Method and system for calculating reservoir porosity based on X-ray fluorescence logging technology
Through the multivariate linear regression analysis method based on X-ray fluorescence well recording technology, the problems of reservoir identification and porosity calculation are solved, and efficient reservoir physical property evaluation and accurate porosity calculation are achieved in the absence of well logging physical property parameters.
Patent Information
- Application Number
- CN202311793423.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-25
- Publication Date
- 2025-06-27
AI Technical Summary
The prior art is difficult to identify and calculate reservoir porosity in a timely, accurate and efficient manner, especially in the absence of logging properties parameters.
Using the method based on X-ray fluorescence well recording technology, the porosity of the reservoir is calculated by selecting sample wells, collecting core samples, performing elemental analysis, screening regional sensitive elements, and establishing a multivariate linear regression equation for effective porosity of the reservoir.
Timely, accurate and efficient reservoir physical properties evaluation under the conditions of lack of logging properties parameters, providing accurate porosity calculations, supporting drilling evaluation and cost savings.
Smart Images
Figure CN120211732A_ABST
Abstract
Description
Background Art
[0002] X-ray fluorescence element logging technology is a technology that can detect the mass percentage content of 36 elements from sodium to uranium in cores and cuttings. After obtaining continuous and quantitative element content data of the target well, the weight percentage content of the element can be calculated. In recent years, good application effects have been achieved in major oil and gas fields across the country. Currently, element logging technology is mainly applied to the identification and naming of special lithologies, horizon division, and auxiliary horizontal well geological steering, but there is less research on the relationship between element logging technology and reservoir pore and permeability conditions. According to rock and mineral data, the factors affecting reservoir physical properties are the particle size and arrangement of particles, and the content of matrix and cement. A large number of geochemical experiments and research results show that the chemical composition of rocks is related to the content and particle size of rock particles, and the content and types of matrix and cement. Therefore, element logging can qualitatively evaluate the physical properties of reservoirs.
[0003] Reservoir physical property evaluation has always been the focus and difficulty of logging oil and gas layer interpretation and evaluation. Currently, logging reservoir physical property evaluation mainly focuses on core conventional physical properties, nuclear magnetic resonance analysis, and rock thin section analysis. With the influence of factors such as compressed exploration costs and complex well conditions, the number of drilled cores has decreased year by year. Under the condition of lacking physical property analysis parameters, the difficulty of oil and gas layer interpretation and evaluation is increasing. Under non-drilled core conditions, reservoir physical property evaluation mostly takes logging data as the main body, and there is no method for logging to evaluate reservoir physical properties yet. Therefore, how to identify reservoirs in a timely, accurate, and efficient manner, that is, how to calculate the porosity of reservoirs, is an important problem that needs to be solved. Summary of the Invention
[0004] In order to solve the above problems in the prior art, that is, how to identify reservoirs in a timely, accurate, and efficient manner, that is, how to calculate the porosity of reservoirs is an important problem that needs to be solved. The present invention provides a method and system for calculating reservoir porosity based on X-ray fluorescence logging technology. The method includes:
[0005] S100, selecting a sample well and collecting core samples; performing XRF element experiments on the core samples according to well depth to obtain the content of elements; and screening the elements of the sample well to obtain analysis elements;
[0006] S200, performing conventional physical property experiments on the core samples according to well depth to obtain the conventional physical property effective porosity of the core of the sample well;
[0007] S300, screening the analysis elements to obtain regionally sensitive elements;
[0008] For S400, perform stepwise regression analysis on the sensitive elements in the region and the effective porosity of the conventional physical properties of the sample well to establish a multiple linear regression equation for the effective porosity of the reservoir; and determine whether the multiple linear regression equation for the effective porosity of the reservoir is reliable. If it is reliable, jump to step S500; if it is not reliable, return to step S300;
[0009] For S500, substitute the content of the sensitive element at any well depth of the sample well into the multiple linear regression equation for the effective porosity of the reservoir, and the corresponding effective porosity of the conventional physical properties can be obtained.
