Methods, systems, equipment, and media for identifying favorable reservoirs in bedrock oil and gas reservoirs based on logging spider maps.

Abstract

This invention provides a method, system, equipment, and medium for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider diagrams. The method includes the following steps: Step 1, obtaining the logging facies curves corresponding to the target well, including deep lateral resistivity curves, compensated density curves, natural gamma curves, microsphere focused resistivity curves, gas logging C2 / C1 ratio curves, well diameter relative to drill bit diameter variation curves, compensated neutron curves, sonic transit time curves, and total hydrocarbon curves; Step 2, standardizing the obtained logging facies curves to obtain standardized values; Step 3, creating a spider diagram based on the standardized values ​​of the obtained logging facies curves; Step 4, classifying the reservoirs corresponding to the target wells based on the obtained spider diagrams. This invention can comprehensively integrate multi-source data, improve the accuracy and reliability of identifying complex bedrock reservoirs, and more accurately identify favorable reservoirs in bedrock oil and gas reservoirs.

Description

Technical Field

[0001] This invention belongs to the field of petroleum exploration technology, and specifically relates to a method, system, equipment and medium for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider maps. Background Technology

[0002] Lithology identification is fundamental to reservoir evaluation and a crucial component of reservoir characterization and well logging interpretation. Only by accurately understanding reservoir lithology can reservoir parameters be precisely calculated, and the hydrocarbon potential of the reservoir be determined. Therefore, the accuracy of lithology identification directly impacts the reliability of reservoir evaluation results.

[0003] Due to the complex reservoir space and strong heterogeneity of bedrock oil and gas reservoirs, traditional single-parameter or two-dimensional methods are insufficient to meet the needs of refined reservoir identification. Spider diagrams, as a multi-parameter comprehensive evaluation method, can intuitively reflect the multidimensional characteristics of reservoirs; however, existing methods mostly focus on the correlation between logging-sensitive parameters and lithological response characteristics, neglecting the potential information between logging parameters and the important indicative role of logging and gas logging parameters in oil and gas layer identification. Facing the heterogeneity and tight, low-permeability characteristics of fractured bedrock oil and gas reservoirs, existing technologies still have shortcomings in terms of identification accuracy and practicality, such as: The invention patent application CN201810884847.9, entitled "Method and System for Identifying Lithology in Complex Strata," discloses a method and system for identifying lithology in complex strata, relating to the field of petroleum geophysical exploration technology. The method includes: selecting sensitive parameters for lithology logging based on the stratigraphic-lithological electrical relationship; establishing a sensitive parameter relationship function; establishing a lithology identification model based on the sensitive parameter relationship function; determining the baseline range of multiple template lithologies in the stratigraphy; and identifying unknown stratigraphic lithologies based on the lithology identification model and the baseline range of the multiple template lithologies. This method establishes relationships by optimizing logging sensitive parameters corresponding to different lithologies through mathematical transformation, determines the lithology identification model, and, based on core-calibrated logging data, determines the baseline values ​​for various lithologies as screening conditions for the lithology identification model to classify lithologies, thereby achieving, to a certain extent, the purpose of quantitative and precise lithology classification.

[0004] The invention patent application CN201510071090.8, entitled "A Five-Dimensional Lithology Identification Method Based on Conventional Well Logging Data," discloses a five-dimensional lithology identification method based on conventional well logging data, relating to the field of oilfield development technology. This invention includes the following steps: Step 1, selecting multiple lithology-sensitive well logging curves as discrimination parameters and extracting their well logging response values ​​for various lithologies; Step 2, establishing standard charts for different lithologies based on the data extracted in Step 1; and Step 3, extracting the discrimination parameters of the sample to be predicted and performing sample prediction based on the established standard charts for different lithologies. This method establishes a series of practical and effective lithology identification charts based on conventional well logging five-dimensional analysis, which can assist in the identification of volcanic rock lithology in oilfields.

