A horizontal well reservoir interpretation method

Abstract

The present application provides a kind of horizontal well reservoir interpretation method, it is related to shale oil and gas field development technical field, the method includes: obtaining the target sensitive parameter corresponding to the fracturing section of target horizontal well, target sensitive parameter includes well logging parameter and geological parameter;According to target sensitive parameter and pre-set target relationship model, determine lithology index value, oiliness index value and pore structure index value;Lithology index value, oiliness index value and pore structure index value are input into comprehensive interpretation model, and the interpretation result of the reservoir type of the fracturing section of target horizontal well is obtained.The present application calculates lithology index, oiliness index and pore structure index by target relationship model, can more comprehensively reflect the characteristics of reservoir, so as to interpret the reservoir type of horizontal well according to multiple indexes by comprehensive interpretation model, can improve the accuracy of reservoir type interpretation, provides accurate data support for horizontal well fracturing segmentation clustering.

Description

Technical Field

[0001] This invention relates to the field of shale oil and gas field development technology, and in particular to a method for interpreting horizontal well reservoirs. Background Technology

[0002] Interpreting reservoir types in oil and gas wells is a key parameter for fracturing and reservoir selection. Currently, commonly used fluid interpretation methods include single-logging parameter cross-plotting, mathematical analysis methods such as Fisher discriminant analysis and grey clustering analysis, and various logging fluid interpretation methods, such as nuclear magnetic resonance logging. These methods are all based on single-discipline interpretation methods of logging or well logging data, and are established based on data from vertical or directional wells.

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

[0004] This invention provides a method for interpreting horizontal well reservoirs to improve the accuracy of reservoir type interpretation.

[0005] A method for interpreting reservoirs in horizontal wells, comprising:

[0006] Obtain the target sensitive parameters corresponding to the fracturing section of the target horizontal well, including logging parameters and geological parameters;

[0007] Based on the target sensitive parameters and the preset target relationship model, the lithology index value, oil-bearing index value, and pore structure index value are determined; wherein, the lithology index value is used to characterize the lithological characteristics of the reservoir, the oil-bearing index value is used to characterize the oil-bearing characteristics of the reservoir, and the pore structure index value is used to characterize the pore structure characteristics of the reservoir.

[0008] The lithology index, oil-bearing index, and pore structure index are input into the integrated interpretation model to obtain the interpretation results of the reservoir type of the fracturing section.

[0009] In some possible implementations, the target relationship model includes a lithological relationship model, an oil-bearing relationship model, and a pore structure relationship model; the logging parameters include deep lateral resistivity, natural gamma ray, sonic transit time curve, and total hydrocarbon logging curve; and the geological parameters include clay content, porosity, water-bearing porosity, oil saturation, and permeability.

[0010] Based on the target sensitive parameters and the preset target relationship model, the lithology index value, oil-bearing index value, and pore structure index value are determined, including:

[0011] The lithology index value is determined based on the deep lateral resistivity, the natural gamma, the sonic transit time curve, the clay content, and the lithological relationship model.

[0012] The oil-bearing index value is determined based on the porosity, the water-bearing porosity, the oil saturation, the total hydrocarbon logging curve, and the oil-bearing relationship model.

[0013] The pore structure index value is determined based on the acoustic time difference curve, the porosity, the permeability, and the pore structure relationship model.

[0014] In some possible implementations, the lithological relationship model is as follows:

[0015] ,

[0016] Where YXZS represents the lithology index; RT represents the deep lateral resistivity; GR represents the natural gamma; SH represents the clay content; and S represents the shale content. AC-RT The envelope representing the acoustic transit time and the deep lateral resistivity is determined by the acoustic transit time curve and the deep lateral resistivity.

[0017] In some possible implementations, the oil-bearing index value is determined based on the porosity, the water-bearing porosity, the oil saturation, the total hydrocarbon logging curve, and the oil-bearing relationship model, including:

[0018] Based on the total hydrocarbon logging curve, determine the total hydrocarbon base value and the total hydrocarbon anomaly amplitude value;

[0019] The oil content index value is determined based on the porosity, natural gamma, water-bearing porosity, oil saturation, total hydrocarbon value, total hydrocarbon anomaly amplitude value, and oil content relationship model.

[0020] In some possible implementations, the oil content relationship model is as follows:

[0021] ,

[0022] Where HYZS represents the oil content index; φ represents porosity; φ w Indicates water-bearing porosity; C 异 The total hydrocarbon anomaly amplitude value is determined by the total hydrocarbon logging curve; C 基 The total hydrocarbon base value is determined by the total hydrocarbon logging curve; S o This indicates the degree of oil saturation.

[0023] In some possible implementations, the pore structure relationship model is as follows:

[0024] ,

[0025] Where KXJGZS represents the porosity index; AC represents the acoustic transit time, determined by the acoustic transit time curve; AC min φ represents the minimum value of the acoustic transit time curve; K represents the porosity; and φ represents the permeability.