[0010] In a preferred embodiment, the method for screening the analysis elements to obtain the regional sensitive elements is as follows:
[0011] Perform Pearson correlation analysis on the effective porosity of the conventional physical properties and each analysis element respectively to obtain the corresponding significance test coefficient P value and correlation coefficient r value;
[0012] If the significance test coefficient P value is less than the first threshold and the absolute value of the correlation coefficient r value is greater than or equal to the second threshold, it indicates that the analysis element is correlated with the effective porosity variable of the conventional physical properties, and this analysis element is determined as the regional sensitive element.
[0013] In a preferred embodiment, the specific method for establishing the multiple linear regression equation for the effective porosity of the reservoir is as follows:
[0014] Using the sensitive element as the independent variable and the effective porosity of the conventional physical properties of the sample well as the dependent variable, establish a multiple linear regression equation for the effective porosity of the reservoir by the stepwise regression analysis method.
[0015] In a preferred embodiment, if the significance test coefficient P value is less than the first threshold and the correlation coefficient r value is negative, it indicates that the analysis element is negatively correlated with the effective porosity of the conventional physical properties;
[0016] If the significance test coefficient P value is less than the first threshold and the correlation coefficient Q value is positive, it indicates that the analysis element is positively correlated with the effective porosity of the conventional physical properties.
[0017] In a preferred embodiment, after establishing the multiple linear regression equation for the effective porosity of the reservoir, judge the regression effect of the multiple linear regression equation for the effective porosity of the reservoir through F test. If the confidence level obtained through F test is less than the third threshold, it is considered that the multiple linear regression equation for the effective porosity of the reservoir is reliable.
[0018] In a preferred embodiment, the elements with the content of the elements in the sample well greater than the set first percentage are screened as analysis elements.
[0019] In a second aspect of the present invention, a method and a system for calculating reservoir porosity based on X-ray fluorescence logging technology are proposed. The system includes:
[0020] An analysis element acquisition module, configured to select a sample well and collect core samples; perform XRF element experiments on the core samples according to well depth to obtain the content of elements; and screen the elements of the sample well to obtain analysis elements;
[0021] A porosity acquisition module, which performs conventional physical property experiments on the core samples according to well depth to obtain the conventional physical property effective porosity of the core of the sample well;
[0022] A sensitive element acquisition module, configured to screen the analysis elements to obtain regional sensitive elements;
[0023] An equation determination module, configured to perform stepwise regression analysis on the regional sensitive elements and the conventional physical property effective porosity of the sample well to establish a multiple linear regression equation for reservoir effective porosity; and determine whether the multiple linear regression equation for reservoir effective porosity is reliable. If it is reliable, the porosity determination module calculates the effective porosity; if it is not reliable, the sensitive element acquisition module re-screens the analysis elements;
[0024] A porosity determination module, which substitutes the content of the sensitive elements at any well depth of the sample well into the multiple linear regression equation for reservoir effective porosity to obtain the corresponding conventional physical property effective porosity.
[0025] In a third aspect of the present invention, an electronic device is proposed, including:
[0026] At least one processor; and
[0027] A memory communicatively connected to at least one of the processors; wherein,
[0028] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned system for calculating reservoir porosity based on X-ray fluorescence logging technology.
[0029] In a fourth aspect of the present invention, a computer-readable storage medium is proposed. 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 system for calculating reservoir porosity based on X-ray fluorescence logging technology.
