Method and system for converting logging element content and mineral content based on XRF

Abstract

The invention belongs to the field of petroleum and natural gas engineering, particularly relates to a method and a system for converting XRF logging elements and mineral content, and aims to solve the problems of long time for judging the mineral content by XRD and low cost performance. The method comprises the steps that XRF logging element and XRD mineral content detection is conducted on a coring sample according to the well depth, and XRF logging element detection data and XRD mineral content detection data are obtained; classifying minerals corresponding to the coring samples to obtain mineral types, extracting elements of the mineral types, and screening the elements to obtain characteristic elements; performing linear regression analysis on the XRF logging element detection data corresponding to the characteristic elements and the XRD mineral content detection data corresponding to the characteristic elements to obtain mineral content models of different mineral types; and substituting XRF logging element data of any well depth into the model to obtain the mineral type content of the well depth. According to the method, the mineral type content can be judged according to the XRF logging elements.

Description

Background Art

[0002] Accurately identifying formation lithology is the basic work in the mud logging industry, and it also provides a basis for correctly selecting construction parameters for safe and rapid drilling. Due to the different and complex types of rocks in different regions, and the mixed composition of cuttings returned to the surface with drilling fluid, under the current conditions of rapid drilling, the cuttings are extremely fine or even powdery, increasing the difficulty of cuttings identification. Due to the differences in the experience of on-site personnel, it may even cause misjudgment of formation lithology.

[0003] Traditional lithology identification is divided into two methods: manual physical observation and microscopic identification. Among them, manual observation is the first-hand information on-site, but it requires high geological experience of the naming personnel. Coupled with the fact that the cuttings caused by rapid drilling are extremely fine or even powdery, due to the simple means, the identification relies on human judgment, there are differences in the experience of on-site personnel, and there is a lack of quantitative judgment criteria, which may even cause misjudgment of lithology. Microscopic observation qualitatively describes formation lithology through the composition of cuttings. Although it is relatively accurate, the process is complex, with high requirements for personnel and equipment, strong subjectivity, long identification cycle, and limited on-site application. Therefore, the traditional method can no longer meet the need for rapid lithology identification in the mud logging field. Currently, there are two methods in the mud logging industry for identifying lithology using X-rays, namely XRD and XRF. Among them, due to the long analysis time and high price of XRD, it has been gradually replaced by XRF logging elements in many fields. However, the mineral composition of XRF logging elements is the most important basis for rock naming. Through the comparison of experimental data of XRD mineral composition and XRF logging element analysis content of core samples at the same depth, it is found that different minerals have a good correspondence with the elements they contain. However, the calculated contents of sandy, muddy, calcareous, and dolomitic substances are different, and the correspondence is not strong, which affects the calculation of mineral content. Summary of the Invention

[0001] In order to solve the above problems in the prior art, that is, the problem of experimental data of XRD mineral composition and XRF logging element analysis content, the present invention provides a method for converting XRF logging elements and mineral content, and this method includes the following steps:

[0002] S100, obtaining core samples of mud logging; detecting XRF logging elements and XRD mineral content of the core samples according to well depth, and obtaining XRF logging element detection data and XRD mineral content detection data corresponding to the well depth of the mud logging;

[0003] S200, based on the XRD mineral content detection data, classifying the minerals corresponding to the core samples to obtain mineral types; extracting elements from the well depth formation corresponding to the mineral types, screening the XRF logging element detection data according to the results of element extraction, and taking the remaining elements after screening as characteristic elements;

[0004] S300, perform a linear regression analysis on the XRF logging element detection data corresponding to the characteristic elements and the XRD mineral content detection data corresponding to the characteristic elements to obtain a mineral content model for different mineral types;

[0005] S400, substitute the XRF logging element detection data corresponding to the characteristic elements at any well depth of the logging into the mineral content model of the corresponding mineral type, and the mineral type content at the corresponding well depth can be obtained.