[0026] In some possible implementations, the comprehensive interpretation model is a three-dimensional chart, in which the three-dimensional coordinate system corresponds to the lithology index, oil-bearing index, and pore structure index, respectively, and different spatial regions of the three-dimensional chart correspond to different reservoir types of horizontal wells.

[0027] In some possible implementations, the reservoir type of the horizontal well includes oil-bearing layers, poor-oil-bearing layers, oil-water co-containing layers, oil-water-bearing layers, water-bearing layers, and dry layers. The testing standards for the reservoir type of the horizontal well are determined in advance using the testing standards for vertical wells. These testing standards include:

[0028] The oil production contribution rate of the oil layer is >8%, and the water production contribution rate of the oil layer is <10%.

[0029] 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 <10%.

[0030] The oil-water co-layer's oil production contribution rate is 1%-5%, and the water production contribution rate of the oil-water co-layer is <10%.

[0031] The oil-bearing water layer has an oil production contribution rate of 0%-1% and a water production contribution rate of <10%.

[0032] The oil production contribution rate of the water layer is 0%, and the water production contribution rate of the water layer is >10%;

[0033] The oil production contribution rate and water production contribution rate of the dry layer are both 0%.

[0034] In some possible implementations, after inputting the lithology index value, the oil-bearing index value, and the pore structure index value into a comprehensive interpretation model to obtain the interpretation result of the reservoir type of the fracturing section, the method further includes:

[0035] Based on the interpretation results of the reservoir types corresponding to the multiple fractured sections in the target horizontal well, the multiple fractured sections are segmented and clustered.

[0036] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0037] This invention determines lithology, oil-bearing, and pore structure indices by using target sensitive parameters corresponding to the fractured section of a target horizontal well and a preset target relationship model. These indices are then input into a comprehensive interpretation model to obtain an interpretation of the reservoir type of the fractured section. By calculating the lithology, oil-bearing, and pore structure indices using the target relationship model, the characteristics of the reservoir can be more comprehensively reflected. Therefore, interpreting the reservoir type of a horizontal well based on multiple indices using the comprehensive interpretation model improves the accuracy of reservoir type interpretation and provides accurate data support for the segmentation and clustering of horizontal well fracturing.

[0038] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0040] Figure 1 This is a flowchart illustrating a horizontal well reservoir interpretation method according to an exemplary embodiment.

[0041] Figure 2 This is a schematic diagram of a three-dimensional plate according to an exemplary embodiment. Detailed Implementation

[0042] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0043] As described in the background section, the methods for interpreting oil and gas well reservoir types in related technologies are all based on data from vertical or directional wells. However, the logging mechanisms for horizontal wells differ significantly from those for vertical and directional wells, and there are also differences in resistivity logging series.

[0044] In vertical wells, the wellbore axis is designed as a vertical line, meaning the path from the wellhead to the bottom of the well remains roughly vertical. This type of well is suitable for areas with stable formations and relatively concentrated oil and gas reservoirs.

[0045] In directional wells, the wellbore axis 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 through vertical wells, such as oil and gas resources in faults, fractures, or complex formations.

[0046] Horizontal wells are a special type of directional well where the wellbore extends almost horizontally after entering the target formation. Horizontal wells maximize the length of the wellbore within the oil and gas reservoir, thereby improving production efficiency. This type of well is particularly suitable for developing thin, fractured, or low-permeability oil and gas reservoirs.

[0047] 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 cross plots.

[0048] Fisher discriminant analysis uses prior knowledge to classify unknown samples. Its main goal is to find an optimal projection direction that maximizes the separation of samples from different classes, thus achieving linear classification. Fisher discriminant analysis has a complex computational model, requiring extensive data preparation and function establishment. It also exhibits high ambiguity and slow computation speed.

[0049] Grey clustering analysis is based on the generation of whitening functions for grey numbers. It summarizes the whitening functions of clusters for different clustering indicators into n grey classes to determine the grey class to which a clustered object belongs. This method requires strong mathematical computational skills, is computationally difficult, and its computational model is not easily applied by on-site production personnel, resulting in relatively weak applicability.

[0050] Nuclear magnetic resonance (NMR) logging data provides multiple parameters, including effective porosity, pore size distribution, pore diameter, pore connectivity, and time constant. Based on these parameters, rock pore structure characteristics, such as porosity, pore distribution, and pore connectivity, can be assessed, thereby determining fluid storage and flow conditions. However, NMR logging data for each oil and gas field block is limited and interpretation costs are high, hindering its widespread use and preventing its general applicability.

[0051] Reference Figure 1 , Figure 1 This is a flowchart illustrating a horizontal well reservoir interpretation method according to an exemplary embodiment, such as... Figure 1 As shown, the horizontal well reservoir interpretation method includes the following steps.

