Reservoir fluid form identification method, device and equipment and storage medium

Abstract

The invention discloses a reservoir fluid form identification method, device and equipment and a storage medium, and belongs to the technical field of oil and gas extraction. Based on the physical experiment data of the sample rock, a plurality of influence factors influencing the water layer resistivity and the oil layer resistivity can be accurately determined, so that the resistivity model (the first water layer resistivity model and the first oil layer resistivity model) can be established through multi-factor fusion, and the accuracy of the established resistivity model can be improved. The logging sensitive curve is determined on the basis of the first water layer resistivity model and the first oil layer resistivity, so that the resistivity model can be determined in a mode of combining physical experiment data and conventional logging information; therefore, the accuracy of the determined resistivity model (the second water layer resistivity model and the second oil layer resistivity model) is improved, and the accuracy of fluid property identification based on the resistivity model (the second water layer resistivity model and the second oil layer resistivity model) is further improved.

Description

Technical Field

[0001] This application relates to the field of clastic rock logging evaluation technology, and in particular to a method, apparatus, equipment and storage medium for identifying reservoir fluid forms. Background Technology

[0002] In the field of geological exploration, identifying reservoir fluid properties can effectively determine the fluid type within a reservoir, such as oil-bearing or water-bearing layers. This is of great significance for predicting promising oil-bearing areas and improving exploration efficiency. Currently, conventional well logging data is used to identify fluid properties. Summary of the Invention

[0003] This application provides a method, apparatus, device, and storage medium for identifying reservoir fluid types, which can improve the accuracy of fluid property identification based on resistivity models (second water layer resistivity model and second oil layer resistivity model). The technical solution is as follows:

[0004] On the one hand, a method for identifying reservoir fluid properties is provided, the method comprising:

[0005] The physical experimental data of the sample rocks are determined. The sample rocks include saturated water rock samples and saturated oil rock samples. The physical experimental data of the saturated water rock samples are the analysis data of the electrical influencing factors of the saturated water rock samples under different laboratory conditions. The physical experimental data of the saturated oil rock samples are the analysis data of the electrical influencing factors of the saturated oil rock samples under different laboratory conditions.

[0006] Based on the physical experimental data of the saturated water rock sample, several first influencing factors of the saturated water rock sample were determined. These first influencing factors are the factors that affect the resistivity of the saturated water rock sample under laboratory conditions, and the multiple first influencing factors include porosity, formation water salinity, carbonate content, median particle size, and clay content.

[0007] Based on the physical experimental data of the saturated oil rock sample, several second influencing factors were determined. These second influencing factors are those that affect the resistivity of the saturated oil rock sample under laboratory conditions, and include bound water porosity, carbonate content, median grain size, and clay content.

[0008] Based on the aforementioned multiple first influencing factors, the first water layer resistivity model of the saturated water rock sample under laboratory conditions is determined;

[0009] Based on the aforementioned multiple second influencing factors, the resistivity model of the first oil layer of the saturated oil rock sample under laboratory conditions was determined;

[0010] Based on the first water layer resistivity model and the first oil layer resistivity model, a well logging sensitive curve is determined, which is the curve showing the change between the formation resistivity and fluid properties;

[0011] Based on the well logging sensitivity curve, the resistivity model of the second water layer of the saturated water rock sample under formation conditions is determined;

[0012] Based on the well logging sensitivity curve, the resistivity model of the second oil layer of the saturated oil rock sample under formation conditions is determined;

[0013] When identifying reservoir fluid properties, the reservoir fluid properties are identified based on the second water layer resistivity model and the second oil resistivity model.

[0014] In one possible implementation, determining the logging sensitivity curve based on the first water layer resistivity model and the first oil layer resistivity model includes:

[0015] Based on the first water layer resistivity model and the second oil layer resistivity model, several third influencing factors are determined. These third influencing factors are factors that affect the resistivity of the formation, and they include water layer resistivity, bound water porosity, carbonate content, and rock particle size.

[0016] Based on the aforementioned third influencing factors, the well logging sensitivity curve is determined.

[0017] In another possible implementation, determining the second water layer resistivity model of the saturated water rock sample under formation conditions based on the well logging sensitivity curve includes:

[0018] The logging sensitivity curve is the spontaneous potential curve, which is used to reflect the electrical difference between the formation and the fluid encountered during drilling; based on the spontaneous potential curve, the amplitude difference of spontaneous potential anomalies and the resistivity of mud filtrate are determined.

[0019] Based on the difference in the amplitude of the spontaneous potential anomaly and the resistivity of the mud filtrate, the resistivity model of the second aquifer of the saturated water-rock sample under formation conditions is determined using the following formula:

[0020] Formula 1: ΔSP=-48.421g(R) mf / R wz -0.007

[0021] Wherein, △SP represents the amplitude difference of the natural potential abnormality, and R mf R represents the resistivity of the mud filtrate. wz This represents the resistivity model of the second water layer.

[0022] In another possible implementation, determining the second water layer resistivity model of the saturated water rock sample under formation conditions based on the well logging sensitivity curve includes:

[0023] The logging sensitivity curve is a natural gamma curve, which is used to reflect the relationship between the natural gamma radiation intensity of radioactive elements in the formation and the content of radioactive fluids in the formation.

[0024] Based on the natural gamma curve, determine the natural gamma curve value, the maximum natural gamma curve value and the minimum natural gamma curve value at the pure sand layer in the same layer group;

[0025] Based on the natural gamma curve values, the maximum natural gamma curve value, and the minimum natural gamma curve value, the second water layer resistivity model of the saturated water rock sample under formation conditions is determined.

[0026] In another possible implementation, determining the second water layer resistivity model of the saturated water rock sample under formation conditions based on the well logging sensitivity curve includes:

[0027] The logging sensitivity curve is a nuclear magnetic resonance logging curve, which is used to reflect the relationship between underground rock formations and fluid properties.

[0028] Based on the nuclear magnetic resonance logging curves, the volume of bound water pores was determined.

[0029] Based on the bound water void volume, the resistivity model of the second water layer of the saturated water rock sample under formation conditions is determined.

[0030] In another possible implementation, the identification of reservoir fluid properties based on the second water layer resistivity model and the second oil resistivity model includes:

[0031] Determine the measured resistivity of the reservoir fluid;

[0032] Determine the first similarity between the measured resistivity and the second water layer resistivity model;

[0033] Determine the second similarity between the measured resistivity and the second oil layer resistivity model;

[0034] The properties of the reservoir fluid are determined based on the first similarity and the second similarity.

[0035] On the other hand, a reservoir fluid property identification device is provided, the device comprising:

[0036] The first determining module is used to determine the physical experimental data of the sample rocks, which include saturated water rock samples and saturated oil rock samples. The physical experimental data of the saturated water rock samples are the analysis data of the electrical influencing factors of the saturated water rock samples under different laboratory conditions, and the physical experimental data of the saturated oil rock samples are the analysis data of the electrical influencing factors of the saturated oil rock samples under different laboratory conditions.

