Sandstone reservoir comprehensive classification method, system and device based on physical property index and oil-water index and medium

Abstract

The application discloses a kind of based on physical property index and oil-water index's conglomerate reservoir comprehensive classification method, system, device and medium, comprising: through GR variation function and median size calculation formula, construct reservoir lithology sorting index, combine reservoir lithology sorting index and gravel content to establish rock structure index, based on mercury injection experiment pore structure parameter model to establish conventional logging curve pore structure parameter model, combine reservoir lithology rock structure index, porosity, clay content and conventional logging curve pore structure parameter model to establish reservoir physical property structure index, by electrical imaging data view formation water resistivity spectrum to construct reservoir oil-water index, comprehensive reservoir physical property structure index and reservoir oil-water index to establish reservoir comprehensive classification method, identify reservoir fluid and distinguish reservoir productivity.The application solves the inaccuracy of single influencing factor in evaluating reservoir productivity, poor universality and the disadvantages of qualitative identification, improves the accuracy of interpretation and evaluation, has better application value.

Description

Technical Field

[0001] This invention belongs to the field of well logging evaluation technology for sandstone and conglomerate oil and gas reservoirs, and relates to a comprehensive classification method, system, device and medium for sandstone and conglomerate reservoirs based on physical property index and oil-water index. Background Technology

[0002] Conglomerate and sandstone reservoirs are generally characterized by deep burial, complex lithology, low porosity, low permeability, complex pore structure, and strong heterogeneity. Their well logging reservoir evaluation, hydrocarbon layer interpretation, geological and engineering applications remain challenging in the domestic and international petroleum exploration fields. Fluid identification and reservoir productivity determination are key aspects of the comprehensive classification and evaluation of conglomerate and sandstone reservoirs. Comprehensive classification of these reservoirs, based on a clear understanding of lithological and reservoir property variations, provides crucial technical support for well logging interpretation.

[0003] Conglomerate reservoirs are mostly thick layers, lacking stable mudstone interlayers, exhibiting dramatic lateral lithofacies variations, poor regional contrast, and indistinct logging curve cyclic characteristics, making reservoir subdivision difficult. The diverse parent rock types of conglomerate bodies, their close-range deposition, and low maturity, along with the gravelly rock framework, severely weaken information about reservoir pore fluid properties, resulting in unclear electrical characteristics at the boundaries of oil-bearing, poor-oil-bearing, water-bearing, and dry layers, making accurate fluid identification challenging. Predicting reservoir productivity using logging data primarily starts from the basic theory of stable flow, identifying the main controlling factors influencing productivity, and using appropriate mathematical methods to establish a productivity index and a productivity prediction model based on these controlling factors. Currently, various reservoir productivity classification methods exist, mainly based on conventional logging curve modeling, using macroscopic parameters and experimental analysis of microscopic parameters to classify reservoir productivity. However, these parameters are not easily extracted, relying primarily on qualitative identification, and are effective in reservoirs with good porosity and permeability, weak heterogeneity, and uniform lithology. However, given the complex pore structure and diverse factors affecting production capacity of sandstone and conglomerate reservoirs, the method of evaluating reservoir production capacity based on conventional well logging data and using single factors is no longer suitable for the current exploration and development of sandstone and conglomerate reservoirs in oilfields. Summary of the Invention

[0004] The purpose of this invention is to address the problem that conventional well logging curve modeling in the prior art is not suitable for the exploration and development of sandstone and conglomerate reservoirs in oil fields due to their complex pore structure and diverse factors affecting production capacity. The invention provides a comprehensive classification method, system, device, and medium for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] A comprehensive classification method for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices includes:

[0007] Based on the conventional GR curve of conglomerate reservoir, a GR variation function is established. The response characteristics of the grain size median of conglomerate reservoir on the conventional logging curve are analyzed to determine the grain size median sensitive curve and obtain the grain size median.

[0008] Based on the GR variogram and grain size median, a reservoir lithological sorting index is established;

[0009] Based on reservoir lithological sorting index and gravel content, a reservoir rock structure index is constructed.

