Carbonate rock lithology identification method and device based on logging parameters and storage medium

Abstract

The invention relates to a carbonate rock lithology identification method and device based on logging parameters and a storage medium, and belongs to the technical field of rock type identification, and the method comprises the steps: calibrating a plurality of carbonate rock slices of a target stratum, and obtaining the logging parameters and types of the corresponding depths of the carbonate rock slices; the carbonate rock slices comprise dolomite slices and limestone slices; drawing a cross plot based on the logging parameters of the corresponding depths of the carbonate rock slices, and determining target parameters according to the cross plot and the type; the target parameters comprise uranium values, thorium values, interval transit time and compensated neutrons; and identifying the lithology of the dolomite and limestone of the non-coring section based on the target parameters. According to the carbonate rock lithology identification method based on the logging parameters, accurate identification of the lithology of the carbonate rock is achieved through joint analysis of multiple parameters such as uranium, thorium, interval transit time and compensated neutrons.

Description

Technical Field

[0001] The present invention relates to the technical field of rock type identification, and in particular to a method, device and storage medium for identifying carbonate rock lithology based on well logging parameters. Background Art

[0002] Carbonate reservoirs contain abundant oil and gas resources and are widely distributed around the globe, offering enormous exploration potential. Of all the super-large oil and gas reservoirs discovered today, carbonate reservoirs account for 35% and their recoverable reserves contribute 50% of the world's total oil and gas production, with oil and natural gas production accounting for 60% and 40% of global production, respectively. Therefore, carbonate reservoirs play a crucial role in oil and gas exploration and development.

[0003] The development of carbonate reservoirs is closely related to their lithology. Calcite and dolomite are the primary minerals in carbonate reservoirs, and they are typically classified as limestone or dolomite based on their mineral content. However, due to the complex diagenesis and the overlapping transformations of multiple tectonic movements, lithology identification in older, deeply buried carbonate reservoirs presents significant challenges. In the absence of extensive downhole core and thin section data, identifying lithology and lithofacies is crucial for determining sedimentary sequences, elucidating sedimentary evolution, and conducting reservoir prediction.

[0004] At present, there is no unified identification method in the domestic research on the lithology and sedimentary microfacies division and identification of marine limestone by well logging. Under the condition of limited coring data, the lithology division of carbonate dolomite and limestone has become the primary technical problem to be solved in dividing different beach development environments and conducting reservoir analysis. Summary of the Invention

[0005] In view of this, it is necessary to provide a carbonate rock lithology identification method, device and storage medium based on logging parameters to achieve the purpose of accurately identifying the lithology of carbonate dolomite and limestone.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for identifying carbonate rock lithology based on well logging parameters, comprising: Calibrate multiple carbonate rock slices of the target formation to obtain logging parameters and types of the carbonate rock slices at corresponding depths; the carbonate rock slices include dolomite slices and limestone slices; Drawing a cross-plot based on the logging parameters of the corresponding depth of the carbonate rock slice, and determining target parameters according to the cross-plot and the type; the target parameters include uranium value, thorium value, sonic time difference and compensated neutron; The lithology of the dolomite and limestone in the non-coring section is identified based on the target parameters.

[0007] In a possible implementation, identifying the lithology of dolomite and limestone in the non-coring section based on the target parameter includes: Identify limestone lithology based on uranium and thorium values; Identify dolomite lithology based on acoustic transit time and compensated neutrons.

[0008] In a possible implementation, identifying the lithology of limestone based on uranium and thorium values ​​includes: The uranium value is normalized to obtain a normalized uranium value; The ratio of the uranium value to the thorium value is normalized to obtain a normalized ratio; determining a rock structure factor based on the normalized uranium value and the normalized ratio; Based on the discriminant function and the rock structure factor, the limestone lithology is identified.

[0009] In a possible implementation, the rock structure factor is expressed as follows:

[0010] in, represents the rock structure factor, represents the normalized uranium value, represents the normalized ratio.

[0011] In a possible implementation, the discriminant function is expressed as follows:

[0012] in, Represents the rock texture factor, 1 represents sparry grain limestone, 2 represents mud-sparry grain limestone, and 3 represents grain-bearing micritic limestone / micritic limestone.

[0013] In a possible implementation, the identification of dolomite lithology based on acoustic transit time and compensated neutrons includes: The dolomite lithology is identified based on the average value of the acoustic wave transit time and the average value of the compensated neutron.

