Geophysical well logging method for distinguishing between shale and tuff facies

Abstract

This invention provides a geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies, comprising: Step 1, collecting data on the area to be studied and determining lithology and sedimentary structures; Step 2, determining the logging and imaging logging characteristics of different lithologies and sedimentary structures, and classifying lithology and sedimentary structures in sections without coring; Step 3, determining logging curves with high correlation to rock components; Step 4, constructing dual rock component indicator parameters F1 and F2 using multiple curves; Step 5, comprehensively identifying lithology using F1 and F2; Step 6, selecting curves that reflect the characteristics of stratigraphic sedimentary structures, constructing parameters indicating stratigraphic sedimentary structures, and determining the lithofacies in conjunction with lithology. This method uses logging data to identify lithofacies and can perform lithofacies identification throughout the entire well section, better distinguishing between mudstone and tuff facies, overcoming the problems of limited coring sections and low accuracy of on-site lithofacies identification, and providing more accurate data support for oil and gas exploration and development prediction.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies. Background Technology

[0002] Domestic exploration practices have shown that shale oil and gas resources have enormous potential, and breakthroughs have been made in shale oil and gas exploration in China over the past decade. Mudstone and shale facies, as fundamental facies in shale oil and gas, have been discovered in several oil and gas basins in China. Because these two types of lithology and facies often occur together, and each facies contains multiple subfacies (e.g., tuff includes dolomitic tuff and calcareous tuff, while mudstone and shale facies include calcareous mudstone, dolomitic mudstone, and tuffaceous mudstone), this introduces some uncertainty into reservoir evaluation and lithology / facies prediction, necessitating facies identification and differentiation.

[0003] Shale rocks have fine grains, and the identification of lithology and lithofacies is mostly based on laboratory techniques such as microscopic examination, X-ray diffraction, and rock composition. However, with the large-scale development of shale oil and gas, the laboratory identification techniques of drilling coring are difficult to meet the needs of exploration and development due to the cost and timeliness of coring. Well logging, as an essential project after drilling completion, has the characteristics of being timely, comprehensive, and rich in data. Studying lithofacies well logging identification can provide accurate and timely lithofacies identification.

[0004] Current lithofacies logging identification technologies mostly rely on a single curve or multiple curves to identify a single lithology or lithofacies. They do not offer identification methods for lithologies and lithofacies with minor differences, especially for sub-type lithofacies.

[0005] Due to the atypical characteristics of mudstone, shale, and tuff, well logging features are difficult to utilize. The main lithology and lithofacies identification methods are as follows.

[0006] Qualitative interpretation of well logging characteristics is a method that extracts and analyzes well logging features contained in single well logging curves or two-dimensional well logging images, and interprets the lithology of formations by combining this with the regularity of actual data. This method is suitable for simple strata with relatively uniform composition or complex lithological formations requiring rough lithological classification. Its application effectiveness is influenced by a combination of factors, including the richness of actual data, the experience of the interpreters, the accuracy of depth positioning, and the complexity of the lithological profile.

[0007] The well logging response equation graphical method is based on a cross-plot chart. It uses point plotting, connecting lines, or scaling of logging lines to graphically interpret the well logging response equations, thereby enabling qualitative identification of rock or mineral types and quantitative determination of mineral content. This method requires first creating a cross-plot chart, and then plotting points on the interpretation chart for solution. This method is affected by factors such as sample data and purity. It works well for relatively simple lithologies but is less effective for complex lithologies or rocks with similar compositions.

[0008] The well logging response equation system solution method is a lithological interpretation method that constructs a system of equations consisting of two or more multivariate linear equations and a volume balance equation based on the well logging response equations of different well logging methods. It then uses mathematical methods to solve the numerical solutions of the variables in this system. This method can obtain numerical solutions for the unknown variables in balanced, overdetermined, and underdetermined equation systems, and has achieved good results in sandstone and mudstone stratigraphic profiles and complex lithological formations with multiple minerals. This method is applicable to the lithological interpretation of single-mineral and dual-mineral models. Its application effectiveness is comprehensively influenced by factors such as the number of well logging curves and their calibration accuracy, the number of mineral types, the applicability of the selected mathematical model and methods, the algorithm's running speed, and the geological constraints of the numerical solution.

[0009] The "core logging" statistical analysis method is based on "lithology logging." It uses statistical methods to directly analyze the relationship between formation lithology and logging parameters, and then utilizes computers for automated processing and interpretation. "Core logging" applies depth matching between geological data and logging data, providing sample data for statistical analysis and validating the final lithology interpretation results. The effectiveness of this method is influenced by a combination of factors, including the complexity of the formation lithology profile, the accuracy of depth matching between geological and logging data, the applicability of the statistical methods, and the experience of the interpreters.