[0010] Pore ​​structure parameters were constructed based on mercury intrusion porosimetry data from sandstone and conglomerate, and a pore structure parameter model for conventional well logging curves was constructed based on the pore structure parameter model from the mercury intrusion porosimetry data from the sandstone and conglomerate.

[0011] Based on the reservoir rock structure index, porosity, clay content, and conventional logging curve pore structure parameter model, a reservoir physical property structure index is established.

[0012] Based on the electrical imaging data of sandstone and conglomerate reservoirs, the apparent formation water resistivity spectrum was obtained, and the variance and width of the apparent formation water resistivity spectrum were extracted to construct the reservoir oil-water index.

[0013] A reservoir classification method is established based on the reservoir physical property structure index and the reservoir oil-water index to identify reservoir fluids and determine reservoir productivity.

[0014] A further improvement of the present invention is that:

[0015] Furthermore, a GR variogram function is established based on the conventional GR curve of the conglomerate reservoir, specifically as follows:

[0016]

[0017] Where v(h) represents the variation function of GR(x); GR(x) represents the regional variable; h represents the distance; N represents the data pair within the region; and i is the sample number within the region.

[0018] Furthermore, the granularity median sensitivity curve is determined, and the granularity median is obtained, specifically as follows:

[0019] LD = 0.04GR - 0.09RT - 0.956 (2)

[0020] Where LD is the median grain size; GR is the gamma logging curve value in API; and RT is the deep resistivity curve value in Ω·m.

[0021] Based on the GR variogram and grain size median, a reservoir lithological sorting index is established, specifically as follows:

[0022] TS=v(h)·|LD| (3)

[0023] Furthermore, based on the reservoir lithological sorting index and gravel content, a reservoir rock structure index is constructed, specifically as follows:

[0024]

[0025] Where TS is the reservoir lithology sorting index; F is the gravel content.

[0026] Furthermore, pore structure parameters were constructed based on mercury intrusion porosimetry data from sandstone and conglomerate rocks, specifically:

[0027]

[0028] Where KJ represents the experimental pore structure parameters; RP represents the average capillary radius in mm; RZ represents the median radius in mm; and PQ represents the displacement pressure in MPa.

[0029] The pore structure parameter model for conventional well logging curves is constructed based on the mercury injection experiment pore structure parameter model in conglomerate. Specifically:

[0030]

[0031] Wherein, PZ represents the pore structure parameter of the logging curve; RT represents the deep resistivity curve value, in Ω·m; RI represents the medium resistivity curve value, in Ω·m; and DEN represents the density curve value, in g / cm³. 3 CNL represents the compensated neutron curve value, in percent.

[0032] Furthermore, based on the reservoir rock structure index, porosity, clay content, and conventional well logging curve pore structure parameter model, a reservoir physical property structure index is established, specifically as follows:

[0033]

[0034] Where J is the reservoir physical property structure index; POR is the reservoir porosity, in %; and SH is the clay content, in %.

[0035] Furthermore, electrical imaging data of sandstone and conglomerate reservoirs were selected to obtain the apparent formation water resistivity spectrum. The variance and width of the apparent formation water resistivity spectrum were extracted to construct the reservoir oil-water index, specifically:

[0036] The apparent formation water resistivity spectrum was obtained based on electrical imaging data, and the mean and width of the apparent formation water resistivity spectrum were extracted, specifically as follows:

[0037]

[0038] in, R represents the average resistivity spectrum of apparent formation water. wai P represents the apparent formation water resistivity. RwaiThe frequency corresponding to the apparent formation water resistivity;

[0039] K = R wax -R wan (9)

[0040] Where K is the width of the apparent formation water resistivity spectrum; R wax R represents the maximum point value of the apparent formation water resistivity spectrum corresponding to the depth point; wan The minimum point value of the apparent formation water resistivity spectrum corresponding to the depth point;

[0041] The construction of the reservoir oil-water index specifically refers to:

[0042]

[0043] A comprehensive classification system for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices includes:

[0044] The first acquisition module establishes a GR variation function based on the conventional GR curve of the conglomerate reservoir, performs response characteristic analysis on the median grain size of the conglomerate reservoir on the conventional logging curve, determines the sensitive curve of the median grain size, and acquires the median grain size.

