Method and system for predicting hydrocarbon source rock and computer readable storage medium

Abstract

The invention relates to a method and system for predicting hydrocarbon source rocks and a computer readable storage medium, and the method comprises the steps: precisely positioning the top and bottom interfaces of the hydrocarbon source rocks in time domain seismic data through well-seismic joint calibration and whole-region tracking, and solving the problem of waveform distortion caused by stratum thickness changes. Then, the characteristic frequency of the hydrocarbon source rock is extracted through time-frequency spectrum analysis, analysis is focused on the characteristics of the hydrocarbon source rock, and non-correlation interference is reduced. Furthermore, according to the characteristic frequency, frequency division explanation is carried out, time domain data is converted into a frequency domain, and waveform clustering analysis is promoted by utilizing the frequency domain advantage. The frequency domain data is processed through a fragrant interpolation method, the data quality is improved, the smoothness and continuity are enhanced, and noise is suppressed. And finally, waveform clustering is carried out, the waveform distortion influence is reduced, and the clustering stability and reliability are improved, so that hydrocarbon source rock plane distribution is visually displayed, and an accurate and reliable geological basis is provided for oil-gas exploration and development.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrocarbon source rock prediction, and in particular to a method and system for predicting hydrocarbon source rocks and a computer-readable storage medium. Background Art

[0002] The frequency, phase, amplitude and other information of seismic data will eventually be reflected in the seismic waveform, that is, the change of the seismic waveform reflects the change of the seismic signal. Based on this theoretical understanding, geophysical exploration applies waveform clustering to seismic phase research, sedimentary microfacies prediction and lithofacies division. The principle is to cluster vectors, which is to classify waveforms with greater similarity into the same category; waveforms with less similarity are divided into different categories. The current waveform clustering method mainly represents typical waveforms of different categories through "clustering centers". First, the initial clustering center is randomly selected, and then the clustering center is iteratively updated until it converges to the real clustering center. Finally, the clustering center is used to divide the waveforms of each seismic channel. The waveform is divided into which category according to which clustering center has the greatest similarity to a certain waveform. Different waveforms represent different seismic phases. Then, the seismic phase transition to sedimentary phase is realized according to the principle of seismic stratigraphy. Most exploration workers use waveform clustering methods to predict reservoir distribution, and some experts and scholars also use this method to describe volcanic rock lithofacies. However, it is the first time that it has been applied to the distribution study of source rocks.

[0003] Traditional waveform clustering methods mostly divide seismic phases based on the similarity of seismic trace waveforms within an equal-time window. However, if the seismic signals are resampled into equal-length signals in the time domain when the thickness of the formation varies greatly, waveform distortion is likely to occur, which limits their use when the thickness of the target layer varies greatly. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method, system and computer-readable storage medium for predicting hydrocarbon source rocks in view of the inaccurate waveform problem existing in the prior art.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for predicting hydrocarbon source rocks, comprising the following steps:

[0006] S1: Obtain post-stack time domain seismic data in the target area and actual drilling data of source rocks encountered;

[0007] S2: performing well-seismic joint calibration based on the actual drilling data and the time-domain seismic data, determining the top and bottom interfaces of the source rock corresponding to the time-domain seismic data, and performing full-area tracking on the time-domain seismic data based on the top and bottom interfaces to obtain the top and bottom surface data of the source rock;

[0008] S3: Analyze the time-domain seismic data using time-frequency spectrum analysis based on the actual drilling data after well-seismic joint calibration to obtain the characteristic frequency of the source rock;

[0009] S4: performing frequency division interpretation on the time domain seismic data according to the characteristic frequency of the source rock to determine the seismic frequency division data corresponding to the characteristic frequency of the source rock;

[0010] S5: converting the time-domain seismic frequency division data into frequency-domain seismic frequency division data, and performing interpolation processing on the frequency-domain seismic frequency division data using the Shannon interpolation method to determine the frequency-domain seismic frequency division data after the interpolation processing;

[0011] S6: Based on the top and bottom surface data of the source rock and the interpolated frequency domain seismic frequency division data, waveform clustering is performed between the top and bottom surfaces to obtain a waveform clustering plane map reflecting the plane distribution of the source rock in the target area.