[0010] Chinese patent application CN201910120898.9 discloses a method for identifying tuff through well logging, comprising the following steps: (1) establishing a comprehensive identification model for tuff formations; (2) obtaining the comprehensive identification index of tuff at each depth sampling point in the formation based on the comprehensive identification model; and (3) determining whether tuff exists in the formation based on the magnitude of the comprehensive identification index of tuff at each depth sampling point. This invention can use well logging methods to perform high-precision identification of tuff formations at the macroscopic level, avoiding the drawbacks of easy misjudgment and low identification accuracy of single well logging curves in the process of identifying tuff, and accurately and quickly identifying tuff layers in the formation.

[0011] Chinese patent application CN201410283509.1 discloses a method for identifying high-quality shale and mudstone using well logging data, belonging to the fields of oil and gas exploration and development and geophysics. This method first performs well logging data preprocessing, including environmental correction, depth correction, and standardization of the logging curves. Then, the following steps are performed: Step 1: Differentiate between shale and mudstone layers and non-shale and mudstone layers using natural gamma ray logging curves; Step 2: Determine the depositional environment of the shale and mudstone layers using the ratios of the thorium and potassium curves and the thorium and uranium curves from the natural gamma ray spectroscopy logging; Step 3: If the depositional environment determined in Step 2 is a reducing, low-energy environment, calculate the content of organic matter and clay minerals in the shale and mudstone layers; Step 4: Based on Step 3, identify high-quality and non-high-quality shale and mudstone layers.

[0012] Chinese patent application CN201610687331.6 discloses a method for studying the fine-grained sedimentary facies of shale and mudstone. This method includes: Step 1, determining a facies classification scheme for fine-grained sedimentary shale and mudstone based on a combination of three elements: rock composition, structure, and organic matter content; Step 2, establishing well logging identification models for mineral composition, organic matter, and structure; Step 3, using the well logging identification model established in Step 2 to identify the facies types of shale and mudstone segments in a single well within the study area; Step 4, constraining and correcting the facies types of shale and mudstone identified by the well logging models; and Step 5, conducting a study on the identification and distribution patterns of fine-grained sedimentary facies of shale and mudstone across the entire area. This method improves the accuracy of predicting the fine-grained sedimentary facies of shale and mudstone, laying a solid theoretical foundation for the exploration and development of shale oil and gas in continental rift basins.

[0013] Chinese patent application CN201611025247.4 discloses a well logging identification method for lacustrine mudstone and shale based on genetic analysis. This method includes: Step 1, analyzing the facies type of mudstone and shale based on actual geological data of the research object; Step 2, analyzing the influence of different genetic types of minerals on the well logging curves based on the genesis and occurrence of major minerals and the morphology of typical well logging curves; Step 3, establishing a well logging identification pattern chart adapted to the research object, referring to the facies logging identification model; Step 4, comparing the well logging curve data of wells requiring facies identification with the identification pattern segment by segment to identify the facies one by one. This well logging identification method for lacustrine mudstone and shale based on genetic analysis is simple to operate and can be widely applied in oilfield exploration research, providing new research ideas and technical means for the exploration of shale oil and gas in continental lacustrine basins.

[0014] The existing technologies described above are significantly different from the present invention and have failed to solve the technical problem we want to address. Therefore, we have invented a new geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies. Summary of the Invention

[0015] The purpose of this invention is to provide a geophysical logging identification method that can more accurately distinguish between shale and tuff facies, and is widely applicable to the field of oil and gas resource geological exploration and development evaluation to differentiate between mudstone and shale facies and tuff facies.

[0016] The objective of this invention can be achieved through the following technical measures: a geophysical logging identification method for distinguishing between shale and tuff facies, comprising:

[0017] Step 1: Collect data on the area to be studied and determine the lithology and sedimentary structures;

[0018] Step 2: Determine the logging and imaging logging characteristics of different lithologies and sedimentary structures;

[0019] Step 3: Determine logging curves that are highly correlated with rock composition;

[0020] Step 4: Construct dual rock composition indicator parameters F1 and F2 using multiple curves;

[0021] Step 5: Use F1 and F2 to comprehensively identify lithology;

[0022] Step 6: Select curves that reflect the characteristics of stratigraphic sedimentary structures, construct parameters indicating stratigraphic sedimentary structures, and determine lithofacies by combining lithology.

