Deep layer lithium-rich brine logging identification method and system, electronic equipment and storage medium

Abstract

This invention discloses a method, system, electronic device, and storage medium for logging identification of deep lithium-rich brine, belonging to the field of deep brine identification technology. The invention determines lithological identification parameters and constructs a lithological identification curve based on processed natural gamma curves and spontaneous potential curves; it uses a three-porosity curve for physical property interpretation and calculates porosity based on the interpretation results and lithological identification parameters; it constructs a water saturation logging interpretation model using porosity and calculates the content of different fluids in the reservoir based on this model; and it constructs a lithium ion content interpretation model using the water saturation calculation results and combined with hydrochemical analysis data to quantitatively interpret the lithium content. This method achieves quantitative interpretation of lithium ion content in deep brine layers, further improving the identification capability of deep lithium-rich brine reservoirs and providing important basis for predicting the vertical and horizontal distribution of deep lithium-rich brines.

Description

Technical Field

[0001] This invention belongs to the field of deep brine identification technology, and specifically relates to a method, system, electronic device and storage medium for logging and identifying deep lithium-rich brine. Background Technology

[0002] Brine, rich in minerals including precious trace elements such as bromine, iodine, and strontium, is a valuable liquid mineral widely used in industries such as salt production, pharmaceuticals, and chlor-alkali manufacturing (PVC, caustic soda). Usable brine resources include sodium salts (NaCl), potassium salts (K₂SO₃, KCl, KNO₃), lithium salts (LiCl, Li₂CO₃), boron (boric acid), magnesium salts (MgCl₂, MgSO₃), and Br. In the exploration of existing thick gravelly porous brine potassium salt deposits, geophysical methods such as magnetotelluric sounding, wide-area electromagnetic sounding, seismic exploration, and well logging have been applied. Results show that each method has its advantages and disadvantages, with conventional well logging, as a geophysical method that can directly measure the formation, showing significant advantages. Well logging technology has long been widely used in coalfields, natural gas, freshwater resources, petroleum, and solid minerals, yielding considerable results and experience; however, its application in deep brine resource exploration is relatively limited.

[0003] There are relatively few logging interpretation methods for deep brine. Early published literature primarily focused on cross-sectional analysis of different logging parameters to identify sensitive parameters and determine their variation ranges. Most methods essentially rely on qualitative analysis of logging data, combined with rock physics or actual data, to identify deep brine layers. High-precision deep brine logging identification generally requires extensive analysis and judgment based on existing data. Summary of the Invention

[0004] To address the aforementioned problems, in a first aspect, this invention proposes a method for identifying deep lithium-rich brine wells through logging, comprising the following steps:

[0005] Acquire data and perform environmental correction and standardization on the data;

[0006] Based on the processed natural gamma curve and spontaneous potential curve, lithological identification parameters are determined and lithological identification curves are constructed.

[0007] Physical properties were interpreted using a three-porosity curve, and porosity was calculated based on the interpretation results and the lithological identification parameters.

[0008] A water saturation logging interpretation model is constructed using the porosity, and the content of different fluids in the reservoir is calculated based on the water saturation logging interpretation model.

[0009] A lithium ion content interpretation model was constructed using water saturation calculations and combined with hydrochemical analysis data to quantitatively explain the lithium content.

[0010] Furthermore, the data includes well logging data, geological data, and oil testing data;

[0011] The well logging data includes natural gamma curves, resistivity curves, spontaneous potential curves, sonic curves, and rock bulk density curves.

[0012] Furthermore, the determination of lithological identification parameters and the construction of lithological identification curves based on the processed natural gamma curve and spontaneous potential curve specifically adopts the following formula:

[0013]

[0014] Among them, V sh These are lithological identification parameters; GR and SP represent the natural gamma curve and spontaneous potential curve, respectively. max and GR min SP represents the maximum and minimum values ​​of natural gamma, respectively. max and SP min These represent the maximum and minimum values ​​of the natural potential, respectively.

[0015] Furthermore, the specific method of interpreting physical properties using the three-porosity curve is as follows:

[0016] The porosity of the rock formation is interpreted based on sonic transit time logging curves, neutron logging curves, and density logging curves.

