Method and device for predicting rock surface porosity of shale oil reservoir, electronic equipment and medium

Abstract

The invention relates to the technical field of unconventional oil-gas exploration, and discloses a method and device for predicting the rock surface porosity of a shale oil reservoir, electronic equipment and a medium. In a method, a shale oil sample is processed to include at least two parallel samples; the acoustic emission system observes the longitudinal wave spectrum characteristics of the first parallel sample and extracts the waveform and signal of the longitudinal wave spectrum in the corresponding time window; converting the longitudinal wave from a time domain to a frequency domain based on discrete Fourier transform to obtain a rock sample longitudinal wave frequency spectrum characteristic curve; quantitatively representing the overall distribution range of the longitudinal wave frequency by using the longitudinal wave centroid frequency Fz of the rock sample based on the frequency distribution characteristics in the longitudinal wave frequency spectrum; and fitting the relationship between the longitudinal wave centroid frequency Fz of the rock sample and the corresponding rock surface porosity phi S, and constructing a surface porosity prediction model. According to the technical scheme, the shale oil reservoir pore structure evaluation method is enriched; the method has a good application effect on the shale oil reservoir, is high in practicability, universality and operability, and has good evaluation precision.

Description

Technical Field

[0001] This invention relates to the field of unconventional oil and gas exploration technology, specifically to a method, apparatus, electronic device, and medium for predicting the porosity of shale oil reservoirs. Background Technology

[0002] The pore structure system is a holistic description of the geometry, size, spatial distribution, and inter-pore combinations of complex and disordered pores and throats within a porous medium. In the field of oil and gas exploration and development, the pore structure system controls reservoir storage performance and seepage characteristics; therefore, it is particularly crucial to achieve a detailed characterization of the pore structure using rock physics experimental techniques (Li et al., 2017; Salahshoor et al., 2017).

[0003] Thin section analysis is one of the most fundamental research methods in petrology. Using a grinding mill, rock samples are ground to an extremely thin thickness (0.03 mm), allowing for the observation and analysis of mineral types, assemblages, grain size, and microstructure within the rock using a polarizing microscope. Cast thin sections are a type of rock thin section where colored liquid adhesive (blue or red) is injected into the pore space of the rock under vacuum and then ground into thin sections. Cast thin sections are primarily used to observe the two-dimensional spatial structure of pores, throats, and their interconnections within the rock; they can clearly identify a series of microscopic parameters of the rock, such as lithology, pore structure type, porosity, interstitial material type, and contact relationships (Zhao et al., 2009; Chen et al., 2016; Du et al., 2021). Multi-scale rock thin section observation is the most basic, intuitive, and rapid analytical method in oil and gas geology research and production, especially in the area of ​​rock morphology and fabrication, where it plays an irreplaceable role (Saif et al., 2017; Shao et al., 2017).

[0004] Traditional optical microscopy has been the primary technique for observing rock structures for many years. While it offers the advantages of being intuitive and quick, its limitations in resolution and magnification prevent it from providing more detailed information on pore structure. Scanning electron microscopy (SEM) can overcome this shortcoming. SEM is an observational method that falls between transmission electron microscopy and optical microscopy. It is widely used to observe the surface ultrastructure and composition of various solid materials. It utilizes a focused high-energy electron beam to scan rock samples. Through the interaction between the beam and the material, various physical information is excited. The collected information is magnified and re-imaged to characterize the microscopic morphology of the material. It features high resolution, large depth of field, and strong stereoscopic effect (Du et al., 2014; Zhang et al., 2022; Li et al., 2022). This technology is widely used in geological research, especially in oil and gas exploration and development. It can clearly observe the surface morphology and compositional differences of minerals at the nanoscale, as well as the pore structure and distribution at the micro- and nano-scale, providing information on the micro-morphology and structure of minerals (Xiao et al., 2018; Huang et al., 2021).