[0014] In a possible implementation, the method further includes: constructing a logging response template based on the logging parameters and types of the corresponding depths of the carbonate rock slices; comparing the uranium value and natural gamma curve segments in the logging curve of the dolomite in the non-coring section with the response template to identify the limestone lithofacies; The uranium value, acoustic transit time, and compensated neutron curve segments in the logging curve of the limestone in the non-coring section are compared with the response template to identify the dolomite lithofacies.

[0015] In a second aspect, the present invention further provides a carbonate rock lithology identification device based on well logging parameters, comprising: a calibration module for calibrating multiple carbonate thin sections of a target formation to obtain logging parameters and types of the carbonate thin sections at corresponding depths; the carbonate thin sections include dolomite thin sections and limestone thin sections; a determination module, configured to draw a cross-plot based on the logging parameters of the carbonate rock slice at the corresponding depth, and determine target parameters according to the cross-plot and the type; the target parameters include uranium value, thorium value, acoustic wave time difference, and compensated neutron; An identification module is used to identify the lithology of dolomite and limestone in the non-coring section based on the target parameters.

[0016] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the carbonate rock lithology identification method based on well logging parameters described in any of the above implementations.

[0017] In a fourth aspect, the present invention also provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the carbonate rock lithology identification method based on logging parameters described in any of the above-mentioned implementation methods.

[0018] The beneficial effects of the present invention are as follows: the carbonate rock lithology identification method, device and storage medium based on logging parameters provided by the present invention obtain the logging parameters and types of the corresponding depths of the carbonate rock thin sections by calibrating multiple carbonate rock slices of the target formation, and draw an intersection diagram based on the logging parameters of the corresponding depths of the carbonate rock thin sections, so as to extract the target parameters for identifying the lithology based on the intersection diagram and the type, and further identify the lithology of dolomite and limestone in the non-coring section based on the target parameters. The method is suitable for lithology identification in the non-coring section, reduces the need for core sampling, and improves the identification efficiency. The method adopts the joint analysis of multiple parameters such as uranium, thorium, acoustic time difference and compensated neutron to comprehensively judge the lithology. The multi-dimensional data fusion effectively reduces the limitations of a single parameter, and uses the logging parameters of the actual carbonate rock thin sections as the calibration basis to achieve accurate identification of the carbonate rock lithology. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic flow chart of an embodiment of a method for identifying carbonate rock lithology based on well logging parameters provided by the present invention; Figure 2 A schematic flow chart of the method for identifying carbonate lithology and sedimentary microfacies using well logging data based on thin section calibration provided by the present invention; Figure 3 This is a schematic diagram of the GR-U correlation of pure limestone sections in some wells provided by the present invention; Figure 4 Schematic diagram of GR-U correlation of continuous thick dolomite sections in some wells provided by the present invention; Figure 5 Schematic diagram of statistical comparison of DT and CNL average values ​​of different dolomite lithologies provided by the present invention; Figure 6 This is a schematic diagram of a non-standard drilling standardized logging curve and a sedimentary facies identification scheme after comparison provided by the present invention; Figure 7 The second schematic diagram of the sedimentary facies identification scheme after comparison of the standardized logging curves for non-standard drilling provided by the present invention; Figure 8 A schematic structural diagram of an embodiment of a carbonate rock lithology identification device based on well logging parameters provided by the present invention; Figure 9 This is a schematic structural diagram of an embodiment of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0022] In the description of the embodiments of the present invention, unless otherwise specified, "plurality" means two or more. "And / or" describes the association relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0023] The terms "first," "second," and so on, used in the embodiments of the present invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, technical features designated as "first" or "second" may explicitly or implicitly include at least one such feature.

[0024] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] The present invention provides a method, device and storage medium for identifying carbonate rock lithology based on well logging parameters, which are described below respectively.

[0026] Figure 1 A flow chart of an embodiment of the carbonate rock lithology identification method based on well logging parameters provided by the present invention is shown in FIG. Figure 1 As shown in Figure 2, the carbonate rock lithology identification methods based on well logging parameters include: S101, calibrating multiple carbonate rock thin sections of a target formation to obtain logging parameters and types of corresponding depths of the carbonate rock thin sections; the carbonate rock thin sections include dolomite thin sections and limestone thin sections; S102, drawing a crossplot based on the logging parameters of the corresponding depth of the carbonate rock slice, and determining target parameters according to the crossplot and the type; the target parameters include uranium value, thorium value, sonic time difference, and compensated neutron; S103: Identify the lithology of the dolomite and limestone in the non-coring section based on the target parameters.

[0027] In S101 , multiple dolomite thin sections and limestone thin sections of the target formation are obtained for calibration. The dolomite and limestone in the target formation are classified through identification and analysis to obtain logging parameters and types of corresponding depths of the carbonate thin sections.

