A method, device, equipment, medium and product for identifying a reservoir fluid type

Abstract

This application provides a method, apparatus, equipment, medium, and product for identifying reservoir fluid types, relating to the field of oil and gas extraction. The method establishes a high-precision numerical model based on instrument and environmental parameters, uses particle transport simulation to generate a standardized energy spectrum database covering complex geological conditions, extracts key parameters characterizing the pure fluid response through spectral analysis, establishes a quantitative dynamic relationship between these parameters and water saturation and fluid type, and forms an adaptive intelligent discrimination chart. This eliminates the influence of wellbore environmental interference and geological heterogeneity, achieving fully automated interpretation from data acquisition to fluid identification. It solves the problem of low identification efficiency caused by parameter distortion and model rigidity in existing technologies, and improves the accuracy of identifying complex reservoir fluid types.

Description

Technical Field

[0001] This application relates to the field of oil and gas extraction, and in particular to a method, apparatus, equipment, medium and product for identifying reservoir fluid types. Background Technology

[0002] Reservoir fluid type identification refers to the technique of determining the type of fluid in formation pores through well logging technology. In oil and gas resource development, due to factors such as strong reservoir heterogeneity, complex fluid systems and wellbore environment, accurate identification of reservoir fluid type is directly related to reserve assessment, development plan optimization and development efficiency, and is crucial for the efficient development of oil and gas resources.

[0003] In existing technologies, pulsed neutron sources are used to excite formation elements, causing neutrons to undergo inelastic scattering or capture reactions with elements such as carbon, oxygen, and chlorine. The generated gamma spectrum is collected by a detector, and key parameters such as the carbon-oxygen yield ratio, chlorine yield, and macroscopic capture cross section are analyzed and extracted. Based on the characteristic differences of the parameters corresponding to different fluid types, the reservoir fluid type in the formation is determined.

[0004] However, existing technologies suffer from low efficiency in reservoir fluid type identification. Current technologies rely on conventional logging parameters for reservoir fluid type identification, making these parameters susceptible to interference from diverse environmental factors. The models struggle to adapt to complex geological conditions, leading to discrepancies between key parameters and actual formation fluid characteristics. This reduces the reliability of fluid type identification results and results in low efficiency in reservoir fluid type identification. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and product for identifying reservoir fluid types, in order to solve the problem of low efficiency in reservoir fluid type identification in the prior art.

[0006] In a first aspect, embodiments of this application provide a method for identifying reservoir fluid types, including:

[0007] The system acquires multiple physical parameters of a preset lithological scanning logging instrument and multiple formation condition parameters of each of the preset environments; wherein, the multiple physical parameters are used to represent the working characteristics of the instrument, and the multiple formation condition parameters are used to represent the formation and wellbore characteristics of each of the environments, and the multiple formation condition parameters include water saturation.

[0008] Based on the multiple physical parameters and the multiple formation condition parameters, a wellbore and formation model is constructed; wherein, the wellbore and formation model is used as a computational carrier for particle transport simulation;

[0009] Based on the wellbore and formation model, particle transport simulation is performed to obtain multiple formation energy spectrum data for each environment; wherein, the multiple formation energy spectrum data are used to represent the characteristics of inelastic scattering of neutrons with preset elements in the formation and the element trapping characteristics;

[0010] Based on the multiple formation energy spectrum data, spectral stripping analysis and formula derivation are performed to obtain multiple first analytical parameters and cross plot formulas; wherein, the multiple first analytical parameters are used to represent the response signal characteristics of multiple preset fluid types in the formation, and the cross plot formulas are used to represent the relationship between the multiple first analytical parameters, the water saturation and the multiple fluid types;

[0011] A cross-plot is drawn based on the plurality of first analytical parameters and the cross-plot formula, and the fluid type is determined based on the cross-plot to obtain the fluid type of each environment; wherein, the fluid type of each environment is used to provide data support for oil and gas development in each environment.

[0012] In one possible design, the step of performing spectral stripping analysis and formula derivation based on the multiple formation energy spectrum data to obtain multiple first analytical parameters and cross-plot formulas includes:

[0013] The multiple formation energy spectrum data are stripped and analyzed to obtain the multiple first analytical parameters;

[0014] The cross-plot formula is derived by deriving the formula based on the multiple first analytical parameters, the water saturation, and the multiple fluid types.

[0015] In one possible design, the step of drawing the intersection plot based on the plurality of first analytical parameters and the intersection plot formula includes:

[0016] The plurality of first analytical parameters are corrected to obtain a plurality of second analytical parameters, and the difference between each first analytical parameter and the second analytical parameter corresponding to each first analytical parameter is calculated.

[0017] The intersection plot is drawn based on the intersection plot formula and multiple differences.

[0018] In one possible design, the correction of the plurality of first analytical parameters to obtain a plurality of second analytical parameters includes:

[0019] The wellbore correction is performed on the plurality of first analytical parameters to obtain a plurality of third analytical parameters; wherein, the wellbore correction is used to eliminate the gamma measurement signal deviation caused by the actual wellbore diameter deviating from the preset standard size;

[0020] The drilling fluid salinity is corrected for the plurality of third analytical parameters to obtain the plurality of second analytical parameters; wherein, the drilling fluid salinity correction is used to eliminate the interference caused by the change in salt concentration in the drilling fluid on the formation capture section measurement value.

[0021] In one possible design, constructing the wellbore and formation model based on the plurality of physical parameters and the plurality of formation condition parameters includes:

[0022] An instrument model is constructed based on the aforementioned physical parameters; wherein the instrument model is used to represent the structural features, neutron source characteristics, and detection response properties of the instrument.

[0023] Based on the instrument model and the multiple formation condition parameters, the wellbore and formation model are constructed.

[0024] In one possible design, after determining the fluid type based on the cross-plot to obtain the fluid type of each environment, the process further includes:

[0025] Obtain the measured fluid type for each of the aforementioned environments;

[0026] The accuracy rate of fluid type determination of the cross plot is calculated based on the fluid type of each environment and the measured fluid type of each environment.

[0027] In response to the fluid type determination accuracy being lower than a preset accuracy threshold, the parameters of the cross plot are optimized to obtain an optimized cross plot.

[0028] Secondly, embodiments of this application provide a reservoir fluid type identification device, comprising:

[0029] The first acquisition module is used to acquire multiple physical parameters of a preset lithological scanning logging instrument and multiple formation condition parameters of each of the preset environments; wherein, the multiple physical parameters are used to represent the working characteristics of the instrument, the multiple formation condition parameters are used to represent the formation and wellbore characteristics of each environment, and the multiple formation condition parameters include water saturation.

[0030] A construction module is used to construct a wellbore and formation model based on the multiple physical parameters and the multiple formation condition parameters; wherein, the wellbore and formation model is used as a computational carrier for particle transport simulation;

[0031] The simulation module is used to perform particle transport simulation based on the wellbore and formation model to obtain multiple formation energy spectrum data for each environment; wherein, the multiple formation energy spectrum data are used to represent the characteristics of inelastic scattering of neutrons with preset elements in the formation and the characteristics of element capture.

[0032] The analysis module is used to perform spectral stripping analysis and formula derivation based on the multiple formation energy spectrum data to obtain multiple first analytical parameters and cross plot formulas; wherein, the multiple first analytical parameters are used to represent the response signal characteristics of multiple preset fluid types in the formation, and the cross plot formulas are used to represent the relationship between the multiple first analytical parameters, the water saturation and the multiple fluid types;

[0033] The judgment module is used to draw a cross plot based on the plurality of first analytical parameters and the cross plot formula, and to judge the fluid type based on the cross plot to obtain the fluid type of each environment; wherein, the fluid type of each environment is used to provide data support for oil and gas development in each environment.