[0005] Researchers can visually observe the type and morphology of rock pore structure using scanning electron microscopy and cast thin sections, but these two testing methods cannot achieve a comprehensive quantitative characterization of pore throat radius. Furthermore, in some areas, due to difficulties in core sampling, a sufficient number of core samples cannot be obtained for cast thin section or scanning electron microscopy observation (Hemes et al., 2015; Li et al., 2015; Xiao et al., 2018; Huang et al., 2020).

[0006] In summary, shale oil reservoirs are characterized by complex lithology and rapid changes in lithofacies, resulting in unique microscopic pore structure features. While scanning electron microscopy (SEM) offers strong visualization and convenience, its use in past studies has primarily been qualitative. Furthermore, SEM observation requires the extraction of partial rock samples, and in some areas, due to difficulties in core sampling, sufficient core data cannot be obtained. Summary of the Invention

[0007] This invention provides a method, apparatus, electronic device, and medium for predicting the porosity of shale oil reservoirs, thereby solving the aforementioned technical problem of the difficulty in quantitatively predicting the porosity of rocks in existing technologies.

[0008] According to a first aspect of the present invention, a method for predicting the porosity of shale oil reservoir rocks is provided, comprising:

[0009] Process shale oil samples into at least two parallel samples;

[0010] The acoustic emission system observes the longitudinal wave spectrum characteristics of the first parallel sample and extracts the waveform and signal of the longitudinal wave spectrum within the corresponding time window.

[0011] Based on the discrete Fourier transform, the longitudinal wave is converted from the time domain to the frequency domain, and the characteristic curve of the longitudinal wave frequency spectrum of the rock sample is obtained.

[0012] Based on the frequency distribution characteristics in the longitudinal wave frequency spectrum, the longitudinal wave center frequency F of the rock sample is used. z Quantitatively characterize the overall distribution range of longitudinal wave frequencies;

[0013] Fitting the longitudinal waveform center frequency F of the rock sample z With the corresponding rock face rate Ф S The relationship between the rock faces was established, and a face rate prediction model was constructed. This involved observing a second parallel sample using scanning electron microscopy and obtaining the face rate Ф of the rock face using image processing software. S .

[0014] Preferably, the samples are dried after being processed into at least two parallel samples.

[0015] Preferably, for the first parallel sample, the waveform and signal of the longitudinal wave spectrum within the window from 0 to 300 μs are extracted as a whole.

[0016] Preferably, the discrete sampled value x(nT) of the continuous signal x(t) is obtained from the characteristic curve of the longitudinal wave frequency spectrum of the rock sample, and the specific formula is as follows:

[0017]

[0018] In the formula, X is a discrete sequence of frequency domain signals.

[0019] X(k) is a frequency domain parameter, k = 0, 1, ..., N-1.

[0020] ω is the angular frequency, in rad / s.

[0021] x is a discrete sequence of signals in the time domain.

[0022] x(n) represents the data at the nth sampling point in the discrete signal x.

[0023] j is an imaginary number.

[0024] N is the length of the discrete signal.

[0025] Preferably, the longitudinal waveform center frequency F of the rock sample is... z definition

[0026]

[0027] In the formula, F z For each discretized frequency value, in kHz,

[0028] A(Fi) is the amplitude of the frequency wave, in mV.

[0029] △F is the difference between adjacent frequencies, in kHz.

[0030] Preferably, the rock surface area ratio Ф S The calculation formula is:

[0031]

[0032] In the formula, Ф S Face rate, %.

[0033] A pi Let be the area of ​​the i-th pore, in μm. 2 Or pixel;

[0034] A is the area of ​​the entire field of view, in μm. 2 Or pixel.

[0035] Preferably, the face rate prediction model is:

[0036] Ф S =AF z +B

[0037] In the formula: F z The transverse waveform's center frequency value is in kHz.

[0038] Ф S The porosity of the rock sample is expressed as a percentage.

