An alkali-containing formation lithology identification method, model construction method and device

Abstract

The application discloses a kind of alkali-containing formation lithology identification method, model construction method and device.The method comprises: based on the well logging data of alkali-containing formation, extracting logging response characteristic set, logging response characteristic set contains at least one logging response characteristic and its corresponding well logging curve;Based on logging response characteristic set, construct alkali-containing formation lithology index F (T) and resistivity differential index F (R);According to alkali-containing formation lithology index F (T) and resistivity differential index F (R) constructs crossplot for lithology identification, obtains the well logging lithology identification model of alkali-containing formation.Can accurately quantitatively identify the lithology of alkali-containing formation.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and in particular to a method and system for identifying the lithology of lacustrine alkaline strata. Background Technology

[0002] Natural alkali is a salt mineral containing more than 20 mineral types, such as Na2CO3 and NaHCO3, formed by evaporation and crystallization under arid to semi-arid climatic conditions. A small amount of natural alkali is developed in the pores of early-formed dolomite, interbedded with argillaceous dolomite. With the advancement of exploration technology and the requirements of integrated geological engineering, the exploration and development technology of alkali-bearing strata is particularly important.

[0003] Currently, scholars both domestically and internationally have conducted extensive research and discussion on well logging identification methods for alkali-bearing strata. However, these methods are primarily qualitative, typically using cross plots to interpret and evaluate soda ash layers. But for alkali-bearing strata, the accuracy of traditional cross plot analysis is low. Methods using curve amplitude variations to identify lithology are limited by human factors and variations in sedimentary conditions across different regions, resulting in low accuracy in lithological judgment of alkali-bearing rocks, often leading to misjudgments and even overlooking alkali-bearing strata, thus delaying exploration. Therefore, there is an urgent need for a method that can accurately and quantitatively identify the lithology of lacustrine alkali-bearing strata. Summary of the Invention

[0004] Since the main controlling factor for the distribution of alkali deposits is the dolomite facies zone, the traditional method of analyzing the lithology of alkali-bearing strata using cross plots has low accuracy. The method of identifying the lithology of alkali-bearing strata by utilizing curve amplitude changes is affected by human factors and is limited by the variation of sedimentary conditions in different regions, resulting in low accuracy and even the omission of alkali-bearing strata, which delays the exploration process. Moreover, the current methods for identifying the lithology of alkali-bearing strata in the world are mainly qualitative, and there is no accurate and efficient method for quantitatively identifying the lithology of alkali-bearing strata.

[0005] The inventors have discovered that due to differences in lithology, there are significant differences in well logging curves between strata with high alkali content and their surrounding rocks. This difference can serve as an important indicator for lithological identification and well logging interpretation of alkali-bearing strata. By establishing a calculation model based on the characteristics of alkali-bearing strata's responses to various well logging methods, accurate and efficient quantitative identification of the lithology of alkali-bearing strata can be achieved even when nuclear magnetic resonance logging data is scarce.

[0006] In view of the above problems and findings, the present invention is proposed to provide a method, model construction method and apparatus for identifying alkali-bearing strata that overcomes or at least partially solves the above problems.

[0007] In a first aspect, embodiments of the present invention provide a method for constructing a lithological identification model for alkali-bearing strata, comprising:

[0008] Based on logging data of alkaline formations, a logging response feature set is extracted, which includes at least one logging response feature and its corresponding logging curve.

[0009] Based on the well logging response feature set, the lithological index F(T) and resistivity differential index F(R) of alkaline formations are constructed.

[0010] Based on the lithological index F(T) and resistivity differential index F(R) of the alkaline formation, a cross plot for lithological identification is constructed, resulting in a well logging lithological identification model for the alkaline formation.

[0011] In one embodiment, extracting a well logging response feature set based on well logging data from alkaline formations includes:

[0012] Based on well logging data of alkaline formations, lithological analysis of the alkaline formations is conducted to extract response characteristics;

[0013] The lithological analysis includes at least one of electrical properties, radiometric properties, and logging properties;

[0014] The response characteristics include at least one of the logging response characteristics of well diameter, wellbore neutron, flush zone resistivity, deep resistivity, density, and natural gamma.

[0015] In one embodiment, the construction of the lithological index F(T) and resistivity differential index F(R) of the alkaline formation based on the well logging response feature set includes:

[0016] Based on the wellbore neutron logging data and density logging data included in the well logging response characteristics, the reservoir factor F(Q), which characterizes reservoir properties, is determined:

[0017]

[0018] In the above formula, CNL represents the compensated neutron logging value, and DEN represents the density logging value;

[0019] Based on the well logging response characteristics including caliper logging data and natural gamma logging data, the clay factor F(S), which characterizes the grain size and clay content, is determined:

[0020]

[0021] In the above formula, CALI represents the caliber logging value, and GR represents the natural gamma logging value;

[0022] Based on the reservoir factor F(Q) and the clay factor F(S), the lithology index F(T) of the alkaline strata is determined:

[0023] F(T) = F(Q) × F(S);

[0024] Based on the resistivity logging data of the flushed zone and the deep resistivity logging data included in the logging response characteristics, the resistivity differential index F(R) is determined:

[0025]

[0026] In the above formula, RT represents the deep resistivity logging value, and RXO represents the flushed zone resistivity logging value.

