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

Abstract

The present application relates to a kind of carbonate lithology identification method based on well logging parameter, device and storage medium, belong to rock type identification technical field, the method includes: the multiple carbonate thin section of target formation is calibrated, obtains the well logging parameter and type of corresponding depth of carbonate thin section;Carbonate thin section includes dolomite thin section and limestone thin section;Based on the well logging parameter of corresponding depth of carbonate thin section plot crossplot, and determine target parameter according to crossplot and type;Target parameter includes uranium value, thorium value, acoustic time difference and compensation neutron;Based on target parameter, the lithology of dolomite and limestone in non-coring section is identified.The present application provides the carbonate lithology identification method based on well logging parameter, is analyzed with multiple parameters such as uranium, thorium, acoustic time difference and compensation neutron, realizes accurate identification to carbonate lithology.

Description

Technical Field

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

[0002] Carbonate reservoirs contain abundant oil and gas resources and are widely distributed globally, possessing enormous exploration potential. Of the various types of super-large oil and gas reservoirs discovered to date, carbonate reservoirs account for 35% of the total, containing 50% of global recoverable oil and gas reserves, with oil production accounting for 60% and natural gas production for the latter, respectively. Therefore, carbonate reservoirs play a crucial role in oil and gas exploration and development.

[0003] The development level and lithology of carbonate reservoirs are closely related. Their main constituent minerals are calcite and dolomite, and they are typically classified as limestone or dolomite based on differences in their constituent mineral content. However, lithological identification of old, deeply buried carbonate reservoirs is often challenging due to complex diagenesis and the superimposed alteration by multiple tectonic movements. In the absence of abundant core and thin-section data downhole, identifying lithology and lithofacies is crucial for determining sedimentary sequences, elucidating sedimentary evolution processes, and conducting reservoir prediction.

[0004] Currently, there is no unified identification method for the lithology and sedimentary microfacies classification of marine limestone logging in China. Under the condition of limited core data, the lithological classification of carbonate dolomite and limestone has become the primary technical problem to be solved in classifying different shoal development environments and carrying out reservoir analysis. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, device and storage medium for identifying carbonate rock lithology based on well logging parameters, so as 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:

[0007] Multiple carbonate rock thin sections of the target formation are calibrated to obtain the logging parameters and types of the carbonate rock thin sections at corresponding depths; the carbonate rock thin sections include dolomite thin sections and limestone thin sections;

[0008] Cross plots are generated based on logging parameters at corresponding depths of the carbonate rock thin sections, and target parameters are determined according to the cross plots and the type; the target parameters include uranium value, thorium value, sonic transit time, and compensated neutrons;

[0009] The lithology of dolomite and limestone in non-core sections is identified based on the target parameters.

[0010] In one possible implementation, identifying the lithology of dolomite and limestone in non-core sections based on the target parameters includes:

[0011] Identify limestone lithology based on uranium and thorium values;

[0012] Dolomite lithology is identified based on acoustic time difference and compensated neutrons.

[0013] In one possible implementation, identifying limestone lithology based on uranium and thorium values ​​includes:

[0014] The uranium value is normalized to obtain the normalized uranium value;

[0015] The ratio of uranium value to thorium value is normalized to obtain the normalized ratio;

[0016] The rock structure factor is determined based on the normalized uranium value and the normalized ratio.

[0017] Based on the discriminant function and the rock structure factor, the lithology of limestone is identified.

[0018] In one possible implementation, the rock structure factor is expressed as follows:

[0019]

[0020] in, Represents rock structure factor, This represents the normalized uranium value. This represents the normalized ratio.

[0021] In one possible implementation, the discriminant function is expressed as follows:

[0022]

[0023] in, The rock structure factor is represented by 1, which represents sparry grain limestone, 2 represents mudstone-sparry grain limestone, and 3 represents grainy micritic limestone / micritic limestone.

[0024] In one possible implementation, identifying dolomite lithology based on acoustic transit time and compensated neutrons includes:

[0025] Dolomite lithology is identified based on the average value of acoustic time difference and the average value of compensated neutrons.

[0026] One possible implementation also includes:

[0027] Based on the logging parameters and types at the corresponding depths of the carbonate rock thin sections, a logging response template is constructed;

[0028] By comparing the uranium value and natural gamma curve segments in the logging curves of the dolomite in the non-core section with the response template, the limestone lithofacies can be identified.

[0029] By comparing the uranium value, sonic transit time, and compensated neutron curve segments in the logging curves of the limestone in the non-cored section with the response template, the dolomite lithofacies can be identified.