[0017] Furthermore, the formula for calculating porosity based on the physical property interpretation results and the lithological identification parameters is as follows:

[0018]

[0019] In the formula: φ represents porosity; ρ represents the density logging value; ρ ma ρ represents the density of a rock when there are no pores. f ρ represents the density of the fluid in the pores. sh C represents the density value of conductive minerals in the rock framework. p V represents the compaction coefficient. sh These are lithology identification parameters.

[0020] Furthermore, the water saturation logging interpretation model is as follows:

[0021]

[0022] In the formula: R w φ represents the formation water resistivity; R represents the porosity. tResistivity logging value; S w denoted as water saturation; a, b, and m are empirical constants.

[0023] Furthermore, the quantitative explanation of lithium content by constructing a lithium-ion content interpretation model using water saturation calculation results and combining them with hydrochemical analysis data includes the following steps:

[0024] Acquire logging data from multiple typical deep lithium-rich brines and form a logging database;

[0025] The logging response characteristics of deep lithium-rich brine layers were determined based on the data in the logging database.

[0026] A lithium-ion content interpretation model was constructed based on the well logging response characteristics and water saturation, combined with hydrochemical analysis data.

[0027] Furthermore, the lithium-ion content explanation model is as follows:

[0028]

[0029] In the formula: γ represents the lithium richness index; C Li Indicates the lithium richness factor; R LLS Represents shallow resistivity logging values; R LLD This represents the deep resistivity logging value.

[0030] Secondly, this invention proposes a deep lithium-rich brine logging identification system, comprising:

[0031] A data preprocessing unit is used to acquire data and perform environmental correction and standardization on the data;

[0032] The lithology identification parameter construction unit is used to determine lithology identification parameters and construct lithology identification curves based on the processed natural gamma curve and spontaneous potential curve;

[0033] The physical property interpretation unit is used to interpret physical properties using the three-porosity curve and calculate porosity based on the physical property interpretation results and the lithological identification parameters.

[0034] A water saturation logging interpretation model construction unit is used to construct a water saturation logging interpretation model using the porosity, and to calculate the content of different fluids in the reservoir based on the water saturation logging interpretation model.

[0035] The lithium-ion content interpretation model construction unit is used to construct a lithium-ion content interpretation model to quantitatively interpret the lithium content by using the water saturation calculation results and combining them with water chemistry analysis data.

[0036] Thirdly, the present invention proposes an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0037] Memory, which stores computer programs;

[0038] The processor, when executing the program stored in the memory, implements the deep lithium-rich brine logging identification method.

[0039] Fourthly, the present invention proposes a computer-readable storage medium storing a computer program, which, when run, executes the deep lithium-rich brine logging identification method.

[0040] The beneficial effects of this invention are:

[0041] This invention identifies deep brine layers through lithological logging interpretation, physical property logging interpretation, and water saturation calculation. Based on this, combined with hydrochemical analysis data, a quantitative expression for lithium ion content is obtained, thereby enabling a quantitative interpretation of lithium ion content in deep brine layers. This effectively realizes an interpretation model for lithium ion content in deep brine layers. Using this model to quantitatively interpret lithium ion content has significant application value for the quantitative evaluation of deep brine layers using conventional logging, and further improves the identification capability of deep lithium-rich brine reservoirs, providing important basis for predicting the vertical and horizontal distribution of deep lithium-rich brines.

[0042] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 The flowchart of the deep lithium-rich brine logging identification method proposed in this invention is shown.

[0045] Figure 2A The following is a logging response characteristic diagram of a well in a lithium-rich brine layer in the Qaidam Basin, as shown in an embodiment of the present invention.

[0046] Figure 2BThis invention illustrates the logging response characteristics of wells in ordinary aquifers in the Qaidam Basin according to an embodiment of the present invention.

[0047] Figure 3A This invention presents a comparison chart of the consistency of acoustic time difference across multiple wells and environmental correction in the Chaidamu-Chaixi area according to an embodiment of the present invention.

[0048] Figure 3B This invention presents a comparison chart of multi-well consistency and environmental correction of natural gamma rays in the Qaidam Basin's western region, as shown in this embodiment of the invention.