[0034] In one possible design, the parsing module includes:

[0035] The analysis unit is used to perform spectral stripping analysis on the multiple formation energy spectrum data to obtain the multiple first analysis parameters;

[0036] The derivation unit is used to derive the cross-plot formula based on the plurality of first analytical parameters, the water saturation, and the plurality of fluid types.

[0037] In one possible design, the determination module includes:

[0038] The correction unit is used to correct the plurality of first analytical parameters to obtain a plurality of second analytical parameters, and to calculate the difference between each first analytical parameter and the second analytical parameter corresponding to each first analytical parameter.

[0039] A drawing unit is used to draw the intersection plot based on the intersection plot formula and multiple differences.

[0040] In one possible design, the correction unit includes:

[0041] A first calibration component is used to perform wellbore calibration on the plurality of first analytical parameters to obtain a plurality of third analytical parameters; wherein, the wellbore calibration is used to eliminate the gamma measurement signal deviation caused by the actual wellbore diameter deviating from the preset standard size;

[0042] The second correction component is used to correct the drilling fluid salinity of the plurality of third analytical parameters to obtain the plurality of second analytical parameters; wherein, the drilling fluid salinity correction is used to eliminate the interference caused by the change in salt concentration in the drilling fluid on the formation capture section measurement value.

[0043] In one possible design, the building block includes:

[0044] The first construction unit is used to construct an instrument model based on the plurality of physical parameters; wherein the instrument model is used to represent the structural features, neutron source characteristics and detection response properties of the instrument;

[0045] The second construction unit is used to construct the wellbore and formation model based on the instrument model and the multiple formation condition parameters.

[0046] In one possible design, the reservoir fluid type identification device further includes:

[0047] The second acquisition module is used to acquire the measured fluid type of each of the environments;

[0048] The calculation module is used to calculate the accuracy of fluid type determination of the cross plot based on the fluid type of each environment and the measured fluid type of each environment.

[0049] An optimization module is used to optimize the parameters of the cross plot in response to the fact that the accuracy of the fluid type determination is lower than a preset accuracy threshold, so as to obtain an optimized cross plot.

[0050] Thirdly, embodiments of this application provide an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0051] The memory stores computer-executed instructions;

[0052] When the processor executes computer execution instructions stored in the memory, it is used to implement the reservoir fluid type identification method as described in any of the first aspects.

[0053] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the reservoir fluid type identification method as described in any of the first aspects.

[0054] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, is used to implement the reservoir fluid type identification method as described in any of the first aspects.

[0055] This application provides a method, apparatus, device, medium, and product for identifying reservoir fluid types. By establishing a high-precision numerical model based on instrument and environmental parameters, and using particle transport simulation to generate a standardized energy spectrum database covering complex geological conditions, key parameters characterizing the pure fluid response are extracted through spectral analysis. A quantitative dynamic relationship between these parameters and water saturation and fluid type is established, forming an adaptive intelligent discrimination chart. This eliminates the influence of wellbore environmental interference and geological heterogeneity, and realizes fully automated interpretation from data acquisition to fluid identification. It solves the problem of low identification efficiency caused by parameter distortion and model rigidity in existing technologies, and improves the accuracy of identifying complex reservoir fluid types. Attached Figure Description

[0056] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0057] Figure 1 This is a schematic diagram illustrating an application scenario of the reservoir fluid type identification method provided in the embodiments of this application;

[0058] Figure 2 A flowchart illustrating the reservoir fluid type identification method provided in this application embodiment. Figure 1 ;

[0059] Figure 3 Provided for the embodiments of this application — Intersection diagram;

[0060] Figure 4 Provided for the embodiments of this application — Intersection diagram;

[0061] Figure 5 Provided for the embodiments of this application — Intersection diagram;

[0062] Figure 6 The corrected wellbore provided for the embodiments of this application , and Calculation results Figure 1 ;

[0063] Figure 7 The corrected wellbore provided for the embodiments of this application , and Calculation results Figure 2 ;

[0064] Figure 8 The corrected wellbore provided for the embodiments of this application , and Calculation results Figure 3 ;

[0065] Figure 9 The corrected wellbore provided for the embodiments of this application , and Calculation results Figure 4 ;

[0066] Figure 10 The corrected drilling fluid salinity for correcting well diameter provided in the embodiments of this application , and Calculation results Figure 1 ;

[0067] Figure 11 The corrected drilling fluid salinity for corrected wellbore diameter provided in the embodiments of this application , and Calculation results Figure 2 ;

[0068] Figure 12 The corrected drilling fluid salinity for correcting well diameter provided in the embodiments of this application , and Calculation results Figure 3 ;

[0069] Figure 13 The corrected drilling fluid salinity for correcting well diameter provided in the embodiments of this application , and Calculation results Figure 4 ;

[0070] Figure 14 The actual identification of fluid properties in the formation provided in the embodiments of this application — , — as well as — Intersection Figure 1 ;

[0071] Figure 15 The actual identification of fluid properties in the formation provided in the embodiments of this application — , — as well as — Intersection Figure 2 ;

[0072] Figure 16 The actual identification of fluid properties in the formation provided in the embodiments of this application — , — as well as — Intersection Figure 3 ;

[0073] Figure 17 The diagrams of the lithological scanning logging apparatus, wellbore, and formation model provided in the embodiments of this application are shown.

[0074] Figure 18 A flowchart illustrating the reservoir fluid type identification method provided in this application embodiment. Figure 2 ;

[0075] Figure 19 A schematic diagram of the structure of the reservoir fluid type identification device provided in the embodiments of this application;

[0076] Figure 20 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application.

[0077] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0078] 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 numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0079] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply difference. It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0080] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation. The embodiments of this application do not specifically limit this. In addition, the method, apparatus, device, medium, and product for identifying reservoir fluid types provided in the embodiments of this application are only examples. A method, apparatus, device, medium, and product for identifying reservoir fluid types may also include more or less content.

[0081] To facilitate a clear description of the technical solutions in the embodiments of this application, some terms and technologies involved in the embodiments of this application will be briefly introduced below:

[0082] Chlorine production: This is a quantitative parameter corresponding to the intensity of gamma-ray signals related to chlorine collected by the detector after neutrons undergo nuclear reactions with chlorine in the formation rocks and fluids during well logging. Its value directly reflects the relative content distribution of chlorine in the formation and is an important indicator for indirectly judging the formation mineralization level.

[0083] Macroscopic capture cross section: It is a comprehensive physical quantity that characterizes the overall neutron capture ability of the formation medium. It is obtained by weighted summation of the microscopic capture cross section of various nuclides in the formation and the corresponding nuclide number density. Its numerical variation is closely related to the content of elements with high capture cross sections, such as chlorine and boron, in the formation. It can reflect the fluid properties and lithological characteristics of the formation.

[0084] Carbon-oxygen production ratio: This is the ratio of the production of carbon and oxygen in the formation, obtained by detecting the characteristic gamma rays produced by carbon and oxygen elements under neutron excitation in the formation using logging instruments and processing the signals. The difference in the content of carbon and oxygen elements in different reservoir fluids will cause this ratio to show significant differences, and it is one of the core parameters for identifying reservoir fluid types.

[0085] 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.

[0086] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0087] To clearly understand the technical solution of this application, the solutions of existing technologies will first be described in detail. In oil and gas resource development, the accurate identification of reservoir fluid types is directly related to reserve assessment, development scheme optimization, and development efficiency, and is crucial for the efficient development of oil and gas resources.

