A method and apparatus for classifying carbonate reservoirs

Abstract

The application discloses a kind of carbonate reservoir classification method and device.The method comprises, for the production well with PLT data in carbonate reservoir, determining the oil production index and physical property index of oil production interval, the oil production index is the ratio of oil production and thickness of oil production interval, and the physical property index is the product of porosity and permeability logarithm of oil production interval;According to the oil production index and physical property index of oil production interval and the corresponding relationship between oil production index and reservoir category determined in advance, the corresponding relationship between the physical property index of oil production interval and reservoir category is determined;According to the porosity and permeability of target carbonate reservoir, the physical property index is determined, and according to the corresponding relationship between physical property index and reservoir category, the category of reservoir is determined.The method takes oil production index as an intermediary, realizes the classification of reservoir category by physical property index, comprehensively considers the reservoir property and percolation property of reservoir, is suitable for carbonate reservoir, especially suitable for the classification of bioclastic limestone reservoir with poor porosity-permeability correlation.

Description

Technical Field

[0001] This invention relates to the field of carbonate reservoir characterization technology, and in particular to a method and apparatus for classifying carbonate reservoirs. Background Technology

[0002] In some regions, Cretaceous carbonate reservoirs are mostly thick and massive, with bioclastic limestone as the main reservoir material. Furthermore, the main oil reservoirs in some oilfields are massive bioclastic limestone reservoirs with enormous oil and gas reserves and production (Sadooni, et al. Cretaceous Research, 2005; Song Xinmin, et al. Petroleum Exploration and Development, 2018; Yu Yichang, et al. Petroleum Exploration and Development, 2018). These reservoirs differ significantly from the genesis and characteristics of carbonate reservoirs in China (Ma Yongsheng, et al. Acta Petrologica Sinica, 2017; Zhao Wenzhi, et al. Petroleum Exploration and Development, 2012; Shen Anjiang, et al. Marine Oil and Gas Geology, 2019; Li Yang, et al. Acta Petrolei Sinica, 2013). Carbonate reservoirs in some regions can develop in both high-energy and low-energy sedimentary environments (Li Fengfeng, et al. Earth Science, 2021; Zhang Meng, et al. Journal of Northeast Petroleum University, 2021). The genesis of these reservoirs is diverse, and the complex structural composition and diagenetic alteration result in strong heterogeneity (Vincent, et al. Marine and Petroleum Geology, 2015; Awadeesian, et al. Arabian Journal of Geosciences, 2015). Local intervals have developed microfractures due to tectonic factors (Chen Peiyuan, et al. Journal of Northeast Petroleum University; 2019). The reservoir micropore structure is complex, with a large range of physical properties and low porosity-permeability correlation (Sadooni. Journal of Petroleum Geology, 2004; Hollis. Petroleum Geoscience, 2011). Among them, the Khasib Formation in a certain oilfield exhibits four types of pore structure: high porosity and medium permeability fine throat type, medium-high porosity and low permeability fine throat type, medium-high porosity and ultra-low permeability micro throat type, and low porosity and ultra-low permeability extremely micro throat type (Deng Hucheng, et al. Acta Petrologica Sinica, 2014); the Mishrif Formation in the West Qurna oilfield includes primary intergranular pores, intragranular pores, secondary dissolution intergranular pores, intragranular dissolution pores, casting pores, dolomite intercrystalline pores, and micropores, with a small number of microfractures and pressure-dissolution fractures (Yao Zixiu, et al. Marine Oil and Gas Geology, 2018); and the Mishrif Formation in another oilfield has reservoir porosity ranging from low porosity to high porosity, with permeability differences of 2 to 3 orders of magnitude, developing ultra-low permeability to high permeability reservoirs (Wang Jun, et al. Petroleum Exploration and Development, 2016). Complex reservoir characteristics make reservoir evaluation difficult; superimposed geological processes lead to different trends in reservoir quality; strong diagenetic alteration leads to convergence of rock properties, resulting in weak differences in geophysical response.There is no clear correlation between reservoir porosity and permeability. Although reservoir porosity is high and oil content is good, the production capacity of single wells varies greatly during the development process. Static reservoir classification is poorly matched with dynamic development and production, making it difficult to guide the deployment of well networks in oilfield development.