[0045] A reservoir lithology sorting index construction module, which establishes a reservoir lithology sorting index based on the GR variability function and the grain size median;

[0046] A reservoir rock structure index construction module, which constructs a reservoir rock structure index based on the reservoir lithology sorting index and gravel content;

[0047] A pore structure parameter construction module is provided, which constructs pore structure parameters based on mercury intrusion porosimetry data from sandstone and conglomerate, and constructs a conventional well logging curve pore structure parameter model based on the mercury intrusion porosimetry pore structure parameter model from the sandstone and conglomerate pore structure parameter model.

[0048] A reservoir physical property structure index construction module is used to establish a reservoir physical property structure index based on the reservoir rock structure index, porosity, clay content and conventional logging curve pore structure parameter model.

[0049] The second acquisition module selects electrical imaging data of sandstone and conglomerate reservoirs, acquires the apparent formation water resistivity spectrum, and extracts the variance and width of the apparent formation water resistivity spectrum to construct the reservoir oil-water index.

[0050] The identification module establishes a reservoir classification method based on the reservoir physical property structure index and the reservoir oil-water index, identifies reservoir fluids, and determines reservoir productivity.

[0051] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the steps of the method described above.

[0052] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] This invention constructs a reservoir lithology sorting index using the GR variation function and grain size median calculation formula. It then establishes a rock structure index by combining the reservoir lithology sorting index and gravel content. Based on the mercury injection experiment pore structure parameter model, it establishes a conventional logging curve pore structure parameter model. Finally, it establishes a reservoir physical property structure index by combining the reservoir lithology rock structure index, porosity, clay content, and the conventional logging curve pore structure parameter model. Using electrical imaging data to visualize the formation water resistivity spectrum, it constructs a reservoir oil-water index. Finally, it establishes a comprehensive reservoir classification method by integrating the reservoir physical property structure index and the reservoir oil-water index, identifying reservoir fluids and determining reservoir productivity. This invention overcomes the shortcomings of traditional methods that rely on single influencing factors to evaluate reservoir productivity, such as inaccuracy, poor universality, and qualitative identification. For untested wells, it can improve the accuracy of interpretation and evaluation based on reservoir map locations, optimize production plans, and has greater application value. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart illustrating the comprehensive classification method for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices according to the present invention.

[0057] Figure 2 This is a schematic diagram of the correlation analysis of particle size median;

[0058] Figure 3 A schematic diagram of a comprehensive classification method for sandstone and conglomerate reservoirs;

[0059] Figure 4 This is a schematic diagram of the structure of the comprehensive classification system for sandstone and conglomerate reservoirs based on physical property index and oil-water index of the present invention. Detailed Implementation

[0060] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0061] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0062] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0063] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0064] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0065] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0066] The present invention will now be described in further detail with reference to the accompanying drawings:

[0067] See Figure 1 This invention discloses a comprehensive classification method for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices, including:

[0068] S101. Based on the conventional GR curve of sandstone and conglomerate reservoir, a GR variation function is established. The response characteristics of the grain size median of sandstone and conglomerate reservoir on the conventional logging curve are analyzed to determine the grain size median sensitive curve and obtain the grain size median.

[0069] The GR variation function is established based on the conventional GR curve of sandstone and conglomerate reservoirs, specifically as follows:

[0070]

[0071] Where v(h) represents the variation function of GR(x); GR(x) represents the regional variable; h represents the distance; N represents the data pair within the region; and i is the sample number within the region.

[0072] See Figure 2 To determine the granularity median sensitivity curve and obtain the granularity median, the specific steps are as follows:

[0073] LD = 0.04GR - 0.09RT - 0.956 (2)

[0074] Where LD is the median grain size; GR is the gamma logging curve value in API; and RT is the deep resistivity curve value in Ω·m.

[0075] S102, based on the GR variability function and grain size median, establishes the reservoir lithology sorting index.

[0076] TS=v(h)·|LD| (3)

[0077] S103 is a reservoir rock structure index constructed based on reservoir lithology sorting index and gravel content.