[0088] In existing technologies, pulsed neutron sources are used to excite formation elements, causing neutrons to undergo inelastic scattering or capture reactions with elements such as carbon, oxygen, and chlorine. The resulting gamma spectrum is then collected by a detector, and key parameters such as the carbon-oxygen yield ratio, chlorine yield, and macroscopic capture cross-section are extracted. The reservoir fluid type is then determined based on the characteristic differences of these parameters corresponding to different fluid types. However, existing technologies rely on conventional logging parameters for reservoir fluid type identification. This makes the identification parameters susceptible to interference from various environmental factors, and the models struggle to adapt to complex geological conditions. Consequently, key parameters deviate from the actual fluid characteristics of the formation, reducing the reliability of the fluid type identification results and leading to low efficiency in reservoir fluid type identification. Therefore, existing technologies suffer from low efficiency in reservoir fluid type identification.

[0089] Therefore, addressing the low efficiency of reservoir fluid type identification in existing technologies, this research found that to solve this problem, a high-precision numerical model can be constructed, and a standardized formation energy spectrum database can be generated using particle transport simulation. This allows for the extraction of key parameters characterizing pure fluid response and the establishment of an adaptive discriminant chart, eliminating environmental interference and geological heterogeneity, thus achieving accurate identification of reservoir fluid types. ① Precise wellbore and formation numerical models can be established, and numerical simulations can be performed using basic principles such as particle transport to generate principle gamma-ray energy spectrum data covering various geological and wellbore conditions. This is equivalent to establishing a pure and complete standard spectrum database, providing a high-quality data source unaffected by random noise in the field for subsequent feature extraction. ② The original energy spectrum data can be analyzed and stripped to eliminate the contributions of lithological framework and wellbore environment, extracting core characteristic parameters directly related only to reservoir fluid type. ③ A quantitative response relationship can be established between fluid identification characteristic parameters and key formation state variables such as water saturation, forming a discriminant function or chart that can be dynamically adjusted according to formation conditions. This enables fluid type identification to automatically adapt to specific geological backgrounds, improving the model's generalization ability and identification accuracy in different reservoirs.

[0090] Specifically, a numerical physical model that accurately reflects the wellbore environment, formation lithology, and fluid distribution can be established. The model can be forward-simulated using basic principles such as particle transport to generate a standardized principle response spectrum library covering expected fluid types and saturation changes. Through advanced spectral analysis and machine learning techniques, the essential characteristic parameters most sensitive to fluid types and their discrimination boundaries can be automatically learned and extracted from this spectrum library to form a dynamic identification chart or classifier that can adapt to the model's preset geological conditions. The actual logging data can be matched and interpreted with this dynamic identification chart to achieve efficient and reliable reservoir fluid type identification.

[0091] This application discloses a method, apparatus, device, medium, and product for identifying reservoir fluid types. By establishing a high-precision numerical model based on instrument and environmental parameters, using particle transport simulation to generate a standardized energy spectrum database covering complex geological conditions, extracting key parameters characterizing the pure fluid response through spectral analysis, establishing a quantitative dynamic relationship between these parameters and water saturation and fluid type, and forming an adaptive intelligent discrimination chart, the application eliminates the influence of wellbore environmental interference and geological heterogeneity. It achieves fully automated interpretation from data acquisition to fluid identification, solves the problem of low identification efficiency caused by parameter distortion and model rigidity in existing technologies, and improves the accuracy of identifying complex reservoir fluid types.

[0092] Based on the above-mentioned inventive discovery, the technical solution of this application is proposed.

[0093] The following describes the application scenarios of the reservoir fluid type identification method provided in the embodiments of the present invention. Figure 1 This is a schematic diagram illustrating an application scenario of the reservoir fluid type identification method provided in this application embodiment. For example... Figure 1 As shown, this application scenario includes a mobile terminal 101 and a server 102. The mobile terminal 101 collects multiple physical parameters and multiple formation condition parameters, and sends them to the server 102. The server 102 constructs a wellbore and formation model based on the multiple physical parameters and formation condition parameters. The server 102 performs particle transport simulation based on the wellbore and formation model to obtain multiple formation energy spectrum data for each environment. The server 102 performs spectral analysis and formula derivation based on the multiple formation energy spectrum data to obtain multiple first analytical parameters and cross-plot formulas. The server 102 draws cross-plots based on the multiple first analytical parameters and cross-plot formulas, and determines the fluid type based on the cross-plots to obtain the fluid type for each environment.

[0094] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0095] Figure 2 A flowchart illustrating the reservoir fluid type identification method provided in this application embodiment. Figure 1 .like Figure 2 As shown, in this embodiment, the execution entity of this invention is a server. Therefore, the reservoir fluid type identification method provided in this embodiment includes the following steps:

[0096] S201. Obtain multiple physical parameters of the preset lithological scanning logging instrument and multiple formation condition parameters of each environment in multiple preset environments; wherein, the multiple physical parameters are used to represent the working characteristics of the instrument, the multiple formation condition parameters are used to represent the formation and wellbore characteristics of each environment, and the multiple formation condition parameters include water saturation.

[0097] Specifically, multiple physical parameters, including detector response characteristics and neutron source emission parameters, can be obtained by consulting the manufacturer's technical specifications and calibration data of the lithology scanning logging instrument. Multiple formation condition parameters for various environments, including water saturation, formation lithology, porosity, permeability, and wellbore size, can be obtained by collecting geological exploration results and conventional logging data of the target area. This step is used to provide realistic basic data for the subsequent construction of wellbore and formation models, ensuring that the model can accurately reflect the working characteristics of the instrument and the real characteristics of the formation and wellbore, and providing reliable data support for subsequent particle transport simulation, energy spectrum analysis, and fluid type determination.

[0098] S202. Based on multiple physical parameters and multiple formation condition parameters, construct a wellbore and formation model; wherein, the wellbore and formation model is used as the computational carrier for particle transport simulation.

[0099] Specifically, a professional numerical simulation platform can be used to divide the model into grids according to the actual formation layer structure and wellbore geometry. The acquired physical parameters and formation condition parameters are then assigned to the corresponding grid cells in the model. The distribution patterns and relationships of each parameter in the model are clarified. By setting boundary conditions and initial conditions, the model is debugged and confirmed, and a three-dimensional wellbore and formation model is constructed. This step is used to build the computing carrier required for particle transport simulation, accurately replicate the working characteristics of the instrument and the real features of the formation and wellbore, provide a realistic computing environment for particle transport simulation, and ensure the smooth progress of the simulation process and the accuracy of the simulation results.

[0100] S203. Based on the wellbore and formation model, particle transport simulation is performed to obtain multiple formation energy spectrum data for each environment; among them, multiple formation energy spectrum data are used to represent the characteristics of inelastic scattering of neutrons with preset elements in the formation and the characteristics of element capture.

[0101] Specifically, the Monte Carlo algorithm can be combined with professional particle transport simulation tools. Based on the correlation between parameter distributions in the wellbore and formation model, simulation parameters such as emission energy and emission intensity of the neutron source can be set. The physical processes and cross-sectional data of inelastic scattering and capture reactions between neutrons and preset elements such as carbon, oxygen, and chlorine in the formation can be defined. The trajectory and interaction process of neutrons in the wellbore and formation can be simulated. The gamma photon counts of different energy ranges received by the detector can be recorded to generate multiple formation energy spectrum data including inelastic scattering energy spectrum and capture energy spectrum. This step is used to obtain basic data that can characterize the interaction characteristics between neutrons and formation elements, present the energy spectrum response characteristics of formation fluids under different environments, and provide direct data input for subsequent spectral analysis, extraction of first analytical parameters, and derivation of cross-plot formulas.