[0030] Secondly, the present invention also provides a carbonate rock lithology identification device based on well logging parameters, comprising:

[0031] The calibration module is used to calibrate multiple carbonate rock thin sections of the target formation and obtain the logging parameters and types of the carbonate rock thin sections at corresponding depths; the carbonate rock thin sections include dolomite thin sections and limestone thin sections;

[0032] The determination module is used to draw a cross plot based on the logging parameters at the corresponding depth of the carbonate rock thin section, and to determine the target parameters according to the cross plot and the type; the target parameters include uranium value, thorium value, sonic transit time, and compensated neutrons;

[0033] The identification module is used to identify the lithology of dolomite and limestone in non-core sections based on the target parameters.

[0034] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein,

[0035] The memory is used to store programs;

[0036] The processor, coupled to the memory, is used to execute the program stored in the memory to implement the carbonate rock lithology identification method based on well logging parameters as described in any of the above implementations.

[0037] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the carbonate rock lithology identification method based on well logging parameters described in any of the above implementations.

[0038] The beneficial effects of this invention are as follows: The method, device, and storage medium for carbonate rock lithology identification based on well logging parameters provided by this invention obtain the well logging parameters and types of carbonate rock thin sections at corresponding depths by calibrating multiple carbonate rock thin sections of the target formation. Cross-plots are then drawn based on the well logging parameters at corresponding depths of the carbonate rock thin sections. Target parameters for lithology identification are extracted based on the cross-plots and types. Furthermore, the lithology of dolomite and limestone in non-core sections is identified based on the target parameters. This method is suitable for lithology identification in non-core sections, reducing the need for core sampling and improving identification efficiency. The method employs multi-parameter joint analysis, including uranium, thorium, sonic transit time, and compensated neutrons, to comprehensively determine lithology. Multi-dimensional data fusion effectively reduces the limitations of single parameters. Using the well logging parameters of actual carbonate rock thin sections as the calibration basis, accurate identification of carbonate rock lithology is achieved. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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 effort.

[0040] Figure 1 This is a schematic flowchart of an embodiment of the carbonate rock lithology identification method based on well logging parameters provided by the present invention.

[0041] Figure 2 A schematic flowchart illustrating the method for identifying carbonate rock lithology and sedimentary microfacies based on thin section calibration using well logging data provided by the present invention;

[0042] Figure 3 This is a schematic diagram of GR-U correlation in some well pure limestone sections provided by the present invention;

[0043] Figure 4 This invention provides a schematic diagram of the GR-U correlation of some continuous thick dolomite sections in a well.

[0044] Figure 5 This invention provides a statistical comparison diagram of the average values ​​of DT and CNL for different dolomite lithologies.

[0045] Figure 6 This is one of the schematic diagrams of the non-standard drilling standardized logging curves and the sedimentary facies identification scheme after comparison provided by the present invention;

[0046] Figure 7 The second schematic diagram of the non-standard drilling standardized logging curve and the sedimentary facies identification scheme after comparison provided by the present invention;

[0047] Figure 8A schematic diagram of an embodiment of the carbonate rock lithology identification device based on well logging parameters provided by the present invention;

[0048] Figure 9 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0050] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0051] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0052] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0053] This invention provides a method, device, and storage medium for identifying carbonate rock lithology based on well logging parameters, which will be described below.

[0054] Figure 1 This is a schematic flowchart of an embodiment of the carbonate rock lithology identification method based on well logging parameters provided by the present invention, as shown below. Figure 1 As shown, methods for identifying carbonate rock lithology based on well logging parameters include:

[0055] S101. Calibrate multiple carbonate rock thin sections of the target formation to obtain the logging parameters and types of the carbonate rock thin sections at corresponding depths; the carbonate rock thin sections include dolomite thin sections and limestone thin sections;

[0056] S102. Draw a cross plot based on the logging parameters at the corresponding depth of the carbonate rock thin section, and determine the target parameters according to the cross plot and the type; the target parameters include uranium value, thorium value, sonic transit time, and compensated neutrons;

[0057] S103. Identify the lithology of dolomite and limestone in non-core sections based on the target parameters.

[0058] In S101, multiple dolomite and limestone thin sections of the target formation are obtained for calibration. Through identification and analysis, the dolomite and limestone in the target formation are classified to obtain the logging parameters and types of the carbonate rock thin sections at corresponding depths.

[0059] Optionally, dolomite can be classified into: laminated microcrystalline dolomite, granular dolomite, and dolomite with metasomatic residual structures. Limestone can be classified into: sparry grain limestone, micritic grain limestone, and micritic limestone.