[0039] A and B are the fitting parameters, which are dimensionless.

[0040] Preferably, the method for predicting the porosity of shale oil reservoir rocks includes:

[0041] Analysis of the longitudinal waveform center frequency F of the rock sample z The corresponding rock face rate Ф S The correlation between them; wherein, based on high correlation, the face rate prediction model is applied; or, based on low correlation, core samples are collected again and the face rate prediction model is reconstructed.

[0042] According to a second aspect of the present invention, an apparatus for predicting the porosity of shale oil reservoir rocks is provided, comprising:

[0043] Core processing module for processing shale oil samples into at least two parallel samples;

[0044] The longitudinal wave spectrum extraction module within the time window is used by the acoustic emission system to observe the longitudinal wave spectrum characteristics of the first parallel sample and extract the waveform and signal of the longitudinal wave spectrum within the corresponding time window.

[0045] The spectrum conversion module is used to convert the longitudinal wave from the time domain to the frequency domain based on the discrete Fourier transform, so as to obtain the characteristic curve of the longitudinal wave frequency spectrum of the rock sample.

[0046] The longitudinal wave center frequency extraction module is used to extract the longitudinal wave center frequency F of the rock sample based on the frequency distribution characteristics in the longitudinal wave frequency spectrum. z Quantitatively characterize the overall distribution range of longitudinal wave frequencies;

[0047] The scanning electron microscope (SEM) image processing module is used to observe the second parallel sample using an SEM and obtain the rock porosity Φ using image processing software. S ;

[0048] The fitting analysis module is used to fit the longitudinal waveform center frequency F of the rock sample. z The corresponding rock face rate Ф S The relationship between; and

[0049] The prediction model building module is used to build face rate prediction models.

[0050] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0051] Memory; and

[0052] processor;

[0053] The memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to implement the method described in any of the above.

[0054] According to a fourth aspect of the present invention, a readable storage medium is provided, wherein computer instructions are stored thereon; wherein, when executed by a processor, the computer instructions implement the method described in any of the preceding claims.

[0055] The technical solution of this invention converts the longitudinal wave from the time domain to the frequency domain based on the discrete Fourier transform, utilizing the centroid frequency F. z It enables the prediction of rock porosity; enriches the evaluation methods for pore structure in shale oil reservoirs, and provides a quantitative evaluation method for assessing the porosity and pore development of shale oil rocks; it has good application effects on shale oil reservoirs, with strong practicality, universality and operability, and good evaluation accuracy.

[0056] The technical solution of this invention is simple and efficient. Without damaging the rock sample, it can predict the porosity of the rock sample and assess the degree of porosity development inside the rock sample, which meets the actual needs of efficient and stable production on site. Attached Figure Description

[0057] Figure 1This is a flowchart of a method for predicting the rock porosity of shale oil reservoirs in one embodiment;

[0058] Figure 2 This is a logic block diagram of a method for predicting the porosity of shale oil reservoir rocks in one embodiment;

[0059] Figure 3 This is a schematic diagram of a device for predicting the porosity of shale oil reservoir rocks in one embodiment.

[0060] Figure 4 This is an example of a diagram showing the porosity extraction effect of shale oil rocks in the Lianggaoshan Formation of the central Sichuan Basin.

[0061] Figure 5 This is a 0-300μs longitudinal wave waveform diagram of shale oil from the Lianggaoshan Formation in the central Sichuan Basin, as shown in one embodiment.

[0062] Figure 6 This is a P-wave frequency distribution curve of shale oil in the Lianggaoshan Formation of the central Sichuan Basin after fast Fourier transform, as described in one embodiment.

[0063] Figure 7 This is a diagram illustrating a prediction model for the porosity of shale oil rocks in the Lianggaoshan Formation of the central Sichuan Basin, as described in one embodiment.