[0005] The invention patent application CN 201610621321.2, entitled "A Method and Apparatus for Identifying Oil-Water Layers," discloses a method and apparatus for identifying oil-water layers, relating to the field of petroleum exploration technology. The method includes: classifying oil-water layers according to their reservoir characteristics within the work area; drawing a spider diagram of the oil-water layer to be tested; and determining the oil-water layer type based on the reservoir classification results and the relationship between the spider diagram of the oil-water layer to be tested and one or more pre-set reference spider diagrams showing the same reservoir type as the oil-water layer to be tested. In this embodiment, by directly determining the oil-water layer type, the method solves the problem of low accuracy in oil-water layer identification caused by the chaotic distribution of data points on the cross-plot when simultaneously identifying different types of oil-water layers. Furthermore, it transforms the qualitative process into a quantitative process, reducing the uncertainty caused by subjective human judgment of oil-water layers.

[0006] The invention patent application CN 201611192067.5, entitled "A Method for Quantitative Identification of Well Logging Lithology Using an Improved Spider Web Diagram," discloses a method for quantitative identification of well logging lithology using an improved spider web diagram, belonging to the field of petroleum and natural gas geology. It proposes an angle parameter set for quantifying the spider web diagram, using this set to quantitatively determine the similarity of the spider web diagrams, thereby achieving quantitative identification of lithology in non-core sections. Through core sample analysis, the lithology type of the target layer is classified; through correlation analysis between well logging curves and rock sample lithology, the best well logging curve for identifying lithology is selected, and a spider web diagram template is created; using the created template, well logging curves, and core analysis data, a typical spider web diagram for each lithological reservoir is created; by calculating the similarity between the angle parameter set and the angle parameter set of the typical spider web diagram for each lithology, the lithology at that depth point is quantitatively determined; following the depth sequence, the reservoir lithology is identified point by point to obtain the lithology of the entire well section. This method solves the problems of complex well logging responses and difficulty in lithology identification due to differences in debris content.

[0007] The aforementioned methods for lithological identification and oil-water layer identification neglect the potential information between multiple logging parameters and the indicative role of logging gas measurements and transformation parameters in oil and gas layer identification. Since fractured reservoirs in bedrock oil and gas reservoirs have extremely strong heterogeneity and tight low permeability, it is difficult to guarantee the accuracy of the identification results of favorable reservoirs in bedrock oil and gas reservoirs. Summary of the Invention

[0008] The purpose of this invention is to provide a method, system, equipment and medium for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider diagrams, which solves the problem of inaccurate identification results in existing methods for identifying favorable reservoirs in bedrock oil and gas reservoirs.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The present invention provides a method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider diagrams, comprising the following steps: Obtain the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curves, compensated density curves, natural gamma curves, microsphere focused resistivity curves, gas logging C2 / C1 ratio curves, well diameter relative to drill bit diameter variation curves, compensated neutron curves, sonic transit time curves, and total hydrocarbon curves. The obtained logging phase curves are standardized to obtain standardized values ​​of the logging phase curves; A spider diagram is created based on the standardized values ​​of the obtained well logging phase curves; The reservoirs corresponding to the target logging wells are classified based on the obtained spider diagrams.

[0010] Preferably, the method for obtaining the wellbore diameter relative to drill bit diameter variation curve δ-CALX is as follows: Obtain the well diameter and drill bit diameter of the logging well in the target area, and obtain the well diameter-to-drill bit diameter variation curve δ-CALX based on the obtained well diameter and drill bit diameter.

[0011] Preferably, before standardizing the obtained logging phase curves, the obtained logging phase curves are preprocessed to obtain preprocessed logging phase curves.

[0012] Preferably, the obtained logging phase curves are standardized using the Log function to obtain the standardized values ​​corresponding to the logging phase curves.

[0013] Preferably, the standardized value is obtained using the following formula: (2) In the formula: Standardized logging values; is the actual logging value; max is the maximum value corresponding to the logging phase curve.