[0060] The logging data for each thin section sample at the corresponding depth within the target formation were statistically analyzed. The logging data included logging parameters, such as natural gamma (GR), uranium value (U), uranium / thorium value (U / Th), sonic transit time (DT), and compensated neutron (CNL).

[0061] In S102, a cross plot is drawn based on the logging parameters and types at corresponding depths of carbonate rock thin sections. For example, by drawing a cross plot based on the GR and U data in the logging data, it can be seen that the main contribution of GR in the dolomite section is U, and the main contribution of GR in the limestone section is U.

[0062] Target parameters for lithology identification are extracted from the logging parameters based on the type of thin section. These target parameters include uranium value, thorium value, sonic transit time, and compensated neutrons.

[0063] In S103, dolomite and limestone from non-core sections are obtained. The lithology of limestone is identified by uranium and thorium values, and the lithology of dolomite is identified by sonic transit time and compensated neutrons.

[0064] The rock structure factor of limestone can be calculated by using uranium and thorium values, thereby identifying the lithology of limestone based on the rock structure factor.

[0065] Based on the results of thin section identification of dolomite, it can be seen that the average DT (diastolic density) value is the lowest among medium- to coarse-grained dolomite types, the average DT value is significantly increased among silty to fine-grained dolomite types, and the average DT value is the highest among muddy to microcrystalline dolomite types. Similarly, the average CNL (cumulonimbus density) value is the highest among medium- to coarse-grained dolomite types, the average CNL value is significantly decreased among silty to fine-grained dolomite types, and the average CNL value is the lowest among muddy to microcrystalline dolomite types. As the dolomite grain size decreases, the average CNL value in the dolomite subtypes shows a clear downward trend. Combining DT and CNL values ​​can effectively distinguish dolomite lithology.

[0066] In summary, the carbonate rock lithology identification method based on well logging parameters provided in this embodiment of the invention obtains the well logging parameters and types at corresponding depths of multiple carbonate rock thin sections of the target formation by calibrating them. A cross-plot is then drawn based on the well logging parameters at corresponding depths of the carbonate rock thin sections. Target parameters for lithology identification are extracted based on the cross-plot and type. Furthermore, the lithology of dolomite and limestone in non-core sections is identified based on these target parameters. This method is suitable for lithology identification in non-core sections, reducing the need for core sampling and improving identification efficiency. The method employs multi-parameter joint analysis, including uranium, thorium, sonic transit time, and compensated neutrons, to comprehensively determine lithology. Multi-dimensional data fusion effectively reduces the limitations of single parameters. Using the well logging parameters of actual carbonate rock thin sections as the calibration basis, accurate identification of carbonate rock lithology is achieved.

[0067] In some embodiments of the present invention, the step of identifying the lithology of dolomite and limestone in non-core sections based on the target parameters includes:

[0068] Identify limestone lithology based on uranium and thorium values;

[0069] Dolomite lithology is identified based on acoustic time difference and compensated neutrons.

[0070] In some embodiments of the present invention, the identification of limestone lithology based on uranium and thorium values ​​includes:

[0071] The uranium value is normalized to obtain the normalized uranium value;

[0072] The ratio of uranium value to thorium value is normalized to obtain the normalized ratio;

[0073] The rock structure factor is determined based on the normalized uranium value and the normalized ratio.

[0074] Based on the discriminant function and the rock structure factor, the lithology of limestone is identified.

[0075] In some embodiments of the present invention, the expression for the rock structure factor is as follows:

[0076]

[0077] in, Represents rock structure factor, This represents the normalized uranium value. This represents the normalized ratio.

[0078] In some embodiments of the present invention, the expression of the discriminant function is as follows:

[0079]

[0080] in, The rock structure factor is represented by 1, which represents sparry grain limestone, 2 represents mudstone-sparry grain limestone, and 3 represents grainy micritic limestone / micritic limestone.

[0081] Optionally, the logging data can be preprocessed to remove outliers before normalization to reduce the differences between data from different wells, keeping the U value between 0 and 5 and the U / Th value between 0 and 10.

[0082] The uranium value (U) and uranium / thorium ratio (U / Th) are normalized using the following formula:

[0083]

[0084] in, For normalized well logging data, The logging data before normalization processing The minimum value in the well logging data before normalization. This represents the maximum value in the well logging data before normalization.