[0028] Alternatively, dolomite can be divided into: laminated microcrystalline dolomite, crystalline dolomite, and dolomite with metasomatic residual texture. Limestone can be divided into: sparry granular limestone, micritic granular limestone, and micritic limestone.

[0029] The logging data of the corresponding depth of each thin section sample in the target formation are collected. The logging data include logging parameters, including natural gamma ray (GR), uranium value (U), uranium / thorium value (U / Th), acoustic transit time (DT), and compensated neutron (CNL).

[0030] In S102, a cross-plot is drawn based on the logging parameters and types of the corresponding depths of the carbonate rock thin section. For example, a cross-plot is drawn based on the GR and U data in the logging data, and it can be found that the main contribution of GR in the dolomite section is U, and the main contribution of GR in the limestone section is U.

[0031] Target parameters for identifying lithology are extracted from the logging parameters according to the type of thin section. The target parameters include uranium value, thorium value, sonic time difference and compensated neutron.

[0032] In S103 , dolomite and limestone in the non-coring section are obtained, and the lithology of the limestone is identified by uranium and thorium values, and the lithology of the dolomite is identified by acoustic transit time and compensated neutrons.

[0033] The rock structure factor of limestone can be calculated through the uranium value and thorium value, and the lithology of limestone can be identified based on the rock structure factor.

[0034] Based on the results of dolomite thin section analysis, it is found that among dolomite types, medium-coarse crystalline dolomite has the smallest average DT value, while powder-fine crystalline dolomite has a significantly larger average DT value, and mud-microcrystalline dolomite has the largest average DT value. It is also found that among dolomite types, medium-coarse crystalline dolomite has the largest average CNL value, while powder-fine crystalline dolomite has a significantly smaller average CNL value, and mud-microcrystalline dolomite has the smallest CNL value. With decreasing dolomite grain size, the average CNL value within each dolomite subtype shows a clear downward trend. Combining DT and CNL values ​​can effectively distinguish dolomite lithologies.

[0035] In summary, the carbonate rock lithology identification method based on logging parameters provided by the embodiment of the present invention obtains the logging parameters and types of the corresponding depths of the carbonate rock thin sections by calibrating multiple carbonate rock thin sections of the target formation, and draws an intersection diagram based on the logging parameters of the corresponding depths of the carbonate rock thin sections, thereby extracting the target parameters for identifying the lithology based on the intersection diagram and the type, and further identifying the lithology of dolomite and limestone in the non-coring section based on the target parameters. The method is suitable for lithology identification in the non-coring section, reduces the need for core sampling, and improves the identification efficiency. It adopts the joint analysis of multiple parameters such as uranium, thorium, acoustic time difference and compensated neutron to comprehensively judge the lithology. The multi-dimensional data fusion effectively reduces the limitations of a single parameter, and uses the logging parameters of the actual carbonate rock thin section as the calibration basis to achieve accurate identification of the carbonate rock lithology.

[0036] In some embodiments of the present invention, identifying the lithology of dolomite and limestone in the non-coring section based on the target parameters includes: Identify limestone lithology based on uranium and thorium values; Identify dolomite lithology based on acoustic transit time and compensated neutrons.

[0037] In some embodiments of the present invention, identifying the lithology of limestone based on uranium and thorium values ​​includes: The uranium value is normalized to obtain a normalized uranium value; The ratio of the uranium value to the thorium value is normalized to obtain a normalized ratio; determining a rock structure factor based on the normalized uranium value and the normalized ratio; Based on the discriminant function and the rock structure factor, the limestone lithology is identified.

[0038] In some embodiments of the present invention, the rock structure factor is expressed as follows:

[0039] in, represents the rock structure factor, represents the normalized uranium value, represents the normalized ratio.

[0040] In some embodiments of the present invention, the discriminant function is expressed as follows:

[0041] in, Represents the rock texture factor, 1 represents sparry grain limestone, 2 represents mud-sparry grain limestone, and 3 represents grain-bearing micritic limestone / micritic limestone.

[0042] Optionally, the well logging data is pre-processed to remove outliers and then normalized to reduce the differences in data between different wells, so that the U value is maintained in the range of 0-5 and the U / Th value is maintained in the range of 0-10.

[0043] The uranium value (U) and uranium / thorium value (U / Th) are normalized as follows:

[0044] in, is the normalized logging data, is the logging data before normalization, is the minimum value of the logging data before normalization, is the maximum value in the logging data before normalization.