[0003] There are many classification schemes for carbonate reservoirs, with the most widely used including: ① Classification based on reservoir genesis, which is the most widely used method for carbonate reservoirs. Zhao Wenzhi et al. classified marine carbonate reservoirs in China into sedimentary, diagenetic, and modified types (Zhao Wenzhi, et al. Petroleum Exploration and Development, 2012); ② Classification based on reservoir space, Li Yang et al. classified marine carbonate reservoirs into porous, fracture-porous, and fracture-vuggy types (Li Yang, et al. Acta Petrolei Sinica, 2012). (2013) This classification is applicable to reservoirs that are structurally controlled and have complex reservoir spaces; ③ Based on permeability, it is divided into high permeability (K≥100mD), medium permeability (10mD≤K<100mD), low permeability (1mD≤K<10mD) and ultra-low permeability (K<1mD). Although the physical property classification can characterize the reservoir quality, it cannot reflect the genesis of the reservoir and is difficult to characterize the distribution law of the reservoir; ④ Based on lithofacies, lithofacies classification is usually based on complex mathematical algorithms to divide the reservoir into several levels according to reservoir physical properties or micro parameters. Although lithofacies classification can characterize the reservoir quality, lithofacies type is the result of the coupling of multiple genesis processes and cannot reflect the main controlling factors of reservoir characteristics.

[0004] Overall, carbonate reservoir classification focuses on applying geological factors such as microscopic pore-throat structure, sedimentary facies, and lithofacies. Reservoir classification parameters are mainly qualitative, with a large overlap range for quantitative parameters. Furthermore, the classification results are not sufficiently integrated with production dynamics (Wang Yuxi et al. Well Logging Technology, 2015; Liu Hangyu et al. Geological Science and Technology Information, 2018; Sun Xiaowei et al. Geological Science and Technology Information, 2017; Zhou Wen et al. Acta Petrologica Sinica, 2014). It is difficult to classify and evaluate reservoirs with high porosity and low permeability, as well as those with low porosity and high permeability. Reservoir classification can be achieved in cored wells, but the correspondence between reservoir type and well logging is poor, making it difficult to extend to non-cored wells.

[0005] For the Cretaceous thick bioclastic limestone reservoirs in certain regions, there is no clear correlation between reservoir porosity and permeability. Although the reservoir porosity is high and the oil content is good, the single-well production capacity varies greatly during the development process. The static reservoir classification is poorly matched with dynamic development and production. Although there are many methods for classifying carbonate reservoirs, none of them are suitable for classifying this type of reservoir. Summary of the Invention

[0006] To enrich process routes and increase the selection space, this invention provides a method and apparatus for classifying carbonate reservoirs, which comprehensively considers the reservoir's storage and permeability, and is applicable to carbonate reservoirs, especially to the classification of thick, complex, highly heterogeneous bioclastic limestone reservoirs.

[0007] In a first aspect, embodiments of the present invention provide a method for classifying carbonate reservoirs, including:

[0008] For production wells with PLT data in carbonate reservoirs, the oil-producing intervals of the production wells and the oil production index and physical property index of the oil-producing intervals are determined. The oil production index is the ratio of the oil production to the thickness of the oil-producing interval, and the physical property index is the product of the average porosity and the logarithm of the average permeability of the oil-producing interval.

[0009] Based on the oil production index and physical property index of the oil-producing interval and the predetermined correspondence between the oil production index and the reservoir category, the correspondence between the physical property index of the oil-producing interval and the reservoir category is determined.

[0010] Based on the porosity and permeability of the target carbonate reservoir, physical property indices are determined. Based on the determined physical property indices and their correspondence with reservoir categories, the category of the target carbonate reservoir is determined.

[0011] Secondly, embodiments of the present invention provide a carbonate reservoir classification device, comprising:

[0012] The data acquisition module is used to determine the oil-producing section and the oil production index and physical property index of the oil-producing section for production wells with PLT data in carbonate reservoirs. The oil production index is the ratio of oil production to thickness of the oil-producing section, and the physical property index is the product of the average porosity and the logarithm of the average permeability of the oil-producing section.

[0013] The correspondence determination module is used to determine the correspondence between the physical property index of the oil-producing segment and the reservoir category based on the oil production index and physical property index of the oil-producing segment and the pre-determined correspondence between the oil production index and the reservoir category.

[0014] The reservoir classification module is used to determine the physical property index based on the porosity and permeability of the target carbonate reservoir, and to determine the category of the target carbonate reservoir based on the determined physical property index and the correspondence between the physical property index and the reservoir category.