[0027] In one embodiment, the construction of a cross-plot for lithology identification based on the lithology index F(T) and resistivity differential index F(R) of the alkali-bearing formation, to obtain a well-logging lithology identification model for the alkali-bearing formation, includes:

[0028] Take the logarithm of the lithological index F(T) and resistivity differential index F(R) of the alkaline strata, respectively;

[0029] In a double logarithmic coordinate system, a linear relationship is established between the lithological index F(T) and the resistivity differential index F(R) of the alkali-bearing strata:

[0030] F(T) = a × F(R) b ;

[0031] Where 'a' represents the first lithology coefficient, which varies between 0.4 and 0.6.

[0032] b represents the second lithology coefficient, which varies between 0.2 and 0.3;

[0033] Based on the linear relationship, a cross plot is established for lithology identification, resulting in a well logging lithology identification model for alkaline formations.

[0034] In one example, the alkaline strata include at least one of soda ash, alkaline fine sandstone, dolomitic fine sandstone, dolomitic siltstone, and dolomitic mudstone.

[0035] In one embodiment, the method further includes: standardizing logging data from alkaline formations and performing core repositioning.

[0036] In one embodiment, the well logging data from the alkaline formation undergoes standardization and core repositioning processing, including:

[0037] Based on the selected standard layers and standard wells, the logging curves of each well in the logging data of the alkaline formation are corrected to obtain standardized logging data.

[0038] Based on the core range shown in the logging curve, the core location data in the core sampling data is corrected to obtain logging data with core repositioning.

[0039] Secondly, embodiments of the present invention provide a method for identifying the lithology of alkali-bearing strata, comprising:

[0040] Inversion of the three-dimensional seismic data volume yields lithological index F(T) inversion data volume and resistivity differential index F(R) inversion data volume for different lithologies of alkali-bearing strata;

[0041] Constraints are applied to the lithology index F(T) inversion data volume and the resistivity differential index F(R) inversion data volume based on the pre-established cross plot to obtain an effective inversion data volume; the cross plot is constructed using the lithology identification model construction method for alkali-bearing formation logging as described above;

[0042] Based on the effective inversion data volume, a fused data volume of different lithologies of alkali-bearing strata is obtained;

[0043] By constraining the fused data volume using the top and bottom boundaries of the target strata interpreted by seismic analysis, a planar predicted distribution map of different lithologies in alkali-bearing strata is obtained.

[0044] Thirdly, embodiments of the present invention provide a device for constructing a lithological identification model for alkali-bearing strata, comprising:

[0045] A response feature acquisition unit is used to extract a well logging response feature set based on well logging data of alkaline formations. The well logging response feature set includes at least one well logging response feature and its corresponding well logging curve.

[0046] Sensitive parameter units are constructed to generate lithological indices F(T) and resistivity differential indices F(R) for alkaline formations based on well logging response feature sets.

[0047] A model unit is constructed to build a cross plot for lithology identification based on the lithology index F(T) and resistivity differential index F(R) of the alkaline formation, thus obtaining a well logging lithology identification model for the alkaline formation.

[0048] In one embodiment, the above-mentioned apparatus further includes;

[0049] The data processing unit is used to standardize and reposition logging data from alkaline formations.

[0050] Fourthly, embodiments of the present invention provide a well logging lithology identification device for alkaline formations, comprising:

[0051] The first acquisition unit is used to invert the three-dimensional seismic data volume to obtain the lithological index F(T) inversion data volume and the resistivity differential index F(R) inversion data volume of different lithologies of alkaline strata;

[0052] The constrained data unit is used to constrain the lithology index F(T) inversion data volume and the resistivity differential index F(R) inversion data volume based on a pre-established cross plot, so as to obtain an effective inversion data volume; the cross plot is constructed using the well logging lithology identification model construction device for alkaline formations as described in any one of claims 9 or 10;

[0053] The second acquisition unit is used to obtain a fused data body of different lithologies of alkali-bearing strata based on the effective inversion data body;

[0054] The lithology distribution prediction unit is used to constrain the fused data volume using the top and bottom boundaries of the target strata interpreted by seismic analysis, and to obtain a planar predicted distribution map of different lithologies in alkali-bearing strata.

[0055] The lithological identification chart was constructed using the lithological identification model construction device for alkali-bearing strata as described above.

[0056] Fifthly, embodiments of the present invention provide a computer storage medium storing computer-executable instructions. When executed by a processor, the computer-executable instructions implement the aforementioned method for constructing a well logging lithology identification model for alkaline formations and / or the aforementioned method for identifying well logging lithology in alkaline formations.

[0057] In a sixth aspect, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for constructing a well logging lithology identification model for alkaline formations as described above and / or the method for identifying well logging lithology for alkaline formations as described above.

[0058] The beneficial effects of the above-mentioned technical solution provided in this embodiment include at least the following:

[0059] The method for constructing a well logging lithology identification model for alkaline formations provided in this embodiment extracts well logging response characteristics and corresponding well logging curves that show significant variations in alkaline formations based on well logging data. These well logging curves with significant variations can serve as important indicators for identifying the lithology of alkaline formations and for well logging interpretation. Based on the selected well logging response characteristics, a lithology index F(T) and an alkali content F(R) that can characterize alkaline formations are constructed. By constructing the mathematical relationship between F(T) and F(R), a cross-plot can be obtained that can quantitatively identify different lithologies in alkaline formations. The constructed cross-plot integrates multiple conventional well logging curves, establishes a quantitative formula and classification standard for lithology identification of alkaline formations, and improves the accuracy of quantitative identification. In the absence of imaging and nuclear magnetic resonance logging data, it can provide an important reference for the classification and identification of different lithologies and reservoir prediction in complex alkaline formations, and has good application effects and prospects for promotion.