[0045] The structural type of the limestone in the non-coring section is identified by the area to which its logging value belongs. For the identification of limestone, the rock structure factor is calculated. ,according to Determination of limestone lithology in the region. The calculation formula is as follows:

[0046] The lithology identification sample set was loaded into the Origin software. In the discriminant analysis module, the lithology column was defined as a grouping variable. The maximum and minimum values ​​of the grouping variable were defined as the maximum and minimum values ​​of the lithology numeric code, respectively. The well logging uranium value (U) and uranium / thorium value (U / Th) columns were defined as independent variables. Through Fisher discriminant analysis, the discriminant function was obtained as follows:

[0047] Wherein, 1 represents sparry grain limestone; 2 represents mud-sparry grain limestone; 3 represents grain-bearing micritic limestone / micritic limestone.

[0048] In some embodiments of the present invention, the identification of dolomite lithology based on acoustic transit time and compensated neutrons includes: The dolomite lithology is identified based on the average value of the acoustic wave transit time and the average value of the compensated neutron.

[0049] Based on the results of dolomite thin section identification, the average DT value of each type of limestone was calculated. It can be found that among the dolomite types, the average DT value of medium-coarse crystal dolomite is the smallest, the average DT value of powder-fine crystal dolomite is significantly increased, and the average DT value of mud-micro crystal dolomite is the largest.

[0050] Calculating the average CNL values ​​for each limestone type reveals that, among dolostone types, medium-coarse-crystalline dolomite has the highest average CNL, while silty-fine-crystalline dolomite has a significantly lower average CNL, and muddy-microcrystalline dolomite has the lowest. The average CNL values ​​for each dolomite subtype show a clear downward trend as dolomite grain size decreases.

[0051] DT and CNL values ​​show obvious characteristics of the subtypes in dolomite. Combining DT and CNL values ​​can better distinguish the lithology of dolomite.

[0052] In addition, based on the thin section analysis results, DT and CNL are cross-plotted to generate processed operational data and establish a dolomite logging lithology identification chart. The structural type of dolomite in the non-cored section is identified by the region to which its logging average value belongs. For dolomite identification, the GR and U values ​​are combined, and the DT and CNL average values ​​are calculated to determine the lithology of the dolomite.

[0053] In some embodiments of the present invention, further comprising: constructing a logging response template based on the logging parameters and types of the corresponding depths of the carbonate rock slices; comparing the uranium value and natural gamma curve segments in the logging curve of the dolomite in the non-coring section with the response template to identify the limestone lithofacies; The uranium value, acoustic transit time, and compensated neutron curve segments in the logging curve of the limestone in the non-coring section are compared with the response template to identify the dolomite lithofacies.

[0054] For lithofacies identification, conventional curves such as GR, U, and U / Th that are more sensitive to sedimentary facies are preferred. By establishing a standard drilling logging curve response template, the logging curves of non-standard drilling wells are compared with the logging curve response template of standard drilling wells to identify sedimentary facies such as inner gentle slope, inner zone of shallow gentle slope, outer zone of shallow gentle slope, and deep gentle slope.

[0055] The carbonate sedimentary facies classification and logging curves of standard drilling were analyzed to determine logging response templates for different sedimentary facies. Specifically, identification plates for dolomite and limestone sedimentary microfacies were established based on core and thin section data, and carbonate rock response templates for standard drilling were constructed. The standardized logging curves of non-standard drilling wells were compared with the logging response templates of dolomite and limestone for standard drilling to classify the dolomite and limestone sedimentary microfacies of non-standard drilling wells.

[0056] For example, the lithofacies of the limestone section can be identified by comparing the uranium value (U) and natural gamma ray (GR) curve segments in the well logging curve. The lithofacies of the dolomite section can be identified by comparing the uranium value (U), sonic transit time (DT), and compensated neutron (CNL) curve segments in the well logging curve.

[0057] Optionally, Figure 2 The present invention provides a flow chart of a method for identifying carbonate lithology and sedimentary microfacies using well logging data based on thin section calibration. The technical solution provided by the present invention can effectively identify carbonate dolomite and limestone lithology and sedimentary microfacies with high identification accuracy.

[0058] like Figure 2 As shown, a method for identifying carbonate lithology and lithofacies using well logging data based on thin section calibration includes: S201. Obtain multiple thin-section data of the target stratum, and classify the lithology and sedimentary facies of the carbonate rock, dolomite, and limestone in the target stratum.

[0059] Obtain identification and analysis of multiple carbonate dolomite and limestone thin sections in the target formation to classify the limestone lithology within the target formation. Determine the lithofacies type based on core description data.