[0023] The objective of this invention can also be achieved through the following technical measures:

[0024] In step 1, collect core photographs and scanned images of the well to be studied, well logging data, especially imaging logging data, well core microscopic data, and well core rock composition analysis data. Match these data depths to conventional well logging depths and draw a comprehensive map of logging, core, imaging, and core analysis data.

[0025] In step 1, using core samples or core images and photographs, the sedimentary structure of the rock is determined through observation of the core samples, image analysis, and microscopic analysis of the core.

[0026] In step 1, the lithology is determined by combining the logging lithology and the data from drilling core analysis and experiments. Among these, the plagioclase obtained from the core is used as the most important indicator for determining the lithology.

[0027] In step 2, the lithology and sedimentary structures determined by drilling core sampling are compared with logging and imaging logging to determine the logging and imaging logging characteristics of different lithologies and sedimentary structures.

[0028] In step 3, the determined lithology and sedimentary structures are plotted on the logging and well logging composite chart according to the matched depth. The differences between different lithologies and sedimentary structures on conventional logging curves are studied. Curves that may identify lithologies and sedimentary structures are initially selected, their correlation is analyzed, and highly correlated curves are screened out.

[0029] In step 3, the differences between rocks such as mudstone and shale are mainly reflected in the differences in rock composition. Curves with high correlation to rock composition are selected to achieve lithology identification through well logging.

[0030] In step 3, the well logging curves with high correlation to rock components are determined based on the rock components obtained from drilling core experiments. The relationship between the important lithology indicator mineral plagioclase and the well logging curves is plotted, and the correlation is analyzed using mathematical fitting methods. The well logging curves include: density, neutron, acoustic wave, photoelectric absorption cross section index, resistivity, spontaneous potential, spontaneous gamma, uranium, thorium, and potassium.

[0031] In step 3, logging curves with a correlation coefficient greater than 0.6 are selected as rock composition indicator curves.

[0032] In step 4, F1 = A1 - A2; F2 = a × B1 + b × B2 + c × B3, where A1 and A2 are one of the preferred normalized density, neutron, acoustic, and photoelectric absorption cross-section index curves, respectively; B1, B2, and B3 are one of the normalized logarithmic resistivity, spontaneous potential, spontaneous gamma, uranium, thorium, and potassium curves, respectively; and a, b, and c are undetermined coefficients, determined based on their correlation with rock composition.

[0033] Step 5 includes:

[0034] Step 51: F1 distinguishes between tuff and mudstone / shale. When F1 > 0.6, the lithology is tuff; when F1 ≤ 0.6, the lithology is mudstone / shale.

[0035] Step 52: Based on the identification of mudstone and shale using F1, F1 and F2 are then combined to distinguish the subtype lithology.

[0036] In step 6, based on the determination of stratigraphic sedimentary structures, a curve that reflects the characteristics of stratigraphic sedimentary structures is selected to construct the stratigraphic sedimentary structure parameter F. 层理 According to F 层理 The parameter sedimentary structure identification value, combined with lithology, determined the lithofacies.

[0037] In step 6, based on the preferred deep and shallow resistivity and porosity curves, indicative stratigraphic sedimentary structural parameters are constructed, specifically:

[0038] F 层理 =a×F AC +b×F DEN +c×F RT +d×F RT-RXO +... (3)

[0039] Among them, F AC =AC / AC 平均值 ;F DEN =DEN / DEN 平均值 ;F RT =lg(RT) / lg(RT) 平均值 );F RT-RXO =lg(RT-RXO) / lg(RT-RXO) 平均值 The average curve represents well sections with similar or identical lithology, exhibiting relatively similar logging characteristics, and with well section lengths ranging from 10 to 100 meters; F 层理 —Stratification parameters for sedimentary structures, with values ​​from smallest to largest corresponding to massive, layered, and laminar structures. Identification thresholds are determined based on the geological characteristics of the study area; AC—Sonic transit time logging value, μs / ft; DEN—Density logging value, g / cm³. 3 ;RT—Deep resistivity logging value, Ω.m;RXO—Shallow resistivity logging value, Ω.m;a,b,c,d are undetermined coefficients, determined based on the relationship between logging curves and formation sedimentary structures.

[0040] The geophysical logging identification method for distinguishing between shale and tuff facies in this invention is a step-by-step, multi-curve approach that can more accurately differentiate between shale and tuff facies and can be widely applied in the field of oil and gas resource geological exploration and development evaluation.