[0049] Figure 3C This invention presents a comparison chart of the resistivity of the Chaidamu-Chaixi area across multiple wells and its environmental correction in an embodiment of the invention.

[0050] Figure 4 The diagram shows the porosity interpretation results of a well in the Chaixi area in an embodiment of the present invention;

[0051] Figure 5 The diagram shows the interpretation results of the logging interpretation method for deep lithium-rich brine layers in an embodiment of the present invention.

[0052] Figure 6 This invention illustrates a logging interpretation and identification scale for deep lithium-rich brine formations in an embodiment of the present invention.

[0053] Figure 7 A schematic diagram of the deep lithium-rich brine logging identification system proposed in this embodiment of the invention is shown.

[0054] Figure 8 A schematic diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] This invention utilizes conventional well logging data to progressively control and eliminate the influence of lithology, physical properties, and hydrocarbon content. Combined with hydrochemical analysis data, it establishes explanatory factors for deep lithium-rich brines, enabling a quantitative interpretation of lithium-ion content in deep brine reservoirs. This allows for more accurate calculations of lithium-ion content in deep brines, such as... Figure 1 As shown, the method for identifying deep lithium-rich brine wells proposed in this invention includes the following steps:

[0057] S1: Acquire data and perform environmental correction and standardization on the data; including: analyzing the quality of logging curves and performing environmental correction and standardization on the logging curves to identify the distribution range of favorable areas for deep brine.

[0058] Among them, environmental correction involves wellbore collapse correction and logging data system error correction, while standardization processing involves histogram comparison analysis of logging curves from different well marker layers to determine that the distribution pattern and range of all curves are consistent.

[0059] The data includes geological data, well logging data, and oil testing and well testing data. Geological data includes sedimentary environment, lithological data, and physical property data. Lithological data includes rock color, composition, structure, cement, cement type, and special minerals. Physical property data reflects the quality of the reservoir, determining the abundance and reserves of the oilfield. Reservoir evaluation can be performed using physical property parameters such as effective porosity, absolute permeability, effective permeability, and porosity-permeability relationship, utilizing well logging responses from sonic logging, density logging, and neutron logging. Geological data can clearly define the lithology and mineral content, and calibrate lithological calculation results.

[0060] Well logging curves, including natural gamma ray curves, resistivity curves, spontaneous potential curves, sonic curves, and rock bulk density, reflect various properties and characteristics of underground formations, such as lithology, hydrocarbon content, water content, and porosity. Natural gamma ray curves characterize the radioactivity of formations by measuring the intensity of gamma rays generated by natural radiation from underground rocks, primarily used for lithology classification, stratigraphic correlation, and determining clay content. Resistivity curves are a well logging method for detecting resistivity at different radial depths; the presence of fractures causes differences in the two lateral directions, and the amplitude difference is related not only to the fracture orientation but also to the fracture opening. Spontaneous potential curves are affected by various factors, including formation thickness, formation resistivity, mud resistivity, and mud invasion zones. Sonic curves reflect the relationship between the reflection of fractures on the sonic curve and the orientation and development of fractures around the wellbore. Sonic transit time can be used to identify horizontal or network fractures but not vertical fractures.

[0061] Well testing data includes data from intermediate testing, well completion testing, and production logging. Based on the well testing data, it can be determined whether the subsequent water saturation calculation is accurate.

[0062] S2: Determine lithology identification parameters and construct lithology identification curves based on the processed natural gamma ray and spontaneous potential curves; calculate the lithology identification curves based on natural gamma ray and spontaneous potential logging to eliminate the influence of lithology. The specific calculation formula is as follows:

[0063]

[0064] Among them, Vsh These are lithological identification parameters; GR and SP represent the natural gamma curve and spontaneous potential curve, respectively. max and GR min SP represents the maximum and minimum values ​​of natural gamma, respectively. max and SP min These represent the maximum and minimum values ​​of the natural potential, respectively.