[0037] The second determining module is used to determine multiple first influencing factors of the saturated water rock sample based on the physical experimental data of the saturated water rock sample. The multiple first influencing factors are the factors that affect the resistivity of the saturated water rock sample under laboratory conditions, and the multiple first influencing factors include porosity, formation water salinity, carbonate content, median particle size and clay content.

[0038] The third determining module is used to determine multiple second influencing factors of the saturated oil rock sample based on the physical experimental data of the saturated oil rock sample. The multiple second influencing factors are the factors that affect the resistivity of the saturated oil rock sample under laboratory conditions, and the multiple second influencing factors include bound water porosity, carbonate content, median particle size and clay content.

[0039] The fourth determining module is used to determine the first water layer resistivity model of the saturated water rock sample under laboratory conditions based on the multiple first influencing factors.

[0040] The fifth determining module is used to determine the first oil layer resistivity model of the saturated oil rock sample under laboratory conditions based on the multiple second influencing factors.

[0041] The sixth determining module is used to determine the logging sensitive curve based on the first water layer resistivity model and the first oil layer resistivity model. The logging sensitive curve is the curve showing the change between the formation resistivity and the fluid properties.

[0042] The seventh determining module is used to determine the second water layer resistivity model of the saturated water rock sample under formation conditions based on the well logging sensitivity curve;

[0043] The eighth determining module is used to determine the second oil layer resistivity model of the saturated oil rock sample under formation conditions based on the well logging sensitivity curve;

[0044] The identification module is used to identify the reservoir fluid properties based on the second water layer resistivity model and the second oil resistivity model when identifying reservoir fluid properties.

[0045] In one possible implementation, the sixth determining module is used to determine multiple third influencing factors based on the first water layer resistivity model and the second oil layer resistivity model. The multiple third influencing factors are factors that affect the resistivity of the formation, and the multiple third influencing factors include water layer resistivity, bound water porosity, carbonate content, and rock particle size; and to determine the logging sensitivity curve based on the multiple third influencing factors.

[0046] In another possible implementation, the logging sensitivity curve is a spontaneous potential (SP) curve, which reflects the electrical difference between the formation and the fluid encountered during drilling. The seventh determining module is used to determine the SP anomaly amplitude difference and the mud filtrate resistivity based on the SP curve. Based on the SP anomaly amplitude difference and the mud filtrate resistivity, the resistivity model of the second water layer of the saturated water rock sample under formation conditions is determined using the following formula:

[0047] Formula 1: ΔSP=-48.421g(R) mf / R wz -0.007

[0048] Wherein, △SP represents the amplitude difference of the natural potential abnormality, and R mf R represents the resistivity of the mud filtrate. wz This represents the resistivity model of the second water layer.

[0049] In another possible implementation, the logging sensitivity curve is a natural gamma curve, which is used to reflect the relationship between the natural gamma radiation intensity of radioactive elements in the formation and the content of radioactive fluids in the formation.

[0050] The seventh determining module is used to determine the natural gamma curve value, the maximum natural gamma curve value and the minimum natural gamma curve value at the pure sand layer in the same layer group based on the natural gamma curve; and to determine the second water layer resistivity model of the saturated water rock sample under the formation conditions based on the natural gamma curve value, the maximum natural gamma curve value and the minimum natural gamma curve value.

[0051] In another possible implementation, the logging sensitivity curve is a nuclear magnetic resonance logging curve, which is used to reflect the relationship between underground rock formations and fluid properties;

[0052] The seventh determining module is used to determine the volume of bound water voids based on the nuclear magnetic resonance logging curve; and to determine the resistivity model of the second water layer of the saturated water rock sample under formation conditions based on the volume of bound water voids.

[0053] In another possible implementation, the identification module is configured to: determine the measured resistivity of the reservoir fluid; determine a first similarity between the measured resistivity and the second water layer resistivity model; determine a second similarity between the measured resistivity and the second oil layer resistivity model; and determine the properties of the reservoir fluid based on the first similarity and the second similarity.

[0054] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one piece of program code, the at least one piece of program code being loaded and executed by the processor to implement the above-described method for identifying reservoir fluid properties.

[0055] On the other hand, a computer-readable storage medium is provided, wherein at least one piece of program code is stored in the storage medium, the at least one piece of program code being loaded and executed by a processor to implement the above-described method for identifying reservoir fluid properties.

[0056] On the other hand, a computer program product is provided, the product storing at least one piece of program code, the at least one piece of program code being executed by a processor to implement the above-described method for identifying reservoir fluid properties.

[0057] In this embodiment, based on physical experimental data of sample rocks, multiple influencing factors affecting the resistivity of water and oil layers can be accurately identified. A first water layer resistivity model and a first oil layer resistivity model are established based on these multiple influencing factors, thereby enabling the fusion of multiple factors to establish resistivity models (first water layer resistivity model and first oil layer resistivity model). Compared to single-factor models, this improves the accuracy of the established resistivity models. Furthermore, the well logging sensitivity curve is determined based on the first water layer resistivity model and the first oil layer resistivity model, both of which are determined based on physical experimental data of sample rocks. Therefore, this application can achieve the determination of resistivity models by combining physical experimental data and conventional well logging data, that is, by combining theoretical data and actual data to determine the resistivity models, thereby improving the accuracy of the determined resistivity models (second water layer resistivity model and second oil layer resistivity model), and further improving the accuracy of fluid property identification based on the resistivity models (second water layer resistivity model and second oil layer resistivity model).

[0058] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating a method for identifying reservoir fluid properties according to an exemplary embodiment of this application;

[0060] Figure 2 This is a flowchart illustrating the construction of a resistivity model, as shown in an exemplary embodiment of this application;

[0061] Figure 3 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of a water layer under laboratory conditions, as shown in an exemplary embodiment of this application.

[0062] Figure 4 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of a water layer under laboratory conditions, as shown in an exemplary embodiment of this application.

[0063] Figure 5 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of a water layer under laboratory conditions, as shown in an exemplary embodiment of this application.

[0064] Figure 6 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of a water layer under laboratory conditions, as shown in an exemplary embodiment of this application.

[0065] Figure 7 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of a water layer under laboratory conditions, as shown in an exemplary embodiment of this application.

[0066] Figure 8 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of a water layer under laboratory conditions, as shown in an exemplary embodiment of this application.

[0067] Figure 9 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of a water layer under laboratory conditions, as shown in an exemplary embodiment of this application.

[0068] Figure 10 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of a water layer under laboratory conditions, as shown in an exemplary embodiment of this application.