[0078]

[0079] Where TS is the reservoir lithology sorting index; F is the gravel content.

[0080] S104. Pore structure parameters were constructed based on mercury injection test data of sandstone and conglomerate, and a conventional logging curve pore structure parameter model was constructed based on the mercury injection test pore structure parameter model of sandstone and conglomerate.

[0081] Pore ​​structure parameters were constructed based on mercury intrusion porosimetry data from sandstone and conglomerate rocks, specifically:

[0082]

[0083] Where KJ represents the experimental pore structure parameters; RP represents the average capillary radius in mm; RZ represents the median radius in mm; and PQ represents the displacement pressure in MPa.

[0084] Based on the pore structure parameter model of mercury intrusion porosimetry experiments in sandstone and conglomerate, a pore structure parameter model for conventional well logging curves is constructed, specifically as follows:

[0085]

[0086] Wherein, PZ represents the pore structure parameter of the logging curve; RT represents the deep resistivity curve value, in Ω·m; RI represents the medium resistivity curve value, in Ω·m; and DEN represents the density curve value, in g / cm³. 3 CNL represents the compensated neutron curve value, in percent.

[0087] S105 establishes a reservoir physical property structure index based on the reservoir rock structure index, porosity, clay content, and conventional logging curve pore structure parameter model.

[0088]

[0089] Where J is the reservoir physical property structure index; POR is the reservoir porosity, in %; and SH is the clay content, in %.

[0090] S106. Select the electrical imaging data of the sandstone and conglomerate reservoir, obtain the apparent formation water resistivity spectrum, and extract the variance and width of the apparent formation water resistivity spectrum to construct the reservoir oil-water index.

[0091] The apparent formation water resistivity spectrum was obtained based on electrical imaging data, and the mean and width of the apparent formation water resistivity spectrum were extracted, specifically as follows:

[0092]

[0093] in, R represents the average resistivity spectrum of apparent formation water. wai P represents the apparent formation water resistivity. Rwai The frequency corresponding to the apparent formation water resistivity;

[0094] K = R wax -R wan (9)

[0095] Where K is the width of the apparent formation water resistivity spectrum; R wax R represents the maximum point value of the apparent formation water resistivity spectrum corresponding to the depth point; wan The minimum point value of the apparent formation water resistivity spectrum corresponding to the depth point;

[0096] The construction of the reservoir oil-water index specifically refers to:

[0097]

[0098] S107, a reservoir classification method is established based on the reservoir physical property structure index and the reservoir oil-water index to identify reservoir fluids and determine reservoir productivity.

[0099] See Figure 3 , Figure 3 The numbers represent the reservoir's fluid production per meter. Based on comprehensive physical property indices and the oilfield's criteria for single-well fluid production per meter, the reservoirs are categorized into three types using dashed lines, representing their fluid production capacity. Using the oil-water index and oil testing results as a basis, oil and water layers are distinguished using solid lines. The overall map is divided into nine regions, representing nine types of reservoirs with different fluid production capacities: oil layers, oil-water co-containment layers, and water layers. By comparing with actual oil testing results and fluid production capacities, the fluid identification and production prediction capabilities of this invention show good agreement.

[0100] Based on the oil-water index and oil testing results, solid lines are used to distinguish between oil and water layers. The overall map is divided into nine regions, representing nine types of reservoirs with different production capacities: oil layers, oil-water co-containment layers, and water layers. The reservoirs are classified into nine categories using a combination of production rate and oil testing, and expressed by a functional relationship. Figure 3 ), as follows: Oil layer, poor oil layer: when N>10 14 e -4.605x And M > 2.2e 0.1708x For rice yield category I, when N < 10 14 e -4.605x And M > 2.2e 0.1708x And N > 10 7 e -4.605x Rice yield is classified as Category II, when N < 10 7 e -4.605x And M > 2.2e 0.1708x Rice yield is classified as Class III; oil and water are in the same layer: when M < 2.2e 0.1708x And M > 6.6e 0.1708x And N > 10 14 e -4.605x For rice yield category I, when N < 10 14 e -4.605x And M < 2.2e 0.1708x And M > 6.6e 0.1708x And N > 10 7 e -4.605x Rice yield is classified as Category II, when N < 10 7 e -4.605x And M < 2.2e 0.1708x And M > 6.6e 0.1708x Rice yield is classified as Class III; aquifer and oil-bearing aquifer: when M < 6.6e 0.1708x And N > 10 14 e -4.605xFor rice yield category I, when N < 10 14 e -4.605x And M < 6.6e 0.1708x And N > 10 7 e -4.605x Rice yield is classified as Category II, when N < 10 7 e -4.605x And M < 6.6e 0.1708x It is classified as rice production category III.