[0014] Preferably, the reservoirs corresponding to the target logging wells are classified based on the obtained spider web map. The specific method is as follows: When the peak values ​​in the spider diagram include well diameter relative to drill bit diameter change δ-CALX, compensated neutron CNC, sonic transit time DT, and total hydrocarbon TG, and the compensated density ZDEN normalized value is less than 0.85, and at the same time, the logging shows oil and gas or the total hydrocarbon TG is greater than 0.1%, then the reservoir corresponding to the target area is a porosity or fractured reservoir, and the corresponding reservoir is a good reservoir or a medium reservoir. When the peak values ​​in the spider diagram include deep lateral resistivity (RD), compensated density (ZDEN), natural gamma (GR), and microsphere focused resistivity (RMSL), and the standardized value of compensated density (ZDEN) is less than 0.85 or the standardized values ​​of compensated neutron (CNC) and borehole diameter variation relative to drill bit diameter (δ-CALX) are both less than 0.7, and at the same time, there are no oil and gas indications in the logging and TG is less than or equal to 0.1%, then the reservoir corresponding to the target area is a relatively tight reservoir, and the corresponding reservoir is a poor reservoir or a non-reservoir.

[0015] A system for identifying favorable reservoirs in bedrock oil and gas reservoirs based on logging spider maps includes: The logging phase curve acquisition unit is used to acquire the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curves, compensated density curves, natural gamma curves, microsphere focused resistivity curves, gas logging C2 / C1 ratio curves, well diameter relative to drill bit diameter variation curves, compensated neutron curves, sonic transit time curves, and total hydrocarbon curves. The standardization processing unit is used to standardize the obtained logging phase curves to obtain standardized values ​​of the logging phase curves. The spider diagram creation unit is used to create a spider diagram based on the standardized values ​​of the obtained well logging phase curves; The reservoir classification unit is used to classify the reservoirs corresponding to the target logging wells based on the obtained spider diagram.

[0016] A computer device, comprising: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, performs the method.

[0017] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method.

[0018] A computer program product comprising a computer program that, when executed by a processor, implements the method.

[0019] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider diagrams. The method uses well logging facies curves, deep lateral resistivity curves (RD), compensated density curves (ZDEN), natural gamma curves (GR), microsphere focused resistivity curves (RMSL), gas logging C2 / C1 ratio curves, well diameter-to-bit diameter variation curves (δ-CALX), compensated neutron curves (CNC), sonic transit time curves (DT), and total hydrocarbon curves (TG) as sensitive logging parameters for identifying favorable reservoirs in bedrock oil and gas reservoirs. The well logging facies curves are then standardized, and spider diagrams are generated. Combined with core analysis and well testing data, criteria for identifying favorable reservoirs in bedrock oil and gas reservoirs are established, thereby achieving a comprehensive evaluation of the physical properties and oil-bearing capacity of the basement reservoirs. Based on well logging data from basement well testing, the comprehensive spider diagrams of well logging data are compared and analyzed under typical well testing conclusions in different blocks and with different lithologies. Good reservoirs with industrial production capacity and dry reservoirs show relatively obvious special markings on the spider diagrams. This invention comprehensively integrates multi-source data, improving the accuracy and reliability of identifying complex bedrock reservoirs. This method effectively overcomes the shortcomings of traditional methods, enabling more precise identification of favorable reservoirs in bedrock oil and gas reservoirs.

[0020] Furthermore, the Log function standardization method can stabilize the spider diagram's appearance across different blocks and lithologies.

[0021] Furthermore, the deep lateral resistivity curve RD, compensated density curve ZDEN, natural gamma curve GR, microsphere focused resistivity curve RMSL, gas logging C2 / C1 ratio curve, wellbore-to-bit diameter variation curve δ-CALX, compensated neutron curve CNC, sonic transit time curve DT, and total hydrocarbon curve TG are arranged in clockwise order to create a spider diagram. This facilitates obtaining obvious morphological characteristics, thus revealing distinct features on the spider diagram for good reservoirs with industrial production capacity and dry reservoirs. Attached Figure Description

[0022] Figure 1 The spider diagram is a representation of the basement reservoir as described in this embodiment of the invention, obtained by standardizing it using the log function. Figure 2 This is a schematic diagram of the effective reservoir identification features of the basement of well Q-71 as described in an embodiment of the present invention. Detailed Implementation