[0102] S204. Based on multiple formation energy spectrum data, perform spectral stripping analysis and formula derivation to obtain multiple first analytical parameters and cross plot formulas; wherein, the multiple first analytical parameters are used to represent the response signal characteristics of multiple preset fluid types in the formation, and the cross plot formulas are used to represent the relationship between multiple first analytical parameters, water saturation and multiple fluid types.

[0103] Specifically, background removal and characteristic peak separation methods can be used to extract first analytical parameters representing the response signal characteristics of multiple pre-defined fluid types from multiple formation energy spectrum data. Then, combined with known water saturation and fluid type data, a quantitative correlation between the first analytical parameters, water saturation, and fluid type is established through a combination of derivation and data fitting, thereby obtaining the cross-plot formula. This step is used to clarify the core characterization of the first analytical parameters, construct the intrinsic relationship between key parameters and core formation parameters and fluid types, and provide direct parameter support and formula basis for subsequent cross-plot drawing and fluid type determination.

[0104] S205. Draw a cross plot based on multiple first analytical parameters and cross plot formulas, and determine the fluid type based on the cross plot to obtain the fluid type of each environment; wherein, the fluid type of each environment is used to provide data support for oil and gas development in each environment.

[0105] Specifically, key parameters from multiple first analytical parameters can be used as the horizontal and vertical axes of the cross plot. Combined with the water saturation data of each environment, the corresponding coordinate points are calculated according to the cross plot formula. The coordinate points of all environments are plotted on the same chart. At the same time, based on the parameter distribution characteristics of known fluid types, exclusive regions for different fluid types are divided. By determining the exclusive region where each environment's coordinate point is located, the corresponding fluid type is determined. This step is used to visualize the quantitative relationship between the first analytical parameters, water saturation, and fluid type, intuitively clarifying the fluid type of each environment and providing direct data support for oil and gas development in each environment.

[0106] The fluid types for each environment can be applied to the accurate assessment of reservoir hydrocarbon potential during oil and gas exploration, well location optimization during development, precise screening of perforated sections, and dynamic adaptation of production process parameters for the green development of complex reservoirs. Simultaneously, it provides data support for reservoir protection, production capacity enhancement, and energy-saving and low-carbon solutions in oil and gas field development. The fluid types for each environment can address issues arising from fluid type identification biases in traditional development, such as missed identification of high-quality reservoirs, ineffective well deployment, and mismatch between production processes and reservoir fluids, leading to high-energy-consuming operations, resource waste, and excessive carbon emissions. This improves the accuracy of reservoir hydrocarbon potential assessment, helps screen high-potential development blocks, and reduces inefficient or ineffective development. By reducing investment in energy-intensive projects such as drilling and perforation, the energy consumption, fuel consumption, and carbon emissions during operations can be minimized. By optimizing well location layout, perforation schemes, and oil production process parameters, the production rate of single wells and oil and gas recovery rate can be increased, while the energy consumption and carbon emissions per unit of oil and gas production can be reduced. This provides a reliable basis for the dynamic control of oil, gas, and water wells, extends the economic exploitation cycle of oil fields, and, especially, accurately locates oil and gas-bearing areas under complex geological conditions, improves the success rate of development, and achieves efficient and rational utilization of oil and gas resources. At the same time, it reduces resource waste and unnecessary energy consumption for technical adjustments during development, reduces carbon emissions, ensures the stability, economy, and low-carbon environmental protection of oil and gas development, and promotes the green and low-carbon transformation of the oil and gas industry.

[0107] This embodiment provides a method for identifying reservoir fluid types. By establishing a high-precision numerical model based on instrument and environmental parameters, and using particle transport simulation to generate a standardized energy spectrum database covering complex geological conditions, the method extracts key parameters characterizing the pure fluid response through spectral analysis, establishes a quantitative dynamic relationship between these parameters and water saturation and fluid type, and forms an adaptive intelligent discrimination chart. This eliminates the influence of wellbore environmental interference and geological heterogeneity, and realizes fully automated interpretation from data acquisition to fluid identification. It solves the problem of low identification efficiency caused by parameter distortion and model rigidity in existing technologies, and improves the accuracy of identifying complex reservoir fluid types.

[0108] In one possible design, S204, based on multiple formation energy spectrum data, performs spectral stripping analysis and formula derivation to obtain multiple first analytical parameters and cross-plot formulas, including:

[0109] S2041. Perform spectral stripping analysis on multiple formation energy spectrum data to obtain multiple first analytical parameters.

[0110] Specifically, methods such as energy spectrum smoothing and denoising, background subtraction, and characteristic peak identification and separation can be used to systematically process multiple formation energy spectrum data. This process removes irrelevant information such as instrument noise and interference signals, accurately locates and extracts characteristic peaks corresponding to elements such as carbon, oxygen, and chlorine related to the preset fluid type, and obtains parameters such as peak area and peak height for each characteristic peak to form multiple first analytical parameters. This step is used to strip effective signals and interference information from complex formation energy spectrum data to obtain pure core parameters with clear physical meaning, providing a reliable data foundation for establishing the correlation between parameters and water saturation and fluid type.

[0111] Among them, several of the first analytical parameters include chlorine production. Macroscopic capture section Compared with carbon and oxygen production The chlorine yield can be obtained by stripping and analyzing the mixed spectrum obtained using the inelastic standard spectrum and the capture standard spectrum of elements. Macroscopic capture section Compared with carbon and oxygen production According to the formula, the chlorine production rate Macroscopic capture section Compared with carbon and oxygen production By combining the three sets of data in pairs to create a cross plot, using a carbonate rock formation model as an example, and combining it with an oxygen closure model, the carbon-oxygen yield ratio was calculated. As shown in equation (1), equation (1) is:

[0112]

[0113] in, Formation porosity, For gas saturation, The density of C atoms in the gas. The density of O atoms in water, These represent the oxygen atom density in a pure carbonate rock framework; all of these are fixed values. For carbon production, This refers to the production of oxygen.

[0114] The capture gamma rays released by the capture reaction between thermal neutrons and the atomic nuclei of stratigraphic elements exhibit varying degrees of decay over time. This reflects the stratigraphic capture capacity of the stratigraphic system, specifically the macroscopic capture section of a pure carbonate stratum. As shown in equation (2), equation (2) is:

[0115]

[0116] Combining equations (1) and (2), we obtain equation (3), which is:

[0117]

[0118] in, This is a macroscopic section of formation water capture. This is a macroscopic trapping section of natural gas (gas phase in the pores). and This is a macroscopic capture section for a specific phase (usually a reference phase or hydrocarbon phase).

[0119] For formations with a certain porosity, as gas saturation increases, the macroscopic trapping section... Chlorine production Increase the carbon-oxygen ratio Based on this reduction, cross-plot fluid identification was performed to obtain the chlorine yield of different types of formation fluids under different water saturation conditions. Macroscopic capture section Compared with carbon and oxygen production .

[0120] Figure 3 Provided for the embodiments of this application — Intersection diagram, Figure 4 Provided for the embodiments of this application — Intersection diagram, Figure 5 Provided for the embodiments of this application — Intersection diagram, such as Figures 3 to 5 As shown, the distribution patterns of different fluids in the diagram are obvious, and the distribution areas of oil, gas, and water are regular. Therefore, these three cross-plots can effectively distinguish water and oil and gas layers and are highly adaptable to formation conditions with high-salinity water.

[0121] S2042. Based on multiple first analytical parameters, water saturation, and various fluid types, the formula is derived to obtain the cross-plot formula.