[0060] The method for constructing a well logging lithology identification model for alkaline formations provided in this embodiment corrects the logging curves of each well in the well logging data of the alkaline formation based on selected standard layers and standard wells. It also corrects the core location data in the core sampling data based on the core range shown in the logging curves, achieving core consistency and obtaining standardized and core-positioned logging data. This standardized logging operation, controlling appropriate correction amounts, ensures the accuracy of the prediction results of the constructed well logging lithology identification model for alkaline formations.

[0061] The method for constructing a well logging lithology identification model for alkaline formations provided in this embodiment obtains the reservoir factor F(Q) through compensated neutron logging and density logging values. F(Q) can characterize the reservoir properties of alkaline formations. The clay factor F(S) is obtained through caliper logging and natural gamma logging values. F(S) can characterize the clay content and grain size of alkaline formations. Using F(Q) and F(S), two factors that can effectively distinguish the lithology of alkaline formations, a lithology index F(T) that can characterize alkaline formations is constructed. Finally, the resistivity differential index F(R) is obtained through deep resistivity logging values ​​and flushed zone resistivity logging values.

[0062] The lithology identification model construction method for alkali-bearing formations provided in this embodiment performs double logarithmic processing on the lithology index F(Q) and resistivity differential index F(R) of alkali-bearing formations, and establishes a linear relationship between the lithology index F(Q) and resistivity differential index F(R) in the double logarithmic coordinates. Based on this linear relationship, a cross plot is established for lithology identification of alkali-bearing formations. The resulting lithology identification model for alkali-bearing formations has greatly enhanced lithology differentiation capabilities and the ability to quantitatively identify different lithologies in alkali-bearing formations. It can effectively identify soda ash layers, alkali-bearing fine sandstone, dolomitic fine sandstone, dolomitic siltstone, dolomitic mudstone, etc.

[0063] The lithological identification method for alkali-bearing strata provided in this embodiment, for predicted alkali-bearing strata, uses three-dimensional seismic data volume and geostatistical inversion method to obtain the resistivity differential index F(R) inversion data volume and the lithological index F(T) inversion data volume of the predicted alkali-bearing strata. The cross-plot constructed by the above-mentioned alkali-bearing strata lithological identification model construction method determines the value range of the resistivity differential index F(R) and the lithological index F(T) of the alkali-bearing strata. Constraints are applied to the obtained resistivity differential index F(R) and lithological index F(T) inversion data volumes of the predicted alkali-bearing strata to obtain the predicted... The resistivity differential index F(R) and lithology index F(T) of the alkali-bearing strata are effectively inverted. By multiplying these two sets of effective inversion data, a fused data volume predicting the alkali-bearing strata is obtained. The top and bottom boundaries of the target strata interpreted by the seismic analysis are used to constrain the fused data volume. Mathematical operations such as root mean square, average, maximum, and summation can be used to calculate the planar predicted distribution map of different lithologies of the predicted alkali-bearing strata. By combining the seismic data volume with depth, the lithology of the alkali-bearing strata can be marked not only at different depths but also on a planar scale.

[0064] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0065] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0066] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0067] Figure 1 This is a flowchart of the construction method for the lithological identification model of alkali-bearing strata in Embodiment 1 of the present invention;

[0068] Figure 2 This is a flowchart illustrating the construction of the lithological index F(T) and resistivity differential index F(R) of alkali-bearing strata in Embodiment 1 of the present invention.

[0069] Figure 3 This is a flowchart illustrating the construction of a cross-plot for lithological identification in Embodiment 1 of the present invention;

[0070] Figure 4 This is a cross plot of alkali index and differential index established in well A01 according to an embodiment of the present invention.

[0071] Figure 5 This is a flowchart of the lithology identification method for alkaline strata in Embodiment 2 of the present invention;

[0072] Figure 6 This is a schematic diagram of the structure of the lithology identification model construction device for alkaline strata in Embodiment 1 of the present invention;

[0073] Figure 7 This is a schematic diagram of the lithology identification device for alkaline formation logging in Embodiment 2 of the present invention. Detailed Implementation

[0074] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0075] Since alkali minerals are found in argillaceous and dolomitic materials, there are significant differences in well logging curves between strata with high alkali content and their surrounding rocks due to differences in lithology. These differences can serve as an important indicator for lithological identification and well logging interpretation. The electrical characteristics of alkali-bearing strata can be summarized as "one large, two highs, and two lows," namely, large well diameter, high wellbore neutrons, high sonic transit time, low density, and low resistivity in the flushed zone.

[0076] For caliper logging, the main purpose is to measure the size of the wellbore at different depths. During the drilling process, due to the soaking and dissolution of drilling fluid, the wellbore size formed by different lithologies is not the same. In alkaline formations, the wellbore diameter is greatly enlarged because the ore is dissolved by the mud water, resulting in a caliper logging curve that is significantly larger than that of the surrounding rock in the alkaline ore layer, i.e., a large wellbore.

[0077] For wellbore neutron logging, the minerals in alkali ores are mostly sodium hydrocarbon (Na2CO3·3NaHCO3), which are also rich in H elements. This increases the hydrogen content of the alkali formation. Hydrogen is the decisive factor in the slowing down of neutrons by rocks. Therefore, in wellbore neutron logging, there is a significant difference between the alkali formation and the surrounding rock. The wellbore neutron logging curve value of the alkali formation is significantly higher than that of the surrounding rock, i.e., high wellbore neutron.