[0041] This geophysical logging identification method, which distinguishes between mudstone and shale facies and tuff facies, can identify lithofacies using logging data. It can identify lithofacies throughout the entire well section, better distinguishing between mudstone and tuff facies, and overcoming the problems of limited core sampling sections and low accuracy of on-site lithofacies identification. This provides more accurate data support for oil and gas exploration and development prediction. Attached Figure Description

[0042] Figure 1 A flowchart of a specific embodiment of the geophysical well logging identification method for distinguishing between mudstone and shale facies of the present invention;

[0043] Figure 2 This is a cross-plot of sound waves and plagioclase content in a specific embodiment of the present invention;

[0044] Figure 3 This is a cross-plot of density versus plagioclase content in a specific embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of preferred logging curves in a specific embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the lithological identification parameter limits in a specific embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram illustrating an example of lithological identification, sedimentary structure classification, and lithofacies classification in a specific embodiment of the present invention;

[0048] Figure 7 This is a schematic diagram illustrating an example of sedimentary structure and lithofacies identification in a specific embodiment of the present invention;

[0049] Figure 8 This is a schematic diagram illustrating an example of lithological identification, sedimentary structure classification, and lithofacies identification in a specific embodiment of the present invention. Detailed Implementation

[0050] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0051] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0052] The geophysical logging identification method for distinguishing between shale and tuff facies, as described in this invention, is commonly used in the field of oil and gas exploration and development. This method, based on laboratory studies of lithology and sedimentary structures from well core samples, performs logging work to differentiate between shale and tuff facies. The main steps are as follows: Step 1: Determine logging curves with high correlation to rock components; Step 2: Construct dual rock component indicator parameters using multiple curves; Step 3: Construct sedimentary structural parameters indicating formations based on resistivity and porosity curves. This method can identify lithology and lithofacies throughout the entire well section, better distinguishing between shale and tuff facies, overcoming the problems of limited core sections and low accuracy in on-site lithofacies identification, and providing more accurate data support for oil and gas exploration and development prediction.

[0053] The following are several specific embodiments of the application of the present invention.

[0054] Example 1

[0055] In a specific embodiment 1 of the present invention, such as Figure 1As shown, Figure 1 This is a flowchart of the geophysical logging identification method for distinguishing between shale and tuff facies according to the present invention. The geophysical logging identification method for distinguishing between shale and tuff facies includes the following steps:

[0056] Step 1: Collect data on the area to be studied and determine the lithology and sedimentary structures.

[0057] Collect core photographs and scanned images from wells in the area to be studied, well logging data, especially imaging logging data, well core microscopic data, and core rock composition analysis data. Match these data depths to conventional well logging depths and create a comprehensive map of logging, core, imaging, and core analysis data.

[0058] By utilizing core samples or images and photographs from wells, and through observation of core samples, image analysis, and microscopic analysis of core samples, the sedimentary structures of rocks can be determined.

[0059] The lithology was determined by combining well logging lithology and data from drilling core microscopy and experimental analysis. Among these, plagioclase obtained from core sampling was used as the most important indicator for determining the lithology.

[0060] Step 2: By comparing the lithology and sedimentary structures determined by drilling cores with well logging and imaging logging, the characteristics of well logging and imaging logging for different lithologies and sedimentary structures are determined.

[0061] Step 3: Plot the determined lithology and sedimentary structures on the well logging and logging composite chart according to the matched depth, study the differences of different lithologies and sedimentary structures on conventional well logging curves, initially select curves that may identify lithologies and sedimentary structures, analyze their correlation, and screen out curves with high correlation.

[0062] The differences between rocks such as mudstone, shale, and tuff mainly lie in their rock composition. Therefore, logging curves with high correlation to rock composition are selected for well logging identification. The determination of logging curves with high correlation to rock composition is based on rock composition obtained from drilling core experiments. Relationship diagrams are plotted between plagioclase, an important indicator of lithology, and the logging curves. Mathematical fitting methods are used to analyze their correlation. The logging curves include: density, neutron, acoustic wave, photoelectric absorption cross-section index, resistivity, spontaneous potential, spontaneous gamma ray, uranium, thorium, and potassium.

[0063] Well logging curves with a correlation coefficient greater than 0.6 were selected as rock composition indicator curves. Figure 2 The intersection diagram of the acoustic (AC) curve and plagioclase shows a clear trend of negative correlation between the two, with a correlation of 71%, which meets the conditions for a high correlation well logging curve with rock composition. Figure 3The intersection plot of the density curve (DEN) and plagioclase shows a clear positive correlation, reaching 66%, which meets the conditions for a high correlation logging curve with rock composition. The neutron curve (CNL) is negatively correlated with plagioclase, reaching 61%, which also meets the conditions for a high correlation logging curve with rock composition. The deep resistivity curve (RT) is negatively correlated with plagioclase, reaching 65%, and the shallow resistivity curve (RXO) is negatively correlated with plagioclase, reaching 61%, which also meets the conditions for a high correlation logging curve with rock composition. The correlations of other curves are less than 60%, which does not meet the conditions and is not selected.