[0060] Optionally, based on the thin section identification results, the well logging curves can be normalized to obtain processed real-time operational data and corresponding identification markers can be constructed. Based on the lithofacies type and identification markers, a lithofacies and sedimentary microfacies identification model can be established. Based on the identification model, the sedimentary microfacies of the target well can be identified.

[0061] S202 , collecting target logging data of the corresponding depth of each thin section sample in the target area, wherein the logging data includes GR value, U value, DT value, and CNL value.

[0062] The logging data of each thin section sample at the corresponding depth in the preset area are counted. The logging data include logging parameters such as natural gamma (GR), uranium value (U), uranium / thorium value (U / Th), time difference of sound wave (DT), and compensated neutron (CNL).

[0063] S203. Make a cross plot of the natural gamma ray GR and the uranium U value, and identify the lithologic characteristics of the limestone in the non-coring section by the area to which its logging value belongs.

[0064] The GR and U data were cross-plotted based on the logging data, confirming that the main contribution to GR in the dolomite section is U, and the main contribution to GR in the limestone section is U.

[0065] S204. Select the natural gamma ray (GR) and uranium U value, which are logging parameters sensitive to the lithology of dolomite, and make a judgment based on the average value of the DT value and the CNL value.

[0066] S205. Establish a standard drilling curve response template, identify the lithofacies of the limestone section by comparing the curve segments of the U value and GR value in the logging curve, and identify the lithofacies of the dolomite section by comparing the curve segments of the U value, DT value, and CNL value in the logging curve.

[0067] The following describes in detail the method for identifying the lithology and sedimentary microfacies of open platform limestone using well logging data based on thin section calibration with reference to specific examples.

[0068] Example 1: Taking the third member of the Ordovician Ying in the Gucheng area of ​​the Tarim Basin as an example, the logging lithology identification of a typical coring well is described in detail.

[0069] S1. Basic geological conditions: The third member of the Ordovician Ying formation in the Gucheng area of ​​the Tarim Basin is a set of marine carbonate rock formations, the lithology of which is mainly dolomite and limestone. The above geological conditions are suitable for the application of the present invention.

[0070] S2. Parameter acquisition: obtain identification and analysis of multiple dolomite and limestone thin sections in the preset area, and classify the structural types of dolomite and limestone in the preset area. Dolomite is divided into the following types according to its structure: laminated microcrystalline dolomite, crystalline dolomite, and dolomite with metasomatic residual structure; limestone is divided into the following types according to its structure: sparry granular limestone, micritic granular limestone, and micritic limestone.

[0071] The logging data of each thin section sample at the corresponding depth in the preset area are counted. The logging data include logging parameters such as natural gamma (GR), uranium value (U), uranium / thorium value (U / Th), time difference of sound wave (DT), and compensated neutron (CNL).

[0072] S3. Based on the logging data, the GR and U data were cross-plotted. The correlation coefficient R value is an important indicator to measure the correlation between the two parameters. It is confirmed that the main contribution of GR in the limestone section is U.

[0073] Figure 3 The GR-U correlation diagram of the pure limestone section of some wells provided by the present invention is as follows: Figure 3 As shown, the GR value and U value of the typical well Gucheng 9 well are R 2 The GR value of Gucheng 15 well is 0.837, and the R value of U value is 2 The correlation coefficient R is greater than 0.9, indicating that the GR value and U value are highly correlated.

[0074] S4. Based on the results of limestone thin section identification, the uranium value (U) and uranium / thorium ratio (U / Th) are normalized to obtain processed real-time running data to reduce the variability of data between different wells, keeping the U value within 0-5 and the U / Th ratio within 0-10.

[0075] (1) In formula (1), X n is the logging data after normalization, X is the logging data before normalization, and X min is the minimum value of the logging data before normalization, X max is the maximum value in the logging data before normalization.

[0076] S5. Calculation of limestone rock structure factor.

[0077] The lithology identification sample set was loaded into the Origin software. In the discriminant analysis module, the lithology column was defined as a grouping variable. The maximum and minimum values ​​of the grouping variable were defined as the maximum and minimum values ​​of the lithology digital code, respectively. The well logging value uranium value (U) and uranium / thorium value (U / Th) columns were defined as independent variables. The discriminant function was obtained through Fisher discriminant analysis, and the rock structure factor was obtained. f .

[0078] (2) S6. Limestone lithology identification, using the following function:

[0079] Wherein, 1 represents sparry grain limestone; 2 represents mud-sparry grain limestone; 3 represents grain-bearing micritic limestone / micritic limestone.

[0080] For example, in the above example, f =0.3456, then Lith=2, and the lithology identified by logging is mud-sparry granular limestone.