[0064] Figure 8 This is a verification diagram of a rock face prediction model for shale oil rocks in the Lianggaoshan Formation of the central Sichuan Basin, as described in one embodiment. Detailed Implementation

[0065] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0066] To enable those skilled in the art to better understand the present invention, the technical solution of one embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0067] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0068] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element, or there may be an intermediate element present. Moreover, in this invention, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element, or "connected" to the other element via a third element.

[0069] Example 1

[0070] Please refer to Figure 1 , Figure 2 This embodiment provides a method for predicting the porosity of shale oil reservoir rocks, including the following steps:

[0071] S1. Process shale oil samples into at least two parallel samples;

[0072] S2. The acoustic emission system observes the longitudinal wave spectrum characteristics of the first parallel sample and extracts the waveform and signal of the longitudinal wave spectrum within the corresponding time window;

[0073] S3. Based on the discrete Fourier transform, the longitudinal wave is converted from the time domain to the frequency domain, and the characteristic curve of the longitudinal wave frequency spectrum of the rock sample is obtained;

[0074] S4. Based on the frequency distribution characteristics in the longitudinal wave frequency spectrum, using the longitudinal wave center frequency F of the rock sample... z Quantitatively characterize the overall distribution range of longitudinal wave frequencies;

[0075] S5. Fit the longitudinal waveform of the rock sample to the center frequency F. z With the corresponding rock face rate Ф S The relationship between the rock faces was established, and a face rate prediction model was constructed. This involved observing a second parallel sample using scanning electron microscopy and obtaining the face rate Ф of the rock face using image processing software. S The rock surface surface ratio Ф S In other words, it is the proportion of the pore area to the entire field of view in a scanning electron microscope image, reflecting the proportion of the area occupied by macropores observed under the microscope.

[0076] In one embodiment, in step S1, the shale oil plunger sample is cut into two parts: Part 1 has a diameter of 25.2 mm and a length of 5 mm, which is the second parallel sample; Part 2 has a diameter and length of 25.2 mm, which is the first parallel sample. The two rock samples are parallel samples cut from the same plunger sample. The processed rock samples are placed in an oven and dried for 15-24 hours for later use.

[0077] In one embodiment, rock sample Part1 was observed using a scanning electron microscope, and the rock image was processed using image processing software to obtain the corresponding rock face ratio (Ф). S And assess the development of rock porosity; the specific formula for calculating porosity is as follows:

[0078]

[0079] In the formula, Ф S Face rate, %.

[0080] A pi Let be the area of ​​the i-th pore, in μm. 2 Or pixel;

[0081] A is the area of ​​the entire field of view, in μm. 2 Or pixel.

[0082] In one embodiment, in step S2, the acoustic emission system is used to observe the acoustic spectral characteristics of rock sample Part2, i.e., the first parallel sample, and the waveform and signal of the longitudinal wave spectrum within the 0-300μs time window are extracted as a whole.

[0083] In one embodiment, in step S3, the acquired longitudinal wave x(t) is subjected to spectral transformation, and the characteristic curve of the longitudinal wave frequency spectrum of the rock sample is obtained by fast Fourier transform, and the discrete sampled value x(nT) of the continuous signal x(t) is obtained. The specific formula is as follows:

[0084]

[0085] In the formula, X is a discrete sequence of frequency domain signals.

[0086] X(k) is a frequency domain parameter, k = 0, 1, ..., N-1.

[0087] ω is the angular frequency, in rad / s.

[0088] x is a discrete sequence of signals in the time domain.

[0089] x(n) represents the data at the nth sampling point in the discrete signal x.

[0090] j is an imaginary number.

[0091] N is the length of the discrete signal.

[0092] In one embodiment, in step S4, the longitudinal waveform center frequency F of the rock sample is... z Defined as:

[0093]

[0094] In the formula, F z For each discretized frequency value, in kHz,

[0095] A(Fi) is the amplitude of the frequency wave, in mV.

[0096] △F is the difference between adjacent frequencies, in kHz.