[0064] Although a single curve shows the highest correlation with plagioclase at 66%, there are many overlapping areas when using a single curve to identify lithology. Therefore, it is necessary to draw a pairwise curve intersection diagram. Figure 4 As can be seen, DEN-AC and AC-RT intersections can amplify lithological indicator signals and better identify lithology.

[0065] Step 4: Construct dual rock composition indicator parameters F1 and F2 using multiple curves. Specifically: F1 = A1 - A2; F2 = a × B1 + b × B2 + c × B3, where A1 and A2 are one of the preferred normalized density, neutron, acoustic, and photoelectric absorption cross-section index curves, respectively. In this embodiment, density and acoustic curves are selected. B1, B2, and B3 are one of the normalized logarithmic resistivity, spontaneous potential, spontaneous gamma, uranium, thorium, and potassium curves, respectively. In this embodiment, logarithmic resistivity is selected. a, b, and c are undetermined coefficients, determined based on their correlation with rock composition.

[0066] Based on the preferred curves and correlations, the specific curves and undetermined coefficient values ​​for F1 and F2 were determined:

[0067] F1=(DEN-Dmax) / (DENmin-Dmax)-(AC-Amin) / (Amax-Amin) (1)

[0068] F2=0.6×[lg(RT)max-lg(RT) / (lg(RT)max-lg(RT)min)]+0.4×[lg

[0069] (RXO)max-lg(RXO) / (lg(RXO)max-lg(RXO)min)](2)

[0070] Wherein, DEN is the density logging value, in g / cm³. 3 Dmax—Maximum density logging value, g / cm³ 3 Environmental correction removes outliers, the same applies below; Dmin—minimum density logging value, g / cm³ 3AC—Sonic transit time logging value, μs / ft; Amax—Maximum sonic transit time logging value, μs / ft; Amin—Minimum sonic transit time logging value, μs / ft; RT—Deep resistivity logging value, Ω.m; RXO—Shallow resistivity logging value, Ω.m; lg(RT)max, lg(RT)min—Maximum and minimum logarithmic deep resistivity values, respectively; lg(RXO)max, lg(RXO)min—Maximum and minimum logarithmic deep resistivity values, respectively.

[0071] Step 5: Use F1 and F2 to comprehensively identify lithology, which is carried out in two steps.

[0072] Step 51, F1 distinguishes between tuff and mudstone / shale, see Figure 5 When F1>0.6, the lithology is tuff; when F1≤0.6, the lithology is mudstone or shale.

[0073] Step 52: Based on the identification of mudstone and shale using F1, F1 and F2 are then combined to distinguish sub-type lithologies, such as dolomitic tuff, tuff, dolomitic mudstone, and calcareous mudstone.

[0074] Based on core analysis, the lithological boundary values ​​for the implementation area were determined. (See...) Figure 5 In tuff, lithology with F2>0.7 is dolomitic tuff, and lithology with F2≤0.7 is dolomitic tuff; in mudstone and shale, lithology with F2>0.6 is tuffaceous mudstone and shale, lithology with 0.6≥F2>0.4 is dolomitic mudstone and shale, and lithology with F2≤0.4 is calcareous mudstone and shale.

[0075] Based on the above parameters, lithology and sedimentary structures are comprehensively identified to determine the lithofacies of the target stratigraphic unit, thereby distinguishing between mudstone and tuff facies. (See...) Figure 6 In the figure, column 4 represents the lithology of cuttings logging, column 5 represents the calculation parameter F1, column 6 represents the calculation parameter F2, and the lithology is identified based on the identification criteria values ​​of F1 and F2 in column 7.

[0076] Step 6: Based on the determination of stratigraphic sedimentary structures, select curves that can reflect the characteristics of stratigraphic sedimentary structures and construct parameters indicating stratigraphic sedimentary structures.

[0077] Based on the selected deep and shallow resistivity and porosity curves, parameters indicating the sedimentary structure of the formation are constructed, specifically:

[0078] F 层理 =a×F AC +b×F DEN +c×F RT +d×F RT-RXO +... (3)

[0079] Among them, FAC =AC / AC 平均值 ;F DEN =DEN / DEN 平均值 ;F RT =lg(RT) / lg(RT) 平均值 );F RT-RXO =lg(RT-RXO) / lg(RT-RXO) 平均值 The average curve represents well sections with similar or identical lithology, where the logging characteristics are not significantly different, and the length of these sections is generally 10–100 meters; F 层理 —Stratification parameters for sedimentary structures, with values ​​from smallest to largest corresponding to massive, layered, and laminar structures. Identification thresholds are determined based on the geological characteristics of the study area; AC—Sonic transit time logging value, μs / ft; DEN—Density logging value, g / cm³. 3 ;RT—Deep resistivity logging value, Ω.m;RXO—Shallow resistivity logging value, Ω.m;a,b,c,d are undetermined coefficients, determined based on the relationship between logging curves and formation sedimentary structures.