[0030] Advantages of the present invention:
[0031] (1) The present invention accurately calculates the effective porosity of a reservoir by using a multiple linear regression method with sensitive elements, and conducts logging reservoir physical property evaluation timely, accurately and efficiently under the condition of lacking logging physical property parameters;
[0032] (2) The present invention is a method for calculating porosity based on XRF element logging technology. It conducts sensitivity evaluation on the detected elements, and uses the stepwise regression analysis method for the qualified sensitive elements to establish a regression equation with the core physical property porosity, so as to realize the quantitative evaluation of the conventional physical property effective porosity of accurate logging physical properties during the drilling process;
[0033] (3) The present invention provides sufficient basis for evaluating reservoir fluid properties while drilling, and provides technical support for the construction party to save costs and make efficient decisions. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] Other features, purposes and advantages of this application will become more obvious by reading the detailed description of the non-restrictive embodiments with reference to the following drawings:
[0035] Figure 1 is a method for identifying a reservoir based on X-ray fluorescence logging technology according to an embodiment of the present invention;
[0036] Figure 2 is a statistical chart of core conventional physical property porosity and permeability according to an embodiment of the present invention;
[0037] Figure 3 is a comparison chart of the calculated value of conventional physical property porosity and the measured value of conventional physical property effective porosity according to an embodiment of the present invention;
[0038] Figure 4 is a schematic structural diagram of a computer system of a server for implementing the method, system and device embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0039] The following further elaborates on this application in conjunction with the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention and are not intended to limit the invention. Additionally, it should be noted that for the sake of description, only parts related to the relevant invention are shown in the drawings.
[0040] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will elaborate on this application in detail with reference to the drawings and embodiments.
[0041] The present invention provides a method for calculating reservoir porosity based on X-ray fluorescence logging technology. As Figure 1 shown, this method includes:
[0001] S100, Select a sample well and collect core samples; perform XRF element experiments on the core samples according to the well depth to obtain the element contents; and screen the elements of the sample well to obtain analysis elements;
[0002] S200, Perform conventional physical property experiments on the core samples according to the well depth to obtain the conventional physical property effective porosity of the core of the sample well;
[0003] S300, Screen the analysis elements to obtain regionally sensitive elements;
[0004] S400, Perform stepwise regression analysis on the regionally sensitive elements and the conventional physical property effective porosity of the sample well to establish a multiple linear regression equation for the reservoir effective porosity; and determine whether the multiple linear regression equation for the reservoir effective porosity is reliable. If it is reliable, jump to step S500; if it is not reliable, return to step S300;
[0005] S500, Substitute the content of the sensitive elements at any well depth of the sample well into the multiple linear regression equation for the reservoir effective porosity to obtain the corresponding conventional physical property effective porosity.
[0006] To more clearly illustrate the method for identifying a reservoir based on X-ray fluorescence logging technology in the present invention, each step in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0007] The method for calculating the reservoir porosity based on X-ray fluorescence logging technology in the first embodiment of the present invention includes steps S100 - S500, and each step is described in detail as follows:
[0008] S100, Select a sample well and collect core samples; perform XRF element experiments on the core samples according to the well depth to obtain the element contents; and screen the elements of the sample well to obtain analysis elements;
[0009] In this embodiment, elements with the content of the elements of the sample well greater than a set first percentage are screened as analysis elements. Specifically, the cored well A in the northern part of the Xinghua structure can be selected as the research well to perform XRF element experiments and conventional physical property experiments. The installation, calibration, verification of the XRF element logging equipment, as well as the production and analysis of samples are carried out in accordance with SY / T 7420-2018 "Specification for X-ray Fluorescence Spectrometry Element Logging". A total of 149 core samples are selected in this study, and 36 elements are analyzed for each sample, including 12 major elements such as Na, Mg, Al, Si, S, Cl, K, Ca, Ba, Ti, Fe, Sr and 24 trace elements. Among them, the set first percentage can be selected as 0.1%, that is, elements with an average content higher than 0.1% of the obtained elements are used as analysis elements; Table 1 shows the XRF element experiment data when the sample well is Well A; Table 1 Experimental data of core XRF elements in Well A
[0010] S200. For the core samples, conventional physical property experiments are carried out according to the well depth to obtain the effective porosity of the conventional physical properties of the core in the sample well;
[0011] In this embodiment, specifically, the cored well Well A in the northern part of the Xinghua structure is selected as the sample well for illustration. Figure 2 It is a statistical chart of the conventional physical property pore permeability of the core in Well A of this embodiment of the present invention. The experimental data of the conventional physical properties of the core obtained from Well A in this embodiment of the present invention are shown in Table 2; Table 2 Experimental data of the conventional physical properties of the core in Well A
[0012] S300. Screen the analysis elements to obtain regionally sensitive elements;
[0013] Screen the analysis elements to obtain regionally sensitive elements. The method is as follows:
[0014] Perform Pearson correlation analysis on the effective porosity of the conventional physical properties and each analysis element respectively to obtain the corresponding significance test coefficient P value and correlation coefficient r value; the specific method for performing Pearson correlation analysis is realized through SPSS data analysis software; the analysis elements are major elements with an average content higher than a set first percentage;
[0015] If the significance test coefficient P value is less than the first threshold and the correlation coefficient r value is negative, it indicates that the analysis element is negatively correlated with the effective porosity of the conventional physical properties;
[0016] If the significance test coefficient P value is less than the first threshold and the correlation coefficient Q value is positive, it indicates that the analysis element is positively correlated with the effective porosity of the conventional physical properties.