[0023] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0024] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0025] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0027] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Example 1 Bedrock reservoirs possess complex storage spaces and heterogeneity, making it difficult to effectively identify oil and gas-bearing reservoirs using conventional logging or well logging data. To achieve a refined evaluation of bedrock oil and gas reservoirs, this embodiment uses core analysis and well testing data as a foundation, employing a multi-index comprehensive evaluation method that combines conventional logging with well logging data to conduct reservoir logging facies studies. To visually represent the reservoir characteristics of different lithologies and physical properties, a spider diagram is used to represent the logging facies. Specifically, through repeated experiments, representative logging facies curve values ​​are selected for the basement reservoirs, standardized using the Log function transformation method to create a spider diagram, and a comprehensive evaluation of the basement reservoirs is made. This establishes a rapid and intuitive method for identifying favorable bedrock oil and gas reservoirs, meeting the technical requirements for reservoir interpretation during the exploration and development of bedrock oil and gas reservoirs.

[0030] Reservoir logging facies is a collection of logging values ​​that reflect the lithological and physical characteristics of the reservoir. To visually represent the reservoir characteristics of different lithologies and physical properties, a spider diagram is used to represent logging facies. The order of coordinates in the spider diagram should emphasize the characteristics of a certain logging curve. The values ​​of curves represented by adjacent coordinates should not all be too high; otherwise, a prominent shape feature cannot be formed. The diagram should, as far as possible, form a simple, easily recognizable, and characteristic graphic while maintaining the relative relationships between various curves.

[0031] Based on logging data, nine logging parameters that are sensitive to lithology, physical properties, and oil-bearing properties were selected for the basement reservoir in this block. These parameters include: deep lateral resistivity (RD), compensated density (ZDEN), natural gamma ray (GR), microsphere focused resistivity (RMSL), gas logging C2 / C1 ratio, borehole diameter relative to drill bit diameter (δ-CALX), compensated neutron (CNC), sonic transit time (DT), and total hydrocarbons (TG). These parameters are arranged clockwise to create a spider diagram.

[0032] Traditional spider web Figure 1 Generally, well logging curves are chosen as parameters. However, with continuous improvements in logging technology, well logging gas values ​​and transformed parameters have become highly effective indicators for identifying oil and gas reservoirs. The Pixler chart component ratio C1 / C2 has shown good results in oil and gas reservoir interpretation. Considering that the overall well logging gas values ​​of the basement reservoir are generally low, and sometimes C2 is close to 0, this embodiment selects C2 / C1 and total hydrocarbon TG as two key parameters for constructing the spider diagram. These nine parameters can reflect the different lithologies, physical properties, and oil-bearing characteristics of the formation from different perspectives, and their combination serves as a distinctive method for identifying favorable reservoirs in the basement.

[0033] Spider diagrams are a multi-index comprehensive evaluation method that integrates information from multiple indicators describing different aspects of the evaluation object to obtain a comprehensive index. This index allows for an overall assessment of the evaluation object and enables horizontal or vertical comparisons. Different logging methods have different geological and physical response mechanisms, and the dimensions, orders of magnitude, and logging instrument measurement states of various logging curves differ. Therefore, to ensure the reliability of the results, data standardization of each logging parameter is necessary before drawing the spider diagram. Deviation standardization sometimes exhibits oversensitivity to certain data, making it difficult to form stable graphic recognition features under the same oil test results, which is not conducive to identifying effective reservoirs, poor reservoirs, and dry reservoirs. Log function transformation standardization, on the other hand, can form relatively stable graphic recognition features under typical oil test results in different blocks and with different lithologies. Therefore, the stable Log function transformation data standardization method of this invention was ultimately selected. Effective reservoir identification focuses on qualitative aspects. A comprehensive spider diagram is drawn using quantitative Log function transformation standardization to provide a qualitative description and classify reservoir properties.

[0034] Based on well logging data from basement wells, this study compares and analyzes common data standardization methods combined with comprehensive spider diagrams from well logging under typical well test results in different blocks and lithologies. Among wells that encountered bedrock reservoirs in three oilfields, typical wells with complete data were selected for analysis. Good reservoirs with industrial production capacity and dry reservoirs showed relatively clear special markings on the spider diagrams. Combining geological, core, well logging oil and gas displays and gas logging, imaging logging, and well test data, a classification standard for bedrock reservoirs was established (see appendix). Figure 1 Appendix 1).