[0012] In one embodiment, the actual drilling data includes acoustic wave data and logging density data, and S2 includes:

[0013] Obtaining velocity data and density data of the source rock according to the acoustic wave data and the well logging density data;

[0014] Based on the velocity data and density data of the source rock, the time domain seismic data is calibrated with well-seismic joint calibration to determine the top and bottom interfaces of the time domain seismic data corresponding to the source rock, and full-area tracking is carried out on the time domain seismic data to obtain the top and bottom surface data of the source rock.

[0015] In one embodiment, the wave resistance characteristics of the source rock include three-peaks and two-valleys characteristics and two-peaks and two-valleys characteristics.

[0016] In one embodiment, the logging data of the actual drilling includes an acoustic wave time difference curve and a density curve, and the time domain seismic data includes an actual seismic record;

[0017] The performing well-seismic joint calibration on the time-domain seismic data based on the wave resistance characteristics of the source rock to determine the top and bottom interfaces of the time-domain seismic data corresponding to the source rock comprises:

[0018] synthesizing the acoustic wave time difference curve and the density curve to obtain a synthetic seismic record;

[0019] By comparing the cross-correlation between the actual seismic record and the synthetic seismic record, the top and bottom interfaces of the time domain seismic data corresponding to the source rock are determined.

[0020] In one embodiment, determining the top and bottom interfaces of the time-domain seismic data corresponding to the source rock by comparing the cross-correlation between the actual seismic record and the synthetic seismic record includes:

[0021] Based on the top-bottom interface in the actual drilling data, the time-domain seismic data is tracked using a three-dimensional seismic phase tracking technology to determine the top and bottom interfaces of the time-domain seismic data corresponding to the source rock.

[0022] In one embodiment, the S3 includes:

[0023] Based on the actual drilling data after the well-seismic joint calibration, the time domain seismic data is analyzed using time-frequency spectrum analysis to obtain the characteristic frequency segment of the source rock, and the average value of the characteristic frequency segment of the source rock is calculated to obtain the characteristic frequency of the source rock.

[0024] In one embodiment, the S4 includes:

[0025] The time-domain seismic data is subjected to structural guidance filtering processing, and the time-domain seismic data after structural guidance filtering processing is subjected to frequency division interpretation according to the characteristic frequency of the source rock to determine the seismic frequency division data corresponding to the characteristic frequency of the source rock.

[0026] In one embodiment, converting the time domain seismic frequency division data into frequency domain seismic frequency division data comprises:

[0027] The time-domain seismic frequency-division data is converted into frequency-domain seismic frequency-division data using the Fourier transform method.

[0028] The present invention also provides a system for predicting hydrocarbon source rocks, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above methods for predicting hydrocarbon source rocks.

[0029] The present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon, wherein the computer program / instruction, when executed by a processor, implements the steps of any of the above-mentioned methods for predicting source rocks.

[0030] The implementation of the present invention has the following beneficial effects: the top and bottom interfaces of the source rock in the time domain seismic data are accurately located through the combined calibration of wells and seismic data and the whole area tracking, thus overcoming the problem of waveform distortion caused by changes in stratum thickness. Subsequently, the characteristic frequency of the source rock is extracted by time-frequency spectrum analysis, so that the analysis focuses on the characteristics of the source rock and reduces non-correlated interference. Further, a frequency division interpretation is performed according to the characteristic frequency, and the time domain data is converted into the frequency domain to promote waveform clustering analysis by utilizing the advantages of the frequency domain. The frequency domain data is processed by the Shannon interpolation method to improve data quality, enhance smoothness and continuity, and suppress noise. Finally, waveform clustering is performed to reduce the impact of waveform distortion, improve clustering stability and reliability, and thus intuitively display the planar distribution of the source rock, providing an accurate and reliable geological basis for oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0032] Figure 1 is a flow chart of an embodiment of a method for predicting source rocks of the present invention;