[0080] Based on the correlation between well logging curves and formation sedimentary structures, the undetermined coefficients in Formula 3 were determined, and the specific algorithm is as follows:

[0081] F 层理 =0.21×F DEN +0.48×F RXO +0.31×F RT-RXO (4)

[0082] F DEN1 =DEN / DEN 平均值 (5)

[0083] F DEN =(F DEN1 -F DEN1max ) / (F DEN1min -F DEN1max (6)

[0084] F RXO1 =lg(RXO) / lg(RXO) 平均值 (7)

[0085] F RXO1 =(F RXO1 -F RXO1max ) / (F RXO1min -F RXO1max (8)

[0086] F (RT-RXO)1 =lg|RT-RXO| / lg|RT-RXO| 平均值 (9)

[0087] F RT-RXO =(F (RT-RXO)1 -F (RT-RXO)1max ) / (F (RT-RXO)1min -F (RT-RXO)1max (10)

[0088] Among them, F 层理 —Stratification parameters for sedimentary structures, with values ​​from smallest to largest corresponding to massive, layered, and lamellar structures; DEN, RT, and RXO are the same as in formulas (1)(2)(3); DEN1max and DEN1min are the maximum and minimum values ​​of the curves, respectively, and other curves are similar.

[0089] Based on core observation and microscopic analysis and F 层理 Comparative analysis determined the sedimentary structure identification value, F. 层理 >0.7 indicates a layered structure, 0.7 ≥ F 层理 >0.3 indicates layered structure, F 层理 ≤0.3 indicates blocky.

[0090] Step 7, according to F 层理 The parameter sedimentary tectonic identification value, combined with lithology, determined the lithofacies, see [reference]. Figure 6 Column 8.

[0091] Example 2

[0092] In a specific embodiment 2 of the present invention, the geophysical logging identification method for distinguishing between shale and tuff facies of the present invention includes the following steps:

[0093] 1. Using core sampling experiments to analyze rock components, the relationship between plagioclase, an important indicator of lithology, and well logging curves was plotted. Cross plots of sonic transit time, density, neutron content, and plagioclase content were also created. Using mathematical fitting methods, the correlations between AC, DEN, CNL, and plagioclase were 0.70, 0.65, and 0.62, respectively, all three curves meeting the correlation index. Analyzing the relationship between the three curves, the combination of AC and DEN, and AC and DEN, can better distinguish different lithologies. Therefore, AC and DEN were selected as rock component indicator curves.

[0094] 2. Lithology is determined by constructing dual rock component indicator parameters using multiple curves, F1 = A1 - A2, where A1 and A2 are the optimized normalized density and acoustic wave, respectively.

[0095] F1=(DEN-Dmax) / (DENmin-Dmax)-(AC-Amin) / (Amax-Amin) (11)

[0096] Wherein, DEN is the density logging value, in g / cm³. 3Dmax—Maximum density logging value, g / cm³ 3 Dmin—Minimum density logging value, g / cm³ 3 AC—Acoustic transit time logging value, μs / ft; Amax—Maximum acoustic transit time logging value, μs / ft; Amin—Minimum acoustic transit time logging value, μs / ft;

[0097] By comparing lithology and rock composition, the relationship between logging resistivity and lithology is obvious, which can be used as a supplement to the F1 parameter to further subdivide the lithology.

[0098] F2=0.6×RT+0.4×Rxo (12)

[0099] Wherein, RT—deep resistivity logging value, Ω.m; RXO—shallow resistivity logging value, Ω.m;

[0100] F1 is used to distinguish between tuff and shale, and F2 is used to further distinguish sub-type lithology. Specific identification parameter thresholds are detailed in [link to relevant documentation]. Figure 5 , Figure 7 Column 4 is for identifying lithology through well logging.

[0101] 3. Based on core and microscopic data, the sedimentary structures of each well section were determined, and these identified sedimentary structural features were plotted on conventional logging maps. (See attached image.) Figure 7 The study determined sedimentary structure indicator curves. Based on the logging curve mechanism, shallow resistivity, sonic logging curves, and the difference in deep and shallow resistivity were optimized. The parameters indicating the sedimentary structure of the formation were constructed using Formula 3, and the specific undetermined coefficients are shown in Formula 13.