[0006] In a preferred embodiment, the method for screening characteristic elements is as follows:

[0007] Based on the XRD mineral content detection data, obtain the minerals corresponding to the core samples; classify the minerals corresponding to the core samples to obtain mineral types; the mineral types include sandy, muddy, calcareous, dolomitic, ferruginous and other types of minerals;

[0008] Study the composition and molecular formula of the mineral types according to the corresponding well depth strata to determine the elemental composition of the mineral types;

[0009] Elements that are present in both the mineral types and the XRF logging element detection data, and whose content in the mineral types is greater than the first threshold, are screened as characteristic elements.

[0010] In a preferred embodiment, classifying the minerals corresponding to the core samples to obtain mineral types specifically includes: classifying quartz and feldspar minerals as sandy; classifying clay minerals as muddy; classifying calcite minerals as calcareous; classifying dolomite minerals and ankerite minerals as dolomitic; classifying pyrite, ilmenite and magnetite as ferruginous; classifying anhydrite, glauberite, analcime, laumontite and fluorite as other.

[0011] In a preferred embodiment, the characteristic elements include: Si, Na, Al, K, Ca, Mg, Fe, S, P, Ti, Mn, Cl.

[0012] The specific method for obtaining the mineral model is as follows:

[0013] Calculate the Pearson correlation coefficients between each mineral type and the characteristic elements respectively through the Pearson correlation coefficient calculation function of SPSS;

[0014] If the Pearson correlation coefficients between each mineral type and the characteristic elements are all greater than the second threshold, then taking the characteristic elements as independent variables and taking sandy, muddy, calcareous, dolomitic, ferruginous and other types of mineral types as dependent variables respectively, perform linear regression analysis on the independent variable and each dependent variable respectively to obtain the mineral content models of different mineral types; otherwise, return to step S200 to re-screen the characteristic elements.

[0015] In a preferred embodiment, the mineral content model of any one mineral type is:

[0016] A certain mineral type = a*Na + b*Mg - c*Al + d*Si + e*S - f*Ca - g*Fe + h*Ti + i*Mn - j*K + k*Cl - l*P + m;

[0001] Wherein, a certain mineral type can be any one of the classification results of sandy, muddy, calcareous, dolomitic, ferruginous and other types of minerals; a, b, c, d, e, f, g, h, i, j, k, l, m are all coefficients obtained through linear regression analysis;

[0002] In the second aspect of the present invention, a system for converting XRF logging elements and mineral content is proposed. The system includes:

[0003] A data acquisition module, configured to acquire a cored sample of logging; perform XRF logging element and XRD mineral content detection on the cored sample according to the well depth to obtain the XRF logging element detection data and XRD mineral content detection data corresponding to the well depth of the logging;

[0004] An element screening module, configured to classify the minerals corresponding to the cored samples based on the XRD mineral content detection data to obtain mineral types; extract elements from the well depth formation corresponding to the mineral types, and screen the XRF logging element detection data according to the results of the element extraction, and use the remaining elements after screening as characteristic elements;

[0005] A model determination module, which performs linear regression analysis on the XRF logging element detection data corresponding to the characteristic elements and the XRD mineral content detection data corresponding to the characteristic elements to obtain the mineral content models of different mineral types;

[0006] A content determination module, which substitutes the XRF logging element detection data corresponding to the characteristic elements at any well depth of the logging into the mineral content model of the corresponding mineral type to obtain the mineral type content corresponding to the well depth.

[0007] In the third aspect of the present invention, an electronic device is proposed, including:

[0008] At least one processor; and

[0009] A memory communicatively connected to at least one of the processors; wherein

[0010] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned method for converting XRF logging elements and mineral content.

[0011] In a fourth aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned method for converting XRF logging elements and mineral content.

[0012] Advantages of the present invention:

[0013] (1) By statistically analyzing a large amount of XRF logging element analysis data of well logging, summarizing the logging and logging response characteristics of drilled wells, and combining the corresponding relationship between measured XRF logging elements and XRD minerals, the present invention applies the SPPS linear regression method to establish a method for converting XRF logging elements and mineral species content;

[0014] (2) The present invention gives full play to the role of XRF logging elements in reservoir identification, and the present invention proposes a method for converting XRF logging elements and mineral content.