[0015] Thirdly, embodiments of the present invention provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-mentioned carbonate reservoir classification method.

[0016] Fourthly, embodiments of this disclosure provide a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described carbonate reservoir classification method.

[0017] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0018] (1) The carbonate reservoir classification method provided in this embodiment of the invention creatively proposes the concepts of oil production index and physical property index, realizes the use of single well production data to define reservoir level, is simple and feasible to operate, combines dynamic and static, and uses dynamic characteristics to guide static classification. The reservoir classification results can not only follow the geological laws, but also have good matching with development and production, thus solving the problem of mismatch between static reservoir classification and development dynamics. The classified reservoir types have important guiding role in carbonate reservoir evaluation, prediction and balanced development.

[0019] (2) The carbonate reservoir classification method provided in this embodiment of the invention uses porosity and permeability as the main parameters for reservoir classification, comprehensively considers the reservoir's storage and permeability, overcomes the problem of poor porosity and permeability correlation in carbonate reservoirs, especially bioclastic limestone reservoirs, and is particularly suitable for two types of reservoirs: high porosity and low permeability and low porosity and high permeability.

[0020] (3) The carbonate reservoir classification method provided in this embodiment of the invention avoids using conventional logging curves to identify reservoir types for non-core wells, and directly uses porosity and permeability data to identify them, thus overcoming the characteristic that reservoir types have a weak response to logging curves.

[0021] 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 particularly pointed out in the written description, claims, and drawings.

[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0023] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0024] Figure 1 This is a flowchart of the carbonate reservoir classification method in Embodiment 1 of the present invention;

[0025] Figure 2A flowchart for determining the correspondence between oil production index and reservoir type in Embodiment 1 of the present invention;

[0026] Figure 3 This is a flowchart illustrating the specific implementation of the bioclastic limestone reservoir classification method in Embodiment 2 of the present invention.

[0027] Figure 4 This is a distribution diagram of the physical property indices of each oil-producing layer in Embodiment 2 of the present invention;

[0028] Figure 5 This is an example diagram illustrating the classification of reservoir types based on physical property index curves in Embodiment 2 of the present invention;

[0029] Figure 6 This is a characteristic map of Class I reservoir in Oilfield A in Embodiment 2 of the present invention;

[0030] Figure 7 This is a characteristic map of the Class II reservoir in Oilfield A in Embodiment 2 of the present invention;

[0031] Figure 8 This is a characteristic map of the Class III reservoir in Oilfield A in Embodiment 2 of the present invention;

[0032] Figure 9 This is a comparison chart of the matching degree between reservoir type and geological understanding in Oilfield A in Embodiment 2 of the present invention;

[0033] Figure 10 This is a schematic diagram of the carbonate reservoir classification device in an embodiment of the present invention. Detailed Implementation

[0034] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0035] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0036] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0037] The principle underlying this invention is as follows: Carbonate reservoirs, especially bioclastic limestone reservoirs, exhibit poor correlation between porosity and permeability due to their diverse pore systems and complex pore structures. Reservoir evaluation requires simultaneous consideration of both reservoir storage and permeability. Therefore, this invention selects both porosity and permeability as evaluation factors, both of which are positively correlated with reservoir quality. Given the large permeability range, the logarithm of permeability is chosen as the factor. Reservoirs are classified using production capacity data, and physical property statistics are performed on oil-producing sections. Each oil-producing section serves as a sample point, corresponding to three parameters: production index, porosity, and permeability. A relationship between production capacity and physical properties is established using physical property functions, with production capacity serving as the basis for reservoir classification. The classification results show a high degree of consistency with development dynamics. The entire research process progresses from producing wells to cored wells to non-cored wells. In non-cored wells, porosity and permeability data are the most fundamental reservoir parameters. Directly utilizing the interpretation results of these two parameters for reservoir classification helps overcome the shortcomings of poor logging matching. The embodiments of the present invention realize the effective classification of strongly heterogeneous carbonate reservoirs, which can provide geological basis for reservoir evaluation and balanced development.

[0038] Example 1

[0039] Embodiment 1 of the present invention provides a method for classifying carbonate reservoirs, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0040] Step S11: For production wells with PLT data in carbonate reservoirs, determine the oil-producing intervals of the production wells and the oil production index and physical property index of the oil-producing intervals.