[0017] If the significance test coefficient P value is less than the first threshold and the absolute value of the correlation coefficient r value is greater than or equal to the second threshold, it indicates that the analysis element is variably correlated with the effective porosity of the conventional physical properties, and this analysis element is determined as a regionally sensitive element. The value of the first percentage can be 0.1%.
[0018] In this embodiment, the first threshold can take a value of 0.05, and the second threshold can take a value of 0.3;
[0019] In this embodiment, based on Well A, a table obtained by performing Pearson correlation analysis on the conventional physical property effective porosity and each analysis element is given for reference; as shown in Table 3, it is the correlation analysis table of each analysis element of Well A and the conventional physical property effective porosity of the core: Table 3 Pearson Correlation Analysis Table of Analysis Elements of Well A and Core Conventional Physical Property Effective Porosity
[0020] In this embodiment, in this study, 12 major elements (Na, Mg, Al, Si, S, Cl, K, Ca, Ba, Ti, Fe, Sr) with an average content higher than 0.1% are used as analysis objects, and the Pearson correlation coefficient is used to test the correlation between the major elements of the II and III oil groups in the northern part of the XH structure and the core conventional physical property effective porosity and horizontal permeability. When the correlation coefficient r > 0, it is regarded as a positive correlation relationship between the two variables; when r < 0, it is regarded as a negative correlation relationship between the two variables. When r ≥ 0.8, it is regarded as a highly correlated relationship between the two variables; when 0.5 ≤ r < 0.8, it is regarded as a moderately correlated relationship between the two variables; when 0.3 ≤ r < 0.5, it is regarded as a lowly correlated relationship between the two variables; when r < 0.3, it indicates that the correlation degree between the two variables is extremely weak and can be regarded as not correlated. It can be seen from the test results that the effective porosity of the reservoir in the II oil group in the northern part of the XH structure is correlated with the elements of Na, Mg, Al, Si, S, Cl, Ca, Fe, Ba, and Sr, and the significance (two-tailed) test coefficient P value is less than 0.05, indicating that the event of no correlation between variables is a very small probability event. The Na element has a moderately positive correlation with the effective porosity (r = 0.532), and the Si and Cl elements have a lowly positive correlation with the effective porosity (r = 0.445, 0.482), indicating that as the contents of the Na, Si, and Cl elements increase, the effective porosity may increase; the Mg, S, Ca, and Fe elements have a moderately negative correlation with the effective porosity (r = -0.561, -0.644, -0.671, -0.574), and the Al, Ba, and Sr elements have a lowly negative correlation with the effective porosity (r = -0.449, -0.368, -0.488), indicating that as the contents of the Mg, S, Ca, Fe, Al, Ba, and Sr elements increase, the effective porosity may decrease;
[0021] S400, perform stepwise regression analysis on the regional sensitive elements and the conventional physical property effective porosity of the sample well, and establish a multiple linear regression equation for the reservoir effective porosity; and judge whether the multiple linear regression equation for the reservoir effective porosity is reliable. If it is reliable, jump to step S500; if it is not reliable, return to step S300;
[0022] The specific method for establishing the multiple linear regression equation for the reservoir effective porosity is as follows:
[0023] Taking all sensitive elements as independent variables and the effective porosity of the conventional physical properties of the sample well as the dependent variable, a multiple linear regression equation for the effective porosity of the reservoir is established using the stepwise regression analysis method. The stepwise regression analysis can be implemented through SPSS data analysis software;
[0024] In this embodiment, based on the Pearson correlation analysis table of the analysis elements of Well A and the effective porosity of the core conventional physical properties, based on the pearson correlation analysis, the effective porosity of the reservoir in the northern part of the XH structure is correlated with elements such as Na, Si, Cl, Mg, S, Ca, Fe, Al, Ba, Sr, etc.; Therefore, the above-mentioned various elements are selected as independent variables, and the best multiple linear regression equation for the effective porosity of the reservoir is established using the stepwise regression analysis method.