[0085] The structure type of limestone in the non-core section is identified by the region to which its logging values ​​belong. For limestone identification, the rock structure factor is calculated. ,according to The lithology of the limestone in the area was determined. The calculation formula is as follows:

[0086]

[0087] The lithology identification sample set was loaded into Origin software. In the discriminant analysis module, the lithology column was defined as a grouping variable, with the maximum and minimum values ​​of the grouping variable defined as the maximum and minimum values ​​of the lithology numerical codes, 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:

[0088]

[0089] In the formula, 1 represents sparry grain limestone; 2 represents mudstone-sparry grain limestone; and 3 represents grainy micritic limestone / micritic limestone.

[0090] In some embodiments of the present invention, the identification of dolomite lithology based on acoustic transit time and compensated neutrons includes:

[0091] Dolomite lithology is identified based on the average value of acoustic time difference and the average value of compensated neutrons.

[0092] Based on the results of dolomite thin section identification, the average DT values ​​of various limestone types were calculated. It can be seen that the average DT value of medium-coarse-grained dolomite is the smallest, the average DT value of fine-grained dolomite is significantly increased, and the average DT value of mudstone-microcrystalline dolomite is the largest.

[0093] Calculating the average CNL (Concentration-to-Liquidity) values ​​for each type of limestone reveals that medium- to coarse-grained dolomite has the highest average CNL, while silty- to fine-grained dolomite shows a significant decrease in average CNL, and muddy-microcrystalline dolomite has the lowest average CNL. Furthermore, the average CNL value decreases significantly with decreasing dolomite grain size across all dolomite subtypes.

[0094] The DT and CNL values ​​show distinct characteristics of subtypes in dolomite, and combining the DT and CNL values ​​can effectively distinguish dolomite lithology.

[0095] Furthermore, based on the results of thin section identification, cross plots of DT and CNL were generated to obtain processed operational data, and a dolomite logging lithology discrimination chart was established. The structural type of dolomite in the non-cored sections was identified by the region to which its average logging value belonged. For dolomite identification, the lithology of dolomite can be determined by combining GR and U values, and calculating the average DT and CNL values.

[0096] In some embodiments of the present invention, it further includes:

[0097] Based on the logging parameters and types at the corresponding depths of the carbonate rock thin sections, a logging response template is constructed;

[0098] By comparing the uranium value and natural gamma curve segments in the logging curves of the dolomite in the non-core section with the response template, the limestone lithofacies can be identified.

[0099] By comparing the uranium value, sonic transit time, and compensated neutron curve segments in the logging curves of the limestone in the non-cored section with the response template, the dolomite lithofacies can be identified.

[0100] For lithofacies determination, conventional curves such as GR, U, and U / Th, which are more sensitive to sedimentary facies, are preferred. By establishing a standard drilling logging curve response template, the logging curves of non-standard wells are compared with the logging curve response template of standard wells to identify sedimentary facies in inner gentle slope, inner zone of shallow gentle slope, outer zone of shallow gentle slope, and deep gentle slope.

[0101] The sedimentary facies of carbonate rocks and the logging curves of standard wells were analyzed to determine logging response templates for different sedimentary facies. Specifically, identification charts for dolomite and limestone sedimentary microfacies were established based on core and thin section data. Carbonate rock response templates for standard wells were constructed. The standardized logging curves of non-standard wells were compared with the dolomite and limestone logging response templates of standard wells to classify the dolomite and limestone sedimentary microfacies of non-standard wells.

[0102] For example, the lithofacies of limestone sections are identified by comparing the curve segments of uranium value (U) and natural gamma (GR) in the well logging curves. The lithofacies of dolomite sections are identified by comparing the curve segments of uranium value (U), sonic transit time (DT), and compensated neutron (CNL) in the well logging curves.

[0103] Optionally, Figure 2 This is a flowchart illustrating the method for identifying carbonate rock lithology and sedimentary microfacies based on thin section calibration using well logging data provided by the present invention. The technical solution provided by the present invention can effectively identify carbonate rock dolomite, limestone lithology and sedimentary microfacies with high accuracy.

[0104] like Figure 2 As shown, a method for identifying carbonate rock lithology and lithofacies based on thin section calibration using well logging data includes:

[0105] S201. Obtain multiple thin section data of the target strata and classify the lithology and sedimentary facies of carbonate rocks, dolomite, and limestone within the target strata.

[0106] Identification and analysis were performed on multiple thin sections of carbonate dolomite and limestone from the target strata, and the lithology of the limestone within the target strata was classified. Based on core description data, the lithofacies type was determined.

[0107] Optionally, based on the results of thin section identification, the logging curves can be normalized to obtain processed real-time operating data, and corresponding identification markers can be constructed. Based on the lithofacies type and identification markers, identification models for lithofacies and sedimentary microfacies are established. The sedimentary microfacies of the target well are then identified based on these identification models.