[0078] For sonic transit logging, the main reflection is the lithology, rock structure and density of the formation. Since the rock structure and density of alkaline formations are significantly different from those of the surrounding rocks, the sonic velocity is much different from that of the surrounding rocks. The sonic transit value of alkaline formations is generally higher than that of dolomite formations and sandstone and conglomerate reservoirs, i.e., high sonic transit.

[0079] For resistivity logging in the flushed zone: The detection depth is very shallow. Due to the increase in well diameter and mud salinity in alkaline formations, the resistivity logging value of the flushed zone is not the resistivity of the mud cake and the infiltrated zone, but the resistivity value of the mud filling the space where the alkaline ore is dissolved by the mud. Therefore, in alkaline formations, the difference between deep resistivity and flushed zone resistivity can be more than 1,000 times, while the resistivity difference of the surrounding rock is generally within 10 times, or even not much different, i.e., low flushed zone resistivity.

[0080] Regarding density logging: Due to the significantly low density of alkali ore, and the fact that the cavities in the alkali-bearing formation are filled with mud, the density of the alkali-bearing formation is also lower. The density logging value of the alkali-bearing formation is significantly lower than that of the surrounding rock, i.e., low density.

[0081] Among the aforementioned logging curves, the changes in well diameter, wall neutron, sonic transit time, flushing zone resistivity, and density curves are most pronounced in alkaline formations. Establishing a computational model based on the logging curve characteristics of alkaline formations allows for accurate and efficient quantitative identification of the lithology of these formations.

[0082] Example 1

[0083] This embodiment provides a method for constructing a lithological identification model for alkaline strata, the process of which is as follows: Figure 1 As shown, the method includes the following steps:

[0084] Step S1: Based on the logging data of alkaline formations, extract the logging response feature set, which includes at least one logging response feature and its corresponding logging curve.

[0085] Step S2: Based on the well logging response feature set, construct the lithological index F(T) and resistivity differential index F(R) of the alkaline formation;

[0086] Step S3: Construct a cross plot for lithology identification based on the lithology index F(T) and resistivity differential index F(R) of the alkaline formation to obtain the well logging lithology identification model for the alkaline formation.

[0087] In some optional embodiments, step S1 above can be implemented in the following ways:

[0088] Based on well logging data of alkaline formations, lithological analysis is performed on the alkaline formations to extract response characteristics. The lithological analysis includes at least one of electrical properties, radiometric properties, and logging characteristics. Alkaline formations possess various lithological characteristics; the analysis includes at least one of electrical properties, radiometric properties, and logging characteristics. Other optional characteristics, such as acoustic properties, may also be included in the lithological analysis of alkaline formations. Response characteristics include at least one of the logging response characteristics of wellbore diameter, wellbore neutron, flushed zone resistivity, deep resistivity, density, and natural gamma.

[0089] There are various logging response characteristics for lithology, and each logging response characteristic corresponds to a logging curve. Among these various logging response characteristics, the logging curves corresponding to well diameter, wellbore neutron, sonic transit time, flushed zone resistivity, deep resistivity, and density show the most significant changes in alkaline formations. Therefore, at least one of the logging response characteristics of well diameter, wellbore neutron, flushed zone resistivity, deep resistivity, density, and natural gamma should be extracted. Of course, other logging response characteristics can also be extracted, such as the logging response characteristic of sonic transit time.

[0090] In some optional embodiments, step S2 above, such as Figure 2 As shown, this can be achieved through the following steps:

[0091] Step S21: Based on the wellbore neutron logging data and density logging data included in the well logging response characteristics, determine the reservoir factor F(Q) characterizing the reservoir properties:

[0092]

[0093] In the above formula, CNL represents the compensated neutron logging value, and DEN represents the density logging value;

[0094] The F(Q) values ​​of dolomitic mudstone, dolomitic siltstone, and dolomitic fine sandstone in alkali-bearing strata gradually increase. This can be explained by specific experimental data: the compensated neutron logging value for the soda ash layer is above 35, and the density logging value is 2.08 (g / cm³). 3 For example, the reservoir factor F(Q) of soda ash reservoirs is above 16.826. Similarly, the compensated neutron logging value for pure sandstone reservoirs is around 2-4, and the density logging value is 2.65 g / cm³. 3 The reservoir factor F(Q) of pure sandstone is around 0.754 to 1.509. Based on this, the reservoir factor F(Q) of alkaline formations is obtained by calculating the quotient of the compensated neutron logging value and the density logging value. In this embodiment, the wellbore neutron logging data used is the compensated neutron logging value.

[0095] Step S22: Based on the wellbore logging data and natural gamma logging data included in the well logging response characteristics, determine the clay factor F(S) characterizing the grain size and clay content:

[0096]

[0097] In the above formula, CALI represents the caliber logging value, and GR represents the natural gamma logging value;

[0098] The soda ash layer exhibits severe wellbore enlargement, extremely low natural gamma ray values, and high clay content. In contrast, the mudstone layer shows little wellbore variation, extremely high natural gamma ray values, and relatively low clay content compared to the soda ash layer. The sandstone layer falls between the mudstone and soda ash layers in terms of wellbore variation, natural gamma ray values, and clay content. The clay factor F(S) of the soda ash, sandstone, and mudstone layers gradually decreases. Based on this, the clay factor F(S) of the alkaline formation is obtained by calculating the quotient of the wellbore logging value and the natural gamma ray logging value.