[0097] In one embodiment, in step S5, the face rate prediction model is:

[0098] Ф S =AF z +B

[0099] In the formula: F z The transverse waveform's center frequency value is in kHz.

[0100] Ф S The porosity of the rock sample is expressed as a percentage.

[0101] A and B are the fitting parameters, which are dimensionless.

[0102] In one embodiment, the method for predicting the porosity of shale oil reservoir rocks includes the following steps:

[0103] S6. Analyze the longitudinal waveform center frequency F of the rock sample. z The corresponding rock face rate Ф S The correlation between them; wherein, based on high correlation, the porosity prediction model described in step S5 is applied, which can predict the porosity of rocks in adjacent or the same strata when only a small portion of rock samples are observed by scanning electron microscopy, in order to assess the porosity development status inside the reservoir. Alternatively, based on low correlation, core samples are collected again and the porosity prediction model is reconstructed.

[0104] The present invention provides a method for predicting the porosity of shale oil reservoirs based on the frequency conversion of the longitudinal wave spectrum of acoustic waves. In this technical solution, for shale oil reservoirs, the velocity domain is converted into the frequency domain based on the longitudinal wave spectrum of acoustic waves, and sensitive parameters are constructed to achieve quantitative prediction of the porosity of the rocks, providing a scientific basis for the exploration and development of shale oil reservoirs.

[0105] Example 2

[0106] Please refer to Figure 3One embodiment provides a device for predicting the porosity of shale oil reservoir rocks, which has the following structure:

[0107] 1. Core processing module

[0108] Core processing module 10 is used to process shale oil samples into at least two parallel samples;

[0109] 2. Longitudinal wave time window spectrum extraction module

[0110] The longitudinal wave spectrum extraction module 20 is used by the acoustic emission system to observe the longitudinal wave spectrum characteristics of the first parallel sample and extract the waveform and signal of the longitudinal wave spectrum within the corresponding time window.

[0111] 3. Spectrum Conversion Module

[0112] The spectrum conversion module 30 is used to convert the longitudinal wave from the time domain to the frequency domain based on the discrete Fourier transform, so as to obtain the characteristic curve of the longitudinal wave frequency spectrum of the rock sample.

[0113] 4. Longitudinal waveform core frequency extraction module

[0114] The longitudinal wave center frequency extraction module 40 is used to extract the longitudinal wave center frequency F of the rock sample based on the frequency distribution characteristics in the longitudinal wave frequency spectrum. z Quantitatively characterize the overall distribution range of longitudinal wave frequencies;

[0115] 5. Scanning electron microscope image processing module

[0116] The scanning electron microscope image processing module 50 is used to observe the second parallel sample using a scanning electron microscope and obtain the rock porosity Φ using image processing software. S ;

[0117] 6. Fitting Analysis Module

[0118] The fitting analysis module 60 is used to fit the longitudinal waveform center frequency F of the rock sample. z The corresponding rock face rate Ф S The relationship between them;

[0119] 7. Predictive Model Building Module

[0120] The prediction model building module 70 is used to build face rate prediction models.

[0121] It should be noted that the apparatus of the present invention is used to implement the methods in the above embodiments, and each module in the apparatus corresponds to each step in the method.

[0122] Example 3

[0123] Based on the same inventive concept, one embodiment of the present invention provides an electronic device, including: a memory and a processor; wherein the memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor using any of the methods described in the above embodiments.

[0124] Example 4

[0125] Based on the same inventive concept, one embodiment of the present invention provides a readable storage medium storing computer instructions; wherein, when the computer instructions are executed by a processor, they implement the method of any one of the above embodiments.

[0126] One or more of the aforementioned computer instructions can form a program.

[0127] The aforementioned program can run on a processor or be stored in memory (or computer-readable medium). Computer-readable medium includes both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable medium does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0128] These computer programs may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes can be implemented using different modules, and different steps can be implemented using different modules.