[0122] Specifically, multiple first analytical parameters and water saturation data corresponding to various fluid types can be collected and analyzed using mathematical methods such as linear fitting and multiple regression. The intrinsic relationship between each first analytical parameter, water saturation, and fluid type can be analyzed, and a mathematical expression that can quantify the correspondence among the three can be constructed. The final form of the expression is determined by verification and optimization, and the cross-plot formula is obtained. This step is used to establish a quantitative relationship between the first analytical parameters, water saturation, and fluid type, providing clear formula support for the subsequent drawing of cross-plots, and enabling the judgment of fluid type to have a clear mathematical basis.

[0123] The technical effect of this scheme in this embodiment is as follows: by first accurately extracting the first analytical parameters reflecting the pure fluid response characteristics from the simulated energy spectrum, and then deriving a quantitative cross-plot formula based on these parameters, water saturation and fluid type, the direct conversion from data to model is realized, which enhances the accuracy of parameter extraction and the reliability of relationship modeling, and provides a data foundation for subsequent intelligent plot construction and efficient fluid type identification.

[0124] In one possible design, S205, the intersection plot is drawn based on multiple first analytical parameters and the intersection plot formula, including:

[0125] S2051. Correct multiple first analytical parameters to obtain multiple second analytical parameters, and calculate the difference between each first analytical parameter and the corresponding second analytical parameter.

[0126] Specifically, by referring to the calibration curves of lithology scanning logging instruments and the standard analytical parameters corresponding to known pure fluid types, and combining the formation condition parameters of various environments, multiple first analytical parameters can be systematically corrected to eliminate the influence of instrument system errors and formation heterogeneity, resulting in multiple second analytical parameters. Then, by subtracting the corresponding second analytical parameter value from the value of each first analytical parameter, the difference value of each parameter is obtained. This step is used to correct the deviations in the first analytical parameters, making the parameters fit the actual fluid response characteristics of the formation. The degree of deviation is quantified by the difference value, providing corrected data support for the subsequent drawing of accurate cross plots using cross plot formulas.

[0127] S2052. Draw a cross plot based on the cross plot formula and multiple differences.

[0128] Specifically, key differences with significant distinguishability among multiple differences can be selected as the horizontal and vertical axes of the cross plot. Substituting the difference data corresponding to each environment into the cross plot formula, the specific coordinate points of each environment in the coordinate system are calculated. At the same time, the coordinate points corresponding to the known fluid types are marked to delineate the exclusive distribution areas of different fluid types in the coordinate system, fully presenting the distribution pattern of each coordinate point and drawing a cross plot. This step is used to present the quantitative relationship between the difference and the fluid type in a visual chart form, clearly defining the distribution range of different fluid types, and providing an intuitive and clear graphical basis for quickly and accurately determining the fluid type of each environment.

[0129] The difference is the numerical difference between the multiple second analytical parameters obtained after correcting the multiple first analytical parameters and the corresponding first analytical parameters, that is, the difference generated by subtracting the corrected second analytical parameter from each first analytical parameter.

[0130] For example, by performing wellbore correction on the wellbore diameter and drilling fluid salinity, the chlorine production of the formation under standard wellbore conditions or when the drilling fluid is a fresh cement slurry can be obtained. Macroscopic capture section Compared with carbon and oxygen production .

[0131] Calculate the difference before and after correction, and record the corrected values ​​as follows: , and Formulas for correcting well diameter and drilling fluid salinity were obtained respectively.

[0132] To correct for the influence of wellbore diameter, it is first necessary to obtain data from a standard wellbore (6 inches). , and Then use the expanded diameter , and The wellbore portion is removed from the simulation. The formation framework is set as sandstone, the drilling fluid as fresh cement slurry, and the well diameter is set to 6-11 inches. Formation energy spectrum data is recorded during the simulation, based on the 6-inch well diameter condition. , and As a benchmark, the difference between the calculated value under 6.5in-11in well diameter conditions and the calculated value under 6in well diameter conditions is denoted as . , and , Figure 6 The corrected wellbore provided for the embodiments of this application , and Calculation results Figure 1 , Figure 7 The corrected wellbore provided for the embodiments of this application , and Calculation results Figure 2 , Figure 8 The corrected wellbore provided for the embodiments of this application , and Calculation results Figure 3 , Figure 9 The corrected wellbore provided for the embodiments of this application , and Calculation results Figure 4 ,like Figures 6 to 9 As shown, , and As shown in equation (4), equation (4) is:

[0133]

[0134]

[0135]

[0136] Where r is the value of the well diameter.

[0137] To correct for the influence of drilling fluid salinity, it is first necessary to determine which portion originates from the wellbore mud. , and Then from the total , and The portion within the wellbore is subtracted. With formation conditions unchanged and a wellbore diameter of 6 inches, the fluid in the wellbore is a saline-cement slurry with a salinity varying from 10,000 ppm to 100,000 ppm. The formation's non-elastic spectrum, trapping spectrum, and trapping gamma time spectrum are simulated and recorded. The results are calculated under different drilling fluid salinity conditions. , and The difference between the calculated value and the value under the corresponding freshwater conditions, i.e. , and . Figure 10 The corrected drilling fluid salinity for correcting well diameter provided in the embodiments of this application , and Calculation results Figure 1 , Figure 11 The corrected drilling fluid salinity for correcting well diameter provided in the embodiments of this application , and Calculation results Figure 2 , Figure 12 The corrected drilling fluid salinity for correcting well diameter provided in the embodiments of this application , and Calculation results Figure 3 , Figure 13 The corrected drilling fluid salinity for correcting well diameter provided in the embodiments of this application , and Calculation results Figure 4 ,like Figures 10 to 13 As shown, , and As shown in equation (5), equation (5) is:

[0138]

[0139]

[0140]

[0141] in, This refers to the salinity parameter of the drilling fluid.

[0142] In a certain area, the test well section , as well as After wellbore correction, the data was plotted in the cross plot, and the results are as follows: Figures 14 to 16 As shown, Figure 14 The actual identification of fluid properties in the formation provided in the embodiments of this application — , — as well as — Intersection Figure 1 , Figure 15 The actual identification of fluid properties in the formation provided in the embodiments of this application — , — as well as — Intersection Figure 2 , Figure 16 The actual identification of fluid properties in the formation provided in the embodiments of this application — , — as well as — Intersection Figure 3 As can be seen from the figure, even though the formation water salinity is high in this well section, all three cross plots can clearly separate the gas layer and the water layer, and the distribution pattern is consistent with the principle, proving that wellbore correction can effectively improve the accuracy of fluid identification.

[0143] A reservoir fluid identification method was used to establish wellbore and formation models for lithology scanning logging instruments under different environments, simulating and recording the energy spectrum of the formation. Based on the calculation model, the chlorine yield of different types of formation fluids under different water saturation conditions was obtained. Macroscopic capture section Compared with carbon and oxygen production Obtain the cross plot formula, combine the pairs to draw the cross plot, and calculate the chlorine yield. Macroscopic capture section Compared with carbon and oxygen production Perform wellbore calibration to correct the actual formation. , and Fluids are identified by substituting them into the cross plot. This application can accurately identify fluid types under complex conditions. By analyzing the influence of various strata factors on the cross plot, chlorine production is calculated using lithological scanning logging. Macroscopic capture section and carbon-oxygen production ratio This method corrects for wellbore influences, thus forming a practical method for identifying fluids in complex reservoirs, providing support for cross-plot fluid identification in complex lithological formations.