[0108] S202. Statistically analyze the target logging data corresponding to the depth of each thin section sample within the target area. The logging data includes GR value, U value, DT value, and CNL value.

[0109] The logging values ​​at the corresponding depths of each thin-section sample within the preset area are statistically analyzed. The logging data includes logging parameters such as natural gamma (GR), uranium value (U), uranium / thorium value (U / Th), sonic transit time (DT), and compensated neutron (CNL).

[0110] S203. By plotting the natural gamma ray (GR) and uranium (U) values, the lithological characteristics of the limestone in the non-core section can be identified by the region to which its logging value belongs.

[0111] By plotting the GR and U data based on the well logging data, it was confirmed that the main contribution of GR in the dolomite section is U, and the main contribution of GR in the limestone section is U.

[0112] S204. Select the logging parameters that are sensitive to the lithology of dolomite, namely natural gamma ray (GR) and uranium (U) values, and make a judgment based on the average values ​​of DT and CNL values.

[0113] S205. Establish a standard drilling curve response template. By comparing the curve segments with U and GR values ​​in the logging curves, identify the lithofacies of the limestone section. By comparing the curve segments with U, DT, and CNL values ​​in the logging curves, identify the lithofacies of the dolomite section.

[0114] The following detailed description, using specific examples, illustrates the method of identifying open platform limestone lithology and sedimentary microfacies based on thin-section calibration and well logging data according to the present invention.

[0115] Example 1: Taking the Ying 3 Member of the Ordovician System in the Gucheng area of ​​the Tarim Basin as an example, this example illustrates the lithology identification through logging using a typical core well.

[0116] S1. Basic geological conditions: The Ordovician Ying 3 section strata in the Gucheng area of ​​the Tarim Basin are a set of marine carbonate rock strata, mainly composed of dolomite and limestone. The above geological conditions are suitable for the application of this invention.

[0117] S2. Parameter acquisition: Acquire identification and analysis of multiple dolomite and limestone thin sections within the preset area, and classify the structural types of dolomite and limestone within the preset area. Dolomite is classified according to structure into: laminated microcrystalline dolomite, crystalline dolomite, and dolomite with replacement residual structure; limestone is classified according to structure into: sparry grain limestone, micritic grain limestone, and micritic limestone.

[0118] The logging values ​​at the corresponding depths of each thin-section sample within the preset area are statistically analyzed. The logging data includes logging parameters such as natural gamma (GR), uranium value (U), uranium / thorium value (U / Th), sonic transit time (DT), and compensated neutron (CNL).

[0119] S3. Based on the well logging data, a cross plot of GR and U data was created. The correlation coefficient R value is an important indicator for measuring the correlation between two parameters, confirming that the main contribution of GR in the limestone section is U.

[0120] Figure 3 This is a schematic diagram of GR-U correlation in some well pure limestone sections provided by the present invention, as shown in the figure. Figure 3 As shown, the GR value and U value of the typical well Gucheng 9 are R2 The GR value of well 15 in the ancient city is 0.837, and the R value of the U value is... 2 The correlation coefficient R was 0.8584, and the correlation coefficient R was greater than 0.9, indicating that the GR value and the U value were highly correlated.

[0121] S4. Based on the results of limestone thin section identification, the uranium value (U) and uranium / thorium value (U / Th) are normalized to obtain processed real-time operating data, so as to reduce the difference between data from different wells and keep the U value between 0 and 5 and the U / Th value between 0 and 10.

[0122] (1)

[0123] In equation (1), X n X represents the normalized logging data, and X represents the unnormalized logging data. min X is the minimum value in the well logging data before normalization. max This represents the maximum value in the well logging data before normalization.

[0124] S5, Calculation of limestone rock structure factor.

[0125] The lithology identification sample set was loaded into Origin software. In the discriminant analysis module, the lithology column was defined as a grouping variable, with the maximum and minimum values ​​of the grouping variable defined as the maximum and minimum values ​​of the lithology numerical codes, 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, yielding the rock structure factor. f .

[0126] (2)

[0127] S6. Limestone lithology identification, using the following function:

[0128]

[0129] In the formula, 1 represents sparry grain limestone; 2 represents mudstone-sparry grain limestone; and 3 represents grainy micritic limestone / micritic limestone.

[0130] For example, the above example f =0.3456, then Lith=2, and the well logging identification indicates that the lithology is mudstone-spar crystal grain limestone.