[0099] Step S23: Determine the lithology index F(T) of the alkali-bearing strata based on the reservoir factor F(Q) and the clay factor F(S):

[0100] F(T) = F(Q) × F(S); (3)

[0101] The reservoir factor F(Q) and argillaceous factor F(S) of alkali-bearing strata can effectively distinguish lithology. The inventors discovered that for rocks of the same lithology in alkali-bearing strata, the lithology index F(T) varies very little. F(T) is only related to the physical properties and lithology of the rock, and is unrelated to the fluids within the rock. A specific experiment can illustrate this: core samples of the same lithology from different wells were selected, and the F(Q) and F(S) of each core sample were obtained. The first core sample had F(Q1) = 15 and F(S1) = 2. The second core sample had F(Q1) = 2 and F(S1) = 15. Q2) = 12, F(S2) = 2.5, F(Q3) = 10, F(S3) = 3, F(Q4) = 6, F(S4) = 5. The lithology index F(T) for each core is calculated as follows: F(T1) = F(T2) = F(T3) = F(T4) = 30. This reveals that the lithology index F(T) of alkaline strata cores of the same lithology remains essentially unchanged. There is an approximately inverse relationship between the reservoir factor F(Q) and the clay factor F(S), as shown below:

[0102] F(Q1)×F(S1)=F(Q2)×F(S2)=……=F(Q n )×F(S n )=F(T n ).

[0103] Step S24: Based on the resistivity logging data of the flushed zone and the deep resistivity logging data included in the logging response characteristics, determine the resistivity differential index F(R):

[0104]

[0105] In the above formula, RT represents the deep resistivity logging value, and RXO represents the flushed zone resistivity logging value.

[0106] The resistivity of the deep soda ash layer is significantly increased, while the resistivity of the flushed zone is low. The resistivity of both the deep and flushed zones of the mudstone layer is low. This difference between the deep resistivity and the flushed zone resistivity can reflect the alkali content. Based on this, the resistivity differential index F(R) is constructed by calculating the quotient of the deep resistivity logging value and the flushed zone resistivity logging value. The higher the F(R) value of the alkali-containing formation, the higher the alkali content.

[0107] In some optional embodiments, step S3 above, such as Figure 3 As shown, this can be achieved in the following way:

[0108] Step S31: Take the logarithm of the lithological index F(T) and resistivity differential index F(R) of the alkaline strata, respectively;

[0109] Step S32: Establish a linear relationship between the lithological index F(T) and the resistivity differential index F(R) of the alkali-bearing strata in a double logarithmic coordinate system:

[0110] F(T) = a × F(R) b ;

[0111] Where 'a' represents the first lithology coefficient, which varies between 0.4 and 0.6.

[0112] b represents the second lithology coefficient, which varies between 0.2 and 0.3;

[0113] In a double logarithmic coordinate system, the lithological index F(T) of alkali-bearing strata is positively correlated with the resistivity difference index F(R). The higher the resistivity difference index F(R) of alkali-bearing strata, the higher the lithological index F(T).

[0114] Step S33: Based on the linear relationship, establish a cross plot for lithology identification to obtain a well logging lithology identification model for alkaline formations.

[0115] See Figure 4 The cross-sectional diagrams shown indicate that, through experiments, the inventors discovered that: the resistivity differential index F(R) of the soda ash layer is above 600, and its lithology index F(T) is between 1 and 10; the resistivity differential index F(R) of the alkali-bearing fine sandstone is between 10 and 1000, and its lithology index F(T) is between 0.6 and 4; the resistivity differential index F(R) of the dolomitic fine sandstone is between 2.4 and 20, and its lithology index F(T) is between 0.5 and 1.4; the resistivity differential index F(R) of the dolomitic siltstone is between 1.75 and 2.6, and its lithology index F(T) is between 0.4 and 1.3; and the resistivity differential index F(R) of the dolomitic mudstone is between 1 and 2, and its lithology index F(T) is between 0.3 and 1.1.

[0116] The boundaries of the intersection chart are determined by the results of core observation and identification of core casting thin sections. Combined with the lithological identification statistics of rock cuttings and the analysis and comparison with well logging data, an accurate intersection chart for identifying the lithology of alkaline strata is determined.

[0117] The lithology index F(T) and resistivity differential index F(R) of alkali-bearing strata cannot effectively distinguish alkali layers, alkali-bearing reservoirs, and mudstone layers when used alone. However, the cross plot has greatly enhanced the ability to distinguish lithology, and can effectively identify soda ash layers, alkali-bearing fine sandstone, dolomitic fine sandstone, dolomitic siltstone, dolomitic mudstone, etc., so as to achieve quantitative identification of different lithologies of alkali-bearing strata.

[0118] In some optional embodiments, the above method further includes: standardizing and core repositioning the logging data of alkaline formations, which can be achieved, for example, in the following ways:

[0119] When standardizing logging data of alkaline formations, the logging curves of each well in the logging data of alkaline formations are corrected according to the selected standard layers and standard wells to obtain standardized logging data.

[0120] Because the logging response characteristics of soda ash and mudstone layers differ significantly from those of reservoirs, making them easily distinguishable, and because they are widely distributed within alkaline formations, a dual-standard-layer standardization method is employed. This method controls appropriate correction amounts to standardize logging data from alkaline formations. For example, a set of mudstone layers and a set of limestone layers are selected as standard layers. Standard layers are generally chosen from widely developed stratigraphic positions and can be selected according to pre-defined rules. For instance, these rules could be: 1. Widely distributed throughout the structural region, with significant thickness and minimal variation; 2. Stable or essentially stable lithology of the stratigraphic position; 3. Obvious corresponding characteristics on the logging curves.