[0069] Figure 11 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of oil reservoir under laboratory conditions, as shown in an exemplary embodiment of this application.

[0070] Figure 12 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of oil reservoir under laboratory conditions, as shown in an exemplary embodiment of this application.

[0071] Figure 13 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of oil reservoir under laboratory conditions, as shown in an exemplary embodiment of this application.

[0072] Figure 14 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of oil reservoir under laboratory conditions, as shown in an exemplary embodiment of this application.

[0073] Figure 15 This is a schematic diagram illustrating the analysis of the influence of different influencing factors on the resistivity of oil reservoir under laboratory conditions, as shown in an exemplary embodiment of this application.

[0074] Figure 16 This is a statistical diagram illustrating the relationship between formation water resistivity, mud filtrate resistivity, and spontaneous potential anomaly amplitude in a portion of a well, as shown in an exemplary embodiment of this application.

[0075] Figure 17 This is a schematic diagram illustrating a resistivity model constructed based on well logging sensitivity curves, as shown in an exemplary embodiment of this application.

[0076] Figure 18 This application illustrates, in an exemplary embodiment, a second water layer resistivity model and a second oil layer resistivity model of the Minghua Town Formation to the Shasan Oil Formation in the coastal area;

[0077] Figure 19 This application illustrates, in an exemplary embodiment, a second water layer resistivity model and a second oil layer resistivity model of the Minghua Town Formation to the Shasan Oil Formation in the coastal area;

[0078] Figure 20 This application illustrates, in an exemplary embodiment, a second water layer resistivity model and a second oil layer resistivity model of the Minghua Town Formation to the Shasan Oil Formation in the coastal area;

[0079] Figure 21 This application illustrates, in an exemplary embodiment, a second water layer resistivity model and a second oil layer resistivity model of the Minghua Town Formation to the Shasan Oil Formation in the coastal area;

[0080] Figure 22 This application illustrates, in an exemplary embodiment, a second water layer resistivity model and a second oil layer resistivity model of the Minghua Town Formation to the Shasan Oil Formation in the coastal area;

[0081] Figure 23 This application illustrates, in an exemplary embodiment, a second water layer resistivity model and a second oil layer resistivity model of the Minghua Town Formation to the Shasan Oil Formation in the coastal area;

[0082] Figure 24 This is a schematic diagram illustrating an exemplary embodiment of the resistivity construction sweet spot evaluation combination technique in the Tangdong Ed3 section;

[0083] Figure 25This is a schematic diagram illustrating an exemplary embodiment of the resistivity construction sweet spot evaluation combination technique in the Tangdong Ed3 section;

[0084] Figure 26 This is a schematic diagram illustrating an exemplary embodiment of the resistivity construction sweet spot evaluation combination technique in the Tangdong Ed3 section;

[0085] Figure 27 This is a schematic diagram illustrating an exemplary embodiment of the resistivity construction sweet spot evaluation combination technique in the Tangdong Ed3 section;

[0086] Figure 28 This is a block diagram illustrating a reservoir fluid property identification device according to an exemplary embodiment of this application;

[0087] Figure 29 This is a block diagram illustrating a computer device in an exemplary embodiment of this application. Detailed Implementation

[0088] To make the technical solution and advantages of this application clearer, the embodiments of this application will be described in further detail below.

[0089] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0090] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the physical experimental data of the sample rocks and the well logging sensitivity curves involved in this application were obtained with full authorization.

[0091] Please refer to Figure 1 This document illustrates a flowchart of a method for identifying reservoir fluid properties according to an exemplary embodiment of this application. The execution entity of this embodiment can be a computer device, as shown in the reference. Figure 1 The method includes:

[0092] Step 101: The computer equipment determines the physical experimental data of the sample rocks, which include saturated water rock samples and saturated oil rock samples. The physical experimental data of the saturated water rock samples are the analysis data of the electrical influencing factors of the saturated water rock samples under different laboratory conditions, and the physical experimental data of the saturated oil rock samples are the analysis data of the electrical influencing factors of the saturated oil rock samples under different laboratory conditions.

[0093] The sample rocks can be from key exploration areas such as coastal areas (Beidagang, Banqiao, Qibei, Nanpi, Kongxi, Yangerzhuang). Different laboratory conditions can include varying temperatures, pressures, formation water, physical properties, lithologies, and degrees of intrusion. Specifically, in this embodiment, computer equipment is used to analyze the influence of different laboratory conditions on reservoir electrical properties in saturated water and oil samples from key exploration areas. This includes the differences in the effects of temperature, pressure, formation water, physical properties, lithology, and intrusion on the properties of different fluids, collecting relevant measurement data, and conducting electrical influencing factor analysis.

[0094] For example, please refer to Figure 2 The computer equipment is first equipped with physical experimental data of sample rocks, including physical experimental data of saturated water rock samples and saturated oil rock samples, and related measurements and data collection and processing of temperature, pressure, formation water, physical properties, lithology and degree of intrusion.

[0095] Step 102: Based on the physical experimental data of the saturated water rock sample, the computer equipment determines several primary influencing factors of the saturated water rock sample. These primary influencing factors are the factors that affect the resistivity of the saturated water rock sample under laboratory conditions, and they include porosity, formation water salinity, carbonate content, median particle size, and clay content.

[0096] Several primary influencing factors can reflect the relationship between the resistivity and physical properties, lithology, and oil content of saturated water rock samples under laboratory conditions; for example, please refer to... Figure 2 This study establishes the relationship between resistivity and physical properties, lithology, and oil content. Physical experimental data from saturated water-bearing rock samples includes the relationships between porosity and resistivity, formation water salinity and resistivity change rate, pressure and resistivity change rate, temperature and resistivity change rate, carbonate content and resistivity, median grain size and resistivity, CEC and resistivity, and clay content and cation exchange capacity (CEC is used to indirectly represent resistivity). Therefore, based on the physical experimental data from saturated water-bearing rock samples, computer equipment identifies five primary influencing factors with a greater impact than the first preset level.