[0102] F 层理 =0.21×F AC +0.48×F RXO +0.31×F RT-RXO (13)

[0103] F 层理 The calculation results are shown below. Figure 7 Column 9, according to F 层理 See parameter-based stratigraphic and sedimentary structure classification. Figure 7 Column 10.

[0104] Based on the comprehensive identification of lithology and sedimentary structures using the above parameters, the lithofacies of the stratigraphic intervals are obtained, see [reference]. Figure 7 The 11th column is used to distinguish between mudstone and tuff facies.

[0105] Example 3

[0106] In a specific embodiment 3 of the present invention, embodiment 3 is an exploration well in the adjacent area of ​​embodiment 1. Embodiment 3 and embodiment 1 are in the same sedimentary environment and have similar lithology and sedimentary structural characteristics. This well did not have core drilling or imaging logging. Therefore, the preferred logging curve and F1 and F2 calculation formula parameters in embodiment 1 were used in the lithology identification process.

[0107] The calculation results are shown below. Figure 8 In the figure, column 3 represents the lithology of cuttings logging, column 6 represents the calculation parameter F1, column 7 represents the calculation parameter F2, and the lithology is identified based on the identification criteria values ​​of F1 and F2 in column 8.

[0108] Curves representing the characteristics of stratigraphic sedimentary structures are constructed based on selected deep and shallow resistivity curves reflecting porosity to indicate stratigraphic sedimentary structural parameters. Example 3 uses a salt-cement slurry drilling fluid, where the deep and shallow resistivity curves largely overlap. Therefore, in Formula 3, only density and deep resistivity are utilized, and the undetermined coefficients are re-determined based on correlation, as shown in Formula 14.

[0109] F 层理 =0.42×F DEN +0.58×F Rt (14)

[0110] Among them, F DEN =DEN / DEN 平均值 ;F RT =lg(RT) / lg(RT) 平均值 The average curve represents well sections with similar or identical lithology, where logging characteristics are not significantly different, and the length of these sections is generally 10–100 meters; F 层理 —Stratigraphic and sedimentary structural identification parameters, with values ​​ranging from small to large corresponding to massive, layered, and laminar structures. Identification thresholds are determined based on the geological characteristics of the study area; DEN—density logging value, g / cm³ 3 RT—Deep-penetration resistivity logging value, Ω·m. Calculation results are shown below. Figure 8 Column 9, according to F 层理 Identified sedimentary structures are shown in Figure 8 The 10th column.

[0111] The lithofacies were re-identified based on the lithology and stratigraphic sedimentary structures identified by well logging. Figure 8 Column 11.

[0112] F1 = DEN - AC (15)

[0113] F2=LG(RT) (16)

[0114] Where DEN and AC are normalized curves; LG(RT) is the normalized logarithmic resistivity.

[0115] Based on the above parameters, the lithology and sedimentary structure are comprehensively identified, and the facies of the stratigraphic section are obtained, thereby distinguishing between mudstone facies and tuff facies.

[0116] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0117] Except for the technical features described in the specification, all other technologies are known to those skilled in the art.