[0144] The technical effect of this scheme in this embodiment is as follows: by introducing a parameter correction step, the first analytical parameter is calibrated to a standardized second analytical parameter, and the difference between the two is calculated as a quantitative compensation for environmental background interference. The cross plot formula is dynamically corrected using this difference and then the cross plot is drawn. This eliminates the systematic measurement deviation caused by instrument response differences and wellbore environment fluctuations, and improves the response sensitivity and discrimination accuracy of the cross plot to the real formation fluid characteristics.

[0145] In one possible design, S2051, multiple first analytical parameters are corrected to obtain multiple second analytical parameters, including:

[0146] S20511. Perform wellbore correction on multiple first analytical parameters to obtain multiple third analytical parameters; wherein, wellbore correction is used to eliminate the gamma measurement signal deviation caused by the actual wellbore diameter deviating from the preset standard size.

[0147] Specifically, actual wellbore measurement data corresponding to various environments can be obtained. Combined with the preset standard wellbore size, the deviation value between the actual wellbore and the standard wellbore is determined. A correction model is established based on the influence law of wellbore diameter on gamma measurement signal. The first analytical parameter is substituted into the model, and the influence of deviation is eliminated through calculation and adjustment to obtain multiple third analytical parameters. This step is used to eliminate the gamma measurement signal deviation caused by the actual wellbore diameter deviating from the standard size, correct the numerical deviation of the first analytical parameter, and make the third analytical parameter more consistent with the actual fluid response characteristics of the formation, providing accurate basic data for subsequent drilling fluid salinity correction.

[0148] S20512. Drilling fluid salinity correction is performed on multiple third analytical parameters to obtain multiple second analytical parameters; among them, drilling fluid salinity correction is used to eliminate the interference caused by changes in salt concentration in the drilling fluid on the formation capture section measurement value.

[0149] Specifically, actual drilling fluid salinity measurement data corresponding to various environments can be obtained. Combined with the influence of salinity changes on formation trapping section measurement values, a targeted correction relationship is established. Multiple third analytical parameters are substituted into this correction relationship for numerical adjustment to eliminate the interference caused by salt concentration changes, resulting in multiple second analytical parameters. This step is used to eliminate the interference caused by salt concentration changes in drilling fluid on formation trapping section measurement values, correct the deviation of the third analytical parameters, and make the second analytical parameters closer to the actual fluid response characteristics of the formation. This provides accurate data support for the subsequent calculation of the difference between the first and second analytical parameters and the drawing of cross-plots.

[0150] The technical effect of this scheme in this embodiment is as follows: by sequentially correcting the wellbore diameter and drilling fluid salinity of the first analytical parameter, the distortion of the gamma spectrum signal caused by the deviation of the actual wellbore size from the standard size and the influence of the change in salt concentration in the drilling fluid on the measurement of the macroscopic capture section of the formation are eliminated respectively. The second analytical parameter that is close to the ideal formation response characteristics is obtained, which reduces the interference of key field variables such as wellbore environment and drilling fluid properties on the identification parameters, and improves the accuracy of fluid type judgment and the applicability of the method in actual complex well conditions.

[0151] In one possible design, S202, based on multiple physical parameters and multiple formation condition parameters, constructs a wellbore and formation model, including:

[0152] S2021. Construct an instrument model based on multiple physical parameters; the instrument model is used to represent the instrument's structural features, neutron source characteristics, and detection response properties.

[0153] Specifically, professional 3D modeling tools can be used to reconstruct the instrument's physical structure based on data such as dimensions, materials, and component layout that characterize the instrument's structural features from multiple physical parameters. Neutron source operating attributes are defined by combining characteristic parameters such as neutron source emission energy and emission intensity. Detector functional parameters are set based on detection response attributes such as detector response efficiency and energy resolution. The assembly relationship and collaborative working logic between various components are clarified, and the instrument model is completed. This step is used to accurately present the instrument's structural features, neutron source characteristics, and detection response attributes, providing realistic instrument-level data support for the subsequent construction of wellbore and formation models based on formation condition parameters, ensuring that the wellbore and formation models accurately replicate the instrument's operating state.

[0154] S2022. Based on the instrument model and multiple formation condition parameters, construct the wellbore and formation model.

[0155] Specifically, a professional numerical simulation platform can be used to construct a basic wellbore and formation structure that includes wellbore geometry and formation physical property distribution, based on data such as wellbore size, formation layering structure, lithology, porosity, and water saturation from multiple formation condition parameters. The constructed instrument model is then embedded into this basic structure according to the actual downhole deployment location. The spatial relative positions of the instrument, the wellbore inner wall, and each formation segment are clarified, and the correspondence between the instrument's detection range and the formation parameter distribution is matched. This completes the synergistic integration of structure and parameters, forming a complete wellbore and formation model. This step is used to accurately integrate the instrument characteristics with the real characteristics of the wellbore and formation, build the complete computing carrier required for particle transport simulation, and ensure that the simulation process closely matches the actual downhole working conditions.

[0156] For example, an instrument model can be established based on the instrument's size, component composition, and other relevant parameters. The neutron generator is a pulsed neutron source (PNG) with a shield made of tungsten metal, the detector crystal is a cerium-doped lanthanum bromide crystal, and a boron sleeve is added near the detector.

[0157] The lithological scanning logging environment model includes the wellbore and formation. The drilling fluid is fresh water slurry. The formation model type needs to include combinations of different fluids, different mineralization, and different water saturation, including gas-bearing water layers and oil-bearing water layers. The simulated formation inelastic scattering gamma spectrum, trapped gamma spectrum, and trapped gamma time spectrum are recorded respectively.

[0158] The instruments, wellbore, and formation model for lithological scanning logging are as follows: The instrument model can be established based on the instrument's dimensions, component composition, and other relevant parameters. Figure 17 The lithological scanning logging apparatus, wellbore, and formation model diagrams provided in the embodiments of this application are as follows: Figure 17 As shown, the instrument size is 4.5 inches. The neutron generator is a high-performance, safe pulsed neutron source (PNG) with a tungsten shield. The detector crystal is a cerium-doped lanthanum bromide crystal with good temperature and pressure resistance, and a boron sleeve is added near the detector. The wellbore radius is 6 inches. A formation model with sandstone as the formation framework is established, with a radial thickness of 85 cm and a height of 150 cm. The formation is uniformly divided into 450 grid cells with a length and width of 5 cm and a porosity of 15%. The drilling fluid is freshwater slurry. The formation fluids are freshwater, gas, oil, 50,000 ppm high-salinity water, and gas-bearing and oil-bearing water layers with water saturation of 20%, 40%, 60%, and 80%, respectively. The inelastic scattering gamma spectrum, trapped gamma spectrum, and trapped gamma time spectrum of the formation are simulated and recorded.

[0159] The technical effect of this scheme in this embodiment is as follows: by constructing an instrument model that characterizes the instrument's own working characteristics and a wellbore and formation model that integrates formation and wellbore features, a refined decomposition and hierarchical modeling of the physical process of particle transport simulation is achieved. This ensures that the model can realistically reflect the complex coupling effect between the actual instrument response and the formation environment, laying a physical simulation foundation for generating high-fidelity formation energy spectrum data, accurately extracting fluid characteristic parameters, and ultimately achieving reliable fluid type identification.

[0160] Figure 18 A flowchart illustrating the reservoir fluid type identification method provided in this application embodiment. Figure 2 In this embodiment, in Figure 2 Based on the provided embodiments, the method for identifying reservoir fluid types is further explained. The method for identifying reservoir fluid types includes:

[0161] S1801. Acquire multiple physical parameters of the preset lithological scanning logging instrument and multiple formation condition parameters of each environment in multiple preset environments; wherein, the multiple physical parameters are used to represent the working characteristics of the instrument, the multiple formation condition parameters are used to represent the formation and wellbore characteristics of each environment, and the multiple formation condition parameters include water saturation.