[0052] In step S201, target sensitive parameters corresponding to the fracturing section of the target horizontal well are obtained, including logging parameters and geological parameters;

[0053] In step S202, based on the target sensitive parameters and the preset target relationship model, the lithology index value, oil-bearing index value, and pore structure index value are determined; wherein, the lithology index value is used to characterize the lithological characteristics of the reservoir, the oil-bearing index value is used to characterize the oil-bearing characteristics of the reservoir, and the pore structure index value is used to characterize the pore structure characteristics of the reservoir.

[0054] In step S203, the lithology index value, the oil-bearing index value, and the pore structure index value are input into the comprehensive interpretation model to obtain the interpretation results of the reservoir type of the fracturing section.

[0055] For example, the target horizontal well is a horizontal well for which reservoir interpretation is to be performed. Reservoir interpretation involves analyzing and evaluating the rock properties, fluid properties, and distribution of oil and gas reservoirs. The results of reservoir type interpretation can provide a basis for oil and gas reservoir development and provide data support for fracturing segmentation and cluster optimization.

[0056] For example, in horizontal wells, to increase oil and gas production, the reservoir surrounding the wellbore is typically fracturing. A fracturing section is a series of independent fracturing zones within a horizontal well, created using hydraulic fracturing technology. Each fracturing section may contain one or more fracturing clusters.

[0057] For example, target sensitive parameters are key parameters that have a significant impact on reservoir interpretation results. These target sensitive parameters include logging parameters and geological parameters. They are selected from logging and geological parameters in the logging data, focusing on those parameters that are more sensitive to oil-water characteristics.

[0058] For example, the target relation model refers to a mathematical model used to calculate the lithology index, oil-bearing index, and pore structure index. The target relation model can determine the lithology index value, oil-bearing index value, and pore structure index value through the target sensitive parameters. The target relation model can be pre-fitted and continuously validated and optimized based on the sensitive parameters of different rock types.

[0059] For example, logging parameters are reservoir physical property parameters obtained through logging and well logging techniques. They reflect the physical and fluid properties of the reservoir, such as resistivity, natural gamma ray, sonic transit time, and total hydrocarbon logging curves. Geological parameters are parameters that reflect the geological characteristics of the reservoir, such as rock type, porosity, water-bearing porosity, oil saturation, formation pressure, and clay content.

[0060] For example, the lithology index is an indicator that quantifies the type and physical properties of reservoir rocks, and can be used to characterize reservoir permeability and stability. The rock type can be sandstone, mudstone, clastic rock, etc. The oil-bearing index is an indicator that characterizes the abundance of oil and gas in the reservoir, and can be used to characterize the distribution and saturation of reservoir fluids, where the reservoir fluids can be oil, gas, or water. The pore structure index can be used to characterize reservoir permeability and fluid flowability, etc.

[0061] For example, the integrated interpretation model is a model that integrates lithology index, oil-bearing index and pore structure index, and is used to interpret reservoir type by combining the three indices and to classify or predict reservoir type in fracturing sections.

[0062] For example, the comprehensive interpretation model can adopt a mathematical model or a neural network model; or it can form a three-dimensional cross plot based on lithology index, oil-bearing index and pore structure index, which can be used to describe different reservoir types based on different regions in the three-dimensional cross plot.

[0063] For example, sensitive parameters corresponding to the fractured section of the target horizontal well can be obtained in advance. For instance, the data can be preprocessed based on drilling data, logging data, fracturing data, oil testing data, and production profile data, including data cleaning and standardization, and then the sensitive parameters corresponding to the fractured section of the target horizontal well can be determined through data correlation analysis.

[0064] For example, key target sensitive parameters are identified by analyzing the sensitivity of logging parameters and geological parameters to reservoir type interpretation. These sensitive parameters can effectively reflect the lithology, oil-bearing capacity, and pore structure characteristics of the reservoir.

[0065] For example, based on target sensitive parameters, lithology index, oil-bearing index, and pore structure index that can characterize reservoir features can be determined through pre-established lithology index, oil-bearing index, pore structure index, and target relationship model.

[0066] For example, the calculated lithology index, oil-bearing index, and pore structure index are input into the integrated interpretation model. The integrated interpretation model, through comprehensive analysis and processing of each index, yields the interpretation results for the reservoir type of the target horizontal well fracturing section.

[0067] For example, by comprehensively considering multiple parameters such as lithology index, oil-bearing index, and pore structure index, the characteristics of the reservoir can be more fully reflected, thereby improving the accuracy of reservoir type interpretation. Furthermore, accurate reservoir type interpretation results help optimize horizontal well development plans. Based on the distribution of different fluid types, the fracturing sections can be rationally segmented and clustered, improving oil and gas recovery and reducing development costs.