[0065] S3: Use the three-porosity curve to interpret the physical properties, and calculate the porosity based on the interpretation results and the lithological identification parameters;

[0066] S4: Construct a water saturation logging interpretation model using the porosity, and calculate the content of different fluids in the reservoir based on the water saturation logging interpretation model; since there is air, oil, natural gas, etc. in the rock pores in addition to water, different fluids will affect the value of the resistivity curve. This invention eliminates the influence of other fluids on the resistivity curve by calculating the water saturation.

[0067] S5: Construct a lithium-ion content interpretation model using water saturation calculations and combined with hydrochemical analysis data to quantitatively explain lithium content, including:

[0068] Acquire logging data from multiple typical deep lithium-rich brines and form a logging database;

[0069] Determine the logging response characteristics of deep lithium-rich brine layers based on data from the logging database;

[0070] A lithium-ion content interpretation model was constructed based on the well logging response characteristics and water saturation, combined with hydrochemical analysis data.

[0071] The lithium content is quantitatively explained based on the lithium ion content interpretation model.

[0072] The water chemical analysis data refers to the data on the content of different ions in formation water analyzed in the laboratory, including the measured lithium content. This allows us to obtain the relationship between the measured lithium ion content and the calculated lithium ion content at the formation water sampling depth, thereby determining the lithium enrichment factor.

[0073] Taking the well logging interpretation study of deep lithium-rich brine layers in the western part of the Qaidam Basin as an example, the above methods will be explained in further detail.

[0074] Oil and gas exploration surveys in the Qaidam Basin reveal a wide distribution of deep brines containing valuable elements such as potassium and lithium in the oil-bearing structural area represented by the southern wing mountains in western Qaidam. These brines are found in various strata from the Paleogene to the Neogene periods and possess significant socio-economic value. Based on structural and stratigraphic lithological characteristics, the brine reservoir lithology is considered to be dominated by calcareous sandstone and calcareous siltstone, with porous and fractured storage spaces. This invention utilizes logging data from 84 wells in the Qaidam Basin to establish a deep brine logging database. Combining water testing data, water sample analysis data, and drilling reports, the logging response characteristics of deep brine in the Qaidam Basin are summarized. Furthermore, a deep brine logging interpretation model is established based on conventional logging data, and a lithium-rich factor is constructed, which can effectively identify lithium-rich brine layers.

[0075] By analyzing the logging results of multiple wells in the Qaidam Basin, we summarized some unique response characteristics of parameters such as resistivity, natural gamma, sonic transit time, and spontaneous potential in deep brine layers. Through statistical analysis of logging data within the basin, we identified the identifiable characteristics of different logging curve amplitudes and morphologies in deep brine layers. The main features are "three lows and two negatives," namely, low natural gamma, low resistivity, low sonic transit time, negative spontaneous potential anomaly, and negative difference in resistivity between shallow and deep layers. Figure 2A and 2B As shown. Figure 2A The logging curve characteristics of the South 12 well water layer are as follows: water-producing interval: 3059-3087m, sampling layer: E32, water sample salinity: 262000 mg / L; Li + Content: 186 mg / L. Figure 2B The logging curve characteristics of well Yiping 2 are as follows: water-producing interval: 3322-3335m; sampling layer: E32; water sample salinity: 218500 mg / L; no Li was detected. + Specifically, it includes the following steps:

[0076] S1: Establish a logging database for water sample wells in the Qaidam Basin and perform environmental correction and multi-well consistency processing on the logging curves of 84 wells in the work area. The comparison results of multi-well consistency and environmental correction of sonic transit time curves, natural gamma curves, and resistivity curves in this embodiment are shown in 3A, 3B, and 3C, respectively.

[0077] S2: Construct lithology identification parameters based on lithology indicator curves to eliminate the influence of lithology; the lithology indicator curves are natural gamma curves and spontaneous potential curves, wherein the spontaneous potential curves do not require standardization, and their measurement results are only relative values;

[0078]

[0079] Among them, V sh These are lithological identification parameters; GR and SP represent the natural gamma curve and spontaneous potential curve, respectively.max and GR min SP represents the maximum and minimum values ​​of natural gamma, respectively. max and SP min These represent the maximum and minimum values ​​of the natural potential, respectively.