[0033] Figure 2 is a flow chart of another embodiment of the method for predicting source rocks of the present invention;

[0034] Figure 3 1 is a schematic diagram of well-seismic joint calibration according to an embodiment of a method for predicting source rocks of the present invention;

[0035] Figure 4 This is a schematic diagram of the seismic phase characteristics of source rocks corresponding to the characteristic frequency 25 Hz in a method for predicting source rocks according to an embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram of clustering characteristics of a method for predicting source rocks in accordance with an embodiment of the present invention based on a 25 Hz characteristic frequency waveform. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The technical problem existing in the background technology is that the traditional waveform clustering method has reduced clustering accuracy and limited application due to waveform distortion under complex geological structures, especially when the stratum thickness varies greatly.

[0039] The present invention provides a method for predicting source rocks, which can achieve the following goals: accurately identify the position of source rocks in time-domain seismic data through well-seismic joint calibration and full-area tracking; use time-frequency spectrum analysis technology to extract characteristic frequencies of source rocks to provide accurate parameters for waveform clustering; use frequency domain processing and interpolation methods to reduce waveform distortion and improve the stability and reliability of waveform clustering; finally, perform efficient waveform clustering on the frequency domain seismic frequency division data after interpolation processing to generate a waveform clustering plane map reflecting the plane distribution of source rocks, provide intuitive and accurate prediction of source rock distribution for oil and gas exploration and development, and significantly improve exploration efficiency and success rate.

[0040] Specifically, Figure 1 As shown, the method for predicting source rocks includes:

[0041] S1: Acquire post-stack time domain seismic data of the target area and actual drilling data of the source rock encountered;

[0042] Specifically, post-stack time-domain seismic data is obtained by seismic exploration technology, which records the reflection and transmission information of underground rock layers to artificially excited seismic waves, and intuitively displays the complexity and diversity of underground geological structures in the form of time domain. At the same time, it is also necessary to obtain actual drilling data such as KP11-4-9d and KP11-4-10 that encounter source rocks, and the actual drilling data of source rocks.

[0043] like Figure 2 As shown, S2: perform well-seismic joint calibration based on actual drilling data and time-domain seismic data to determine the top and bottom interfaces of the source rock corresponding to the time-domain seismic data, and conduct full-area tracking on the time-domain seismic data based on the top and bottom interfaces to obtain the top and bottom surface data of the source rock.

[0044] The actual drilling data includes acoustic wave data and logging density data. Step S2 includes: obtaining velocity data and density data of the source rock according to the acoustic wave data and logging data; performing joint well-seismic calibration on the time domain seismic data based on the velocity data and density data of the source rock to determine the top and bottom interfaces of the time domain seismic data corresponding to the source rock, and performing full-area tracking on the time domain seismic data to obtain the top and bottom surface data of the source rock.

[0045] Among them, the wave resistance characteristics of source rocks include three peaks and two valleys and two peaks and two valleys. The logging data of actual drilling includes acoustic wave time difference curves and density curves, and the time domain seismic data includes actual seismic records.

[0046] Among them, the time domain seismic data is calibrated with well-seismic joint based on the wave resistance characteristics of the source rock to determine the top and bottom interfaces of the time domain seismic data corresponding to the source rock, including: tracking the time domain seismic data using three-dimensional seismic phase tracking technology based on the top and low interfaces in the actual drilling data to determine the top and bottom interfaces of the time domain seismic data corresponding to the source rock.