[0129] Example 5

[0130] Please refer to Figures 1-8In this embodiment, to verify the effectiveness of the method of the present invention in the quantitative evaluation of rock porosity in shale oil reservoirs, the method for predicting rock porosity in shale oil reservoirs involved in the above embodiments was applied to the Lianggaoshan Formation shale oil reservoir in the central Sichuan Basin, and the porosity of actual core samples was verified using scanning electron microscopy.

[0131] This embodiment describes a method for quantitatively predicting the porosity of shale oil reservoir rocks using core acoustic wave experimental data. The steps are as follows:

[0132] S1. For the Lianggaoshan Formation shale oil in the central Sichuan Basin, 10 complete plunger samples were drilled. Each core was cut into two parts with a uniform diameter of 25.2 mm and lengths of 5 mm and 30 mm respectively. The two ends of the plunger sample were ground flat and perpendicular to the axis of the cylinder. There were no defects on the cylinder surface and the two ends. The cut plunger samples were placed in an oven and dried for 24 hours.

[0133] S2. Ten plunger samples, each 5 mm in length, were observed using a scanning electron microscope. The porosity of the rocks was extracted using image processing software. The processing results are shown below. Figure 4 The porosity is the proportion of the pore area to the total field of view in a scanning electron microscope image, reflecting the proportion of the area occupied by macropores that can be observed under the microscope.

[0134] S3. Using an acoustic emission system, the longitudinal wave spectrum waveforms and signals of five 30mm long rock samples were extracted within a time window of 0–300μs. Figure 5 Furthermore, the frequency spectrum characteristic curve of the rock sample was obtained using the fast Fourier transform.

[0135] S4. Frequency distribution curve for quantitative characterization of longitudinal wave frequency spectrum ( Figure 6 ), using centroid frequency (F z This reflects the distribution range of longitudinal wave frequencies in the rock core. While obtaining the centroid frequencies of these five rock samples, we also obtain their corresponding porosity.

[0136] S5. Fitting the P-wave spectral centroid frequency and porosity parameters of 5 rock samples, the linear relationship between the two is obtained as follows (see porosity prediction model). Figure 7 ):

[0137] Ф S =0.0217F z -5.0846

[0138] S6. The longitudinal wave center frequency (F) of the remaining 5 rock samples was obtained using an acoustic emission system. z Substituting these values ​​into the above fitting model, the predicted facet ratios of these five rock samples were obtained. The predicted facet ratios were then cross-referenced with the facet ratios obtained from the actual image processing software. The predicted facet ratios and actual facet ratios showed a positive correlation, and the correlation coefficient was relatively high. Figure 8This indicates that the obtained rock porosity prediction model has high accuracy, and the method can accurately predict the porosity of rocks without scanning electron microscopy. It meets the practical needs of cost-effectiveness and high efficiency in field applications, and has achieved good results in the Lianggaoshan Formation shale oil field in the central Sichuan Basin.

[0139] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting the porosity of shale oil reservoir rocks, characterized in that, include: Process shale oil samples into at least two parallel samples; The acoustic emission system observes the longitudinal wave spectrum characteristics of the first parallel sample and extracts the waveform and signal of the longitudinal wave spectrum within the corresponding time window. Based on the discrete Fourier transform, the longitudinal wave is converted from the time domain to the frequency domain, and the characteristic curve of the longitudinal wave frequency spectrum of the rock sample is obtained. Based on the frequency distribution characteristics in the longitudinal wave frequency spectrum, the longitudinal wave center frequency F of the rock sample is used. z Quantitatively characterize the overall distribution range of longitudinal wave frequencies; Fitting the longitudinal waveform center frequency F of the rock sample z With the corresponding rock face rate Ф S The relationship between the rock faces was established, and a face rate prediction model was constructed. This involved observing a second parallel sample using scanning electron microscopy and obtaining the face rate Ф of the rock face using image processing software. S .