[0080] S3: Using the results of lithology-density-core analysis porosity cross-plot and density logging curves, porosity is calculated to locate reservoirs with higher physical properties; porosity interpretation results from a well in the Chaixi area are shown in the figure below. Figure 4 As shown, Figure 4 This includes conventional logging curves, inductive resistivity curves, nuclear magnetic resonance logging curves, porosity curves, and rock mineral content curves. The physical properties mentioned in the logging mainly include porosity and permeability; reservoirs with high porosity are generally considered to have good physical properties. The formula for calculating porosity is as follows:

[0081]

[0082] In the formula: φ represents porosity; ρ represents the density logging value; ρ ma ρ represents the density of a rock when there are no pores. f ρ represents the density of the fluid in the pores. sh C represents the density value of conductive minerals in the rock framework. p V represents the compaction coefficient; sh These are lithology identification parameters.

[0083] S4: Using the porosity described above, a water saturation logging interpretation model is constructed to calculate the content of different fluids in the reservoir. The specific model is as follows:

[0084]

[0085] In the formula: R w R represents the resistivity of formation water. t Resistivity logging value; S w denoted as water saturation; a, b, and m are empirical constants.

[0086] S5: Based on the deep brine logging database formed by data from 84 typical wells, the logging response characteristics of deep lithium-rich brine layers are identified as "three lows and two negatives": low natural gamma, low resistivity, low sonic transit time, negative spontaneous potential anomaly, and negative difference in resistivity between deep and shallow layers. Based on the above characteristics, a lithium ion content interpretation model is constructed to quantitatively interpret the lithium content.

[0087]

[0088] In the formula: γ represents the lithium richness index; C Li Indicates the lithium richness factor; R LLS Represents shallow resistivity logging values; R LLD This represents the deep resistivity logging value.

[0089] The interpretation results of the deep lithium-rich brine layer logging interpretation method constructed in this embodiment are as follows: Figure 5 As shown, the logging interpretation and identification scale for deep lithium-rich brine formations is as follows: Figure 6 As shown, the vertical axis represents the lithium enrichment factor (dimensionless), and the vertical axis represents the lithium ion content (in mg / mL).

[0090] Based on the same inventive concept, one embodiment of the present invention proposes a deep lithium-rich brine logging identification system, such as... Figure 7 As shown, it specifically includes:

[0091] A data preprocessing unit is used to acquire data and perform environmental correction and standardization on the data;

[0092] The lithology identification parameter construction unit is used to determine lithology identification parameters and construct lithology identification curves based on the processed natural gamma curve and spontaneous potential curve;

[0093] The physical property interpretation unit is used to interpret physical properties using the three-porosity curve and calculate porosity based on the physical property interpretation results and the lithological identification parameters.

[0094] A water saturation logging interpretation model construction unit is used to construct a water saturation logging interpretation model using the porosity, and to calculate the content of different fluids in the reservoir based on the water saturation logging interpretation model.

[0095] The lithium-ion content interpretation model construction unit is used to construct a lithium-ion content interpretation model to quantitatively interpret the lithium content by using the water saturation calculation results and combining them with water chemistry analysis data.

[0096] Another exemplary embodiment of the present invention provides an electronic device. For example... Figure 8 As shown, the electronic device includes at least one processor 801, at least one communication interface 802, at least one memory 803, and at least one communication bus 804; wherein the processor 801, communication interface 802, and memory 803 communicate with each other through the communication bus 804.

[0097] Memory 803 stores computer programs;

[0098] The processor 801 is used to execute the program stored in the memory 803 to implement the deep lithium-rich brine logging identification method.

[0099] Optionally, the communication interface can be an interface of a communication module, such as the interface of a GSM module; the processor may be a CPU, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The memory may include high-speed RAM and may also include non-volatile memory, such as at least one disk storage device. The memory stores a program, and the processor calls the program stored in the memory to execute some or all of the above-described method embodiments.