[0035] Specifically, the method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on logging spider maps provided in this embodiment includes the following steps: Step 1: Obtain the logging phase curves corresponding to the target logging well, including the deep lateral resistivity curve RD, the compensated density curve ZDEN, the natural gamma curve GR, the microsphere focused resistivity curve RMSL, the gas logging C2 / C1 ratio curve, the compensated neutron curve CNC, the sonic transit time curve DT, and the total hydrocarbon curve TG. The method for obtaining the δ-CALX curve, which represents the change in well diameter relative to drill bit diameter, is as follows: Obtain the wellbore diameter and drill bit diameter of the logging well in the target area, and obtain the wellbore diameter relative to drill bit diameter variation curve δ-CALX based on the obtained wellbore diameter and drill bit diameter: δ-CALX =∣CALX-BIT∣(1) In the formula: δ-CALX is the change in well diameter relative to drill bit diameter, mm; CALX is the well diameter, mm; BIT is the drill bit diameter, mm.

[0036] Step 2: Preprocess the nine obtained logging facies curves to correct logging data from different companies at different times for the block, eliminate systematic errors, and obtain the nine preprocessed logging facies curves.

[0037] Step 3: Use the Log function transformation method to standardize the nine preprocessed logging phase curves to obtain the standardized values ​​corresponding to the logging phase curves.

[0038] (2) In the formula: Standardized logging values; is the actual logging value; max is the maximum value corresponding to the logging phase curve.

[0039] Step 4, as shown in the appendix Figure 1 As shown, a spider diagram is created by arranging the deep lateral resistivity curve RD, compensated density curve ZDEN, natural gamma curve GR, microsphere focused resistivity curve RMSL, gas logging C2 / C1 ratio curve, wellbore-to-bit diameter variation curve δ-CALX, compensated neutron curve CNC, sonic transit time curve DT, and total hydrocarbon curve TG in a clockwise order. In this embodiment, arranging the deep lateral resistivity curve RD, compensated density curve ZDEN, natural gamma curve GR, microsphere focused resistivity curve RMSL, gas logging C2 / C1 ratio curve, wellbore-to-bit diameter variation curve δ-CALX, compensated neutron curve CNC, sonic transit time curve DT, and total hydrocarbon curve TG in a clockwise order facilitates the identification of distinct morphological characteristics, thus providing clear and unique markers for good reservoirs with industrial production capacity and dry reservoirs on the spider diagram.

[0040] Step 5: Combine the spider diagram obtained in Step 4 with well logging oil and gas displays and well logging gas measurements to classify the reservoirs in the target area, as shown in Table 1. When the peak values ​​in the spider diagram include well diameter relative to drill bit diameter change δ-CALX, compensated neutron CNC, sonic transit time DT, and total hydrocarbon TG, and the compensated density ZDEN normalized value is less than 0.85, and at the same time, there are oil and gas shows in the logging or the total hydrocarbon TG is greater than 0.1%, then the reservoir in the target area is a porous or fractured reservoir, which is a good or medium reservoir. When the peak values ​​in the spider diagram include deep lateral resistivity (RD), compensated density (ZDEN), natural gamma (GR), and microsphere focused resistivity (RMSL), and the standardized value of compensated density (ZDEN) is less than 0.85 or the standardized values ​​of compensated neutron (CNC) and borehole diameter variation relative to drill bit diameter (δ-CALX) are both less than 0.7, and at the same time, there are no oil and gas indications in the logging and TG is less than or equal to 0.1%, then the reservoir in the target area is a relatively tight reservoir, and this reservoir is a poor reservoir or a non-reservoir.

