illustratively, 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%.

[0093] 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.

[0094] 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.

[0095] Table 1 Vertical well testing standards and shale oil horizontal well testing standards

[0096] 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.

[0097] Table 2 Oil test results of target horizontal wells according to horizontal well oil test standards

[0098] 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.

[0099] 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.

[0100] 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, the oil content index and the pore structure index respectively, and different spatial regions of the three-dimensional map correspond to different reservoir types of horizontal wells.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] In some possible implementations, after establishing a comprehensive interpretation model for interpreting the reservoir type of the horizontal well, the method further includes: Obtain actual oil test results and sensitive parameter data of the fracturing section of the sample horizontal well; Determining a sample lithology index, a sample oil content index, and a sample pore structure index of the sample horizontal well according to the sensitive parameter data of the fracturing section of the sample horizontal well and the target relationship model; Inputting the sample lithology index, the sample oil content index, and the sample pore structure index into the three-dimensional chart to obtain the test results corresponding to the sample horizontal well; The three-dimensional chart is optimized and verified based on the actual oil test results and the test oil test results.

[0105] For example, the sample horizontal well is a horizontal well used to test and validate the integrated interpretation model. Actual well testing results and sensitive parameter data for the sample horizontal well can be obtained in advance. Sensitive parameters in the well logging parameters include deep lateral resistivity, natural gamma ray, acoustic transit time curves, and total hydrocarbon logging curves. Sensitive parameters in the geological parameters include shale content, porosity, water porosity, oil saturation, and permeability.

[0106] For example, the test oil test result is a reservoir type interpretation result obtained by inputting the sample lithology index, sample oiliness index and sample pore structure index into a three-dimensional map, which is used for comparison with the actual oil test result.

[0107] For example, the sample lithology index, sample oiliness index, and sample pore structure index can be calculated based on the sensitive parameter data of the sample horizontal well. By comparing the actual oil test results with the test oil test results, the three-dimensional map is adjusted and verified to improve the accuracy and reliability of the model.

[0108] For example, after completing the process of establishing the target relationship model and the comprehensive interpretation model, data from newly drilled wells can be used for verification. As shown in Table 3 below, for a shale oil well with 16 horizontal sections tested, the reservoir interpretation model of the present invention was used to obtain the logging and mud logging data of the horizontal well's fractured section cluster, calculate the lithologic index, oil content index, and pore structure index of the section cluster, determine the test results using a three-dimensional intersection chart, and determine the actual test results based on the oil production contribution rate and water production contribution rate of the horizontal well. The final determination of the reservoir type interpretation conformity of the three-dimensional intersection chart was 81.25%, compared to the interpretation conformity of the two-dimensional parameter interpretation method of 73%, an overall improvement of 12.4%.

[0109] Table 3 Oil test results for sample horizontal wells

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Likewise, although the present disclosure 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 disclosure 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 described above (e.g., elements, resources, etc.), 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. In addition, although particular features of the present disclosure 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."

[0114] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure 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 present disclosure being indicated by the appended claims.

[0115] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for constructing a horizontal well reservoir interpretation model, characterized in that: include: Obtaining target oil test results and target parameters corresponding to the fracturing stages of the target horizontal well, wherein the target parameters include well logging parameters and geological parameters, and the target oil test results include actual reservoir types corresponding to different fracturing stages; performing a correlation analysis on the target parameters according to the target oil test results to determine sensitive parameters among the target parameters that are sensitive to oil-water characteristics; According to the sensitive parameters, a target relationship model is constructed, wherein the target relationship model is used to determine the lithology index, oil content index and pore structure index of the reservoir, wherein the lithology index is used to characterize the lithology characteristics of the reservoir, the oil content index is used to characterize the oil content characteristics of the reservoir, and the pore structure index is used to characterize the pore structure characteristics of the reservoir; A comprehensive interpretation model for interpreting the reservoir type of the horizontal well is established, wherein the comprehensive interpretation model is used to obtain an interpretation result of the reservoir type of the fracturing section based on the lithology index, the oil content index and the pore structure index.

2. The method according to claim 1, characterized in that The target relationship model includes a lithology relationship model for calculating the lithology index, an oil content relationship model for calculating the oil content index, and a pore structure relationship model for calculating the pore structure index; the sensitive parameters in the logging parameters include deep lateral resistivity, natural gamma, acoustic wave time difference curve, and total hydrocarbon logging curve; the sensitive parameters in the geological parameters include shale content, porosity, water porosity, oil saturation, and permeability; Constructing a target relationship model based on the sensitive parameters includes: constructing the lithologic relationship model according to the deep lateral resistivity, the natural gamma ray, the acoustic wave transit time curve, and the shale content; constructing the oil-bearing relationship model according to the porosity, the water-porosity, the oil saturation and the full hydrocarbon logging curve; The pore structure relationship model is constructed according to the acoustic wave time difference curve, the porosity and the permeability.

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 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.

5. 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.

6. The method according to any one of claims 1 to 5, characterized in that Obtain the target oil test results corresponding to the fracturing stage of the target horizontal well, including: Obtaining the water production contribution rate and oil production contribution rate of the fracturing section of the target horizontal well by using a fracturing tracing technology; According to the set horizontal well oil test standard and the water production contribution rate and oil production contribution rate of the fracturing section of the target horizontal well, the target oil test result corresponding to the fracturing section of the target horizontal well is determined.

7. The method according to claim 6, characterized in that Before determining a target oil test result corresponding to the fracturing section of the target horizontal well based on a set horizontal well oil test standard and a water production contribution rate and an oil production contribution rate of the fracturing section of the target horizontal well, the method further includes: The horizontal well testing standard is established based on the water production contribution rate and oil production contribution rate corresponding to different reservoir types in the vertical well testing standard.

8. The method according to claim 7, characterized in that The reservoir types in the horizontal well oil test standard include oil layer, poor oil layer, oil-water layer, oil-water layer, water layer and dry layer; 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. According to the method described in any one of claims 1 to 5, 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, the oil content index and the pore structure index respectively, and different spatial areas of the three-dimensional map correspond to different reservoir types of horizontal wells.

10. The method according to claim 9, characterized in that After establishing a comprehensive interpretation model for interpreting the reservoir type of the horizontal well, the method further includes: Obtain actual oil test results and sensitive parameter data of the fracturing section of the sample horizontal well; Determining a sample lithology index, a sample oil content index, and a sample pore structure index of the sample horizontal well according to the sensitive parameter data of the fracturing section of the sample horizontal well and the target relationship model; Inputting the sample lithology index, the sample oil content index, and the sample pore structure index into the three-dimensional chart to obtain the test results corresponding to the sample horizontal well; The three-dimensional chart is optimized and verified based on the actual oil test results and the test oil test results.

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