[0068] This invention determines lithology, oil-bearing, and pore structure indices by using target sensitive parameters corresponding to the fractured section of a target horizontal well and a preset target relationship model. These indices are then input into a comprehensive interpretation model to obtain an interpretation of the reservoir type of the fractured section. By calculating the lithology, oil-bearing, and pore structure indices using the target relationship model, the characteristics of the reservoir can be more comprehensively reflected. Therefore, interpreting the reservoir type of a horizontal well based on multiple indices using the comprehensive interpretation model improves the accuracy of reservoir type interpretation and provides accurate data support for the segmentation and clustering of horizontal well fracturing.

[0069] In some possible implementations, the target relationship model includes a lithological relationship model, an oil-bearing relationship model, and a pore structure relationship model; the logging parameters include deep lateral resistivity, natural gamma ray, sonic transit time curve, and total hydrocarbon logging curve; and the geological parameters include clay content, porosity, water-bearing porosity, oil saturation, and permeability.

[0070] Based on the target sensitive parameters and the preset target relationship model, the lithology index value, oil-bearing index value, and pore structure index value are determined, including:

[0071] The lithology index value is determined based on the deep lateral resistivity, the natural gamma, the sonic transit time curve, the clay content, and the lithological relationship model.

[0072] The oil-bearing index value is determined based on the porosity, the water-bearing porosity, the oil saturation, the total hydrocarbon logging curve, and the oil-bearing relationship model.

[0073] The pore structure index value is determined based on the acoustic time difference curve, the porosity, the permeability, and the pore structure relationship model.

[0074] For example, lithological relationship models, oil-bearing relationship models, and pore structure relationship models can be pre-established. Specifically, a lithological relationship model is established based on sensitive parameters reflecting lithological characteristics, such as deep lateral resistivity, natural gamma ray, sonic transit time curves, and clay content; an oil-bearing relationship model is established based on sensitive parameters reflecting oil-bearing characteristics, such as porosity, water-bearing porosity, oil saturation, and total hydrocarbon logging curves; and a pore structure relationship model is established based on sensitive parameters reflecting pore structure characteristics, such as sonic transit time curves, porosity, and permeability.

[0075] For example, deep lateral resistivity is a logging parameter. Deep lateral resistivity uses the principle of focused current and constrains the current path of the main electrode by shielding the 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 formation fluids.

[0076] 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 naturally radioactive formations, and can be used to determine lithology and sedimentary environment. Different rocks contain different amounts of radioactive elements, resulting in varying intensities of natural gamma rays. For instance, rocks with higher clay content typically exhibit stronger natural gamma ray intensities.

[0077] For example, sonic transit time curves can be obtained through sonic transit time logging, reflecting the elastic properties of rocks and closely related to porosity, lithology, and compaction degree. Sonic transit time refers to the time difference between the emission and reception of a sonic wave. Sonic transit time curves help determine formation porosity, analyze rock permeability, and assist in assessing rock hardness, among other things. The shape of sonic transit time curves varies among different formations, and changes in the curves can reveal the rock structure, providing a basis for oil and gas reservoir assessment. Formations with high porosity or well-developed fractures typically have larger sonic transit times.

[0078] For example, clay content refers to the percentage of clay (clay-like materials) in the total volume of a formation, used to assess formation characteristics or material quality. Methods for detecting clay content include natural gamma logging, sonic transit-time logging, and neutron-density cross-plotting.

[0079] 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. The greater the total porosity of a reservoir, the larger the pore spaces within the rock. Porosity can be used to reflect the reservoir's storage capacity.

[0080] For example, water-bearing 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 represent the portion of the effective pore space in a rock or soil that is occupied by water. Water-bearing porosity is related to reservoir water content.

[0081] For example, oil saturation is the ratio of the oil-bearing volume in the effective pores of an oil reservoir to the effective pore volume of the rock. It can be used to represent the proportion of oil contained in the effective pore space of a rock or soil. Oil saturation can be used to reflect the oil-bearing nature of a reservoir. It can be obtained through Archie's Equation combined with resistivity data inversion, etc.

[0082] For example, the total hydrocarbon logging curve is the 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-bearing properties.

[0083] 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 means of nuclear magnetic resonance or core calibration models, where the core calibration model can be the Kozeny-Carman formula.

[0084] For example, logging parameters and geological parameters of the horizontal well fracturing section are obtained. The logging parameters include deep lateral resistivity, natural gamma ray, sonic transit time curve, and total hydrocarbon logging curve. The geological parameters include clay content, porosity, water porosity, oil saturation, and permeability.

[0085] For example, logging parameters and geological parameters are matched with three relational models. Specifically, the lithology index is calculated based on deep lateral resistivity, natural gamma ray, sonic transit time curve, clay content, and the first relational model; the oil-bearing index is calculated based on porosity, natural gamma ray, water-bearing porosity, oil saturation, total hydrocarbon logging curve, and the second relational model; and the pore structure index is calculated based on sonic transit time curve, porosity, and permeability, and the third relational model.