[0101] See Figure 4 This invention discloses a comprehensive classification system for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices, comprising:

[0102] The first acquisition module establishes a GR variation function based on the conventional GR curve of the conglomerate reservoir, performs response characteristic analysis on the median grain size of the conglomerate reservoir on the conventional logging curve, determines the sensitive curve of the median grain size, and acquires the median grain size.

[0103] A reservoir lithology sorting index construction module, which establishes a reservoir lithology sorting index based on the GR variability function and the grain size median;

[0104] A reservoir rock structure index construction module, which constructs a reservoir rock structure index based on the reservoir lithology sorting index and gravel content;

[0105] A pore structure parameter construction module is provided, which constructs pore structure parameters based on mercury intrusion porosimetry data from sandstone and conglomerate, and constructs a conventional well logging curve pore structure parameter model based on the mercury intrusion porosimetry pore structure parameter model from the sandstone and conglomerate pore structure parameter model.

[0106] A reservoir physical property structure index construction module is used to establish a reservoir physical property structure index based on the reservoir rock structure index, porosity, clay content and conventional logging curve pore structure parameter model.

[0107] The second acquisition module selects electrical imaging data of sandstone and conglomerate reservoirs, acquires the apparent formation water resistivity spectrum, and extracts the variance and width of the apparent formation water resistivity spectrum to construct the reservoir oil-water index.

[0108] The identification module establishes a reservoir classification method based on the reservoir physical property structure index and the reservoir oil-water index, identifies reservoir fluids, and determines reservoir productivity.

[0109] An embodiment of the present invention provides a terminal device. This terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0110] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0111] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0112] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0113] The memory can be used to store the computer program and / or module. The processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0114] If the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0115] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive classification method for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices, characterized in that, include: Based on the conventional GR curve of conglomerate reservoir, a GR variation function is established. The response characteristics of the grain size median of conglomerate reservoir on the conventional logging curve are analyzed to determine the grain size median sensitive curve and obtain the grain size median. Based on the GR variogram and grain size median, a reservoir lithological sorting index is established; Based on reservoir lithological sorting index and gravel content, a reservoir rock structure index is constructed. Pore ​​structure parameters were constructed based on mercury intrusion porosimetry data from sandstone and conglomerate, and a pore structure parameter model for conventional well logging curves was constructed based on the pore structure parameter model from the mercury intrusion porosimetry data from the sandstone and conglomerate. Based on the reservoir rock structure index, porosity, clay content, and conventional well logging curve pore structure parameter model, a reservoir physical property structure index is established, specifically as follows: in, J It is a reservoir physical property structure index; POR Reservoir porosity, in % %. SH Clay content, in % It is the reservoir rock structure index; PZ Pore ​​structure parameters for well logging curves; Based on electrical imaging data of sandstone and conglomerate reservoirs, the apparent formation water resistivity spectrum was obtained, and the variance and width of the apparent formation water resistivity spectrum were extracted to construct the reservoir oil-water index, specifically: in, The reservoir oil-water index; This represents the average value of the apparent formation water resistivity spectrum. K The width of the apparent formation water resistivity spectrum; A reservoir classification method is established based on the reservoir physical property structure index and the reservoir oil-water index to identify reservoir fluids and determine reservoir productivity.

2. The comprehensive classification method for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices according to claim 1, characterized in that, The establishment of the GR variogram function based on the conventional GR curve of sandstone and conglomerate reservoirs is specifically as follows: in, v ( h )express GR ( x The variation function of ). GR ( x () represents a region variable; h Indicates distance; N Indicates data pairs within a region; i This refers to the sample number within the region.