[0131] S7. Based on the well logging data, create a cross-plot of the GR and U data, such as... Figure 4 As shown, Figure 4This invention provides a schematic diagram of the GR-U correlation in some continuous thick dolomite sections of wells. The correlation coefficient R is an important indicator for measuring the correlation between two parameters, confirming that the main contribution of GR to the dolomite section is U. For example, the R value of GR and U in the typical well Gucheng 16 is shown. 2 The value is 0.657, indicating that the GR value and the U value are highly correlated.

[0132] S8. Based on the results of dolomite thin section identification, the average DT value of medium-coarse-grained dolomite was calculated to be the smallest, the average DT value of fine-grained dolomite was significantly increased, and the average DT value of muddy-microcrystalline dolomite was the largest.

[0133] S9. Based on the results of thin section identification of dolomite, the average CNL value was calculated to be the largest for medium-coarse-grained dolomite, significantly smaller for silty-fine-grained dolomite, and smallest for muddy-microcrystalline dolomite. The average CNL value showed a clear downward trend among the dolomite subtypes as the dolomite grain size decreased.

[0134] Figure 5 This is a statistical comparison diagram of the average values ​​of DT and CNL for different dolomite lithologies provided by the present invention, as shown in the figure. Figure 5 As shown, DT and CNL values ​​exhibit distinct characteristics of subtypes in dolomite, and combining DT and CNL values ​​can effectively distinguish different types of dolomite.

[0135] Example 2: Taking the Ordovician system in the Gucheng area of ​​the Tarim Basin as an example, this example illustrates the process of logging curve identification using a typical core well.

[0136] S1. Basic geological conditions: The Ordovician strata in the Gucheng area of ​​the Tarim Basin are a set of marine carbonate rock strata. The sedimentary background of the Middle-Lower Ordovician carbonate rocks in the Gucheng area can be generally interpreted as a gentle slope type platform, mainly developing five sedimentary facies: inner gentle slope, inner shallow gentle slope zone, outer shallow gentle slope zone, deep gentle slope, and outer gentle slope. The above geological conditions are suitable for the application of this invention.

[0137] S2. Based on the above embodiment one, the carbonate rock sedimentary microfacies identification method provided in this embodiment of the invention constructs logging response templates for different microfacies of dolomite and limestone in standard drilling, including: analyzing the thin section identification charts of dolomite and limestone particle beaches and the logging curves of standard drilling to determine logging response templates for different microfacies.

[0138] S3. Specifically, the lithofacies of the limestone section are identified by comparing the curve segments of uranium value (U) and natural gamma (GR) in the well logging curve.

[0139] The value and curve of U can reflect the redox properties of the diagenetic environment. In a reducing diagenetic environment, the rocks are rich in uranium, while in an oxidizing diagenetic environment, the rocks are poor in uranium. Thorium and uranium have specific significance in the identification of sedimentary environment and sedimentary facies. Thorium-containing compounds are products of parent rock weathering and are difficult to dissolve in water. Therefore, the low thorium content of rocks indicates that their sedimentary environment is far from the parent rock area.

[0140] The GR value and curve shape can reflect the hydrodynamics during grain shoal deposition. The stronger the hydrodynamics, the faster the deposition rate, and the lower the GR value. Conversely, the slower the deposition rate, the more radioactive materials the sediments adsorb from the water, and the higher the content of radioactive clay minerals, thus resulting in a higher GR value. The U and GR curves of limestone grain shoal microfacies become low-value curves.

[0141] S4. Specifically, the lithofacies of the dolomite section are identified by comparing the curve segments of uranium value (U), sonic transit time (DT), and compensated neutron (CNL) in the well logging curve.

[0142] Conventional reservoir porosity calculations primarily rely on core-calibrated logging, utilizing regression analysis of porosity curves against density, neutron, and acoustic porosity curves derived from core physical property analysis. In conventional logging curves, simple composite reservoirs show increased CNL and DT, decreased density, and relatively small differences in deep and shallow lateral resistivity. Due to the lack of inter-dissolution communication between pores and fractures, and minimal drilling fluid intrusion into the wellbore, fracture porosity is increased, resulting in lower acoustic velocity deviation. However, the highly heterogeneous nature of carbonate reservoirs weakens the correlation of this regression analysis, making accurate calculation of carbonate reservoir porosity difficult relying on a single logging curve.

[0143] S5. For the identification of the completed carbonate rock logging curves, please refer to [link / reference needed]. Figure 6-7 Table 1 shows the sedimentary facies classification and microfacies characteristics of the Ordovician carbonate rocks. Figure 6-7 It contains parameters such as GR curve, U curve, depth, lithological columnar structure, and sedimentary microfacies.