[0015] (3) The present invention can identify the mineral species content in a timely, accurate and quantitative manner, and solve the thorny problems of lithology identification while drilling, such as the mixing of wellbore cuttings and poor representativeness of logging analysis samples. Description of the Drawings

[0016] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects and advantages of the present application will become more apparent:

[0017] Figure 1 is a method for converting XRF logging elements and mineral content according to an embodiment of the present invention;

[0018] Figure 2 is a comprehensive XRF logging element diagram of well logging according to an embodiment of the present invention;

[0019] Figure 3 is a schematic structural diagram of a computer system of a server for implementing the method, system and device embodiments of the present application. Detailed Embodiments

[0020] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention, rather than limiting the invention. Additionally, it should be noted that for the convenience of description, only the parts related to the relevant invention are shown in the drawings.

[0021] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and embodiments.

[0022] The present invention provides a method for converting XRF logging elements and mineral contents, as Figure 1 shown, for calculating the mineral contents at different well depths of logging, and further identifying the lithology of the logging formation according to the mineral contents. The method includes the following steps:

[0023] S100, obtaining a core sample of the logging; detecting the XRF logging elements and XRD mineral contents of the core sample according to the well depth, and obtaining the XRF logging element detection data and XRD mineral content detection data corresponding to the well depth of the logging;

[0024] S200, based on the XRD mineral content detection data, classifying the minerals corresponding to the core sample to obtain mineral types; extracting elements from the well depth formation corresponding to the mineral types, screening the XRF logging element detection data according to the results of the element extraction, and taking the remaining elements after screening as characteristic elements;

[0025] S300, performing a linear regression analysis on the XRF logging element detection data corresponding to the characteristic elements and the XRD mineral content detection data corresponding to the characteristic elements to obtain a mineral content model for different mineral types;

[0026] S400, substituting the XRF logging element detection data corresponding to the characteristic elements at any well depth of the logging into the mineral content model of the corresponding mineral type, and the mineral type content corresponding to the well depth can be obtained.

[0027] The XRF logging element logging technology can continuously and quantitatively detect the information of element contents in underground rock samples. Especially under modern rapid drilling conditions such as PDC drilling and air drilling, the morphology of the cuttings samples collected is more fragmented and powdered. The XRF logging element logging has the advantages of fast analysis speed, lower requirements for samples, and being not limited by the sample shape, reducing problems such as inaccurate and non-quantitative manual identification, and can quickly and real-time provide the detection results at the logging site, and has been widely used at present;

[0028] The earth's crust is composed of rocks, rocks are composed of minerals, and minerals are composed of collections of chemical elements. The content of elements varies greatly depending on the type of sediment. For example, certain sedimentary rocks are enriched in specific trace elements. When its chemical composition is relatively stable, it can be reflected by the corresponding elements, which is a prerequisite for converting elements into minerals.

[0029] X-ray fluorescence analysis uses primary X-ray photons or other microscopic particles to excite atoms in the substance to be measured, causing them to produce secondary characteristic X-ray fluorescence. The fluorescence X-rays of different elements have their own specific wavelengths. X-ray fluorescence is the composition of elements with determined wavelengths emitted by the analyzed sample under X-ray irradiation. X-ray fluorescence analysis technology (X Radial Fluorescence), abbreviated as XRF, is a new technology based on two mature theories: rock geochemistry and X-ray fluorescence analysis. It has the advantages of fast analysis speed and being not limited by the shape of the sample.

[0030] To more clearly illustrate the method for converting XRF logging elements and mineral content of the present invention, the following will detail each step in the embodiments of the present invention with reference to the accompanying drawings.