[0041] Specifically, PLT data stands for Production Logging Test.

[0042] The oil production index is the ratio of oil production to thickness in an oil-producing layer, while the physical property index is the product of the average porosity and the logarithm of the average permeability in an oil-producing layer.

[0043] Step S12: Determine the correspondence between the physical property index of the oil-producing section and the reservoir category based on the oil production index and physical property index of the oil-producing section and the predetermined correspondence between the oil production index and the reservoir category.

[0044] For the correspondence between the oil production index and reservoir type, please refer to [link / reference needed]. Figure 2 As shown, it can be predetermined in the following way:

[0045] Step S21: From the production wells with PLT data in the carbonate reservoir, screen out production wells with only one oil-producing interval. Based on the oil production of the screened production wells and the correspondence between oil production and production capacity category, determine the production capacity category of the production wells.

[0046] The production capacity category of a production well can include high-yield, medium-yield, and low-yield.

[0047] Step S22: Based on the oil production index of the oil-producing wells in each production capacity category, establish the correspondence between the oil production index and the reservoir category.

[0048] One approach is to classify high-yield reservoirs as Class I, medium-yield reservoirs as Class II, and low-yield reservoirs as Class III. Further, the production index of all high-yield wells with a single oil-producing segment is statistically analyzed to determine the production index boundary between Class I and Class II reservoirs; similarly, the production index of all low-yield wells with a single oil-producing segment is statistically analyzed to determine the production index boundary between Class II and Class III reservoirs, thus establishing the correspondence between production index and reservoir category.

[0049] For example, the average or median value of the oil production index of the oil-producing intervals of high-yield wells can be used as the boundary between the oil production index of Class I and Class II reservoirs; the average or median value of the oil production index of the oil-producing intervals of low-yield wells can be used as the boundary between the oil production index of Class II and Class III reservoirs.

[0050] Step S13: Determine the physical property index based on the porosity and permeability of the target carbonate reservoir, and determine the category of the target carbonate reservoir based on the determined physical property index and the correspondence between the physical property index and the reservoir category.

[0051] The carbonate reservoir classification method provided in Embodiment 1 of this invention creatively proposes the concepts of oil production index and physical property index, realizing the determination of reservoir grade using single-well production data. It is simple and feasible to operate, combining dynamic and static methods, with dynamic characteristics guiding static classification. The reservoir classification results not only follow geological laws but also have good matching with development and production, solving the problem of mismatch between static reservoir classification and development dynamics. The classified reservoir types have important guiding role in carbonate reservoir evaluation, prediction, and balanced development.

[0052] The carbonate reservoir classification method provided in Embodiment 1 of this invention uses porosity and permeability as the main parameters for reservoir classification, comprehensively considering the reservoir's storage and permeability, and overcomes the problem of poor porosity-permeability correlation in carbonate reservoirs, especially bioclastic limestone reservoirs. It is particularly suitable for two types of reservoirs: high porosity and low permeability, and low porosity and high permeability.

[0053] The carbonate reservoir classification method provided in Embodiment 1 of this invention avoids using conventional logging curves to determine reservoir type for non-core wells, and directly uses porosity and permeability data for identification, thus overcoming the characteristic of weak response between reservoir type and logging curve.

[0054] Example 2

[0055] Embodiment 2 of the present invention provides a specific application of a bioclastic limestone reservoir classification method, the process of which is as follows: Figure 3 As shown, it includes the following steps:

[0056] Step S31: Based on the characteristics of reservoir development and production, clarify the boundaries for classifying single-well productivity categories (high-yield, medium-yield, and low-yield).

[0057] High-yield wells in Oilfield A typically produce more than 5,000 barrels per day, and can even reach tens of thousands of barrels per day, while the production of ordinary wells is mostly between 1,000 and 5,000 barrels per day. Wells with a production of less than 1,000 barrels per day are generally considered to be low-yield wells.

[0058] Step S32: From the production wells with PLT data, select production wells with only one oil-producing segment. Based on the oil production of the selected production wells and the correspondence between oil production and production capacity category, determine the production capacity category of the production wells. Based on the oil production index of the oil-producing segment of each production capacity category, establish the correspondence between the oil production index and the reservoir category.