[0041] Table 1 Classification Criteria for Basement Reservoirs

[0042] In this embodiment, through repeated experiments, the following logging parameters were determined as sensitive logging parameters for identifying favorable reservoirs in bedrock oil and gas reservoirs: deep lateral resistivity curve RD, compensated density curve ZDEN, natural gamma curve GR, microsphere focused resistivity curve RMSL, gas logging C2 / C1 ratio curve, wellbore-to-bit diameter variation curve δ-CALX, compensated neutron curve CNC, sonic transit time curve DT, and total hydrocarbon curve TG. Comparative analysis revealed that the spider diagrams obtained using the Log function standardization method remained stable under typical oil testing results for different blocks and lithologies. Therefore, the basement reservoirs were divided into zones and blocks, and their standardization was achieved using the Log function transformation method to create spider diagrams. Combined with core analysis and oil testing data, a criterion for identifying favorable reservoirs in bedrock oil and gas reservoirs was established, thereby achieving an overall assessment of the basement reservoir properties and oil-bearing capacity. Based on the logging data from the basement oil testing wells, a comparative analysis of the comprehensive spider diagrams of the logging data under typical oil testing results for different blocks and lithologies was conducted. Good reservoirs with industrial production capacity and dry reservoirs showed relatively obvious special markings on the spider diagrams. The analysis results show that the method of the present invention has a good application effect on identifying different types of bedrock reservoirs and improves the accuracy of identifying favorable bedrock reservoirs.

[0043] Example 2 This embodiment applies the method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider diagrams to bedrock reservoirs. Figure 1 In the data, the deep lateral resistivity RD, compensated density ZDEN, natural gamma ray GR, microsphere focused resistivity RMSL, gas logging C2 / C1 ratio, borehole diameter relative to drill bit diameter change δ-CALX, compensated neutron CNC, sonic transit time DT, and total hydrocarbon TG are all logging values ​​standardized using the Log function conversion method.

[0044] As attached Figure 2 As shown, taking well Q-71 as an example, this well is located at a local high point of an ancient buried hill. There is no fluorescent oil and gas indication in the logging, the gas measurement value is moderate, the resistivity at different depths is low but the difference is small, the porosity shows local porosity development, the imaging logging shows a small number of fractures, local development of network fractures, and relatively developed pores.

[0045] Nine logging phase curves from well Q-71 were preprocessed: deep lateral resistivity curve RD, compensated density curve ZDEN, natural gamma curve GR, microsphere focused resistivity curve RMSL, gas logging C2 / C1 ratio curve, well diameter relative to drill bit diameter variation curve δ-CALX, compensated neutron curve CNC, sonic transit time curve DT, and total hydrocarbon curve TG. The nine preprocessed logging phase curves were standardized using the Log function transformation method. Then, the nine standardized logging phase curves were arranged clockwise to create a spider diagram, namely, the deep lateral resistivity curve RD, the compensated density curve ZDEN, the natural gamma curve GR, the microsphere focused resistivity curve RMSL, the gas logging C2 / C1 ratio curve, the borehole diameter relative to drill bit diameter variation curve δ-CALX, the compensated neutron curve CNC, the sonic transit time curve DT, and the total hydrocarbon curve TG.

[0046] The spider diagram peaks are on the well diameter relative to drill bit diameter variation curve δ-CALX, the compensated neutron curve CNC, and the sonic transit time curve DT. The ZDEN normalized value of the compensated density curve is less than 0.85. Therefore, it can be concluded that the reservoir of well Q-71 is a good or medium reservoir.

[0047] For well Q-71, perforations were made from 2673.0 to 2691.0 meters deep. After fracturing, the daily oil production during testing was 5.4 m³ / s. 3 A large amount of gas was produced, and the oil test concluded that it was an oil and gas reservoir. The oil test results are consistent with the conclusions obtained using the calculation method of this invention.

[0048] Example 3 The bedrock oil and gas reservoir favorable reservoir identification system based on logging spider diagrams provided in this embodiment includes: The logging phase curve acquisition unit is used to acquire the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curves, compensated density curves, natural gamma curves, microsphere focused resistivity curves, gas logging C2 / C1 ratio curves, well diameter relative to drill bit diameter variation curves, compensated neutron curves, sonic transit time curves, and total hydrocarbon curves. The standardization processing unit is used to standardize the obtained logging phase curves to obtain standardized values ​​of the logging phase curves. The spider diagram creation unit is used to create a spider diagram based on the standardized values ​​of the obtained well logging phase curves; The reservoir classification unit is used to classify the reservoirs corresponding to the target logging wells based on the obtained spider diagram.