[0100] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed, implements some or all of the above-described method embodiments. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0101] Although the present invention 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying deep lithium-rich brine wells, characterized in that, Includes the following steps: Acquire data and perform environmental correction and standardization on the data; Based on the processed natural gamma curve and spontaneous potential curve, lithological identification parameters are determined and lithological identification curves are constructed. Physical properties were interpreted using a three-porosity curve, and porosity was calculated based on the interpretation results and the lithological identification parameters. A water saturation logging interpretation model is constructed using the porosity, and the content of different fluids in the reservoir is calculated based on the water saturation logging interpretation model. A lithium ion content interpretation model was constructed using water saturation calculations and combined with hydrochemical analysis data to quantitatively explain the lithium content. The lithium-ion content interpretation model is as follows: In the formula: γ Indicates the lithium richness index; C Li Indicates the lithium richness factor; R LLS This represents shallow resistivity logging values; R LLD This represents the deep resistivity logging value.

2. The method for identifying deep lithium-rich brine wells according to claim 1, characterized in that, The data includes well logging data, geological data, and oil testing data; The well logging data includes natural gamma curves, resistivity curves, spontaneous potential curves, sonic curves, and rock bulk density curves.

3. The method for identifying deep lithium-rich brine wells according to claim 1, characterized in that, The specific formula for determining lithological identification parameters and constructing lithological identification curves based on the processed natural gamma curve and spontaneous potential curve is as follows: Among them, V sh These are lithological identification parameters; GR and SP represent the natural gamma curve and spontaneous potential curve, respectively. and These represent the maximum and minimum values ​​of natural gamma, respectively. and These represent the maximum and minimum values ​​of the natural potential, respectively.

4. The method for identifying deep lithium-rich brine wells according to claim 1, characterized in that, The specific method of interpreting physical properties using the three-porosity curve is as follows: The porosity of the rock formation is interpreted based on sonic transit time logging curves, neutron logging curves, and density logging curves.

5. The method for identifying deep lithium-rich brine wells according to claim 1, characterized in that, The formula for calculating porosity based on the physical property interpretation results and the lithological identification parameters is as follows: In the formula: Indicates porosity; ρ This represents the density logging value; ρ ma This represents the density value of a rock when there are no pores. ρ f This represents the density value of the fluid in the pores; ρ sh This indicates the density value of conductive minerals in the rock framework; C p V represents the compaction coefficient; sh These are lithology identification parameters.

6. The method for identifying deep lithium-rich brine wells according to claim 1 or 4, characterized in that, The water saturation logging interpretation model is as follows: In the formula: R w The resistivity of formation water; Indicates porosity. R t These are resistivity logging values; S w denoted as water saturation; a, b, and m are empirical constants.

7. The method for identifying deep lithium-rich brine wells according to claim 1, characterized in that, The quantitative interpretation of lithium content by constructing a lithium ion content interpretation model using water saturation calculation results and combining hydrochemical analysis data includes the following steps: Acquire logging data from multiple typical deep lithium-rich brines and form a logging database; The logging response characteristics of deep lithium-rich brine layers were determined based on data from the logging database. A lithium-ion content interpretation model was constructed based on the well logging response characteristics and water saturation, combined with hydrochemical analysis data.

8. A deep lithium-rich brine logging and identification system, characterized in that, include: A data preprocessing unit is used to acquire data and perform environmental correction and standardization on the data; The lithology identification parameter construction unit is used to determine lithology identification parameters and construct lithology identification curves based on the processed natural gamma curve and spontaneous potential curve; The physical property interpretation unit is used to interpret physical properties using the three-porosity curve and calculate porosity based on the physical property interpretation results and the lithological identification parameters. A water saturation logging interpretation model construction unit is used to construct a water saturation logging interpretation model using the porosity, and to calculate the content of different fluids in the reservoir based on the water saturation logging interpretation model. The lithium ion content interpretation model construction unit is used to construct a lithium ion content interpretation model to quantitatively interpret the lithium content by using the water saturation calculation results and combining water chemistry analysis data. The lithium-ion content interpretation model is as follows: In the formula: γ Indicates the lithium richness index; C Li Indicates the lithium richness factor; R LLS This represents shallow resistivity logging values; R LLD This represents the deep resistivity logging value.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, which stores computer programs; A processor, when executing a program stored in a memory, implements the deep lithium-rich brine logging identification method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is run, it performs the deep lithium-rich brine logging identification method as described in any one of claims 1-7.

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