3. The comprehensive classification method for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices according to claim 2, characterized in that, The determination of the granularity median sensitivity curve and the acquisition of the granularity median are specifically as follows: in, LD This is the median particle size. GR Gamma logging curve values, in API. RT Values ​​are for deep resistivity curves, in Ω·m. The reservoir lithological sorting index, based on the GR variogram and median grain size, is established as follows: in, It is the reservoir lithology sorting index.

4. The comprehensive classification method for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices according to claim 3, characterized in that, The reservoir rock structure index is constructed based on the reservoir lithology sorting index and gravel content, specifically as follows: Where TS is the reservoir lithological sorting index; f is the gravel content. Gravel content at each depth.

5. The comprehensive classification method for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices according to claim 4, characterized in that, The pore structure parameters constructed based on mercury intrusion porosimetry data from sandstone and conglomerate are as follows: in, KJ These are the experimental pore structure parameters; RP Average capillary radius, in mm; RZ The median radius is in mm. PQ The pressure is the exhaust pressure, in MPa. The pore structure parameter model for conventional well logging curves is constructed based on the mercury injection experiment pore structure parameter model in conglomerate. Specifically: in, RT These are values ​​from the deep resistivity curve, in Ω·m. RI The values ​​are from the resistivity curve, in Ω·m. DEN These are density curve values, in g / cm³. 3 ; CNL To compensate for neutron curve values, in units of %.

6. The comprehensive classification method for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices according to claim 1, characterized in that, The method involves obtaining the apparent formation water resistivity spectrum based on electrical imaging data of sandstone and conglomerate reservoirs, extracting the variance and width of the apparent formation water resistivity spectrum, and constructing a reservoir oil-water index. Specifically: The apparent formation water resistivity spectrum was obtained based on electrical imaging data, and the mean and width of the apparent formation water resistivity spectrum were extracted, specifically as follows: in, R waj This represents the apparent resistivity of formation water. P Rwaj The frequency corresponding to the apparent formation water resistivity; in, R wax The maximum point value of the apparent formation water resistivity spectrum corresponding to the depth point; R wan This represents the minimum point value of the apparent formation water resistivity spectrum corresponding to the depth point.

7. A comprehensive classification system for sandstone and conglomerate reservoirs based on physical property indices and oil-water indices, characterized in that, include: The first acquisition module establishes a GR variation function based on the conventional GR curve of the conglomerate reservoir, performs response characteristic analysis on the median grain size of the conglomerate reservoir on the conventional logging curve, determines the sensitive curve of the median grain size, and acquires the median grain size. A reservoir lithology sorting index construction module, which establishes a reservoir lithology sorting index based on the GR variability function and the grain size median; A reservoir rock structure index construction module, which constructs a reservoir rock structure index based on the reservoir lithology sorting index and gravel content; A pore structure parameter construction module is provided, which constructs pore structure parameters based on mercury intrusion porosimetry data from sandstone and conglomerate, and constructs a conventional well logging curve pore structure parameter model based on the mercury intrusion porosimetry pore structure parameter model from the sandstone and conglomerate pore structure parameter model. A reservoir physical property structure index construction module, which establishes a reservoir physical property structure index based on reservoir rock structure index, porosity, clay content, and conventional well logging curve pore structure parameter model, specifically: in, J It is a reservoir physical property structure index; POR Reservoir porosity, in % % SH Clay content, in % It is the reservoir rock structure index; PZ Pore ​​structure parameters for well logging curves; The second acquisition module selects electrical imaging data of sandstone and conglomerate reservoirs, acquires the apparent formation water resistivity spectrum, and extracts the variance and width of the apparent formation water resistivity spectrum to construct the reservoir oil-water index. Specifically: in, The reservoir oil-water index; This represents the average value of the apparent formation water resistivity spectrum. K The width of the apparent formation water resistivity spectrum; The identification module establishes a reservoir classification method based on the reservoir physical property structure index and the reservoir oil-water index, identifies reservoir fluids, and determines reservoir productivity.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.

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