[0025] The linear relationship between the effective porosity of the second oil group in the northern part of the XH structure and the three selected explanatory variables Ca, Fe, and Sr elements is very close. The adjusted R-square of the third model in the multiple linear stepwise regression analysis model is 0.633, and the overall explanatory degree of the independent variable to the dependent variable reaches 63.3%, with a good fitting effect and a relatively stable model.
[0026] Y = -0.754×Ca - 5.091×Al - 52.946×Sr + 24.382
[0027] Y is the dimensionless porosity; Ca, Al, and Sr are the contents of the chemical elements calcium, aluminum, and strontium, respectively.
[0028] After establishing the multiple linear regression equation for the effective porosity of the reservoir, the regression effect of the multiple linear regression equation for the effective porosity of the reservoir is judged by the F-test. If the confidence level obtained by the F-test is less than the third threshold, it is considered that the multiple linear regression equation for the effective porosity of the reservoir is reliable. Specifically, the F-test can be implemented through data analysis software, such as python or spss software. Table 4 Summary Table of Element Regression Models for the Second Oil Group in the Northern Part of the XH Structure Model R R-squared Adjusted R-squared Standard error of estimate 1 <![CDATA[.599 a > .359 .355 5.0791 2 <![CDATA[.771 b > .595 .589 4.0541 3 <![CDATA[.800 c > .641 .633 3.8310 Note: a. Predictor variables: (Constant), Ca / calcium b. Predictor variables: (Constant), Ca / calcium, Fe / iron c. Predictor variables: (Constant), Ca / calcium, Fe / iron, Sr / strontium Table 5 Coefficient Table of Element Regression Models for the Second Oil Group in the Northern Part of the XH Structure
[0029] In this embodiment, specifically, the third threshold can be taken as 0.001 for reference; based on Well A, the regression effect of the regression model is judged by the F-test. From the results, F = 84.948 and the confidence level < 0.001. It is considered that the regression model has passed the F-test with a confidence level of 0.001, that is, the fitted equation has statistical significance. In the northern part of Xinghua Structure, for the II and III oil groups, the elements Ca, Fe, and Sr have a significant negative impact on the effective porosity of the reservoir (β < 0, p < 0.05).
[0030] S500, substituting the content of the sensitive element at any well depth of the sample well into the multiple linear regression equation of the effective porosity of the reservoir, the corresponding effective porosity of the conventional physical properties can be obtained.
[0031] In this embodiment, taking the newly drilled Well E as an example, this well is a pre-exploration well located in the middle and lower part of the trap of Block 11 of Xinghua Structure in Xinghua. The cored interval of this well is 5674.6 - 5681.0 m, and the formation is the II oil group of Linhe Formation. The lithology is mainly brownish-grey oil-impregnated fine sandstone, grey oil-spotted fine sandstone, and grey oil-stained argillaceous siltstone, with thin layers of grey gypsum-bearing mudstone intercalated. A total of 12 core samples were selected for analysis in this section. Through the multiple linear regression equation of the effective porosity of the elements, the calculated value of the effective porosity was compared with the measured value of the effective porosity of the conventional physical properties, as Figure 3 shown, the calculation error is small, and it has strong application significance.