[0097] For example, Figures 3-10This is an analysis of the effects of different influencing factors on water layer resistivity under laboratory conditions, based on... Figures 3-10 It can be seen that porosity and formation water salinity are the primary influencing factors of resistivity in saturated water rock samples. Among these, lower porosity leads to a weaker correlation between porosity and formation factors; for the same porosity, the variation in formation factors can range by orders of magnitude. Formation factor calculation models considering only porosity cannot meet the accuracy requirements; formation water salinity must also be considered in the relationship between formation factors and porosity. Saturated water resistivity-porosity models established by region and formation water salinity show significantly improved accuracy, generally exhibiting an exponential decrease in resistivity with increasing porosity. However, the model coefficients differ across regions and strata. Formation water salinity and porosity jointly constrain the electrical properties of saturated water reservoirs; the rate of change in electrical properties increases exponentially with formation water salinity. Higher formation water salinity results in a more significant decrease in the electrical properties of saturated water reservoirs, and better physical properties also lead to a more pronounced decrease in electrical properties. Mineral composition, lithological grain size, clay content, and clay type all influence the resistivity of saturated water to some extent. When the carbonate rock content is greater than 5%, resistivity increases with increasing carbonate rock content. Increased median grain size and coarser skeletal particles also contribute to increased resistivity. Different strata exhibit varying conductivity due to clay content; higher clay content leads to increased conductivity and decreased resistivity. Pressure significantly affects measurement results; increased pressure leads to increased resistivity. The degree of increase is closely related to physical properties; poorer physical properties result in greater sensitivity to pressure changes. Temperature also significantly affects measurement results; increased temperature leads to decreased resistivity. The degree of decrease is closely related to physical properties; better physical properties result in greater sensitivity to temperature changes.

[0098] In the subsequent evaluation of fluid properties, conventional logging data should be combined. The three influencing factors of formation pressure, temperature, and clay type can be defined by different regions and formation groups. Therefore, in the factors affecting the resistivity of pure water formation, the remaining five factors closely related to the logging response should be considered. Thus, the computer determines several primary influencing factors, including porosity, formation water salinity, carbonate content, median particle size, and clay content.

[0099] In related technologies, conventional logging data is used to identify reservoir fluid properties. However, this process only focuses on optimizing the parameters of the resistivity model, neglecting the analysis of factors affecting resistivity values; or it considers only one influencing factor, thus failing to comprehensively analyze the logging response characteristics, resulting in a decrease in the accuracy of fluid property identification. This application, however, considers multiple influencing factors, thereby comprehensively analyzing the logging response characteristics and improving the accuracy of fluid property identification.

[0100] Step 103: Based on the physical experimental data of the saturated oil rock sample, the computer equipment determines several secondary influencing factors of the saturated oil rock sample. These secondary influencing factors are the factors that affect the resistivity of the saturated oil rock sample under laboratory conditions, and include bound water porosity, carbonate content, median grain size, and clay content.

[0101] Several primary influencing factors can reflect the relationship between resistivity and physical properties, lithology, and oil-bearing properties of saturated oil rock samples under laboratory conditions. The physical experimental data of saturated oil rock samples include the relationships between bound water saturation and the rate of increase in reservoir resistivity, porosity and the rate of increase in reservoir resistivity, carbonate content and reservoir resistivity, median grain size and reservoir resistivity, and CEC and reservoir resistivity. Therefore, based on the physical experimental data of saturated oil rock samples, computer equipment identifies the four primary influencing factors with the greatest impact.

[0102] For example, Figures 11-15 This is an analysis of the effects of different influencing factors on reservoir resistivity under laboratory conditions, based on... Figures 11-15 It can be seen that the resistivity and porosity of saturated oil generally follow Archie's formula. The main controlling factor for the increase in oil reservoir resistivity is the saturation of bound water; as the saturation of bound water increases, the increase in resistivity decreases. Porosity is the second most important factor; as porosity increases, the increase in resistivity increases. Mineral composition, lithological grain size, clay content, and clay type all influence the increase in oil reservoir resistivity to some extent. When the carbonate rock content is greater than 5%, the increase in resistivity increases with increasing carbonate rock content. A larger median grain size and coarser skeletal grains also lead to a larger increase in resistivity. Different strata have different clay content and conductivity; as clay content increases conductivity, the increase in resistivity decreases.

[0103] Step 104: The computer equipment determines the first water layer resistivity model of the saturated water rock sample under laboratory conditions based on multiple primary influencing factors.

[0104] Based on several primary influencing factors, the computer equipment determined the first water layer resistivity model of the saturated water rock sample under laboratory conditions using the following formula 2.

[0105] Formula 2: First water layer resistivity model Ro = F(porosity, formation water salinity, carbonate content, median particle size, clay content).

[0106] Step 105: The computer equipment determines the first oil layer resistivity model of the saturated oil rock sample under laboratory conditions based on multiple secondary influencing factors.

[0107] The first oil layer resistivity model of saturated oil rock samples under laboratory conditions can be represented by the oil layer resistivity increase rate model; correspondingly, based on multiple second influencing factors, the computer equipment determines the first oil layer resistivity model of saturated oil rock samples under laboratory conditions through the following formula three.

[0108] Formula 3: Oil Reservoir Resistivity Increase Model: I = F(Bound Water Porosity, Carbonate Content, Median Particle Size, Shale Content). For example, please refer to [reference needed]. Figure 2 Computer equipment was used to construct resistivity models of the first water layer and the first oil layer under laboratory conditions.

[0109] Step 106: The computer equipment determines the logging sensitivity curve based on the first water layer resistivity model and the first oil layer resistivity model. The logging sensitivity curve is the curve showing the change between the formation resistivity and the fluid properties.

[0110] Laboratory studies have shown that, besides the influence of oil content, other limiting factors for reservoir resistivity include formation water resistivity, bound water porosity, carbonate content, and rock particle size. To accurately simulate oil and water layer resistivity under formation conditions, it is necessary to select appropriate logging sensitivity curves. Accordingly, this step can be as follows: Using a computer-based model of first water layer resistivity and second oil layer resistivity, multiple third influencing factors are identified. These factors influence formation resistivity and include water layer resistivity, bound water porosity, carbonate content, and rock particle size. Based on these third influencing factors, logging sensitivity curves are determined. For example, based on these factors, the logging sensitivity curves could be spontaneous potential curves, natural gamma ray curves, or nuclear magnetic resonance (NMR) logging curves. Among them, the spontaneous potential curve is used to reflect the relationship between the natural gamma radiation intensity of radioactive elements in the formation and the content of radioactive fluids in the formation; the natural gamma curve is used to reflect the relationship between the natural gamma radiation intensity of radioactive elements in the formation and the content of radioactive fluids in the formation; and the nuclear magnetic resonance logging curve is used to reflect the relationship between underground rock strata and fluid properties.

[0111] Step 107: The computer equipment determines the resistivity model of the second water layer of the saturated water rock sample under formation conditions based on the well logging sensitivity curve.

[0112] In one possible implementation, the logging sensitivity curve is the spontaneous potential (SP) curve, which reflects the electrical difference between the formation and the fluid encountered during drilling. Based on the changes in the amplitude of the SP anomaly and the resistivity of the drilling mud filtrate, a formation water resistivity model is calculated. Accordingly, this step can be: The computer equipment determines the SP anomaly amplitude difference and the drilling mud filtrate resistivity based on the SP anomaly curve; based on the SP anomaly amplitude difference and the drilling mud filtrate resistivity, the resistivity model of the second water layer under formation conditions is determined using the following formula:

[0113] Formula 1: ΔSP=-48.421g(R) mf / R wz -0.007

[0114] Where △SP represents the difference in amplitude of spontaneous potential abnormality, R mf R represents the resistivity of the mud filtrate. wz This represents the resistivity model of the second water layer.