[0121] Because different wells have different logging sequences, the logging values ​​for the same mudstone formation, such as resistivity and density, obtained from different wells will also differ. Therefore, it is necessary to select the well with the most complete logging series, the most curves, and the best logging results as the standard well. The measurement data of other wells are adjusted based on the measurement data of the standard well to achieve a more consistent result. The standard well can be selected according to preset rules. For example, the preset rules may be: 1. It has relatively systematic drilling and coring data; 2. It has strong regional representativeness in terms of structure and lithology; 3. The logging series is complete, accurate, and comprehensive, and the standard well plays a calibration role.

[0122] Standardization methods can use the frequency histogram method, which is more intuitive and more efficient for data types. For example, the soda ash layer has the characteristics of low natural gamma and high compensated neutrons, while the mudstone layer has the characteristics of high natural gamma and low compensated neutrons. Based on the histogram, the high-frequency values ​​of natural gamma and compensated neutrons in the soda ash layer and mudstone layer are selected as representative values. Of course, other methods can also be selected, such as the trend surface method. This invention does not limit this.

[0123] When performing core repositioning processing on logging data of alkaline formations, the core location data in the core sampling data is corrected according to the core range shown in the logging curve to obtain the logging data with core repositioning processing.

[0124] Core repositioning involves using large-scale micro-lateral logging, natural gamma ray logging, or other relevant logging curves to correct the core sequence, depth, and thickness based on characteristics such as core length, wear, recovery rate, and lithology. This ensures consistency in lithology and electrical properties, guaranteeing accuracy for subsequent operations. Specifically, core repositioning can involve adjusting the precise location of the core at depth. The depth indicated during core sampling is adjusted based on the range of the core shown on the logging curve. For example, if a core is taken at depth A, with the core marker at 4990 meters, but the logging curve at that location shows mudstone only detected at 5000 meters, the depth indicated by the core marker and the logging curve are inconsistent. Therefore, the core marker position is adjusted based on the lithological variation curve characteristics in the logging curve to ensure more accurate positioning.

[0125] Example 2

[0126] Embodiment 2 of the present invention provides a method for identifying the lithology of alkali-bearing strata, such as... Figure 5 As shown, it includes the following steps:

[0127] Step S51: Invert the 3D seismic data volume to obtain the lithology index F(T) inversion data volume and the resistivity differential index F(R) inversion data volume of different lithologies in the alkali-bearing strata;

[0128] Step S52: Constrain the lithology index F(T) inversion data volume and resistivity differential index F(R) inversion data volume based on the pre-established cross plot to obtain the effective inversion data volume; the cross plot is constructed using the lithology identification model construction method for alkali-bearing formation logging described above;

[0129] Step S53: Based on the effective inversion data volume, obtain the fused data volume of different lithologies in the alkali-bearing strata;

[0130] The fusion of data volumes of different lithologies in alkali-bearing strata can be achieved through mathematical operations that multiply the effective inversion data volumes.

[0131] Step S54: Constrain the fused data volume using the top and bottom boundaries of the target strata interpreted by the seismic analysis to obtain a planar predicted distribution map of different lithologies in the alkali-bearing strata.

[0132] The constraints on the fused data volume can be obtained by using mathematical operations such as root mean square, average, maximum and summation to derive a plane prediction distribution map of different lithologies in alkali-bearing strata. Of course, other mathematical operations can also be used, and this invention does not limit them.

[0133] To illustrate the above-mentioned method for constructing a well logging lithology identification model and a well logging lithology identification method for alkaline formations, a specific example is provided. The detailed implementation process of the well logging lithology identification method for alkaline formations is as follows:

[0134] 1. Based on the standard layer logging data of the standard well, the logging data of well A01 was standardized: the correction amount for the density logging value of well A01 was 0.2 (g / cm³). 3 The correction value for neutron logging is around 3%.

[0135] 2. The core repositioning depth is 5373.7~5390.81m. After sorting out the detailed core profile data of the cored section, the core depth of the cored section is moved by +3.11m.

[0136] 3. Lithological analysis was conducted on the electrical, radiological, and logging characteristics of alkaline formations. The logging response characteristics of well diameter, wellbore neutrons, density, flushing zone resistivity, deep resistivity, and natural gamma were extracted, and the corresponding logging curves were obtained.

[0137] 4. Calculate the quotient of the neutron logging value and the density logging value in the wellbore to construct the reservoir factor F(Q);

[0138] 5. Calculate the quotient of the wellbore logging value and the natural gamma logging value to construct the clay factor F(S);

[0139] 6. Selecting alkaline strata cores of the same lithology at different depths, although the logging values ​​corresponding to each core are different, the lithology index F(T) corresponding to each core remains basically unchanged. According to calculation, the lithology index F(T) of the soda ash layer is around 10.