Claims

1. A geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies, characterized in that, The geophysical logging identification methods for distinguishing between shale and tuff facies include: Step 1: Collect data on the area to be studied and determine the lithology and sedimentary structures; Step 2: Determine the logging and imaging logging characteristics of different lithologies and sedimentary structures; Step 3: Determine logging curves that are highly correlated with rock composition; Step 4: Construct dual rock composition indicator parameters F1 and F2 using multiple curves, including: F1 = A1 - A2; F2 = a × B1 + b × B2 + c × B3, where A1 and A2 are one of the preferred normalized density, neutron, acoustic, and photoelectric absorption cross-section index curves, respectively; B1, B2, and B3 are one of the normalized logarithmic resistivity, spontaneous potential, natural gamma, uranium, thorium, and potassium curves, respectively; a, b, and c are undetermined coefficients, determined based on their correlation with rock composition; Based on the preferred curves and correlations, the specific curves and undetermined coefficient values ​​for F1 and F2 were determined: （1） F2=0.6×[lg(RT)max-lg(RT) / ( lg(RT)max −lg(RT)min)]+0.4×[lg(RXO)max-lg(RXO) / ( lg(RXO)max −lg(RXO)min)] (2) DEN—density log value, g / cm 3 Dmax—density log maximum value, g / cm 3 , environmental correction after removing outliers, the same below; Dmin—density log minimum value, g / cm 3 AC—acoustic travel time log value, μs / ft; Amax—acoustic travel time log maximum value, μs / ft; Amin—acoustic travel time log minimum value, μs / ft; RT—deep investigation resistivity log value, Ω.m; RXO—shallow investigation resistivity log value, Ω.m; lg(RT)max, lg(RT)min—log deep investigation resistivity maximum value, minimum value, respectively; lg(RXO)max, lg(RXO)min—log deep investigation resistivity maximum value, minimum value, respectively; Step 5: Use F1 and F2 to comprehensively identify lithology, including: Step 51: F1 distinguishes between tuff and mudstone / shale. When F1 > 0.6, the lithology is tuff; when F1 ≤ 0.6, the lithology is mudstone / shale. Step 52: Based on the identification of mudstone and shale using F1, F1 and F2 are then combined to distinguish the lithology of subclasses. Step 6: Select curves that reflect the characteristics of stratigraphic sedimentary structures, construct parameters indicating stratigraphic sedimentary structures, and determine the lithofacies based on lithology, including: Based on the identification of stratigraphic sedimentary structures, curves that reflect the characteristics of stratigraphic sedimentary structures are selected to construct the stratigraphic sedimentary structure parameter F. 层理 According to F 层理 Parameters for sedimentary tectonic identification values ​​are combined with lithology to determine lithofacies; Based on the selected deep and shallow resistivity and porosity curves, parameters indicating the sedimentary structure of the formation are constructed, specifically: F 层理 =a×F AC +b×F DEN +c×F RT +d×F RT-RXO + ... (3) Among them, F AC =AC / AC 平均值 ;F DEN =DEN / DEN 平均值 ;F RT =lg(RT) / lg(RT 平均值 );F RT-RXO =lg(RT-RXO) / lg(RT-RXO) 平均值 The average curve represents well sections with similar or identical lithology, exhibiting relatively similar logging characteristics, and with well section lengths ranging from 10 to 100 meters; F 层理 —Stratification parameters for sedimentary structures, with values ​​from smallest to largest corresponding to massive, layered, and laminar structures. Identification thresholds are determined based on the geological characteristics of the study area; AC—Sonic transit time logging value, μs / ft; DEN—Density logging value, g / cm³. 3 ;RT—Deep resistivity logging value, Ω.m;RXO—Shallow resistivity logging value, Ω.m;a,b,c,d are undetermined coefficients, determined based on the relationship between logging curves and formation sedimentary structures.

2. The geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies according to claim 1, characterized in that, In step 1, collect core photographs and scanned images of the well to be studied, well logging data, especially imaging logging data, well core microscopic data, and well core rock composition analysis data. Match these data depths to conventional well logging depths and draw a comprehensive map of logging, core, imaging, and core analysis data.

3. The geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies according to claim 2, characterized in that, In step 1, using core samples or core images and photographs, the sedimentary structure of the rock is determined through observation of the core samples, image analysis, and microscopic analysis of the core.

4. The geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies according to claim 3, characterized in that, In step 1, the lithology is determined by combining the logging lithology and the data from drilling core analysis and experiments. Among these, the plagioclase obtained from the core is used as the most important indicator for determining the lithology.

5. The geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies according to claim 1, characterized in that, In step 2, the lithology and sedimentary structures determined by drilling core sampling are compared with logging and imaging logging to determine the logging and imaging logging characteristics of different lithologies and sedimentary structures.

6. The geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies according to claim 1, characterized in that, In step 3, the determined lithology and sedimentary structures are plotted on the logging and well logging composite chart according to the matched depth. The differences between different lithologies and sedimentary structures on conventional logging curves are studied. Curves that may identify lithologies and sedimentary structures are initially selected, their correlation is analyzed, and highly correlated curves are screened out.

7. The geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies according to claim 6, characterized in that, In step 3, the differences between rocks such as mudstone and shale are mainly reflected in the differences in rock composition. Curves with high correlation to rock composition are selected to achieve lithology identification through well logging.

8. The geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies according to claim 7, characterized in that, In step 3, the well logging curves with high correlation to rock components are determined based on the rock components obtained from drilling core experiments. The relationship between the important lithology indicator mineral plagioclase and the well logging curves is plotted, and the correlation is analyzed using mathematical fitting methods. The well logging curves include: density, neutron, acoustic wave, photoelectric absorption cross section index, resistivity, spontaneous potential, spontaneous gamma, uranium, thorium, and potassium.

9. The geophysical logging identification method for distinguishing between mudstone and shale facies and tuff facies according to claim 8, characterized in that, In step 3, logging curves with a correlation coefficient greater than 0.6 are selected as rock composition indicator curves.

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