[0086] In this way, by calculating lithology index, oil-bearing index and pore structure index, the characteristics of different aspects of the reservoir can be more comprehensively reflected, which can improve the accuracy of reservoir type interpretation, make the reservoir interpretation method more applicable to the characteristics of horizontal well reservoirs, and improve the scientificity and reliability of the interpretation results.

[0087] In some possible implementations, the lithological relationship model is as follows:

[0088] ,

[0089] Where YXZS represents the lithology index; RT represents the deep lateral resistivity in Ω·m; GR represents the natural gamma ray in API; SH represents the clay content in %; and S represents the shale content in % of the total shale. AC-RT The envelope representing the acoustic transit time and the deep lateral resistivity is determined by the acoustic transit time curve and the deep lateral resistivity.

[0090] In well logging interpretation, the envelope of sonic transit time and deep lateral resistivity refers to the distribution boundary of sonic transit time and deep lateral resistivity in a cross-plot. It is used to define the numerical range of specific geological features, such as lithology and fluid type. The envelope can be used to delineate the response regions of different reservoir types.

[0091] Among them, the envelope of acoustic transit time and deep lateral resistivity can be used to distinguish different lithologies such as sandstone, mudstone, and carbonate rocks, as well as for fluid type identification, such as dividing the response regions of oil layers, gas layers, water layers, or dry layers.

[0092] For example, sonic transit time and deep lateral resistivity logging curves for the target well section can be obtained. Data preprocessing operations such as outlier removal and instrument response bias correction are then performed. Next, a distribution map of all data points is plotted with sonic transit time as the horizontal axis and deep lateral resistivity as the vertical axis. Algorithms such as K-means and DBSCAN are used to automatically divide the data into clusters, extract the boundaries of each cluster, calculate the density contour lines of the data distribution, and select the boundaries of high-density areas as the envelope surface.

[0093] For example, interactive cross-plot tools can be provided based on well logging interpretation software (such as Techlog and Petrel) to support manual drawing or automatic generation of envelope surfaces.

[0094] For example, the accuracy of the envelope can be verified using core data, well test results, or imaging logging. The envelope boundary can be iteratively optimized based on new well data to adapt to regional geological changes.

[0095] In this way, based on the above lithological relationship model, the lithological index value can be obtained through deep lateral resistivity, natural gamma, sonic transit time curve, and clay content.

[0096] In some possible implementations, the oil-bearing index value is determined based on the porosity, the water-bearing porosity, the oil saturation, the total hydrocarbon logging curve, and the oil-bearing relationship model, including:

[0097] Based on the total hydrocarbon logging curve, determine the total hydrocarbon base value and the total hydrocarbon anomaly amplitude value;

[0098] The oil content index value is determined based on the porosity, natural gamma, water-bearing porosity, oil saturation, total hydrocarbon value, total hydrocarbon anomaly amplitude value, and oil content relationship model.

[0099] For example, in a total hydrocarbon logging curve, the total hydrocarbon logging data remains stable within a certain value range with minimal fluctuations; this value is called the total hydrocarbon base value. When the real-time logging data rises to more than three times the total hydrocarbon base value, it is called a total hydrocarbon gas logging anomaly, which is manifested in the logging curve as an increase in the curve amplitude of more than three times. Once an anomaly appears in the total hydrocarbon curve, it indicates an increase in the total amount of alkane in the drilling fluid, which must be taken seriously by field logging technicians to prevent leakage of oil and gas formations. The total hydrocarbon anomaly amplitude value is the difference between the abnormal peak value of the total hydrocarbon logging curve in the oil and gas section and the total hydrocarbon base value, indicating the relative content of oil and gas in the formation.

[0100] For example, stable sections without oil and gas indications can be identified from the total hydrocarbon logging curve to determine the total hydrocarbon base value. The peak value of the total hydrocarbon logging curve in the oil and gas section can then be found, and the difference between the peak value and the total hydrocarbon base value can be calculated to obtain the total hydrocarbon anomaly amplitude value.

[0101] For example, the total hydrocarbon base value and total hydrocarbon anomaly amplitude value are combined with parameters such as porosity, natural gamma, water-bearing porosity, and oil saturation to calculate the oil-bearing index value through an oil-bearing relationship model. The total hydrocarbon base value and anomaly amplitude value provide quantitative information on oil and gas content in the model, and together with other geological parameters, they more comprehensively and accurately reflect the oil-bearing characteristics of the reservoir.

[0102] In some possible implementations, the oil content relationship model is as follows:

[0103] ,

[0104] Where HYZS represents the oil content index; φ represents porosity, in percentage terms. w This indicates the water-bearing porosity, expressed in %; C 异 The value represents the total hydrocarbon anomaly, expressed as a percentage, determined by the total hydrocarbon logging curve; C 基 This represents the total hydrocarbon base value, expressed as a percentage, determined through the total hydrocarbon logging curve; S o Indicates oil saturation, expressed in percent.