[0032] Although the various steps were described in the above sequential 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.
[0033] The system for identifying a reservoir based on X-ray fluorescence logging technology according to the second embodiment of the present invention is used to calculate the porosity of the reservoir. Based on the above system for calculating the porosity of the reservoir based on X-ray fluorescence logging technology, the system includes:
[0034] An analysis element acquisition module, configured to select a sample well and collect core samples; perform XRF element experiments on the core samples according to the well depth to obtain the content of the elements; and screen the elements of the sample well to obtain analysis elements;
[0035] A porosity acquisition module, which performs conventional physical property experiments on the core samples according to the well depth to obtain the effective porosity of the conventional physical properties of the core of the sample well;
[0036] A sensitive element acquisition module, configured to screen the analysis elements to obtain regional sensitive elements;
[0037] An equation determination module is configured to perform stepwise regression analysis on the region-sensitive elements and the conventional physical property effective porosity of the sample wells, establish a multiple linear regression equation for the reservoir effective porosity, and determine whether the multiple linear regression equation for the reservoir effective porosity is reliable. If it is reliable, the porosity determination module calculates the effective porosity. If it is not reliable, the sensitive element acquisition module re-screens the analysis elements.
[0038] The porosity determination module substitutes the content of the sensitive elements at any well depth of the sample wells into the multiple linear regression equation for the reservoir effective porosity, and the corresponding conventional physical property effective porosity can be obtained.
[0039] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related descriptions of the above-described system can refer to the corresponding process in the foregoing method embodiments, and will not be elaborated herein.
[0040] It should be noted that the system for identifying reservoirs based on X-ray fluorescence logging technology provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be allocated to different functional 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 used to distinguish each module or step, and are not regarded as an improper limitation of the present invention.
[0041] An electronic device according to the third embodiment of the present invention includes:
[0042] At least one processor; and
[0043] A memory communicatively connected to at least one of the processors; wherein,
[0044] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the method for calculating reservoir porosity based on X-ray fluorescence logging technology described above.
[0045] 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 method for calculating reservoir porosity based on X-ray fluorescence logging technology described above.
[0046] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes and related descriptions of the above-described storage device and processing device can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0047] 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 the two. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), 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. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of each example have been generally described according to their 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.
[0048] The following refers to Figure 4 , 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 4 The shown server is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.
[0049] As Figure 4 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.
[0050] 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. as well as 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 out therefrom is installed into the storage section 608 as required.
[0051] 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 method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the 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 functions defined in the method of the present application are performed. It should be noted that the above computer-readable medium in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The 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 that can be used by or in conjunction with an instruction execution system, apparatus, or device. And in the present application, the 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, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted with any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
[0052] 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 cases involving 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 (for example, by using an Internet service provider to connect through the Internet).
[0053] 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 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 can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can 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.
[0054] The terms "first", "second", etc. are used to distinguish similar objects and not to describe or represent a specific order or sequence.
[0055] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or device / equipment that comprises a series of elements includes not only those elements but also other elements not explicitly listed, or also includes elements inherent to those processes, methods, articles, or devices / equipment.
[0056] 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 fall within the protection scope of the present invention.