[0031] The method for converting XRF logging elements and mineral content in the first embodiment of the present invention includes steps S100 - S400, and each step is described in detail as follows:

[0032] S100, obtain the core samples of the logging; perform XRF logging element and XRD mineral content detection on the core samples according to the well depth, and obtain the XRF logging element detection data and XRD mineral content detection data corresponding to the well depth of the logging;

[0033] In this embodiment, the XRF logging element detection data and XRD mineral content detection data for a certain path are given, as shown in Table 1 and Table 2;

[0034] Table 1 Statistical data of XRF logging element analysis for logging Table 2 Statistical data of XRD mineral analysis for logging

[0035] S200, based on the XRD mineral content detection data, classify the minerals corresponding to the core samples to obtain the mineral types; extract elements from the well depth formation corresponding to the mineral types, and screen the XRF logging element detection data according to the results of the element extraction, and use the remaining elements after screening as characteristic elements;

[0036] In this embodiment, based on the XRD mineral content detection data, the minerals corresponding to the core samples are obtained; the minerals corresponding to the core samples are classified to obtain mineral types; the mineral types include sandy, muddy, calcareous, dolomitic, ferruginous and other types of minerals; classifying the minerals corresponding to the core samples to obtain mineral types specifically includes: quartz and feldspar minerals are classified as sandy; clay minerals are classified as muddy; calcite minerals are classified as calcareous; dolomite minerals and ankerite minerals are classified as dolomitic; pyrite, ilmenite and magnetite are classified as ferruginous; ferruginous; anhydrite, glauberite, analcime, laumontite and fluorite are classified as other.

[0037] Study the composition and molecular formula of the mineral types according to the corresponding well depth strata to determine the elemental composition of the mineral types;

[0038] Elements that are present in both the mineral types and the XRF logging element detection data, and whose content in the mineral types is greater than the first threshold, are selected as characteristic elements. In this embodiment, the characteristic elements include: Si, Na, Al, K, Ca, Mg, Fe, S, P, Ti, Mn, Cl.

[0039] Specifically, the first threshold is not specifically limited and is determined according to the specific application scenario. Among them, sandstones mainly composed of quartz and feldspar minerals have relatively high Si and Na element contents; mudstones mainly composed of clay minerals have relatively high Al and K element contents; limestones mainly composed of calcite minerals have relatively high Ca element contents; dolomitic rocks mainly composed of dolomite minerals have relatively high Mg element contents; lithologies mainly composed of iron minerals such as pyrite have relatively high Fe, S, and P element contents. The correspondence between minerals, mineral types and 12 characteristic elements is shown in Table 3: Table 3 Conversion correspondence table between XRD minerals and XRF logging elements

[0040] S300, perform linear regression analysis on the XRF logging element detection data corresponding to the characteristic elements and the XRD mineral content detection data corresponding to the characteristic elements to obtain mineral content models for different mineral types;

[0041] In this embodiment, the specific method for obtaining the mineral model is:

[0042] Calculate the Pearson correlation coefficients between each mineral type and the characteristic elements using the Pearson correlation coefficient calculation function in SPSS; by using the bivariate analysis function in the SPSS statistical analysis software and selecting "Pearson" in the correlation coefficients, the correlation coefficients can be obtained.

[0043] The Pearson correlation coefficients between XRD minerals and the preferred XRF characteristic elements have moderate - high correlations, which is a prerequisite for performing linear regression using SPSS. As shown in Table 4, a table of the Pearson correlation coefficients between XRD minerals and the preferred XRF characteristic elements is given: Table 4 - Corresponding Table of Pearson Correlations between XRD Mineral Types and XRF Characteristic Elements

[0044] If the Pearson correlation coefficients between each mineral type and the characteristic elements are all greater than the second threshold, then take the characteristic elements as the independent variable, and take sandy, muddy, calcareous, dolomitic, ferruginous, and other mineral types as the dependent variables respectively, and perform linear regression analysis on the independent variable and each dependent variable to obtain the mineral content models for different mineral types; otherwise, return to step S200 to re - screen the characteristic elements; the mineral content models for different mineral types can be implemented through the SPSS data statistical analysis software;

[0045] In this embodiment, the mineral content model for any one mineral type is:

[0046] A certain mineral type = a*Na + b*Mg - c*Al + d*Si + e*S - f*Ca - g*Fe + h*Ti + i*Mn - j*K + k*Cl - l*P + m;

[0047] Among them, a certain mineral classification result can be any one of the classification results of sandy, muddy, calcareous, dolomitic, ferruginous, and other mineral types; a, b, c, d, e, f, g, h, i, j, k, l, m are all coefficients obtained through linear regression analysis.