[0115] For example, Figure 16 This is a statistical graph showing the relationship between formation water resistivity, mud filtrate resistivity, and spontaneous potential anomaly amplitude in a subset of wells. Based on Figure 16 The amplitude of the spontaneous potential anomaly in cocoa has a good correlation with the resistivity of mud filtrate / formation water resistivity, and its expression is shown in Formula 1.

[0116] In another possible implementation, the logging sensitivity curve is the natural gamma curve, which reflects the relationship between the natural gamma radiation intensity of radioactive elements in the formation and the content of radioactive fluids in the formation. Accordingly, this step can be: the computer equipment determines the natural gamma curve value, the maximum natural gamma curve value and the minimum natural gamma curve value at the pure sand layer in the same layer group based on the natural gamma curve; based on the natural gamma curve value, the maximum natural gamma curve value and the minimum natural gamma curve value, the resistivity model of the second water layer of the saturated water rock sample under the formation conditions is determined.

[0117] The computer equipment uses Formula 4 to determine the resistivity model of the second water layer of the saturated water rock sample under the formation conditions, based on the natural gamma curve values, the maximum natural gamma curve values, and the minimum natural gamma curve values.

[0118] Formula 4: Second water layer resistivity model = ((GR-GR) min ) / (GR max -GR min ))

[0119] In another possible implementation, the logging sensitivity curve is a nuclear magnetic resonance (NMR) logging curve, which reflects the relationship between subsurface rock formations and fluid properties. Ideally, NMR logging should obtain accurate bound water pore volume, and the 120-inch resistivity curves of pure oil and water layers from the oil testing results should be used as the resistivity of the oil and water layers in the model construction. Accordingly, this step can be: using computer equipment to determine the bound water pore volume based on the NMR logging curve; and based on the bound water pore volume, determining the resistivity model of the second water layer under formation conditions for the saturated water rock sample.

[0120] The steps for determining the resistivity model of the second aquifer of a saturated water rock sample under formation conditions based on the void volume of bound water using computer equipment can be as follows: the computer equipment determines the relative gamma value of the saturated water rock sample; based on the void volume of bound water and the relative gamma value, the resistivity model of the second aquifer of the saturated water rock sample under formation conditions is determined using the following formula five.

[0121] Formula 5: Second water layer resistivity model = F(bound water pore volume, gamma relative value).

[0122] In other embodiments, the logging sensitivity curve is the M2RX curve, which is the water-retaining layer resistivity curve. Accordingly, the computer equipment determines a second water-retaining layer resistivity model for the saturated water-rock sample under formation conditions based on the M2RX curve. For example, the computer equipment directly uses the M2RX curve as the second water-retaining layer resistivity model; or, the computer equipment corrects the M2RX curve to obtain the second water-retaining layer resistivity model. For example, please refer to... Figure 17 The logging sensitivity curve can be the spontaneous potential curve, the spontaneous gamma curve, the M2RX curve, or the nuclear magnetic resonance curve. Based on the spontaneous potential curve, the spontaneous gamma curve, the M2RX curve, or the nuclear magnetic resonance curve, a second water layer resistivity model and a second oil layer resistivity model are constructed.

[0123] The second water layer resistivity model can be a resistivity curve or the first relationship data, which is the relationship between total bound water porosity and water layer resistivity.

[0124] Step 108: The computer equipment determines the resistivity model of the second oil layer of the saturated oil rock sample under formation conditions based on the well logging sensitivity curve.

[0125] In some embodiments, this step is similar to step 107, and will not be repeated here. For example, please refer to [link to relevant documentation]. Figure 2 Under the guidance of laboratory models, computer equipment selects optimal well logging sensitive curves and establishes resistivity models of the second water layer and the second oil layer under formation conditions.

[0126] It should be noted that steps 107-108 are the process of establishing the second water layer resistivity model and the second oil layer resistivity model, while step 109 is the process of using the second water layer resistivity model and the second oil layer resistivity model to identify (or evaluate) fluid properties. Therefore, steps 107-108 only need to be executed once. It is not necessary to repeatedly determine the second water layer resistivity model and the second oil layer resistivity model in subsequent steps. Step 109 can be directly executed to identify the fluid properties using the second water layer resistivity model and the second oil layer resistivity model.

[0127] The second oil reservoir resistivity model can be a resistivity curve or second relationship data, which represents the relationship between total bound water porosity and oil reservoir resistivity. For example, please refer to... Figure 18-23 , Figures 18-23 It is the resistivity model of the second water layer and the second oil layer from the Minghuazhen Formation to the Shasan Oil Formation in the coastal area.

[0128] Step 109: When identifying reservoir fluid properties, the computer equipment identifies reservoir fluid properties based on the second water layer resistivity model and the second oil resistivity model.

[0129] For example, please continue to refer to Figure 2 The computer equipment identifies the properties of the reservoir fluid based on a second water layer resistivity model and a second oil layer resistivity model. In one possible implementation, both the second water layer resistivity and the second oil layer resistivity are resistivity curves; correspondingly, this step can be: the computer equipment determines the measured resistivity of the reservoir fluid; determines the first similarity between the measured resistivity and the second water layer resistivity model; determines the second similarity between the measured resistivity and the second oil layer resistivity model; and determines the properties of the reservoir fluid based on the first and second similarities. For example, if the first similarity is greater than the second similarity, the computer equipment determines the reservoir fluid to be a water layer; if the first similarity is less than the second similarity, the computer equipment determines the reservoir fluid to be an oil layer.

[0130] In another possible implementation, the second water layer resistivity model represents the first relationship between total bound water porosity and water layer resistivity, and the second relationship represents the second relationship between total bound water porosity and oil layer resistivity. Correspondingly, this step can be: computer equipment determines the measured resistivity of the reservoir fluid, determines the total bound water porosity of the reservoir fluid, and based on the total bound water porosity of the reservoir fluid, determines the first resistivity and the second resistivity of the reservoir fluid using the second water layer resistivity model and the second oil layer resistivity model respectively; determines the difference between the measured resistivity and the first resistivity to obtain the first difference; determines the difference between the measured resistivity and the second resistivity to obtain the second difference; if the first difference is greater than the second difference, the reservoir fluid is determined to be an oil layer; if the first difference is less than the second difference, the reservoir fluid is determined to be a water layer.