[0049] Example 9 This embodiment 9 provides a computer device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of a computer method.

[0050] When the processor executes the computer program, it implements the steps of the above-described computer method, for example: A method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on logging spider maps, the method includes the following steps: Step 1: Obtain the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curve, compensated density curve, natural gamma curve, microsphere focused resistivity curve, gas logging C2 / C1 ratio curve, well diameter relative to drill bit diameter change curve, compensated neutron curve, sonic transit time curve, and total hydrocarbon curve. Step 2: Standardize the obtained logging phase curves to obtain standardized values ​​of the logging phase curves; Step 3: Create a spider diagram based on the standardized values ​​of the obtained well logging phase curves; Step 4: Classify the reservoirs corresponding to the target logging wells based on the obtained spider web diagram.

[0051] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, for example: The logging phase curve acquisition unit is used to acquire the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curves, compensated density curves, natural gamma curves, microsphere focused resistivity curves, gas logging C2 / C1 ratio curves, well diameter relative to drill bit diameter variation curves, compensated neutron curves, sonic transit time curves, and total hydrocarbon curves. The standardization processing unit is used to standardize the obtained logging phase curves to obtain standardized values ​​of the logging phase curves. The spider diagram creation unit is used to create a spider diagram based on the standardized values ​​of the obtained well logging phase curves; The reservoir classification unit is used to classify the reservoirs corresponding to the target logging wells based on the obtained spider diagram.

[0052] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a preset function, the instruction segments describing the execution process of the computer program in the computer device.

[0053] For example, the computer program can be divided into: a well logging facies curve acquisition unit, a standardization processing unit, a spider diagram creation unit, and a reservoir classification unit. The specific functions of each module are as follows: The logging phase curve acquisition unit is used to acquire the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curves, compensated density curves, natural gamma curves, microsphere focused resistivity curves, gas logging C2 / C1 ratio curves, well diameter relative to drill bit diameter variation curves, compensated neutron curves, sonic transit time curves, and total hydrocarbon curves. The standardization processing unit is used to standardize the obtained logging phase curves to obtain standardized values ​​of the logging phase curves. The spider diagram creation unit is used to create a spider diagram based on the standardized values ​​of the obtained well logging phase curves; The reservoir classification unit is used to classify the reservoirs corresponding to the target logging wells based on the obtained spider diagram.

[0054] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above are examples of computer devices and do not constitute a limitation on the computer device; it may include more components than described above, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0055] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor, or any conventional processor, etc. The processor is the control center of the computer device, connecting various parts of the computer device through various interfaces and lines.

[0056] The memory can be used to store the computer program and / or module, and the processor implements various functions of the computer device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.

[0057] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0058] Example 10 This embodiment 10 also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements a method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on logging spider diagrams. The method includes the following steps: Step 1: Obtain the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curve, compensated density curve, natural gamma curve, microsphere focused resistivity curve, gas logging C2 / C1 ratio curve, well diameter relative to drill bit diameter change curve, compensated neutron curve, sonic transit time curve, and total hydrocarbon curve. Step 2: Standardize the obtained logging phase curves to obtain standardized values ​​of the logging phase curves; Step 3: Create a spider diagram based on the standardized values ​​of the obtained well logging phase curves; Step 4: Classify the reservoirs corresponding to the target logging wells based on the obtained spider web diagram.

[0059] If the modules / units integrated in the computer system are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0060] Based on this understanding, all or part of the processes in the above-described method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-described computer method. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0061] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0062] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0063] Example 11 This embodiment 11 provides a computer product, which includes a computer program stored in a computer-readable storage medium. The processor of the computer device reads the computer program from the computer-readable storage medium and executes the computer program, enabling the computer device to perform a method for identifying favorable bedrock oil and gas reservoirs based on logging spider maps. This method includes the following steps: Step 1: Obtain the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curve, compensated density curve, natural gamma curve, microsphere focused resistivity curve, gas logging C2 / C1 ratio curve, well diameter relative to drill bit diameter change curve, compensated neutron curve, sonic transit time curve, and total hydrocarbon curve. Step 2: Standardize the obtained logging phase curves to obtain standardized values ​​of the logging phase curves; Step 3: Create a spider diagram based on the standardized values ​​of the obtained well logging phase curves; Step 4: Classify the reservoirs corresponding to the target logging wells based on the obtained spider web diagram.