[0105] In this way, based on the above oil content relationship model, the oil content index value can be obtained through porosity, natural gamma, water porosity, oil saturation, total hydrocarbon value, and total hydrocarbon anomaly amplitude value.

[0106] In some possible implementations, the pore structure relationship model is as follows:

[0107] ,

[0108] Where KXJGZS represents the porosity index; AC represents the acoustic transit time, in μs / m, determined by the acoustic transit time curve; AC min φ represents the minimum value of the acoustic transit time curve, in μs / m; φ represents porosity, in %; and K represents permeability, in mD.

[0109] In this way, based on the above pore structure relationship model, the oil content index value can be obtained through the acoustic time difference curve, porosity, and permeability.

[0110] In some possible implementations, the comprehensive interpretation model is a three-dimensional chart, in which the three-dimensional coordinate system corresponds to the lithology index, oil-bearing index, and pore structure index, respectively, and different spatial regions of the three-dimensional chart correspond to different reservoir types of horizontal wells.

[0111] For example, a 3D chart is a 3D visualization tool that uses three axes of a 3D coordinate system to represent lithology index, oil-bearing index, and pore structure index, with different spatial regions corresponding to different reservoir types. Reservoir type refers to different types of fluids present in the reservoir, such as oil layer, gas layer, water layer, and oil-water co-containment.

[0112] like Figure 2 As shown, the lithology index, oil-bearing index, and pore structure index of different fracturing sections can be represented in a three-dimensional coordinate system. Specifically, the red fracturing section corresponds to an oil-bearing layer, the black fracturing section to a dry layer, the pink fracturing section to a poor-oil layer, the light blue fracturing section to an oil-water co-containment layer, and the dark blue fracturing section to an oil-water-bearing layer.

[0113] In this way, by comprehensively considering three key factors—lithology, oil content, and pore structure—the 3D map can more fully reflect the characteristics of the reservoir, thereby improving the accuracy of reservoir type interpretation. At the same time, the 3D map provides an intuitive visualization tool, allowing complex reservoir type distributions to be displayed visually. By observing the location and distribution of different areas in the 3D map, the fluid properties of the reservoir can be quickly understood.

[0114] In some possible implementations, the reservoir type of the horizontal well includes oil-bearing layers, poor-oil-bearing layers, oil-water co-containing layers, oil-water-bearing layers, water-bearing layers, and dry layers. The testing standards for the reservoir type of the horizontal well are determined in advance using the testing standards for vertical wells. These testing standards include:

[0115] The oil production contribution rate of the oil layer is >8%, and the water production contribution rate of the oil layer is <10%.

[0116] 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 <10%.

[0117] The oil-water co-layer's oil production contribution rate is 1%-5%, and the water production contribution rate of the oil-water co-layer is <10%.

[0118] The oil-bearing water layer has an oil production contribution rate of 0%-1% and a water production contribution rate of <10%.

[0119] The oil production contribution rate of the water layer is 0%, and the water production contribution rate of the water layer is >10%;

[0120] The oil production contribution rate and water production contribution rate of the dry layer are both 0%.

[0121] For example, the different types and contents of fluids present in reservoirs of different reservoir types in horizontal wells can be pre-determined according to the testing standards for vertical wells. The reservoir types in horizontal wells can be ranked from highest to lowest based on their oil production contribution rate, including oil-bearing layers, poor-oil-bearing layers, oil-water co-existing layers, oil-water-bearing layers, water-bearing layers, and dry layers.

[0122] For example, the oil production contribution rate and water production contribution rate of a horizontal well can be determined using fracturing tracing technology. These contribution rates are dynamic data of horizontal well production after fracturing, are variable, and their acquisition is costly. In contrast, logging parameters and geological parameters are static data, less expensive, and more convenient for interpreting reservoir types.

[0123] In this method, a tracer is added to the fracturing fluid, and the concentration change (light intensity value) of the tracer in the flowback fluid is intensively sampled and monitored during the flowback process. The light intensity value of the tracer is proportional to the production volume of the product fluid, and its physicochemical properties in the product fluid are stable. Utilizing its fixed excitation and emission spectra, the oil and product filtrate is placed in a cuvette, and the excitation and emission wavelengths are set. Samples are intensively collected during the flowback process to monitor the concentration change of the tracer in the flowback fluid, thereby determining the production status and contribution of each stage.

[0124] 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. Interpretive standards for reservoir types can be established based on dynamic data, and a target relationship model established based on static data can be used to determine the reservoir type. Then, the actual reservoir type determined by the corresponding dynamic data can be used to optimize the target relationship model.