[0047] Specifically, the logging data of the actual drilling wells were analyzed to extract the wave resistance characteristics of the source rock (such as "three peaks and two valleys" and "two peaks and two valleys"), and then the time domain seismic data was jointly calibrated based on these characteristics. By comparing the cross-correlation between the synthetic seismic records and the actual seismic records (frequency, wave resistance and layer matching), the top and bottom interfaces of the source rock in the seismic data were accurately determined. The fine calibration of the KP11-4-9d and KP11-4-10 wells verified the accuracy of the wave resistance characteristics and revealed the specific morphology of the source rock section on the seismic profile. Then, the full-area tracking was carried out on the time domain seismic data to obtain complete top and bottom surface data of the source rock.

[0048] like Figure 3 and Figure 4As shown, S3: Based on the actual drilling data after well-seismic joint calibration, the time-domain seismic data is analyzed using time-frequency spectrum analysis to obtain the characteristic frequency of the source rock.

[0049] Furthermore, based on the actual drilling data after the joint calibration of well and seismic, the time-domain seismic data is analyzed using time-frequency spectrum analysis to obtain the characteristic frequency segments of the source rock, and the average value of the characteristic frequency segments of the source rock is calculated to obtain the characteristic frequency of the source rock.

[0050] Specifically, based on the actual drilling data after well-seismic joint calibration, the time-frequency spectrum analysis technology was used to conduct an in-depth analysis of the time-domain seismic data. The main purpose of this analysis is to reveal the characteristic frequency of the source rock in the time-domain seismic data. Specifically, by applying time-frequency spectrum analysis to the time-domain seismic data, the frequency range where the source rock characteristics change most significantly, i.e., the characteristic frequency range, was identified as 22-28Hz.

[0051] In order to more accurately quantify the characteristic frequency of the source rock, the frequency values ​​in the characteristic frequency band were calculated and their average value was obtained, thus obtaining the characteristic frequency of the source rock, which is 25Hz, based on the average value of the 22-28Hz characteristic frequency band. It is worth noting that the 22-28Hz characteristic frequency band is directly obtained through single-well time-frequency spectrum analysis, which reflects the unique frequency characteristics of the source rock in the seismic response.

[0052] S4: Perform frequency division interpretation on the time domain seismic data according to the characteristic frequency of the source rock to determine the seismic frequency division data corresponding to the characteristic frequency of the source rock.

[0053] Furthermore, the time-domain seismic data are subjected to structural guidance filtering processing, and the time-domain seismic data after structural guidance filtering processing are subjected to frequency division interpretation according to the characteristic frequency of the source rock to determine the seismic frequency division data corresponding to the characteristic frequency of the source rock.

[0054] Specifically, in order to improve the accuracy and reliability of frequency division interpretation, a structural guidance filtering processing technology was introduced. Structural guidance filtering is a filtering method based on the direction of geological structures. It can remove noise interference in seismic data while maintaining the characteristics of geological structures. The time domain seismic data is first subjected to structural guidance filtering to eliminate unnecessary noise and improve the signal-to-noise ratio of seismic data. Subsequently, the filtered seismic data is subjected to frequency division interpretation based on the characteristic frequency of the source rock. This step ensures that the frequency division analysis is performed on cleaner and clearer seismic data, thereby improving the accuracy and credibility of the seismic frequency division data corresponding to the characteristic frequency of the source rock.

[0055] S5: Convert the time domain seismic frequency division data into frequency domain seismic frequency division data, and use the Shannon interpolation method to interpolate the frequency domain seismic frequency division data.

[0056] Furthermore, the Fourier transform method is used to convert the time domain seismic frequency division data into frequency domain seismic frequency division data.

[0057] Specifically, by applying the Fourier transform technology, the data was converted from the time domain to the frequency domain. Then, in order to optimize the quality of frequency domain seismic frequency division data, the Sinc interpolation method (i.e. Shannon interpolation algorithm) was introduced. This algorithm uses the sinc function as the interpolation kernel and effectively eliminates the discontinuity and noise in the data through fine convolution operations, significantly improving the smoothness, resolution and signal-to-noise ratio of the data. This process not only enhances the ability to express data details, but also provides more accurate and reliable data support for subsequent geological structure analysis, source rock distribution assessment and reservoir characteristics research.