[0131] Appendix Figures 24-27 This paper presents an application example of resistivity-based sweet spot evaluation technology in the Tangdong Ed3 section. A comparison of the T2 spectra of four wells reveals differences in pore structure. Well Tangdong 6X1 is predominantly medium to large pore size, while Tangdong 9X6, 20X3, and 13X3 are predominantly medium to small pore size. Based on reservoir classification and tight oil sweet spot prediction results from NMR, Tangdong 6X1 has good natural production capacity, while the other three wells require fracturing to achieve industrial production. A comparison of measured and constructed resistivity shows that the measured resistivity of Tangdong 13X3 is located in the constructed resistivity water zone, while the top of the measured resistivity of Tangdong 6X1 is located in the constructed resistivity oil zone, and the bottom is located in the constructed resistivity water zone. The measured resistivity of the other two wells is located in the constructed resistivity oil zone. Tangdong 13X3 is interpreted as an oil-bearing water layer, Tangdong 6X1 as a top-oil, bottom-water layer, and the other two wells as oil layers. The oil testing results are consistent with the interpretation conclusions.

[0132] In this embodiment, based on physical experimental data of sample rocks, multiple influencing factors affecting the resistivity of water and oil layers can be accurately identified. A first water layer resistivity model and a first oil layer resistivity model are established based on these multiple influencing factors, thereby enabling the fusion of multiple factors to establish resistivity models (first water layer resistivity model and first oil layer resistivity model). Compared to single-factor models, this improves the accuracy of the established resistivity models. Furthermore, the well logging sensitivity curve is determined based on the first water layer resistivity model and the first oil layer resistivity model, both of which are determined based on physical experimental data of sample rocks. Therefore, this application can achieve the determination of resistivity models by combining physical experimental data and conventional well logging data, that is, by combining theoretical data and actual data to determine the resistivity models, thereby improving the accuracy of the determined resistivity models (second water layer resistivity model and second oil layer resistivity model), and further improving the accuracy of fluid property identification based on the resistivity models (second water layer resistivity model and second oil layer resistivity model).

[0133] Furthermore, the fluid property identification based on resistivity models (second water layer resistivity model and second oil layer resistivity model) in this application embodiment can improve the interpretation accuracy. It only uses conventional logging curves and does not require additional special methods, thus achieving low cost and providing strong technical support for exploration and development, and increased reserves and production in key areas.

[0134] Please refer to Figure 28 This illustration shows a block diagram of a reservoir fluid property identification device according to an exemplary embodiment of this application. The device includes:

[0135] The first determining module 2801 is used to determine the physical experimental data of the sample rocks, which include saturated water rock samples and saturated oil rock samples. The physical experimental data of the saturated water rock samples are the analysis data of the electrical influencing factors of the saturated water rock samples under different laboratory conditions, and the physical experimental data of the saturated oil rock samples are the analysis data of the electrical influencing factors of the saturated oil rock samples under different laboratory conditions.

[0136] The second determining module 2802 is used to determine multiple first influencing factors of saturated water rock samples based on physical experimental data of saturated water rock samples. The multiple first influencing factors are the factors that affect the resistivity of saturated water rock samples under laboratory conditions, and the multiple first influencing factors include porosity, formation water salinity, carbonate content, median particle size and clay content.

[0137] The third determining module 2803 is used to determine multiple secondary influencing factors of saturated oil rock samples based on physical experimental data of saturated oil rock samples. These multiple secondary influencing factors are factors that affect the resistivity of saturated oil rock samples under laboratory conditions, and include bound water porosity, carbonate content, median grain size, and clay content.

[0138] The fourth determination module 2804 is used to determine the first water layer resistivity model of saturated water rock sample under laboratory conditions based on multiple first influencing factors.

[0139] The fifth determination module 2805 is used to determine the first oil layer resistivity model of saturated oil rock samples under laboratory conditions based on multiple second influencing factors;

[0140] The sixth determining module 2806 is used to determine the logging sensitive curve based on the first water layer resistivity model and the first oil layer resistivity model. The logging sensitive curve is the curve showing the change between the formation resistivity and the fluid properties.

[0141] The seventh module 2807 is used to determine the resistivity model of the second water layer of saturated water rock sample under formation conditions based on well logging sensitivity curves.

[0142] The eighth determination module 2808 is used to determine the resistivity model of the second oil layer of saturated oil rock sample under formation conditions based on well logging sensitivity curves;

[0143] The identification module 2809 is used to identify reservoir fluid properties based on the second water layer resistivity model and the second oil resistivity model when identifying reservoir fluid properties.

[0144] In one possible implementation, the sixth determining module 2806 is used to determine multiple third influencing factors based on the first water layer resistivity model and the second oil layer resistivity model. The multiple third influencing factors are factors that affect the resistivity of the formation, and the multiple third influencing factors include water layer resistivity, bound water porosity, carbonate content and rock particle size; and to determine the logging sensitivity curve based on the multiple third influencing factors.

[0145] In another possible implementation, the logging sensitivity curve is the spontaneous potential (SP) curve, which reflects the electrical difference between the formation and the fluid encountered during drilling. The seventh determination module 2807 is used to determine the SP anomaly amplitude difference and mud filtrate resistivity based on the SP curve. Based on the SP anomaly amplitude difference and mud filtrate resistivity, the resistivity model of the second water layer under formation conditions is determined using the following formula:

[0146] Formula 1: ΔSP=-48.421g(R) mf / R wz-0.007

[0147] Where △SP represents the difference in amplitude of spontaneous potential abnormality, R mf R represents the resistivity of the mud filtrate. wz This represents the resistivity model of the second water layer.

[0148] In another possible implementation, the logging sensitivity curve is the natural gamma curve, which is used to reflect the relationship between the natural gamma radiation intensity of radioactive elements in the formation and the content of radioactive fluids in the formation.

[0149] The seventh determination module 2807 is used to determine the natural gamma curve value, the maximum natural gamma curve value and the minimum natural gamma curve value at the pure sand layer in the same layer group based on the natural gamma curve; and to determine the second water layer resistivity model of the saturated water rock sample under the formation conditions based on the natural gamma curve value, the maximum natural gamma curve value and the minimum natural gamma curve value.

[0150] In another possible implementation, the logging sensitivity curve is the nuclear magnetic resonance logging curve, which is used to reflect the relationship between underground rock formations and fluid properties;

[0151] The seventh determination module 2807 is used to determine the volume of bound water voids based on nuclear magnetic resonance logging curves; and to determine the resistivity model of the second water layer of saturated water rock samples under formation conditions based on the volume of bound water voids.

[0152] In another possible implementation, the identification module 2809 is used to determine the measured resistivity of the reservoir fluid; determine a first similarity between the measured resistivity and the second water layer resistivity model; determine a second similarity between the measured resistivity and the second oil layer resistivity model; and determine the properties of the reservoir fluid based on the first and second similarities.