[0140] 7. Calculate the quotient of the deep resistivity logging value and the flushing zone resistivity logging value, and construct the resistivity differential index F(R);

[0141] 8. Take the logarithm of F(T) and F(R) respectively. Utilize the linear relationship between F(T) and F(R) in a double logarithmic coordinate system. Through the intersection of logging data at different depths in well A01, obtain the first lithology coefficient a = 0.469 and the second lithology coefficient b = 0.254. Calculate the regression coefficient R. 2=0.8013, proving that the accuracy of the first lithology coefficient a and the second lithology coefficient b is high. Figure 4 As shown, the cross plot of lithology index and resistivity differential index of well A01 is obtained;

[0142] 9. Invert the 3D seismic data volume to obtain the lithological index F(T) inversion data volume and resistivity differential index F(R) inversion data volume of the predicted alkali-bearing strata with different lithologies;

[0143] 10. Constrain the obtained inversion data volume by using the range of resistivity differential index F(R) and lithology index F(T) of different lithologies of alkali-bearing strata determined by the pre-obtained cross plot, and obtain the effective inversion data volume for predicting different lithologies of alkali-bearing strata.

[0144] 11. Perform a mathematical operation by multiplying the effective inversion data volumes of different lithologies of the predicted alkali-bearing strata to obtain a fused data volume of different lithologies of the predicted alkali-bearing strata.

[0145] 12. Using the top and bottom boundaries of the target strata interpreted by the seismic analysis, the fused data volume of different lithologies of the predicted alkali-bearing strata is constrained. Using mathematical operations such as root mean square, average, maximum, and summation, a plane prediction distribution map of different lithologies of the alkali-bearing strata is calculated.

[0146] Based on the same inventive concept, embodiments of the present invention also provide a device for constructing a lithological identification model for alkali-bearing strata, the structure of which is as follows: Figure 6 As shown, it includes:

[0147] The response feature acquisition unit 61 is used to extract a well logging response feature set based on well logging data of alkaline formations. The well logging response feature set includes at least one well logging response feature and its corresponding well logging curve.

[0148] Sensitive parameter unit 62 is constructed to construct the lithological index F(T) and resistivity differential index F(R) of alkaline formations based on the well logging response feature set;

[0149] Model unit 63 is constructed to build a cross plot for lithology identification based on the lithology index F(T) and resistivity differential index F(R) of the alkaline formation, thus obtaining a well logging lithology identification model for the alkaline formation.

[0150] In some optional embodiments, the above-mentioned well logging lithology identification model construction device for alkaline formations further includes:

[0151] Data processing unit 64 is used for standardization and core repositioning of logging data from alkaline formations.

[0152] Based on the same inventive concept, this invention also provides a well logging lithology identification device for alkaline formations, the structure of which is as follows: Figure 7 As shown, it includes:

[0153] The first acquisition unit 71 is used to invert the three-dimensional seismic data volume to obtain the lithology index F(T) inversion data volume and the resistivity differential index F(R) inversion data volume of different lithologies of alkaline strata;

[0154] Constraint data unit 72 is used to constrain the lithology index F(T) inversion data volume and resistivity differential index F(R) inversion data volume based on a pre-established cross plot to obtain an effective inversion data volume; the cross plot is constructed using the lithology identification model construction device for alkaline formation logging as described above;

[0155] The second acquisition unit 73 is used to obtain a fused data volume of different lithologies of alkaline strata based on the effective inversion data volume;

[0156] The lithology distribution prediction unit 74 is used to constrain the fused data volume using the top and bottom boundaries of the target strata interpreted by seismic interpretation, and to obtain a planar predicted distribution map of different lithologies in alkali-bearing strata.

[0157] The lithology identification chart was constructed using the well logging lithology identification model construction device for lacustrine alkaline formations as described above.

[0158] This invention provides a computer storage medium, comprising: computer executable instructions stored in the computer storage medium, wherein when the computer executable instructions are executed by a processor, the method for constructing a well logging lithology identification model for alkaline formations as described above.

[0159] This invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for constructing a well logging lithology identification model for alkaline formations as described above.

[0160] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0161] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0162] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0163] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0164] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0165] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0166] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for constructing a lithological identification model for alkaline strata, characterized in that, include: Based on logging data of alkaline formations, a logging response feature set is extracted, which includes at least one logging response feature and its corresponding logging curve. Based on the well logging response feature set, the lithological index F(T) and resistivity differential index F(R) of alkaline formations are constructed. Based on the lithological index F(T) and resistivity differential index F(R) of the alkaline formation, a cross plot for lithological identification is constructed, and a well logging lithological identification model for the alkaline formation is obtained. The lithological index F(T) and resistivity differential index F(R) of alkali-bearing strata were constructed, including: Based on the wellbore neutron logging data and density logging data included in the well logging response characteristics, the reservoir factor F(Q), which characterizes reservoir properties, is determined: ； In the above formula, CNL represents the compensated neutron logging value, and DEN represents the density logging value; Based on the well logging response characteristics including caliper logging data and natural gamma logging data, the clay factor F(S), which characterizes the grain size and clay content, is determined: ； In the above formula, CALI represents the caliber logging value, and GR represents the natural gamma logging value; Based on the reservoir factor F(Q) and the clay factor F(S), the lithology index F(T) of the alkaline strata is determined: ； Based on the resistivity logging data of the flushed zone and the deep resistivity logging data included in the logging response characteristics, the resistivity differential index F(R) is determined: ； In the above formula, RT represents the deep resistivity logging value, and RXO represents the flushed zone resistivity logging value.

2. The method as described in claim 1, characterized in that, The logging response feature set extracted from the logging data based on alkaline formations includes: Based on well logging data of alkaline formations, lithological analysis of the alkaline formations is conducted to extract response characteristics; The lithological analysis includes at least one of electrical properties, radiometric properties, and logging properties; The response characteristics include at least one of the logging response characteristics of well diameter, wellbore neutron, flush zone resistivity, deep resistivity, density, and natural gamma.