[0162] S1802. Based on multiple physical parameters and multiple formation condition parameters, construct a wellbore and formation model; wherein, the wellbore and formation model is used as the computational carrier for particle transport simulation.

[0163] S1803. Based on the wellbore and formation model, particle transport simulation is performed to obtain multiple formation energy spectrum data for each environment; among them, multiple formation energy spectrum data are used to represent the characteristics of inelastic scattering of neutrons with preset elements in the formation and the characteristics of element capture.

[0164] S1804. Based on multiple formation energy spectrum data, perform spectral stripping analysis and formula derivation to obtain multiple first analytical parameters and cross plot formulas; wherein, the multiple first analytical parameters are used to represent the response signal characteristics of multiple preset fluid types in the formation, and the cross plot formulas are used to represent the relationship between multiple first analytical parameters, water saturation and multiple fluid types.

[0165] S1805. Draw a cross plot based on multiple first analytical parameters and cross plot formulas, and determine the fluid type based on the cross plot to obtain the fluid type of each environment; wherein, the fluid type of each environment is used to provide data support for oil and gas development in each environment.

[0166] S1801-S1805 are similar to S201-S205, and will not be described again in this embodiment.

[0167] S1806. Obtain the measured fluid type for each environment.

[0168] Specifically, the measured fluid type of each environment can be determined by conducting fluid sampling analysis and core experiments on the corresponding downhole formations, or by collecting existing oil testing and production results for that environment. This step provides reference data on the actual fluid type to be compared with the identification results, and provides a direct basis for calculating the accuracy of fluid type judgment in the cross plot.

[0169] S1807. Calculate the accuracy of fluid type determination in cross plots based on the fluid type of each environment and the measured fluid type of each environment.

[0170] Specifically, we can first count the number of environments in each environment whose fluid type obtained from the cross plot matches the measured fluid type, and then calculate the ratio of this number to the total number of environments involved in the calculation to obtain the fluid type judgment accuracy of the cross plot. This step is used to intuitively reflect the effect of the cross plot on fluid type judgment, and provide clear data reference for whether the subsequent judgment accuracy meets the standard and whether the parameters of the cross plot need to be optimized.

[0171] S1808. In response to the fluid type determination accuracy being lower than a preset accuracy threshold, the parameters of the cross plot are optimized to obtain an optimized cross plot.

[0172] Specifically, the relationship between the first analytical parameters and water saturation and fluid type can be re-examined, the coefficients in the cross plot formula can be adjusted, the key differences in the selection of the horizontal and vertical axes can be optimized, and the parameter characteristics corresponding to the environment in which the judgment was wrong can be adjusted in a targeted manner to determine the optimized parameter configuration. The optimized cross plot can then be drawn. This step is used to correct the factors that affect the accuracy of the judgment in the original cross plot, improve the accuracy of fluid type judgment, provide more reliable graphical basis for the accurate judgment of fluid types in various environments, and ensure the practicality and stability of the identification method.

[0173] The technical effect of this scheme in this embodiment is as follows: by introducing a feedback verification mechanism for measured fluid types, the model judgment result is compared with the actual data to calculate and monitor the accuracy of fluid type judgment, and when the accuracy is lower than the preset threshold, the parameters of the cross plot are optimized, thereby realizing dynamic evaluation and closed-loop correction of the recognition model, enhancing the adaptability and continuous optimization capability of the method in practical applications, and ensuring that the fluid type recognition result can maintain high accuracy and reliability for a long time.

[0174] Figure 19 A schematic diagram of the structure of the reservoir fluid type identification device provided in this application embodiment. Figure 19 As shown, the reservoir fluid type identification device includes:

[0175] The first acquisition module 1901 is used to acquire multiple physical parameters of a preset lithological scanning logging instrument and multiple formation condition parameters of each environment in a preset variety of environments; wherein, the multiple physical parameters are used to represent the working characteristics of the instrument, the multiple formation condition parameters are used to represent the formation and wellbore characteristics of each environment, and the multiple formation condition parameters include water saturation.

[0176] Module 1902 is used to construct wellbore and formation models based on multiple physical parameters and multiple formation condition parameters; the wellbore and formation models are used as the computational carrier for particle transport simulation.

[0177] Simulation module 1903 is used to perform particle transport simulation based on wellbore and formation models to obtain multiple formation energy spectrum data for various environments; among them, multiple formation energy spectrum data are used to represent the characteristics of inelastic scattering of neutrons with preset elements in the formation and the characteristics of element capture.

[0178] The analysis module 1904 is used to perform spectral stripping analysis and formula derivation based on multiple formation energy spectrum data to obtain multiple first analytical parameters and cross plot formulas. Among them, the multiple first analytical parameters are used to represent the response signal characteristics of multiple preset fluid types in the formation, and the cross plot formulas are used to represent the relationship between multiple first analytical parameters, water saturation and multiple fluid types.

[0179] The judgment module 1905 is used to draw a cross plot based on multiple first analytical parameters and cross plot formulas, and to judge the fluid type based on the cross plot to obtain the fluid type of each environment; wherein, the fluid type of each environment is used to provide data support for oil and gas development in each environment.

[0180] In one possible design, parsing module 1904 includes:

[0181] The analysis unit is used to perform spectral stripping analysis on multiple formation energy spectrum data to obtain multiple first analysis parameters.

[0182] The derivation unit is used to derive the formula based on multiple first analytical parameters, water saturation, and various fluid types, and obtain the cross-plot formula.

[0183] In one possible design, the decision module 1905 includes:

[0184] The correction unit is used to correct multiple first analytical parameters to obtain multiple second analytical parameters, and to calculate the difference between each first analytical parameter and the corresponding second analytical parameter.

[0185] The drawing unit is used to draw a cross-plot based on the cross-plot formula and multiple differences.

[0186] In one possible design, the correction unit includes:

[0187] The first calibration component is used to perform wellbore calibration on multiple first analytical parameters to obtain multiple third analytical parameters; wherein, wellbore calibration is used to eliminate the gamma measurement signal deviation caused by the actual wellbore diameter deviating from the preset standard size.

[0188] The second correction component is used to correct the drilling fluid salinity of multiple third analytical parameters to obtain multiple second analytical parameters; among them, the drilling fluid salinity correction is used to eliminate the interference caused by the change in salt concentration in the drilling fluid on the formation capture section measurement value.

[0189] In one possible design, building module 1902 includes:

[0190] The first building unit is used to construct an instrument model based on multiple physical parameters; the instrument model is used to represent the instrument's structural features, neutron source characteristics, and detection response properties.

[0191] The second building unit is used to build the wellbore and formation model based on the instrument model and multiple formation condition parameters.

[0192] In one possible design, the reservoir fluid type identification device further includes:

[0193] The second acquisition module is used to acquire the measured fluid type in each environment.

[0194] The calculation module is used to calculate the accuracy of fluid type determination in cross plots based on the fluid type of each environment and the measured fluid type of each environment.

[0195] The optimization module is used to optimize the parameters of the cross plot in response to the fact that the accuracy of fluid type determination is lower than the preset accuracy threshold, so as to obtain the optimized cross plot.

[0196] The reservoir fluid type identification device provided in this embodiment can perform... Figure 2 and Figure 18 The technical solution of an embodiment of a reservoir fluid type identification method is shown, and its implementation principle and technical effects are similar to those of... Figure 2 and Figure 18 The embodiment of the reservoir fluid type identification method shown is similar and will not be described in detail here.