[0048] Specifically, based on the above data, 6 mineral content models for sandy, muddy, calcareous, dolomitic, ferruginous, and other are given for reference. The specific models are as follows:

[0049] Sandy = 0.044*Na + 5.232*Mg - 3.642*Al + 2.534*Si + 6.494*S - 4.668*Ca - 4.139*Fe + 56.158*Ti + 66.996*Mn - 7.324*K + 0.945*Cl - 6.699*P + 22.455;

[0050] Argillaceous = 2.539 * Na + 4.646 * Mg + 2.05 * Al - 0.978 * Si - 1.11 * Si - 0.585 * Ca - 0.299 * Fe - 19.632 * Ti + 14.422 * Mn + 1.721 * K + 104.362 * Cl + 44.579 * P + 26.825;

[0051] Calcareous = 0.745 * Na - 9.926 * Mg - 0.753 * Al - 0.186 * Si - 38.377 * P - 1.978 * Si - 21.664 * Cl + 9.667 * K + 4.515 * Ca - 23.218 * Ti + 2.034 * Fe - 30.044 * Mn - 1.405;

[0052] Dolomitized = -0.462 * Na + 7.024 * Mg - 0.868 * Al - 0.643 * Si + 13.469 * P - 1.815 * Si - 18.373 * Cl - 0.288 * K - 0.792 * Ca - 14.824 * Ti + 0.504 * Fe - 26.011 * Mn + 30.358;

[0053] Ferruginous = -1.314 * Na - 4.454 * Mg + 1.542 * Al - 0.175 * Si - 16.043 * P + 2.269 * Si - 72.633 * Cl - 0.438 * K + 1.287 * Ca + 9.583 * Ti + 0.57 * Fe - 0.422 * Mn - 2.185;

[0054] Others = 100 - Silty - Argillaceous - Calcareous - Dolomitized - Ferruginous;

[0055] S400, substituting the XRF logging element detection data of the characteristic elements at any well depth of the logging into the mineral content model of the corresponding mineral species, the mineral species content at the corresponding well depth can be obtained.

[0056] In this embodiment, a corresponding table of the mineral species calculated using the above model and the mineral species analyzed by XRD is given; as shown in Table 5: Table 5 - Corresponding Table of Mineral Species Before and After Calculation

[0057] In this embodiment, the lithology of the core description and naming in the cored interval of 1985.00 - 2022.55 m of the logging is sandstone, as shown in Figure 2 -(1). The average sand content calculated by the XRF logging elements before the present invention is 38%, as shown in Figure 2-(2), as shown in Table 5. The average sandy content calculated by XRD minerals is 66%, as Figure 2 -(4), as shown in Table 5. The average sandy content calculated by applying the model of the present invention is 67%, as Figure 2 -(3), as shown in Table 5. The average sandy content calculated by ECS formation element logging is 68%, as Figure 2 -(5). By applying a method for converting XRF logging elements and mineral content, the average sandy content calculated for the well section 1985.00 - 2022.55m is 67%, which is basically consistent with the sandy content calculated by logging and consistent with the core description. According to the national standard "Classification and Naming Scheme for Sedimentary Rocks" GB / T17412.2, the main mineral component content greater than 50% is used as the basic name of the rock, and the lithology is named sandstone. This fully verifies the reliability and feasibility of the present invention.

[0058] In summary, the content of the present invention is not limited to the above embodiments. Those skilled in the same field can easily propose other embodiments within the technical guiding ideology of the present invention, but such embodiments are all included within the scope of the present invention.

[0059] Although the various steps are described in the above order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reversed order, and these simple changes are all within the protection scope of the present invention.