[0059] High-yield wells correspond to reservoir type I reservoirs with an oil production index greater than 100 barrels / day / meter; medium-yield wells correspond to reservoir type II reservoirs with an oil production index between 20 barrels / day / meter and 100 barrels / day / meter; low-yield wells correspond to reservoir type III reservoirs with an oil production index less than 20 barrels / day / meter. See also... Figure 4 As shown.

[0060] Step S33: Determine the oil production index, average porosity, and average permeability of the oil-producing intervals of all production wells with PLT data. One oil-producing interval corresponds to one sample point.

[0061] Step S34: Plot all sample points onto the porosity-permeability cross-plot and distinguish sample points of the same reservoir category according to the boundary of the oil production index corresponding to the reservoir category.

[0062] Step S35: Multiply the logarithmic values ​​of porosity and permeability of the sample points as two variables to obtain the physical property index P=Φ×log10(k), and determine the P value boundary for different types of reservoirs.

[0063] See Figure 5 As shown, the limits of the physical property index P are 10.8 and 26.1.

[0064] Step S36: Project the core physical property data of the target reservoir into the porosity-permeability cross plot, and classify the reservoir into high-yield, medium-yield, or low-yield based on the physical property index P.

[0065] Step S37: Based on data from core samples, thin sections of cast bodies, and mercury intrusion porosimetry, analyze the geological genesis, physical property distribution range, and microstructure of different reservoirs, and fine-tune the reservoir type under the control of the geological model.

[0066] Type I reservoirs are mainly developed in high-energy sedimentary environments such as shallow shoals, and are almost undeveloped in low-energy sedimentary environments. Figure 6 The shallow water environment is characterized by intense scouring, resulting in rocks with low mud content and predominantly high-energy open-water bioclastic debris. The original rock properties are relatively good, allowing for easy infiltration by dissolving fluids. Sea level drop and intense dissolution of the upper part of the shoal have increased pore volume and throat radius, leading to a simultaneous increase in reservoir porosity and permeability. The reservoirs are predominantly high-porosity and high-porosity with medium permeability, with a smaller number being medium-porosity and medium-permeability. Reservoir pores are mainly intergranular pores and intergranular dissolution pores, containing a small amount of residual intergranular pores and molded pores. Pore connectivity is good, with a high pore-throat ratio and porosity greater than 20%. The reservoir pore-throat structure exhibits a dual-modal characteristic, with distribution curves showing two peaks at 0.1–1 μm and 1–10 μm. Pore-throat sorting is poor, with a high proportion of large pore throats. The reservoir displacement pressure is typically less than 80 psia.

[0067] Type II reservoirs develop in both high-energy and low-energy environments. High-energy environments are dominated by barrier shoals and shore shoals, with a small number being foreshore environments. Low-energy environments are dominated by lacustrine facies. These reservoirs mainly form during periods of sea-level decline. Figure 7 Type II reservoirs are mainly characterized by medium-porosity and medium-permeability, medium-porosity and low-permeability, and high-porosity and low-permeability, with a small amount of low-porosity and medium-permeability. Pores are primarily composed of residual intergranular pores, matrix dissolution pores, and molding pores, with localized dense filling and good local connectivity. Pore throats exhibit a single-mode medium-throat distribution, with peak values ​​ranging from 0.1 to 1 μm. Large pore throats are less developed, with a wider peak value distribution range, indicating good pore throat sorting. Reservoir displacement pressures typically range from 80 to 300 psia.

[0068] Type III reservoirs are dominated by low-energy sediments, with lacustrine facies reservoirs accounting for nearly half of the total. Figure 8The reservoir is primarily controlled by later constructive diagenesis, resulting in numerous mold pores and intergranular pores. Bioturbation promotes the development of lagoonal reservoirs. Reservoir properties are predominantly mesoporous and low-permeability, with minor amounts of low-porosity and low-permeability, low-porosity and ultra-low-permeability, mesoporous and ultra-low-permeability, and high-porosity and low-permeability. Pores are mainly composed of matrix micropores, mold pores, biocavity pores, and intergranular pores, with a minor amount of granular micropores. Pore throats exhibit a single-mode distribution, with peak values ​​between 0.1 and 1 μm. Large pore throats are almost nonexistent, indicating good pore throat sorting. Reservoir displacement pressure is high, typically greater than 100 psia, reaching a maximum of 500 psia. A small number of reservoirs have displacement pressures less than 10 psia, possibly due to fracture factors.