3. The method as described in claim 1, characterized in that, The method involves constructing a cross-plot for lithology identification based on the lithology index F(T) and resistivity differential index F(R) of the alkaline formation, resulting in a well logging lithology identification model for the alkaline formation, including: Take the logarithm of the lithological index F(T) and resistivity differential index F(R) of the alkaline strata, respectively; In a double logarithmic coordinate system, a linear relationship is established between the lithological index F(T) and the resistivity differential index F(R) of the alkali-bearing strata: ； Where 'a' represents the first lithology coefficient, which varies between 0.4 and 0.

6. b represents the second lithology coefficient, which varies between 0.2 and 0.3; Based on the linear relationship, a cross plot is established for lithology identification, resulting in a well logging lithology identification model for alkaline formations.

4. The method according to any one of claims 1-3, characterized in that: The alkaline strata include at least one of the following: soda ash layer, alkaline fine sandstone, dolomitic fine sandstone, dolomitic siltstone, and dolomitic mudstone.

5. The method as described in claim 1, characterized in that, It also includes standardization processing of logging data from alkaline formations and core repositioning processing.

6. The method as described in claim 5, characterized in that, The logging data from the alkaline formation were standardized and core repositioning was performed, including: Based on the selected standard layers and standard wells, the logging curves of each well in the logging data of alkaline formations are corrected to obtain standardized logging data. Based on the core range shown in the logging curve, the core location data in the core sampling data is corrected to obtain logging data with core repositioning.

7. A method for identifying the lithology of alkali-bearing strata, characterized in that, include: Inversion of the three-dimensional seismic data volume yields lithological index F(T) inversion data volume and resistivity differential index F(R) inversion data volume for different lithologies of alkali-bearing strata; Constraints are applied to the lithology index F(T) inversion data volume and the resistivity differential index F(R) inversion data volume based on a pre-established cross plot to obtain an effective inversion data volume; the cross plot is constructed using the lithology identification model construction method for alkali-bearing formation logging as described in any one of claims 1-6; Based on the effective inversion data volume, a fused data volume of different lithologies of alkali-bearing strata is obtained; By constraining the fused data volume using the top and bottom boundaries of the target strata interpreted by seismic analysis, a planar predicted distribution map of different lithologies in alkali-bearing strata is obtained.

8. A device for constructing a lithological identification model for alkaline strata, characterized in that, include: A response feature acquisition unit is used to extract a well logging response feature set based on well logging data of alkaline formations. The well logging response feature set includes at least one well logging response feature and its corresponding well logging curve. Sensitive parameter units are constructed to generate lithological indices F(T) and resistivity differential indices F(R) for alkaline formations based on well logging response feature sets. A model unit is constructed to build a cross plot for lithology identification based on the lithology index F(T) and resistivity differential index F(R) of the alkaline formation, thereby obtaining a well logging lithology identification model for the alkaline formation. The construction of the lithological index F(T) and resistivity differential index F(R) for alkaline strata includes: Based on the wellbore neutron logging data and density logging data included in the well logging response characteristics, the reservoir factor F(Q), which characterizes reservoir properties, is determined: ； In the above formula, CNL represents the compensated neutron logging value, and DEN represents the density logging value; Based on the well logging response characteristics including caliper logging data and natural gamma logging data, the clay factor F(S), which characterizes the grain size and clay content, is determined: ； In the above formula, CALI represents the caliber logging value, and GR represents the natural gamma logging value; Based on the reservoir factor F(Q) and the clay factor F(S), the lithology index F(T) of the alkaline strata is determined: ； Based on the resistivity logging data of the flushed zone and the deep resistivity logging data included in the logging response characteristics, the resistivity differential index F(R) is determined: ； In the above formula, RT represents the deep resistivity logging value, and RXO represents the flushed zone resistivity logging value.

9. The apparatus as claimed in claim 8, characterized in that, Also includes; The data processing unit is used to standardize and reposition logging data from alkaline formations.

10. A well logging lithology identification device for alkaline formations, characterized in that, include: The first acquisition unit is used to invert the three-dimensional seismic data volume to obtain the lithological index F(T) inversion data volume and the resistivity differential index F(R) inversion data volume of different lithologies of alkaline strata; The constrained data unit is used to constrain the lithology index F(T) inversion data volume and the resistivity differential index F(R) inversion data volume based on a pre-established cross plot, so as to obtain an effective inversion data volume; the cross plot is constructed using the well logging lithology identification model construction device for alkaline formations as described in any one of claims 8 or 9; The second acquisition unit is used to obtain a fused data body of different lithologies of alkali-bearing strata based on the effective inversion data body; The lithology distribution prediction unit is used to constrain the fused data volume using the top and bottom boundaries of the target strata interpreted by seismic analysis, and to obtain a planar predicted distribution map of different lithologies in alkali-bearing strata. The lithology identification chart is constructed using the lithology identification model construction device for alkaline formation logging as described in any one of claims 8-9.

11. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the method for constructing a well logging lithology identification model for alkaline formations as described in any one of claims 1-6 and / or the method for identifying well logging lithology for alkaline formations as described in claim 7.

12. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method for constructing a well logging lithology identification model for alkaline formations as described in any one of claims 1-6 and / or the method for identifying lithology in well logging of alkaline formations as described in claim 7.

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