[0060] A system for converting XRF logging elements and mineral content according to the second embodiment of the present invention, based on the above method for converting XRF logging elements and mineral content, the system includes:

[0061] A data acquisition module, configured to acquire the cored samples of logging; perform XRF logging element and XRD mineral content detection on the cored samples according to the well depth, and obtain the XRF logging element detection data and XRD mineral content detection data corresponding to the well depth of the logging;

[0062] An element screening module, configured to, based on the XRD mineral content detection data, classify the minerals corresponding to the cored samples to obtain mineral types; extract elements from the well depth formation corresponding to the mineral types, and screen the XRF logging element detection data according to the results of the element extraction, and use the remaining elements after screening as characteristic elements;

[0063] A model determination module, perform linear regression analysis on the XRF logging element detection data corresponding to the characteristic elements and the XRD mineral content detection data corresponding to the characteristic elements, and obtain the mineral content models of different mineral types;

[0064] The content determination module can obtain the mineral species content at the corresponding well depth by substituting the XRF logging element detection data corresponding to the characteristic elements at any well depth of the logging into the mineral content model of the corresponding mineral species.

[0065] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process and related explanations of the above-described system can refer to the corresponding process in the foregoing method embodiments, and will not be elaborated herein.

[0066] It should be noted that the system for converting XRF logging elements and mineral content provided in the above embodiments is only illustrated by dividing the above function modules. In actual applications, the above functions can be allocated to different function modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further split into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing each module or step, and are not regarded as an improper limitation of the present invention.

[0067] An electronic device according to the third embodiment of the present invention includes:

[0068] At least one processor; and

[0069] A memory communicatively connected to at least one of the processors; wherein,

[0070] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above method for converting XRF logging elements and mineral content.

[0071] A computer-readable storage medium according to the fourth embodiment of the present invention stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above method for converting XRF logging elements and mineral content.

[0072] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process and related explanations of the above-described storage device and processing device can refer to the corresponding process in the foregoing method embodiments, and will not be elaborated herein.

[0073] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field. For the sake of clearly illustrating the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in the form of electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0074] Reference is made below to Figure 3 , which shows a schematic structural diagram of a computer system of a server for implementing the method, system, and device embodiments of the present application. Figure 3 The server shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0075] As Figure 3 shown, the computer system includes a central processing unit (CPU, Central Processing Unit) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM, Read Only Memory) 602 or the program loaded from the storage section 608 into the random access memory (RAM, Random Access Memory) 603. In the RAM 603, various programs and data required for system operation are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. The input / output (I / O, Input / Output) interface 605 is also connected to the bus 604.

[0076] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as required. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 610 as required so that a computer program read therefrom is installed into the storage section 608 as required.

[0077] In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the methods of the present application are performed. It should be noted that the computer-readable medium described above in the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable medium can send, propagate, or transmit a program for use by or combined with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0078] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0080] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or represent a specific order or sequence.

[0081] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a series of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent in such process, method, article, or apparatus / device.

[0082] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A method for converting XRF logging elements and mineral contents, which is used to calculate the mineral contents at different well depths during logging, and further identify the lithology of the logging formation based on the mineral contents. It is characterized in that, The method includes the following steps: S100, obtaining a coring sample of mud logging; detecting XRF logging elements and XRD mineral contents of the coring sample according to well depth, and obtaining XRF logging element detection data and XRD mineral content detection data corresponding to the well depth of the mud logging. S200, classifying the minerals corresponding to the coring sample based on the XRD mineral content detection data to obtain mineral types; extracting elements from the mineral types according to the chemical molecular formulas of different mineral types, screening the XRF logging element detection data according to the results of element extraction, and taking the remaining elements after screening as characteristic elements. S300, performing linear regression analysis on the XRF logging element detection data corresponding to the characteristic elements and the XRD mineral content detection data corresponding to the characteristic elements to obtain mineral content models of different mineral types. S400, substituting the XRF logging element detection data corresponding to the characteristic elements at any well depth of the mud logging into the mineral content models of the corresponding mineral types, and the mineral type content corresponding to the well depth can be obtained.