[0125] For example, the reservoir type of horizontal wells can be pre-classified based on accurate dynamic data, namely oil production contribution rate and water production contribution rate. After establishing the target relationship model, the classification result can be obtained through static data, namely logging parameters and geological parameters, as well as the target relationship model. The dynamic data classification result can be used as a standard to compare with the static data classification result, thereby verifying the effectiveness and reliability of the target relationship model.

[0126] For example, vertical well testing can provide relatively accurate data on production capacity and fluid properties, including oil production and water cut. As shown in Table 1 below, by analyzing the results of vertical well testing, quantitative standards for different reservoir types in horizontal wells can be established.

[0127] Table 1. Standards for oil testing in vertical wells and horizontal wells for shale oil.

[0128]

[0129] Then, as shown in Table 2 below, the target oil testing results of different fractured sections of the horizontal well can be determined according to the established oil testing standards for the fractured sections of the horizontal well.

[0130] Table 2. Oil testing results determined according to horizontal well testing standards.

[0131] 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

[0132] Then, based on the target oil test results, correlation analysis can be performed on the target parameters to determine the sensitive parameters that are sensitive to oil-water characteristics. A target relationship model can then be constructed based on these sensitive parameters. During the construction of the target relationship model, the model can be continuously verified and optimized through extensive correlation analysis of target oil test results and target parameters until the final target relationship model is obtained.

[0133] In this way, reservoir types can be classified by the testing standards established by the dynamic parameters of horizontal wells. This provides a clear quantitative standard for the interpretation of reservoir types based on static parameters of horizontal wells, ensuring the consistency and comparability of the interpretation results and reducing errors caused by subjective judgment.

[0134] In some possible implementations, after inputting the lithology index value, the oil-bearing index value, and the pore structure index value into a comprehensive interpretation model to obtain the interpretation result of the reservoir type of the fracturing section, the method further includes:

[0135] Based on the interpretation results of the reservoir types corresponding to the multiple fractured sections in the target horizontal well, the multiple fractured sections are segmented and clustered.

[0136] For example, the interpretation result is obtained by comprehensively analyzing parameters such as reservoir lithology index, oil-bearing index, and pore structure index to determine the reservoir type. Reservoir types include oil-bearing layers, poor-oil-bearing layers, oil-water co-containing layers, oil-water-bearing layers, water-bearing layers, and dry layers.

[0137] For example, in horizontal well fracturing operations, based on the interpretation results of reservoir fluid types, multiple fracturing sections of the horizontal well are further divided into different segments and clusters to enable more precise fracturing and improve oil and gas recovery.

[0138] For example, based on lithology, oil content, and pore structure indices, a comprehensive interpretation model can be used to obtain the reservoir fluid type interpretation results for each fracturing section. Based on these interpretation results, a decision can be made on how to segment and cluster these sections. For instance, sections interpreted as oil-bearing or poor-oil-bearing layers can be prioritized for fracturing to increase oil and gas production; sections interpreted as oil-water co-layers or oil-water-bearing layers can have fracturing parameters adjusted and the stimulation scheme optimized based on the oil and water content ratio; and sections interpreted as water-bearing or dry layers can have fracturing operations 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 the horizontal well, providing guidance for subsequent fracturing operations.

[0139] In this way, by segmenting and clustering the reservoir based on 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 segments, improve fracturing efficiency, avoid unnecessary modification of ineffective or inefficient segments, and improve resource utilization efficiency.

[0140] This invention is the first to establish a multi-parameter fluid interpretation method that integrates logging and well logging for shale oil horizontal wells, and for the first time establishes a comprehensive splitting coefficient based on static production data and static geological parameters, thereby achieving a refined interpretation of reservoir fluids in shale oil horizontal wells.

[0141] In one instance, for the Nth section of a horizontal section of a shale oil well, according to the reservoir interpretation model of the present invention, logging and well logging data of the fractured section cluster of the horizontal well were taken, and the lithology index, oil-bearing index and pore structure index of the section cluster were calculated. The reservoir type was determined by three-dimensional cross plot, and the actual reservoir type was determined based on the oil production contribution rate and water production contribution rate of the horizontal well. Finally, the reservoir type interpretation accuracy of the three-dimensional cross plot was determined to be 81.25%, which is 12.4% higher than the interpretation accuracy of 73% of the two-dimensional parameter interpretation method.

[0142] This invention, among numerous fluid interpretation methods, is the first to consider the dynamic production data after cluster fracturing in horizontal well sections. It effectively combines static geological data with post-fracturing dynamic production data, and fully incorporates the characteristics of unconventional lithological reservoirs. Lithology controls pore structure, and pore structure controls fluid occurrence. Compared to cross-plot methods, this invention improves accuracy; compared to mathematical analysis methods, it is simpler to operate and easier to implement; and compared to nuclear magnetic resonance logging, it reduces costs and is easier to promote and apply in the field.