[0144] Table 1: Sedimentary facies classification and microfacies characteristics of Ordovician carbonate rocks

[0145]

[0146] The well logging response patterns in the confined platform with dolomite as the main component, specifically the gentle slope and shallow gentle slope zones, are as follows: Figure 6 The logging response curves on the inner gentle slope are characterized by medium to high U and GR values, high R, and low CNL. Figure 7 The logging curve response in the shallow and gentle slope inner zone shows a toothed pattern of medium to low GR and U values, with a high R value.

[0147] The well logging responses of the outer zone and outer gentle slope of the open platform dominated by limestone are as follows: Figure 7The logging curve response in the shallow and gentle slope outer zone is characterized by low GR and low U value; Figure 7 The logging curves for deep and gentle slopes show that the storm shoal microfacies exhibit low U values ​​and U / Th values, while the static cement microfacies exhibit medium to high U values ​​and U / Th values.

[0148] The detailed study of the Ordovician system in the Tarim Basin has yielded excellent results, which have laid a solid foundation for clarifying the spatial distribution of microfacies and predicting reservoirs.

[0149] This invention identifies and analyzes multiple thin sections of dolomite and limestone within a pre-defined area, classifying the lithology and sedimentary facies types of the dolomite and limestone within the pre-defined area. It then statistically analyzes the well logging values ​​corresponding to the depth of each thin section sample within the pre-defined area. These well logging values ​​include natural gamma ray (GR) and uranium (U) values, which are sensitive to limestone lithology, and are determined in conjunction with the U / Th ratio. A cross-plot of the GR and U values ​​is created, and the structural type of the limestone in the non-core section is identified by the region to which its well logging value belongs. The invention selects the GR and U values, which are sensitive to dolomite lithology, and combines them with the average values ​​of DT and CNL for determination. Based on this, a standard drilling logging curve response template is established, which can effectively identify sedimentary facies types. The method for identifying carbonate dolomite and limestone structures based on thin section calibration and well logging data provided by this invention can significantly improve the accuracy of identifying open platform limestone structure types based on well logging data, thereby improving the prediction of favorable reservoir distribution.

[0150] To better implement the carbonate rock lithology identification method based on well logging parameters in this invention embodiment, based on the carbonate rock lithology identification method based on well logging parameters, the corresponding method is as follows: Figure 8 As shown, this embodiment of the invention also 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:

[0151] The calibration module 810 is used to calibrate multiple carbonate rock thin sections of the target formation and obtain the logging parameters and types of the carbonate rock thin sections at corresponding depths; the carbonate rock thin sections include dolomite thin sections and limestone thin sections;

[0152] The determination module 820 is used to draw a cross plot based on the logging parameters at the corresponding depth of the carbonate rock thin section, and to determine the target parameters according to the cross plot and the type; the target parameters include uranium value, thorium value, sonic transit time and compensated neutrons;

[0153] The identification module 830 is used to identify the lithology of dolomite and limestone in non-core sections based on the target parameters.

[0154] The carbonate rock lithology identification device 800 based on well logging parameters provided in the above embodiments can realize the technical solutions described in the embodiments of the carbonate rock lithology identification method based on well logging parameters. The specific implementation principles of each module or unit can be found in the corresponding content in the embodiments of the carbonate rock lithology identification method based on well logging parameters, and will not be repeated here.

[0155] 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 components of the electronic device 900 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0156] In some embodiments, processor 901 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 902 or process data, such as the carbonate rock lithology identification method based on well logging parameters in this invention.

[0157] In some embodiments, processor 901 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 901 may be local or remote. In some embodiments, processor 901 may be implemented on a cloud platform. In some embodiments, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0158] In some embodiments, memory 902 may be an internal storage unit of electronic device 900, such as a hard disk or memory of electronic device 900. In other embodiments, memory 902 may also be an external storage device of electronic device 900, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 900.

[0159] Furthermore, the memory 902 may include both internal storage units of the electronic device 900 and external storage devices. The memory 902 is used to store application software and various types of data installed on the electronic device 900.

[0160] In some embodiments, display 903 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touchscreen, etc. Display 903 is used to display information from electronic device 900 and to display a visual user interface. Components 901-903 of electronic device 900 communicate with each other via a system bus.

[0161] In one embodiment, when processor 901 executes a carbonate rock lithology identification program based on well logging parameters stored in memory 902, the following steps can be implemented:

[0162] Multiple carbonate rock thin sections of the target formation are calibrated to obtain the logging parameters and types of the carbonate rock thin sections at corresponding depths; the carbonate rock thin sections include dolomite thin sections and limestone thin sections;

[0163] Cross plots are generated based on logging parameters at corresponding depths of the carbonate rock thin sections, and target parameters are determined according to the cross plots and the type; the target parameters include uranium value, thorium value, sonic transit time, and compensated neutrons;

[0164] The lithology of dolomite and limestone in non-core sections is identified based on the target parameters.