Claims
1. A method for calculating reservoir porosity based on X-ray fluorescence logging technology, characterized in that The method includes: S100, selecting a sample well and collecting core samples; performing XRF element experiments on the core samples according to well depth to obtain the contents of elements; and screening the elements of the sample well to obtain analysis elements; S200, performing conventional physical property experiments on the core samples according to well depth to obtain the conventional physical property effective porosity of the core of the sample well; S300, screening the analysis elements to obtain regional sensitive elements; S400, performing stepwise regression analysis on the regional sensitive elements and the conventional physical property effective porosity of the sample well to establish a multiple linear regression equation for reservoir effective porosity; and determining whether the multiple linear regression equation for reservoir effective porosity is reliable. If it is reliable, jump to step S500. If it is not reliable, return to step S300; S500, substituting the content of the sensitive element at any well depth of the sample well into the multiple linear regression equation for reservoir effective porosity to obtain the corresponding conventional physical property effective porosity.
2. The method for calculating the porosity of a reservoir based on X-ray fluorescence logging technology according to claim 1, wherein The method for screening the analysis elements to obtain regional sensitive elements is as follows: Performing Pearson correlation analysis on the conventional physical property effective porosity and each analysis element respectively to obtain the corresponding significance test coefficient P value and correlation coefficient r value; If the significance test coefficient P value is less than the first threshold and the absolute value of the correlation coefficient r value is greater than or equal to the second threshold, it indicates that the analysis element is correlated with the conventional physical property effective porosity variable, and the analysis element is determined as a regional sensitive element.
3. The method for calculating reservoir porosity based on X-ray fluorescence logging technology according to claim 1, wherein The specific method for establishing a multiple linear regression equation for reservoir effective porosity is as follows: Using the sensitive element as the independent variable and the conventional physical property effective porosity of the sample well as the dependent variable, and using the stepwise regression analysis method to establish a multiple linear regression equation for reservoir effective porosity.
4. The method for calculating reservoir porosity based on X-ray fluorescence logging technology according to claim 2, wherein If the significance test coefficient P value is less than the first threshold and the correlation coefficient r value is negative, it indicates that the analysis element is negatively correlated with the conventional physical property effective porosity; If the significance test coefficient P value is less than the first threshold and the correlation coefficient Q value is positive, it indicates that the analysis element is positively correlated with the conventional physical property effective porosity.
5. The method for calculating the porosity of a reservoir based on X-ray fluorescence logging technology according to claim 3, wherein, After establishing the multiple linear regression equation for reservoir effective porosity, the regression effect of the multiple linear regression equation for reservoir effective porosity is judged by F test. If the confidence level obtained by F test is less than the third threshold, the multiple linear regression equation for reservoir effective porosity is considered reliable.
6. The method for calculating reservoir porosity based on X-ray fluorescence logging technology according to claim 1, characterized in that Elements with a content greater than a set first percentage in the elements of the sample well are screened as analysis elements.
7. A system for calculating reservoir porosity based on X-ray fluorescence logging technology, which is used to calculate reservoir porosity and is based on the method for identifying reservoirs based on X-ray fluorescence logging technology according to any one of the above claims 1-6, characterized in that, The system includes: An analysis element acquisition module, configured to select a sample well and collect core samples; perform XRF element experiments on the core samples according to well depth to obtain the contents of elements; and screen the elements of the sample well to obtain analysis elements; A porosity acquisition module, configured to perform conventional physical property experiments on the core samples according to well depth to obtain the conventional physical property effective porosity of the core of the sample well; A sensitive element acquisition module, configured to screen the analysis elements to obtain region-sensitive elements; An equation determination module, configured to perform stepwise regression analysis on the region-sensitive elements and the conventional physical property effective porosity of the sample well to establish a multiple linear regression equation for the reservoir effective porosity; and determine whether the multiple linear regression equation for the reservoir effective porosity is reliable. If it is reliable, the porosity determination module calculates the effective porosity; if it is not reliable, the sensitive element acquisition module re-screens the analysis elements; The porosity determination module, by substituting the content of the sensitive elements at any well depth of the sample well into the multiple linear regression equation for the reservoir effective porosity, can obtain the corresponding conventional physical property effective porosity.
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 identifying a reservoir based on X-ray fluorescence logging technology according to any one of claims 1-6.
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 identifying a reservoir based on X-ray fluorescence logging technology according to any one of claims 1-6.