2. The method for predicting the porosity of shale oil reservoir rocks according to claim 1, characterized in that, After processing into at least two parallel samples, they are dried.

3. The method for predicting the porosity of shale oil reservoir rocks according to claim 1, characterized in that, For the first parallel sample, the waveform and signal of the longitudinal wave spectrum within the window from 0 to 300 μs were extracted as a whole.

4. The method for predicting the porosity of shale oil reservoir rocks according to claim 1, characterized in that, From the characteristic curve of the longitudinal wave frequency spectrum of the rock sample, the discrete sampled value x(nT) of the continuous signal x(t) is obtained, and the specific formula is as follows: In the formula, X is a discrete sequence of frequency domain signals. X(k) is a frequency domain parameter, k = 0, 1, ..., N-1. ω is the angular frequency, in rad / s. x is a discrete sequence of signals in the time domain. x(n) represents the data at the nth sampling point in the discrete signal x. j is an imaginary number. N is the length of the discrete signal.

5. The method for predicting the porosity of shale oil reservoir rocks according to claim 1, characterized in that, The longitudinal waveform of the rock sample has a center frequency F. z Defined as: In the formula, F z For each discretized frequency value, in kHz, A(Fi) is the amplitude of the frequency wave, in mV. △F is the difference between adjacent frequencies, in kHz.

6. The method for predicting the porosity of shale oil reservoir rocks according to claim 1, characterized in that, The rock face rate Ф S The calculation formula is: In the formula, Ф S Face rate, %. A pi Let be the area of ​​the i-th pore, in μm. 2 Or pixel; A is the area of ​​the entire field of view, in μm. 2 Or pixel.

7. The method for predicting the porosity of shale oil reservoir rocks according to claim 1, characterized in that, The face rate prediction model is as follows: F S =OFF z +B In the formula: F z The transverse waveform's center frequency value is in kHz. Ф S The porosity of the rock sample is expressed as a percentage. A and B are the fitting parameters, which are dimensionless.

8. The method for predicting the porosity of shale oil reservoirs according to any one of claims 1-7, characterized in that, The method for predicting the porosity of shale oil reservoir rocks includes: Analysis of the longitudinal waveform center frequency F of the rock sample z The corresponding rock face rate Ф S The correlation between them; wherein, based on high correlation, the face rate prediction model is applied; or, based on low correlation, core samples are collected again and the face rate prediction model is reconstructed.

9. A device for predicting the porosity of shale oil reservoir rocks, characterized in that, include: Core processing module for processing shale oil samples into at least two parallel samples; The longitudinal wave spectrum extraction module within the time window is used by the acoustic emission system to observe the longitudinal wave spectrum characteristics of the first parallel sample and extract the waveform and signal of the longitudinal wave spectrum within the corresponding time window. The spectrum conversion module is used to convert the longitudinal wave from the time domain to the frequency domain based on the discrete Fourier transform, so as to obtain the characteristic curve of the longitudinal wave frequency spectrum of the rock sample. The longitudinal wave center frequency extraction module is used to extract the longitudinal wave center frequency F of the rock sample based on the frequency distribution characteristics in the longitudinal wave frequency spectrum. z Quantitatively characterize the overall distribution range of longitudinal wave frequencies; The scanning electron microscope (SEM) image processing module is used to observe the second parallel sample using an SEM and obtain the rock porosity Φ using image processing software. S ; The fitting analysis module is used to fit the longitudinal waveform center frequency F of the rock sample. z The corresponding rock face rate Ф S The relationship between; and The prediction model building module is used to build face rate prediction models.

10. An electronic device, characterized in that, include: Memory; and processor; The memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to implement the method according to any one of claims 1 to 8.

11. A readable storage medium, characterized in that, The readable storage medium stores computer instructions; wherein, when the computer instructions are executed by a processor, they implement the method described in any one of claims 1 to 8.

Images