[0153] In this embodiment, based on physical experimental data of sample rocks, multiple influencing factors affecting the resistivity of water and oil layers can be accurately identified. A first water layer resistivity model and a first oil layer resistivity model are established based on these multiple influencing factors, thereby enabling the fusion of multiple factors to establish resistivity models (first water layer resistivity model and first oil layer resistivity model). Compared to single-factor models, this improves the accuracy of the established resistivity models. Furthermore, the well logging sensitivity curve is determined based on the first water layer resistivity model and the first oil layer resistivity model, both of which are determined based on physical experimental data of sample rocks. Therefore, this application can achieve the determination of resistivity models by combining physical experimental data and conventional well logging data, that is, by combining theoretical data and actual data to determine the resistivity models, thereby improving the accuracy of the determined resistivity models (second water layer resistivity model and second oil layer resistivity model), and further improving the accuracy of fluid property identification based on the resistivity models (second water layer resistivity model and second oil layer resistivity model).

[0154] It should be noted that the reservoir fluid property identification device provided in the above embodiments is only illustrated by the division of the above functional modules when identifying reservoir fluid properties. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the reservoir fluid property identification device and the reservoir fluid property identification method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0155] Please refer to Figure 29 , Figure 29 A structural block diagram of a computer device 2900 provided in an exemplary embodiment of this application is shown. The computer device 2900 may be a portable mobile computer device, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The computer device 2900 may also be referred to as a user device, portable computer device, laptop computer device, desktop computer device, or other names.

[0156] Typically, computer device 2900 includes a processor 2901 and a memory 2902.

[0157] Processor 2901 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 2901 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 2901 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 2901 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, processor 2901 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0158] The memory 2902 may include one or more computer-readable storage media, which may be non-transitory. The memory 2902 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 2902 is used to store at least one piece of program code, which is executed by the processor 2901 to implement the operations performed by the computer device in the in-vehicle display method provided in the method embodiments of this application.

[0159] In some embodiments, the computer device 2900 may optionally include a peripheral device interface 2903 and at least one peripheral device. The processor 2901, memory 2902, and peripheral device interface 2903 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 2903 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 2904, a display screen 2905, a camera assembly 2906, an audio circuit 2907, and a power supply 2908.

[0160] Peripheral device interface 2903 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 2901 and memory 2902. In some embodiments, processor 2901, memory 2902 and peripheral device interface 2903 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 2901, memory 2902 and peripheral device interface 2903 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0161] The radio frequency (RF) circuit 2904 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 2904 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 2904 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the RF circuit 2904 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 2904 can communicate with other computer devices through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 2904 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0162] Display screen 2905 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 2905 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 2901 for processing. In this case, display screen 2905 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 2905, disposed on the front panel of computer device 2900; in other embodiments, there may be at least two display screens 2905, disposed on different surfaces of computer device 2900 or in a folded design; in still other embodiments, display screen 2905 may be a flexible display screen, disposed on a curved or folded surface of computer device 2900. Furthermore, display screen 2905 may also be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 2905 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0163] The camera assembly 2906 is used to acquire images or videos. Optionally, the camera assembly 2906 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the computer device, and the rear-facing camera is located on the back of the computer device. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 2906 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0164] The audio circuit 2907 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting them into electrical signals that are input to the processor 2901 for processing, or to the radio frequency circuit 2904 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, positioned at different locations within the computer device 2900. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 2901 or the radio frequency circuit 2904 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 2907 may also include a headphone jack.

[0165] Power supply 2908 is used to supply power to the various components in computer device 2900. Power supply 2908 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 2908 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0166] In some embodiments, the computer device 2900 further includes one or more sensors 2909. The one or more sensors 2909 include, but are not limited to, an accelerometer 2910, a gyroscope 2911, a pressure sensor 2912, an optical sensor 2913, and a proximity sensor 2914.

[0167] Accelerometer 2910 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by computer device 2900. For example, accelerometer 2910 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 2901 can control display screen 2905 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 2910. Accelerometer 2910 can also be used for games or for acquiring user motion data.

[0168] The gyroscope sensor 2911 can detect the orientation and rotation angle of the computer device 2900. The gyroscope sensor 2911 can work in conjunction with the accelerometer sensor 2910 to acquire 3D motion data from the user on the computer device 2900. Based on the data acquired by the gyroscope sensor 2911, the processor 2901 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0169] Pressure sensor 2912 can be disposed on the side bezel of computer device 2900 and / or on the lower layer of display screen 2905. When pressure sensor 2912 is disposed on the side bezel of computer device 2900, it can detect the user's grip signal on computer device 2900, and processor 2901 can perform left / right hand recognition or quick operation based on the grip signal collected by pressure sensor 2912. When pressure sensor 2912 is disposed on the lower layer of display screen 2905, processor 2901 can control operable controls on the UI interface based on the user's pressure operation on display screen 2905. Operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0170] Optical sensor 2913 is used to collect ambient light intensity. In one embodiment, processor 2901 can control the display brightness of display screen 2905 based on the ambient light intensity collected by optical sensor 2913. Specifically, when the ambient light intensity is high, the display brightness of display screen 2905 is increased; when the ambient light intensity is low, the display brightness of display screen 2905 is decreased. In another embodiment, processor 2901 can also dynamically adjust the shooting parameters of camera assembly 2906 based on the ambient light intensity collected by optical sensor 2913.

[0171] The proximity sensor 2914, also known as a distance sensor, is typically located on the front panel of the computer device 2900. The proximity sensor 2914 is used to detect the distance between the user and the front of the computer device 2900. In one embodiment, when the proximity sensor 2914 detects that the distance between the user and the front of the computer device 2900 is gradually decreasing, the processor 2901 controls the display screen 2905 to switch from a screen-on state to a screen-off state; when the proximity sensor 2914 detects that the distance between the user and the front of the computer device 2900 is gradually increasing, the processor 2901 controls the display screen 2905 to switch from a screen-off state to a screen-on state.

[0172] Those skilled in the art will understand that Figure 29 The structure shown does not constitute a limitation on the computer device 2900, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0173] This application also provides a computer-readable storage medium storing at least one line of program code, which is loaded and executed by a processor to implement the reservoir fluid property identification method described in any of the above implementations. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage device.

[0174] This application also provides a computer program product that stores at least one piece of program code, which is loaded and executed by a processor to implement the reservoir fluid property identification method shown in the above embodiments.

[0175] In some embodiments, the computer program product involved in the present application can be deployed and executed on a computer device, or on multiple computer devices located in one location, or on multiple computer devices distributed in multiple locations and interconnected through a communication network. Multiple computer devices distributed in multiple locations and interconnected through a communication network can form a blockchain system.