[0081] S7. Make a cross plot of GR and U data based on the well logging data, such as Figure 4 As shown, Figure 4 The GR-U correlation diagram of some continuous thick dolomite sections in the present invention is provided. The correlation coefficient R value is an important indicator to measure the correlation between two parameters, which proves that the main contribution of GR in the dolomite section is U. For example, the R value of the GR value of the typical well Gucheng 16 well is 2 It is 0.657, indicating that the GR value and U value are highly correlated.

[0082] S8. Based on the results of dolomite thin section identification, it is calculated that the DT average value of medium-coarse crystal dolomite is the smallest among the dolomite types, the DT average value of powder-fine crystal dolomite is significantly increased, and the DT average value of mud-micro crystal dolomite is the largest.

[0083] S9. Based on the results of dolomite thin section analysis, the average CNL of medium-coarse crystalline dolomite is calculated to be the highest, while that of fine-grained crystalline dolomite is significantly lower, and that of mud-microcrystalline dolomite is the lowest. The average CNL of each dolomite subtype decreases significantly with decreasing dolomite grain size.

[0084] Figure 5 The statistical comparison diagram of the DT and CNL average values ​​of different dolomite lithologies provided by the present invention is as follows: Figure 5 As shown in the figure, DT and CNL values ​​show obvious characteristics of the subtypes in dolomite. Combining DT and CNL values ​​can better distinguish different types of dolomite.

[0085] Example 2: Taking the Ordovician system in the Gucheng area of ​​the Tarim Basin as an example, the logging curve identification of a typical coring well is described in detail.

[0086] S1. Basic geological conditions: The Ordovician strata in the Gucheng area of ​​the Tarim Basin are a set of marine carbonate strata. The sedimentary background of the Middle and Lower Ordovician carbonate rocks in the Gucheng area is generally interpreted as a gentle slope platform, with five main sedimentary phases developed: inner gentle slope, shallow gentle slope inner zone, shallow gentle slope outer zone, deep gentle slope, and outer gentle slope. The above geological conditions are suitable for the application of the present invention.

[0087] S2. Based on the above-mentioned embodiment 1, the carbonate rock sedimentary microfacies identification method provided in the embodiment of the present invention constructs logging response templates for different microfacies of dolomite and limestone for standard drilling, including: analyzing thin-section identification plates of dolomite and limestone grain banks and logging curves of standard drilling to determine the logging response templates for different microfacies.

[0088] S3. Specifically, the limestone lithofacies are identified by comparing the uranium value (U) and natural gamma (GR) curve segments in the well logging curve.

[0089] The value and curve of U can reflect the redox properties of the diagenetic environment. In a reducing diagenetic environment, the rock is rich in uranium, and in an oxidizing diagenetic environment, the rock is depleted in uranium. Thorium and uranium elements are of specific significance in the identification of sedimentary environments and sedimentary phases. Thorium-containing compounds are the products of weathering of parent rocks and are insoluble in water. Therefore, the low thorium content in rocks indicates that their sedimentary environment is far away from the parent rock area.

[0090] The GR value and curve shape reflect the hydrodynamic forces during grain bank deposition. Stronger hydrodynamic forces lead to faster sedimentation rates and lower GR values. Conversely, slower sedimentation rates lead to higher GR values ​​due to the adsorption of more radioactive substances from the water column and an increase in the content of radioactive clay minerals. The U and GR curves for the limestone grain bank microfacies exhibit low-value curves.

[0091] S4. Specifically, the lithofacies of the dolomite section are identified by comparing the uranium value (U), acoustic transit time (DT), and compensated neutron (CNL) curve segments in the well logging curve.

[0092] Conventional reservoir porosity calculations are primarily based on core calibration logging, which uses regression analysis of porosity from core physical property analysis with density, neutron, and acoustic porosity curves. Conventional logging curves show increased CNL and DT in simple composite reservoirs, decreased density, and a small difference in deep and shallow lateral resistivity. Because pores and fractures lack interconnected dissolution, drilling fluid intrusion into the wellbore is minimal, resulting in increased fracture porosity and lower acoustic velocity deviation. However, the high heterogeneity of carbonate reservoirs weakens the correlation of this regression analysis, making accurate calculation of carbonate reservoir porosity difficult based on a single logging curve.

[0093] S5. The constructed carbonate rock logging curve identification can be found in Figure 6-7 , and obtained Table 1 Ordovician carbonate sedimentary facies division and microfacies characteristics, Figure 6-7 It contains parameters such as GR curve, U curve, depth, lithologic columnarity, and sedimentary microfacies.