2. The method for converting XRF logging elements and mineral content according to claim 1, characterized in that The method for screening characteristic elements is: Based on the XRD mineral content detection data, obtaining the minerals corresponding to the coring sample; classifying the minerals corresponding to the coring sample to obtain mineral types; the mineral types include sandy, muddy, calcareous, dolomitic, ferruginous and other types of minerals. Studying the composition and molecular formula of the mineral types according to the well depth formation corresponding to the mineral types to determine the element composition of the mineral types. Elements that are present in both the mineral types and the XRF logging element detection data, and whose content in the mineral types is greater than the first threshold, are screened as characteristic elements.

3. The method for converting XRF logging elements and mineral content according to claim 2, wherein Classifying the minerals corresponding to the coring sample to obtain mineral types, specifically: quartz and feldspar minerals are classified as sandy; clay minerals are classified as muddy; calcite minerals are classified as calcareous; dolomite minerals and ankerite minerals are classified as dolomitic. Pyrite, ilmenite and magnetite are classified as ferruginous; ferruginous; anhydrite, glauberite, analcime, laumontite and fluorite are classified as other.

4. The method for converting XRF logging elements and mineral contents according to claim 3, characterized in that The characteristic elements include: Si, Na, Al, K, Ca, Mg, Fe, S, P, Ti, Mn, Cl.

5. The method for converting XRF logging elements and mineral content according to claim 3, characterized in that, The specific method for obtaining the mineral model is: Calculating the Pearson correlation coefficients between each mineral type and the characteristic elements respectively through the Pearson correlation coefficient calculation function of SPSS. If the Pearson correlation coefficients between each mineral type and the characteristic elements are all greater than the second threshold, then taking the characteristic elements as independent variables, and taking sandy, muddy, calcareous, dolomitic, ferruginous and other types of mineral types as dependent variables respectively, performing linear regression analysis on the independent variable and each dependent variable respectively to obtain mineral content models of different mineral types; otherwise, return to step S200 to re-screen the characteristic elements.

6. The method for converting XRF logging elements and mineral content according to claim 5, characterized in that, The mineral content model of any one mineral type is: A certain mineral species = a*Na + b*Mg - c*Al + d*Si + e*S - f*Ca - g*Fe + h*Ti + i*Mn - j*K + k*Cl - l*P + m; Among them, the classification result of a certain mineral can be any one of the classification results of sandy, muddy, calcareous, dolomitic, ferruginous and other types of minerals; a, b, c, d, e, f, g, h, i, j, k, l, m are all coefficients obtained through linear regression analysis.

7. A system for converting XRF logging elements and mineral content, characterized in that, Based on the method for converting XRF logging elements and mineral content described in any one of the above claims 1-6, the system includes: A data acquisition module, configured to acquire the cored samples of logging; perform XRF logging element and XRD mineral content detection on the cored samples according to the well depth, and obtain the XRF logging element detection data and XRD mineral content detection data corresponding to the well depth of the logging; An element screening module, configured to, based on the XRD mineral content detection data, classify the minerals corresponding to the cored samples to obtain the mineral species; extract elements from the well-depth formation corresponding to the mineral species, and perform element screening on the XRF logging element detection data according to the results of the element extraction, and use the remaining elements after screening as characteristic elements; A model determination module, which performs linear regression analysis on the XRF logging element detection data corresponding to the characteristic elements and the XRD mineral content detection data corresponding to the characteristic elements to obtain a mineral content model for different mineral species; A content determination module, which brings the XRF logging element detection data corresponding to the characteristic elements at any well depth of the logging into the mineral content model of the corresponding mineral species, and the mineral species content corresponding to the well depth can be obtained.

8. An electronic device, characterized in that, Comprising: At least one processor; And A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the method for converting XRF logging elements and mineral content described in any one of claims 1-8.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the method for converting XRF logging elements and mineral content described in any one of claims 1-8.

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