[0165] It should be understood that when the processor 901 executes the carbonate rock lithology identification program based on well logging parameters in the memory 902, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0166] Furthermore, this embodiment of the invention does not specifically limit the type of electronic device 900 mentioned. Electronic device 900 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can 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 invention, electronic device 900 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0167] Accordingly, embodiments of the present invention also provide a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the carbonate rock lithology identification method based on well logging parameters provided in the above-described method embodiments.

[0168] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0169] The above provides a detailed description of the carbonate rock lithology identification method, device, and storage medium based on well logging parameters provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A carbonate lithology identification method based on well logging parameters, characterized in that, The method comprises the following steps: calibrating a plurality of carbonate rock slices of a target formation to obtain logging parameters and types of corresponding depths of the carbonate rock slices; the carbonate rock slices include 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 types; the target parameters include uranium values, thorium values, acoustic time differences and compensated neutrons; identifying the lithology of dolomite and limestone in a non-coring section based on the target parameters; wherein identifying the lithology of limestone in the non-coring section based on the target parameters comprises: normalizing the uranium values to obtain normalized uranium values; normalizing the ratio of the uranium values and the thorium values to obtain normalized ratios; determining a rock structure factor based on the normalized uranium values and the normalized ratios; identifying the limestone lithology based on a discriminant function and the rock structure factor; wherein the expression of the rock structure factor is as follows: wherein, represents a rock structure factor, represents a normalized uranium value, represents a normalized ratio; U / Th wherein, 1 = sparry-grainstone, 2 = wacke-sparry-grainstone, 3 = grainy-wacke / dolomite.

2. The method for identifying carbonate lithology based on well logging parameters according to claim 1, characterized in that, the expression of the discriminant function is as follows: identifying the lithology of dolomite in the non-coring section based on the target parameters comprises:

3. The method for identifying carbonate lithology based on well logging parameters according to claim 2, characterized in that, identifying the dolomite lithology based on the acoustic time differences and the compensated neutrons. wherein identifying the dolomite lithology based on the acoustic time differences and the compensated neutrons comprises:

4. The method for identifying carbonate lithology based on well logging parameters according to claim 1, characterized in that, identifying the dolomite lithology based on the average value of the acoustic time differences and the average value of the compensated neutrons. The method further comprises the following steps: constructing a logging response template based on the logging parameters and the types of the corresponding depths of the carbonate rock slices; comparing the curve segments of the uranium values and the natural gamma in the logging curve of the dolomite in the non-coring section with the response template to identify the limestone facies; 5. A device for identifying carbonate lithology based on well logging parameters, characterized in that, comparing the curve segments of the uranium values, the acoustic time differences and the compensated neutrons in the logging curve of the limestone in the non-coring section with the response template to identify the dolomite facies. The method comprises the following steps: a calibration module configured to calibrate a plurality of carbonate rock slices of a target formation to obtain logging parameters and types of corresponding depths of the carbonate rock slices; the carbonate rock slices include dolomite slices and limestone slices; a determination module configured to draw a cross plot based on the logging parameters of the corresponding depths of the carbonate rock slices, and determine target parameters according to the cross plot and the types; the target parameters include uranium values, thorium values, acoustic time differences and compensated neutrons; an identification module configured to identify the lithology of dolomite and limestone in a non-coring section based on the target parameters; wherein identifying the lithology of limestone in the non-coring section based on the target parameters comprises: normalizing the uranium values to obtain normalized uranium values; normalizing the ratio of the uranium values and the thorium values to obtain normalized ratios; determining a rock structure factor based on the normalized uranium values and the normalized ratios; identifying the limestone lithology based on a discriminant function and the rock structure factor; wherein, represents a rock structure factor, represents a normalized uranium value, wherein the expression of the rock structure factor is as follows: represents a normalized ratio; U / Th wherein, 1 = sparry-grainstone, 2 = wacke-sparry-grainstone, 3 = grainy-wacke / dolomite.

6. An electronic device, comprising: the expression of the discriminant function is as follows: The method comprises the following steps: a memory and a processor, wherein the memory is configured to store a program; 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 logging parameters according to any one of claims 1 to 4.

7. A computer readable storage medium characterized in that, A computer readable storage medium for storing a program or instructions readable by a computer, which, when executed by a processor, enables the steps of the method for identifying carbonate lithology based on well logging parameters according to any one of claims 1 to 4 to be implemented.

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