[0069] Step S38: For non-cored wells, based on the interpretation of porosity and permeability data from well logging, the reservoir type is classified by truncation through the physical property index P value.

[0070] Step S39: In non-core wells, verify whether the reservoir type is consistent with geological patterns to ensure that the reservoir type matches existing geological knowledge.

[0071] Reservoir type matches geological understanding, such as Figure 9 As shown, the original classification interpreted the entire barrier shoal as a Class II reservoir. However, measured data revealed differences in physical properties between the top and bottom of the shoal, with the top exhibiting superior properties compared to the bottom, a distinction the original classification failed to make. The new classification interprets it as a Class I reservoir. In the new classification, the top of the shoal is classified as a Class I reservoir, and the bottom as a Class II reservoir. The original classification interpreted lagoons as Class II reservoirs, while the new classification classifies them as Class III, with a small number classified as Class II and interlayers. The interspersed distribution of intraplatform shoals and lagoons shows a good match between reservoir type variations and sedimentary environments.

[0072] Based on the inventive concept of this invention, embodiments of this invention also provide a carbonate reservoir classification device, the structure of which is as follows: Figure 10 As shown, it includes:

[0073] The data acquisition module 101 is used to determine the oil-producing section and the oil production index and physical property index of the oil-producing section for production wells with PLT data in carbonate reservoirs. The oil production index is the ratio of oil production to thickness of the oil-producing section, and the physical property index is the product of the average porosity and the logarithm of the average permeability of the oil-producing section.

[0074] The correspondence determination module 102 is used to determine the correspondence between the physical property index of the oil-producing segment and the reservoir category based on the oil production index and physical property index of the oil-producing segment and the pre-determined correspondence between the oil production index and the reservoir category.

[0075] The reservoir classification module 103 is used to determine the physical property index based on the porosity and permeability of the target carbonate reservoir, and to determine the category of the target carbonate reservoir based on the determined physical property index and the correspondence between the physical property index and the reservoir category.

[0076] In some embodiments, the correspondence determination module 102 is further configured to predetermine the correspondence between the oil production index and the reservoir category in the following manner:

[0077] From production wells with PLT data in carbonate reservoirs, production wells with only one oil-producing segment are selected. Based on the oil production of the selected production wells and the correspondence between oil production and production capacity category, the production capacity category of the production wells is determined. Based on the oil production index of the oil-producing segment of each production capacity category, the correspondence between the oil production index and the reservoir category is established.

[0078] In some embodiments, if the porosity and permeability of the target carbonate reservoir are obtained through core data, the reservoir classification module 103 is further configured to:

[0079] By combining core data, cast thin section data, and / or mercury intrusion porosimetry data of the target carbonate reservoir, as well as pre-statistical data on the development environment, composition characteristics, physical properties, and microstructure of various reservoirs, the category of the target carbonate reservoir is corrected.

[0080] In some embodiments, if the porosity and permeability of the target carbonate reservoir are obtained through well logging data, the reservoir classification module 103 is further configured to:

[0081] Based on geological characteristics, the classification of the target carbonate reservoir is revised.

[0082] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0083] Based on the inventive concept of the present invention, embodiments of the present invention also provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-mentioned carbonate reservoir classification method.

[0084] Based on the inventive concept of this invention, this embodiment of the invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned carbonate reservoir classification method.

[0085] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0086] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0087] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than those stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby clearly incorporated into the detailed description, wherein each claim stands alone as a preferred embodiment of the invention.

[0088] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0089] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0090] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0091] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for classifying carbonate reservoirs, characterized in that, include: For production wells with PLT data in carbonate reservoirs, the oil-producing intervals of the production wells and the oil production index and physical property index of the oil-producing intervals are determined. The oil production index is the ratio of the oil production to the thickness of the oil-producing interval, and the physical property index is the product of the average porosity and the logarithm of the average permeability of the oil-producing interval. Based on the oil production index and physical property index of the oil-producing interval and the pre-determined correspondence between the oil production index and the reservoir category, the correspondence between the physical property index of the oil-producing interval and the reservoir category is determined; specifically, this includes determining the oil production index, average porosity, and average permeability of the oil-producing interval of the production well with PLT data, with one sample point corresponding to one oil-producing interval. The sample points were plotted on the porosity-permeability logarithmic cross plot. Based on the boundary of the oil production index corresponding to the reservoir category, the sample points of the same reservoir category were distinguished. The logarithmic values ​​of porosity and permeability of the sample points were multiplied as two variables to obtain the physical property index value. The boundary of the physical property index value of different types of reservoirs was determined, and the correspondence between the physical property index of the oil-producing section and the reservoir category was obtained. Based on the porosity and permeability of the target carbonate reservoir, physical property indices are determined. Based on the determined physical property indices and their correspondence with reservoir categories, the category of the target carbonate reservoir is determined.