[0197] Figure 20 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application. Figure 20 As shown, the electronic device 200 includes at least one processor 2001 and a memory 2002. The electronic device 200 also includes a communication component 2003. The processor 2001, memory 2002, and communication component 2003 are connected via a bus 2004.

[0198] In a specific implementation, at least one processor 2001 executes computer execution instructions stored in memory 2002, causing at least one processor 2001 to implement a reservoir fluid type identification method of the above embodiment.

[0199] The specific implementation process of processor 2001 can be found in the above method embodiments, and its implementation principle and technical effect are similar, so it will not be repeated here.

[0200] In the above embodiments, it should be understood that the processor 2001 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0201] The memory 2002 may include high-speed RAM memory, and may also include non-volatile memory NVM, such as at least one disk storage.

[0202] Bus 2004 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Bus 2004 can be divided into address bus, data bus, control bus, etc. For ease of illustration, the bus 2004 in the accompanying drawings of this application is not limited to only one bus or one type of bus.

[0203] The above description of the functions implemented by electronic devices and main control devices has introduced the solutions provided by the embodiments of the present invention. It is understood that, in order to implement the above functions, the electronic device or main control device includes hardware structures and / or software modules corresponding to the execution of each function. By combining the units and algorithm steps of the various examples described in the embodiments of the present invention, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of the present invention.

[0204] This application also provides a computer-readable storage medium storing computer-executable instructions. When executed by a processor, these instructions are used to implement a reservoir fluid type identification method according to the above embodiments. In the specific implementation of the aforementioned reservoir fluid type identification method, each module can be implemented as a processor.

[0205] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0206] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in application-specific integrated circuits (ASICs). Alternatively, the processor and the readable storage medium can exist as discrete components in an electronic device or a host device.

[0207] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement a reservoir fluid type identification method according to the above embodiments.

[0208] The computer program is stored in a readable storage medium, and at least one processor can read the computer program from the readable storage medium and execute the computer program to perform the scheme provided in any of the above embodiments.

[0209] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.

[0210] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for identifying reservoir fluid types, characterized in that, include: The system acquires multiple physical parameters of a preset lithological scanning logging instrument and multiple formation condition parameters of each of the preset environments; wherein, the multiple physical parameters are used to represent the working characteristics of the instrument, and the multiple formation condition parameters are used to represent the formation and wellbore characteristics of each of the environments, and the multiple formation condition parameters include water saturation. Based on the multiple physical parameters and the multiple formation condition parameters, a wellbore and formation model is constructed; wherein, the wellbore and formation model is used as a computational carrier for particle transport simulation; Based on the wellbore and formation model, particle transport simulation is performed to obtain multiple formation energy spectrum data for each environment; wherein, the multiple formation energy spectrum data are used to represent the characteristics of inelastic scattering of neutrons with preset elements in the formation and the element trapping characteristics; Based on the multiple formation energy spectrum data, spectral stripping analysis and formula derivation are performed to obtain multiple first analytical parameters and cross plot formulas; wherein, the multiple first analytical parameters are used to represent the response signal characteristics of multiple preset fluid types in the formation, and the cross plot formulas are used to represent the relationship between the multiple first analytical parameters, the water saturation and the multiple fluid types; A cross-plot is drawn based on the plurality of first analytical parameters and the cross-plot formula, and the fluid type is determined based on the cross-plot to obtain the fluid type of each environment; wherein, the fluid type of each environment is used to provide data support for oil and gas development in each environment.

2. The method for identifying reservoir fluid types according to claim 1, characterized in that, The process involves stripping and analyzing the spectral data of the multiple formations and deriving formulas to obtain multiple first analytical parameters and cross-plot formulas, including: The multiple formation energy spectrum data are stripped and analyzed to obtain the multiple first analytical parameters; The cross-plot formula is derived by deriving the formula based on the multiple first analytical parameters, the water saturation, and the multiple fluid types.

3. The method for identifying reservoir fluid types according to claim 1, characterized in that, The step of drawing a cross-plot based on the plurality of first analytical parameters and the cross-plot formula includes: The plurality of first analytical parameters are corrected to obtain a plurality of second analytical parameters, and the difference between each first analytical parameter and the second analytical parameter corresponding to each first analytical parameter is calculated. The intersection plot is drawn based on the intersection plot formula and multiple differences.

4. The method for identifying reservoir fluid types according to claim 3, characterized in that, The correction of the plurality of first analytical parameters yields a plurality of second analytical parameters, including: The wellbore correction is performed on the plurality of first analytical parameters to obtain a plurality of third analytical parameters; wherein, the wellbore correction is used to eliminate the gamma measurement signal deviation caused by the actual wellbore diameter deviating from the preset standard size; The drilling fluid salinity is corrected for the plurality of third analytical parameters to obtain the plurality of second analytical parameters; wherein, the drilling fluid salinity correction is used to eliminate the interference caused by the change in salt concentration in the drilling fluid on the formation capture section measurement value.

5. The method for identifying reservoir fluid types according to claim 1, characterized in that, The step of constructing a wellbore and formation model based on the multiple physical parameters and the multiple formation condition parameters includes: An instrument model is constructed based on the aforementioned physical parameters; wherein the instrument model is used to represent the structural features, neutron source characteristics, and detection response properties of the instrument. Based on the instrument model and the multiple formation condition parameters, the wellbore and formation model are constructed.

6. The method for identifying reservoir fluid types according to claim 1, characterized in that, After determining the fluid type based on the cross-plot to obtain the fluid type of each environment, the process further includes: Obtain the measured fluid type for each of the aforementioned environments; The accuracy rate of fluid type determination of the cross plot is calculated based on the fluid type of each environment and the measured fluid type of each environment. In response to the fluid type determination accuracy being lower than a preset accuracy threshold, the parameters of the cross plot are optimized to obtain an optimized cross plot.

7. A reservoir fluid type identification device, characterized in that, include: The first acquisition module is used to acquire multiple physical parameters of a preset lithological scanning logging instrument and multiple formation condition parameters of each of the preset environments; wherein, the multiple physical parameters are used to represent the working characteristics of the instrument, the multiple formation condition parameters are used to represent the formation and wellbore characteristics of each environment, and the multiple formation condition parameters include water saturation. A construction module is used to construct a wellbore and formation model based on the multiple physical parameters and the multiple formation condition parameters; wherein, the wellbore and formation model is used as a computational carrier for particle transport simulation; The simulation module is used to perform particle transport simulation based on the wellbore and formation model to obtain multiple formation energy spectrum data for each environment; wherein, the multiple formation energy spectrum data are used to represent the characteristics of inelastic scattering of neutrons with preset elements in the formation and the characteristics of element capture. The analysis module is used to perform spectral stripping analysis and formula derivation based on the multiple formation energy spectrum data to obtain multiple first analytical parameters and cross plot formulas; wherein, the multiple first analytical parameters are used to represent the response signal characteristics of multiple preset fluid types in the formation, and the cross plot formulas are used to represent the relationship between the multiple first analytical parameters, the water saturation and the multiple fluid types; The judgment module is used to draw a cross plot based on the plurality of first analytical parameters and the cross plot formula, and to judge the fluid type based on the cross plot to obtain the fluid type of each environment; wherein, the fluid type of each environment is used to provide data support for oil and gas development in each environment.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; When the processor executes the computer execution instructions stored in the memory, it is used to implement the reservoir fluid type identification method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the reservoir fluid type identification method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, is used to implement the reservoir fluid type identification method as described in any one of claims 1 to 6.

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