[0064] It should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods.

[0065] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider maps, characterized in that, Includes the following steps: Obtain the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curves, compensated density curves, natural gamma curves, microsphere focused resistivity curves, gas logging C2 / C1 ratio curves, well diameter relative to drill bit diameter variation curves, compensated neutron curves, sonic transit time curves, and total hydrocarbon curves. The obtained logging phase curves are standardized to obtain standardized values ​​of the logging phase curves; A spider diagram is created by arranging the standardized deep lateral resistivity curve, compensated density curve, natural gamma curve, microsphere focused resistivity curve, gas logging C2 / C1 ratio curve, wellbore relative drill bit diameter change curve, compensated neutron curve, sonic transit time curve, and total hydrocarbon curve in a clockwise direction. The reservoirs corresponding to the target logging wells are classified based on the obtained spider diagrams.

2. The method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider diagrams according to claim 1, characterized in that, The method for obtaining the δ-CALX curve, which represents the change in well diameter relative to drill bit diameter, is as follows: Obtain the well diameter and drill bit diameter of the logging well in the target area, and obtain the well diameter-to-drill bit diameter variation curve δ-CALX based on the obtained well diameter and drill bit diameter.

3. The method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider diagrams according to claim 1, characterized in that, Before standardizing the obtained well logging phase curves, preprocessing is performed to obtain preprocessed well logging phase curves.

4. The method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on logging spider diagrams according to claim 1, characterized in that, The obtained logging phase curves are standardized using the Log function to obtain the standardized values ​​corresponding to the logging phase curves.

5. The method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on logging spider diagrams according to claim 1 or 4, characterized in that, The standardized value is obtained using the following formula: （2） In the formula: Standardized logging values; is the actual logging value; max is the maximum value corresponding to the logging phase curve.

6. The method for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider diagrams according to claim 1, characterized in that, The reservoirs corresponding to the target logging wells are classified based on the obtained spider web map. The specific method is as follows: When the peak values ​​in the spider diagram include well diameter relative to drill bit diameter change δ-CALX, compensated neutron CNC, sonic transit time DT, and total hydrocarbon TG, and the compensated density ZDEN normalized value is less than 0.85, and at the same time, the logging shows oil and gas or the total hydrocarbon TG is greater than 0.1%, then the reservoir corresponding to the target area is a porosity or fractured reservoir, and the corresponding reservoir is a good reservoir or a medium reservoir. When the peak values ​​in the spider diagram include deep lateral resistivity (RD), compensated density (ZDEN), natural gamma (GR), and microsphere focused resistivity (RMSL), and the standardized value of compensated density (ZDEN) is less than 0.85 or the standardized values ​​of compensated neutron (CNC) and borehole diameter variation relative to drill bit diameter (δ-CALX) are both less than 0.7, and at the same time, there are no oil and gas indications in the logging and TG is less than or equal to 0.1%, then the reservoir corresponding to the target area is a relatively tight reservoir, and the corresponding reservoir is a poor reservoir or a non-reservoir.

7. A system for identifying favorable reservoirs in bedrock oil and gas reservoirs based on well logging spider maps, characterized in that, include: The logging phase curve acquisition unit is used to acquire the logging phase curves corresponding to the target logging well. The logging phase curves include deep lateral resistivity curves, compensated density curves, natural gamma curves, microsphere focused resistivity curves, gas logging C2 / C1 ratio curves, well diameter relative to drill bit diameter variation curves, compensated neutron curves, sonic transit time curves, and total hydrocarbon curves. The standardization processing unit is used to standardize the obtained logging phase curves to obtain standardized values ​​of the logging phase curves. The spider diagram creation unit is used to create a spider diagram based on the standardized values ​​of the obtained well logging phase curves; The reservoir classification unit is used to classify the reservoirs corresponding to the target logging wells based on the obtained spider diagram.

8. A computer device, characterized in that, include: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, performs the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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