2. The method as described in claim 1, characterized in that, The correspondence between the oil production index and the reservoir type is predetermined in the following manner: From production wells with PLT data in carbonate reservoirs, production wells with only one oil-producing interval are selected. Based on the oil production of the selected production wells and the correspondence between oil production and production capacity category, the production capacity category of the production wells is determined. Based on the oil production index of the oil-producing wells in each production capacity category, a correspondence between the oil production index and the reservoir category is established.

3. The method as described in claim 2, characterized in that, The correspondence between oil production and production capacity category is as follows: oil production greater than 5,000 barrels / day is a high-production well; oil production no more than 5,000 barrels / day and no less than 1,000 barrels / day is a medium-production well; and oil production less than 1,000 barrels / day is a low-production well. The correspondence between the oil production index and the reservoir category is as follows: an oil production index greater than 100 barrels / day / meter is a Class I reservoir; an oil production index not greater than 100 barrels / day / meter and not less than 20 barrels / day / meter is a Class II reservoir; and an oil production index less than 20 barrels / day / meter is a Class III reservoir.

4. The method as described in claim 3, characterized in that, The determination of the correspondence between the physical property indices of the oil-producing interval and the reservoir category includes: The correspondence between the physical property index of the oil-producing interval and the reservoir category is determined as follows: a physical property index greater than 26.1 is a Class I reservoir, a physical property index not greater than 26.1 and not less than 10.8 is a Class II reservoir, and a physical property index less than 10.8 is a Class III reservoir.

5. The method as described in claim 1, characterized in that, The porosity and permeability of the target carbonate reservoir are obtained through core sampling data or well logging data.

6. The method as described in claim 5, characterized in that, If the porosity and permeability of the target carbonate reservoir are obtained through core sampling data, the following are also included: By combining core data, cast thin section data, and / or mercury intrusion porosimetry data of the target carbonate reservoir, as well as pre-statistical data on the development environment, composition characteristics, physical properties, and microstructure of various reservoirs, the category of the target carbonate reservoir is corrected.

7. The method as described in claim 5, characterized in that, If the porosity and permeability of the target carbonate reservoir are obtained through well logging data, the following are also included: Based on geological characteristics, the classification of the target carbonate reservoir is revised.

8. The method according to any one of claims 1 to 7, characterized in that, Both the carbonate reservoir and the target carbonate reservoir are bioclastic limestone reservoirs.

9. A carbonate reservoir classification device, characterized in that, include: The data acquisition module is used to determine the oil-producing section and the oil production index and physical property index of the oil-producing section for production wells with PLT data in carbonate reservoirs. The oil production index is the ratio of oil production to thickness of the oil-producing section, and the physical property index is the product of the average porosity and the logarithm of the average permeability of the oil-producing section. The correspondence determination module is used to determine the correspondence between the physical property index of an oil-producing segment and the reservoir category based on the oil production index and physical property index of the oil-producing segment and a pre-determined correspondence between the oil production index and the reservoir category. Specifically, it is used to determine the oil production index, average porosity, and average permeability of the oil-producing segment of a production well with PLT data, with one sample point corresponding to one oil-producing segment; the sample points are plotted on a porosity-permeability logarithmic intersection chart, and sample points of the same reservoir category are distinguished according to the boundary of the oil production index corresponding to the reservoir category; the logarithmic values ​​of porosity and permeability of the sample points are multiplied as two variables to obtain the physical property index value, the boundary of the physical property index value of different types of reservoirs is determined, and the correspondence between the physical property index of the oil-producing segment and the reservoir category is obtained. The reservoir classification module is used to determine the physical property index based on the porosity and permeability of the target carbonate reservoir, and to determine the category of the target carbonate reservoir based on the determined physical property index and the correspondence between the physical property index and the reservoir category.

10. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the carbonate reservoir classification method according to any one of claims 1 to 8.

11. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the carbonate reservoir classification method according to any one of claims 1 to 8.

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