[0143] Furthermore, the term “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 compared to other aspects or designs. Rather, the use of the term “exemplary” is intended to present the concept 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 A or B” is intended to mean any of the natural inclusive arrangements. That is, “X applies A or B” satisfies any of the foregoing instances if X applies A; X applies B; or both X applies A and B. Additionally, unless otherwise specified or clear from the context to refer to the singular form, the articles “a” and “an” as used in this application and the appended claims are generally understood to mean “one or more.”

[0144] Similarly, although the 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 invention includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components described above (e.g., elements, resources, etc.), unless otherwise indicated, the terminology used to describe such components is intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if structurally not equivalent to the disclosed structure. Furthermore, although specific features of the 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. Moreover, with regard to the terms “comprising,” “owning,” “having,” “having,” or variations thereof as used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term “including.”

[0145] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the appended claims.

[0146] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method of horizontal well reservoir interpretation, characterized in that, 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 relationship model, wherein the lithology index value is used to represent the lithology characteristics of the reservoir, the oiliness index value is used to represent the oiliness characteristics of the reservoir, and the pore structure index value is used to represent the pore structure characteristics of the reservoir; 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 fracturing section; the target relationship model comprises a lithology relationship model, an oiliness relationship model and a pore structure relationship model, the logging parameters comprise deep lateral resistivity, natural gamma, acoustic time difference curve and total hydrocarbon logging curve, and the geological parameters comprise shale content, porosity, water-bearing porosity, oil saturation and permeability; 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 relationship model, comprising: determining the lithology index value according to the deep lateral resistivity, the natural gamma, the acoustic time difference curve, the shale content and the lithology relationship model; determining the oiliness index value according to the porosity, the water-bearing porosity, the oil saturation, the total hydrocarbon logging curve and the oiliness relationship model; determining the pore structure index value according to the acoustic time difference curve, the porosity, the permeability and the pore structure relationship model; determining the oiliness index value according to the porosity, the water-bearing porosity, the oil saturation, the total hydrocarbon logging curve and the oiliness relationship model, comprising: determining a total hydrocarbon base value and a total hydrocarbon abnormal amplitude value according to the total hydrocarbon logging curve; determining the oiliness index value according to the porosity, the natural gamma, the water-bearing porosity, the oil saturation, the total hydrocarbon base value, the total hydrocarbon abnormal amplitude value and the oiliness relationship model.

2. The method of claim 1, wherein, The lithology relationship model is: ， wherein YXZS represents a lithology index; RT represents a deep lateral resistivity; GR represents a natural gamma; SH represents shale content; S AC-RT represents an envelope surface of acoustic traveltime and deep lateral resistivity, determined by the acoustic traveltime curve and the deep lateral resistivity.

3. The method of claim 1, wherein, The oiliness relationship model is: ， where HYZS represents the oil-bearing index; φ represents the porosity; φ w represents the water-bearing porosity; C 异 represents the total hydrocarbon abnormal amplitude value, determined by the total hydrocarbon logging curve; C 基 represents the total hydrocarbon base value, determined by the total hydrocarbon logging curve; S o represents the oil-bearing saturation.

4. The method of claim 1, wherein, The pore structure relationship model is: ， wherein KXJGZS represents the pore structure index; AC represents the acoustic traveltime, determined by the acoustic traveltime curve; AC min represents the minimum value of the acoustic traveltime curve; φ represents the porosity; and K represents the permeability.

5. The method according to any one of claims 1 to 4, characterized in that, The comprehensive interpretation model is a three-dimensional graph, and the three-dimensional coordinates of the three-dimensional graph correspond to the lithology index, the oiliness index and the pore structure index respectively, and different spatial regions of the three-dimensional graph correspond to different reservoir types of the horizontal well.

6. The method according to any one of claims 1 to 4, characterized in that, The reservoir types of the horizontal well comprise oil layer, poor oil layer, oil-water layer, oil-water layer, water layer and dry layer, and the test oil standards of the reservoir types of the horizontal well are determined in advance by the vertical well test oil standards, and the test oil standards comprise: the oil layer has an oil production contribution rate of >8% and a water production contribution rate of <10%; the poor oil layer has an oil production contribution rate of 5%-8% and a water production contribution rate of <10%; the oil-water layer has an oil production contribution rate of 1%-5% and a water production contribution rate of <10%; the oil-water layer has an oil production contribution rate of 0%-1% and a water production contribution rate of <10%. The oil production contribution rate of the water layer is 0%, and the water production contribution rate of the water layer is >10%; The oil production contribution rate and the water production contribution rate of the dry layer are both 0%.

7. The method according to any one of claims 1 to 4, characterized in that, After inputting the lithology index value, the oil-bearing 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 comprises: According to the interpretation result of the reservoir type corresponding to each of the plurality of fracturing sections in the target horizontal well, the plurality of fracturing sections are segmented and clustered.

Images