[0094] Table 1: Sedimentary facies division and microfacies characteristics of Ordovician carbonate rocks

[0095] The logging curve responses of the gentle slope and shallow gentle slope zone in the restricted platform dominated by dolomite are as follows: Figure 6 The inner gentle slope logging curve response is characterized by medium to high U and GR values, high R, and low CNL; Figure 7 The logging curve response of the shallow gentle slope inner zone is characterized by medium-low GR and U values ​​and high R value.

[0096] The logging curve responses of the outer zone and outer gentle slope of the open platform dominated by limestone are: Figure 7The logging curve response of the shallow and gentle slope outer zone is characterized by low GR and low U value; Figure 7 The deep gentle slope logging curve response shows that the storm beach microfacies exhibits low U values ​​and U / Th values, while the static cement microfacies exhibits medium to high U values ​​and U / Th values.

[0097] In the detailed study of the Ordovician System in the Tarim Basin, the sedimentary phase types of carbonates were finely divided, and good results were achieved, laying a good foundation for clarifying the spatial distribution of microphases and reservoir prediction.

[0098] The present invention obtains identification and analysis of multiple dolomite and limestone thin sections within a preset area, classifying the lithology and sedimentary facies types of the dolomite and limestone within the preset area. The well logging values ​​corresponding to the depth of each thin section sample within the preset area are then calculated. The well logging values ​​include the natural gamma ray grading (GR) and uranium U-value, which are sensitive logging parameters reflecting the lithology of the limestone, and are combined with the U / Th ratio for determination. A cross-plot of the natural gamma ray grading and uranium U-value is plotted, and the structural type of the limestone in the non-cored section is identified by the region to which its well logging values ​​belong. The natural gamma ray grading (GR) and uranium U-value, which are sensitive logging parameters reflecting the lithology of the dolomite, are selected and combined with the average value of the DT value and CNL value for determination. Based on this, a standard drilling logging curve response template is established, which can effectively identify the sedimentary facies type. The method provided by the present invention for identifying carbonate dolomite and limestone structures using well logging data based on thin section calibration can significantly improve the accuracy of identifying the structural type of open platform limestone based on well logging data, thereby improving the prediction of favorable reservoir distribution.

[0099] In order to better implement the carbonate rock lithology identification method based on well logging parameters in the embodiment of the present invention, based on the carbonate rock lithology identification method based on well logging parameters, correspondingly, Figure 8 As shown, an embodiment of the present invention further provides a carbonate rock lithology identification device based on well logging parameters. The carbonate rock lithology identification device 800 based on well logging parameters includes: A calibration module 810 is configured to calibrate multiple carbonate thin sections of a target formation to obtain logging parameters and types of the carbonate thin sections at corresponding depths; the carbonate thin sections include dolomite thin sections and limestone thin sections; a determination module 820 for drawing a crossplot based on the logging parameters at the corresponding depth of the carbonate rock slice, and determining target parameters according to the crossplot and the type; the target parameters include uranium value, thorium value, acoustic wave time difference, and compensated neutron; The identification module 830 is configured to identify the lithology of the dolomite and limestone in the non-coring section based on the target parameters.

[0100] The carbonate rock lithology identification device 800 based on well logging parameters provided in the above embodiment can implement the technical solution described in the above embodiment of the carbonate rock lithology identification method based on well logging parameters. The specific implementation principles of the above modules or units can be found in the corresponding contents in the above embodiment of the carbonate rock lithology identification method based on well logging parameters, which will not be repeated here.

[0101] like Figure 9 As shown, the present invention also provides an electronic device 900. The electronic device 900 includes a processor 901, a memory 902 and a display 903. Figure 9 Only some of the components of the electronic device 900 are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0102] In some embodiments, the processor 901 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 902, such as the carbonate rock lithology identification method based on well logging parameters in the present invention.

[0103] In some embodiments, the processor 901 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 901 may be local or remote. In some embodiments, the processor 901 may be implemented on a cloud platform. In some embodiments, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, multiple clouds, or any combination thereof.

[0104] In some embodiments, the memory 902 may be an internal storage unit of the electronic device 900, such as a hard disk or memory of the electronic device 900. In other embodiments, the memory 902 may also be an external storage device of the electronic device 900, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 900.

[0105] Furthermore, the memory 902 may include both an internal storage unit of the electronic device 900 and an external storage device. The memory 902 is used to store application software installed in the electronic device 900 and various data.

[0106] In some embodiments, the display 903 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touchscreen. The display 903 is used to display information on the electronic device 900 and to display a visual user interface. Components 901-903 of the electronic device 900 communicate with each other via a system bus.