[0176] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0177] The above description is only for the purpose of enabling those skilled in the art to understand the technical solution of this application, and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for identifying reservoir fluid properties, characterized in that, The method includes: The physical experimental data of the sample rocks are determined. The sample rocks include saturated water rock samples and saturated oil rock samples. The physical experimental data of the saturated water rock samples are the analysis data of the electrical influencing factors of the saturated water rock samples under different laboratory conditions. The physical experimental data of the saturated oil rock samples are the analysis data of the electrical influencing factors of the saturated oil rock samples under different laboratory conditions. Based on the physical experimental data of the saturated water rock sample, several first influencing factors of the saturated water rock sample were determined. These first influencing factors are the factors that affect the resistivity of the saturated water rock sample under laboratory conditions, and the multiple first influencing factors include porosity, formation water salinity, carbonate content, median particle size, and clay content. Based on the physical experimental data of the saturated oil rock sample, several second influencing factors were determined. These second influencing factors are those that affect the resistivity of the saturated oil rock sample under laboratory conditions, and include bound water porosity, carbonate content, median grain size, and clay content. Based on the aforementioned multiple first influencing factors, the first water layer resistivity model of the saturated water rock sample under laboratory conditions is determined; Based on the aforementioned multiple second influencing factors, the resistivity model of the first oil layer of the saturated oil rock sample under laboratory conditions was determined; Based on the first water layer resistivity model and the first oil layer resistivity model, a well logging sensitive curve is determined, which is the curve showing the change between the formation resistivity and fluid properties; Based on the well logging sensitivity curve, the resistivity model of the second water layer of the saturated water rock sample under formation conditions is determined; Based on the well logging sensitivity curve, the resistivity model of the second oil layer of the saturated oil rock sample under formation conditions is determined; When identifying reservoir fluid properties, the reservoir fluid properties are identified based on the second water layer resistivity model and the second oil resistivity model.

2. The method according to claim 1, characterized in that, The determination of the logging sensitivity curve based on the first water layer resistivity model and the first oil layer resistivity model includes: Based on the first water layer resistivity model and the second oil layer resistivity model, several third influencing factors are determined. These third influencing factors are factors that affect the resistivity of the formation, and they include water layer resistivity, bound water porosity, carbonate content, and rock particle size. Based on the aforementioned third influencing factors, the well logging sensitivity curve is determined.

3. The method according to claim 1, characterized in that, The determination of the second water layer resistivity model of the saturated water rock sample under formation conditions based on the well logging sensitivity curve includes: The logging sensitivity curve is the spontaneous potential curve, which is used to reflect the electrical difference between the formation and the fluid encountered during drilling; based on the spontaneous potential curve, the amplitude difference of spontaneous potential anomalies and the resistivity of mud filtrate are determined. Based on the difference in the amplitude of the spontaneous potential anomaly and the resistivity of the mud filtrate, the resistivity model of the second aquifer of the saturated water-rock sample under formation conditions is determined using the following formula: Formula 1: ΔSP=-48.421g(R) mf / R WZ -0.007 Wherein, △SP represents the amplitude difference of the natural potential abnormality, and R mf R represents the resistivity of the mud filtrate. wz This represents the resistivity model of the second water layer.

4. The method according to claim 1, characterized in that, The determination of the second water layer resistivity model of the saturated water rock sample under formation conditions based on the well logging sensitivity curve includes: The logging sensitivity curve is a natural gamma curve, which is used to reflect the relationship between the natural gamma radiation intensity of radioactive elements in the formation and the content of radioactive fluids in the formation. Based on the natural gamma curve, determine the natural gamma curve value, the maximum natural gamma curve value and the minimum natural gamma curve value at the pure sand layer in the same layer group; Based on the natural gamma curve values, the maximum natural gamma curve value, and the minimum natural gamma curve value, the second water layer resistivity model of the saturated water rock sample under formation conditions is determined.

5. The method according to claim 1, characterized in that, The determination of the second water layer resistivity model of the saturated water rock sample under formation conditions based on the well logging sensitivity curve includes: The logging sensitivity curve is a nuclear magnetic resonance logging curve, which is used to reflect the relationship between underground rock formations and fluid properties. Based on the nuclear magnetic resonance logging curves, the volume of bound water pores was determined. Based on the bound water void volume, the resistivity model of the second water layer of the saturated water rock sample under formation conditions is determined.

6. The method according to claim 1, characterized in that, The identification of reservoir fluid properties based on the second water layer resistivity model and the second oil resistivity model includes: Determine the measured resistivity of the reservoir fluid; Determine the first similarity between the measured resistivity and the second water layer resistivity model; Determine the second similarity between the measured resistivity and the second oil layer resistivity model; The properties of the reservoir fluid are determined based on the first similarity and the second similarity.

7. A device for identifying reservoir fluid properties, characterized in that, The device includes: The first determining module is used to determine the physical experimental data of the sample rocks, which include saturated water rock samples and saturated oil rock samples. The physical experimental data of the saturated water rock samples are the analysis data of the electrical influencing factors of the saturated water rock samples under different laboratory conditions, and the physical experimental data of the saturated oil rock samples are the analysis data of the electrical influencing factors of the saturated oil rock samples under different laboratory conditions. The second determining module is used to determine multiple first influencing factors of the saturated water rock sample based on the physical experimental data of the saturated water rock sample. The multiple first influencing factors are the factors that affect the resistivity of the saturated water rock sample under laboratory conditions, and the multiple first influencing factors include porosity, formation water salinity, carbonate content, median particle size and clay content. The third determining module is used to determine multiple second influencing factors of the saturated oil rock sample based on the physical experimental data of the saturated oil rock sample. The multiple second influencing factors are the factors that affect the resistivity of the saturated oil rock sample under laboratory conditions, and the multiple second influencing factors include bound water porosity, carbonate content, median particle size and clay content. The fourth determining module is used to determine the first water layer resistivity model of the saturated water rock sample under laboratory conditions based on the multiple first influencing factors. The fifth determining module is used to determine the first oil layer resistivity model of the saturated oil rock sample under laboratory conditions based on the multiple second influencing factors. The sixth determining module is used to determine the logging sensitive curve based on the first water layer resistivity model and the first oil layer resistivity model. The logging sensitive curve is the curve showing the change between the formation resistivity and the fluid properties. The seventh determining module is used to determine the second water layer resistivity model of the saturated water rock sample under formation conditions based on the well logging sensitivity curve; The eighth determining module is used to determine the second oil layer resistivity model of the saturated oil rock sample under formation conditions based on the well logging sensitivity curve; The identification module is used to identify the reservoir fluid properties based on the second water layer resistivity model and the second oil resistivity model when identifying reservoir fluid properties.

8. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one piece of program code, which is loaded and executed by the processor to implement the method for identifying reservoir fluid properties as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one piece of program code, which is loaded and executed by a processor to implement the method for identifying reservoir fluid properties as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The product stores at least one piece of program code, which is executed by a processor to implement the method for identifying reservoir fluid properties as described in any one of claims 1 to 6.

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