[0107] In one embodiment, when the processor 901 executes the carbonate rock lithology identification program based on well logging parameters in the memory 902, the following steps may be implemented: Calibrate multiple carbonate rock slices of the target formation to obtain logging parameters and types of the carbonate rock slices at corresponding depths; the carbonate rock slices include dolomite slices and limestone slices; Drawing a cross-plot based on the logging parameters of the corresponding depth of the carbonate rock slice, and determining target parameters according to the cross-plot and the type; the target parameters include uranium value, thorium value, sonic time difference and compensated neutron; The lithology of the dolomite and limestone in the non-coring section is identified based on the target parameters.

[0108] It should be understood that, when the processor 901 executes the carbonate lithology identification program based on well logging parameters in the memory 902 , in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.

[0109] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 900 mentioned. The electronic device 900 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or the like. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices equipped with IOS, Android, Microsoft, or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 900 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0110] Accordingly, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions of the carbonate rock lithology identification method based on logging parameters provided in the above-mentioned method embodiments.

[0111] Those skilled in the art will appreciate that all or part of the process flow of the above-described method embodiment can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0112] The above is a detailed introduction to the carbonate rock lithology identification method, device and storage medium based on logging parameters provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for identifying carbonate rock lithology based on well logging parameters, characterized in that: include: Calibrate multiple carbonate rock slices of the target formation to obtain logging parameters and types of the carbonate rock slices at corresponding depths; the carbonate rock slices include dolomite slices and limestone slices; Drawing a cross-plot based on the logging parameters of the corresponding depth of the carbonate rock slice, and determining target parameters according to the cross-plot and the type; the target parameters include uranium value, thorium value, sonic time difference and compensated neutron; The lithology of the dolomite and limestone in the non-coring section is identified based on the target parameters.

2. The method for identifying carbonate rock lithology based on well logging parameters according to claim 1, characterized in that: The identifying the lithology of dolomite and limestone in the non-coring section based on the target parameters includes: Identify limestone lithology based on uranium and thorium values; Identify dolomite lithology based on acoustic transit time and compensated neutrons.

3. The method for identifying carbonate rock lithology based on well logging parameters according to claim 2, characterized in that: The identification of limestone lithology based on uranium and thorium values ​​includes: The uranium value is normalized to obtain a normalized uranium value; The ratio of the uranium value to the thorium value is normalized to obtain a normalized ratio; determining a rock structure factor based on the normalized uranium value and the normalized ratio; Based on the discriminant function and the rock structure factor, the limestone lithology is identified.

4. The method for identifying carbonate rock lithology based on well logging parameters according to claim 3, characterized in that: The expression of the rock structure factor is as follows: in, represents the rock structure factor, represents the normalized uranium value, represents the normalized ratio.

5. The method for identifying carbonate rock lithology based on well logging parameters according to claim 3, characterized in that: The expression of the discriminant function is as follows: in, Represents the rock texture factor, 1 represents sparry grain limestone, 2 represents mud-sparry grain limestone, and 3 represents grain-bearing micritic limestone / micritic limestone.

6. The method for identifying carbonate rock lithology based on well logging parameters according to claim 2, characterized in that: The identification of dolomite lithology based on acoustic wave transit time and compensated neutron includes: The dolomite lithology is identified based on the average value of the acoustic wave transit time and the average value of the compensated neutron.

7. The method for identifying carbonate rock lithology based on well logging parameters according to claim 1, characterized in that: Also includes: constructing a logging response template based on the logging parameters and types of the corresponding depths of the carbonate rock slices; comparing the uranium value and natural gamma curve segments in the logging curve of the dolomite in the non-coring section with the response template to identify the limestone lithofacies; The uranium value, acoustic transit time, and compensated neutron curve segments in the logging curve of the limestone in the non-coring section are compared with the response template to identify the dolomite lithofacies.

8. A carbonate rock lithology identification device based on well logging parameters, characterized in that: include: a calibration module for calibrating multiple carbonate thin sections of a target formation to obtain logging parameters and types of the carbonate thin sections at corresponding depths; the carbonate thin sections include dolomite thin sections and limestone thin sections; a determination module, configured to draw a cross-plot based on the logging parameters of the carbonate rock slice at the corresponding depth, and determine target parameters according to the cross-plot and the type; the target parameters include uranium value, thorium value, acoustic wave time difference, and compensated neutron; An identification module is used to identify the lithology of dolomite and limestone in the non-coring section based on the target parameters.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the steps of the method for identifying carbonate rock lithology based on well logging parameters as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the carbonate rock lithology identification method based on logging parameters as described in any one of claims 1 to 7.

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