[0058] like Figure 5 As shown, S6: waveform clustering is performed between the top and bottom surfaces based on the top and bottom surface data of the source rock and the frequency domain seismic frequency division data after interpolation processing, so as to obtain a waveform clustering plane map reflecting the plane distribution of the source rock in the target area.

[0059] Specifically, Figure 5 The application results of waveform clustering technology based on characteristic frequency in describing the distribution of source rocks are shown. The figure associates specific waveform patterns in seismic data with the geological characteristics of source rocks through waveform clustering analysis, thus revealing the distribution of source rocks in underground space.

[0060] exist Figure 5 In the figure, Model 1 and Model 2 show typical "valley-peak" characteristic waveforms. These waveform patterns mainly reflect the seismic phase characteristics of source rocks. Seismic phase is a comprehensive form of expression reflecting the characteristics of underground geological bodies in seismic data. The "valley-peak" characteristics identified by waveform clustering technology provide an important basis for identifying source rocks. In contrast, Models 3 and 4 mainly reflect geological structural characteristics such as dividing grooves and faults. These characteristics are also manifested as specific waveform patterns in seismic data, but they are different from the "valley-peak" characteristics of source rocks. Through waveform clustering analysis, these different waveform patterns can be distinguished, thereby more accurately identifying source rocks and other geological structures.

[0061] Based on the top and bottom surface data of the source rock and the frequency domain seismic frequency division data after interpolation, waveform clustering analysis between the top and bottom surfaces was performed. This analysis process fully considers the vertical variation characteristics of the source rock and the high-resolution information in the frequency domain seismic data. Through cluster analysis, a waveform clustering plane map reflecting the plane distribution of the source rock in the target area was obtained. In the waveform clustering plane map, areas with the same waveform characteristics as model 1 and model 2 are identified as source rocks. These areas are marked with specific colors or symbols in the map, which intuitively shows the distribution of the source rock on the plane. This achievement not only provides direct evidence for the distribution of source rocks, but also provides important geological basis for subsequent oil and gas exploration and development.

[0062] The present invention also provides a system for predicting hydrocarbon source rocks, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to perform any of the steps of the above-mentioned method for predicting hydrocarbon source rocks.

[0063] The present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon, and when the computer program / instruction is executed by a processor, the steps of any of the above methods for predicting hydrocarbon source rocks are implemented.

[0064] Beneficial effects: by obtaining post-stack time-domain seismic data of the target area and actual drilling data of the source rock encountered; performing joint well-seismic calibration based on the actual drilling data and time-domain seismic data, determining the top and bottom interfaces of the source rock corresponding to the time-domain seismic data, and conducting full-area tracking on the time-domain seismic data based on the top and bottom interfaces, and obtaining the top and bottom surface data of the source rock; using time-frequency spectrum analysis to analyze the time-domain seismic data based on the actual drilling data after joint well-seismic calibration, and obtaining the characteristic frequency of the source rock; performing frequency division interpretation on the time-domain seismic data according to the characteristic frequency of the source rock, and determining the seismic frequency division data corresponding to the characteristic frequency of the source rock; converting the time-domain seismic frequency division data into frequency-domain seismic frequency division data, and interpolating the frequency-domain seismic frequency division data using the Shannon interpolation method, and determining the frequency-domain seismic frequency division data after interpolation; performing waveform clustering between the top and bottom surfaces of the source rock and the frequency-domain seismic frequency division data after interpolation, and obtaining a waveform clustering plane map reflecting the plane distribution of the source rock in the target area. The present invention accurately locates the position of source rocks in time-domain seismic data through well-seismic joint calibration and full-area tracking, and extracts characteristic frequencies by combining time-frequency spectrum analysis technology, effectively reducing waveform distortion caused by changes in formation thickness and improving the stability and reliability of waveform clustering. Furthermore, waveform clustering is efficiently performed on frequency-domain seismic frequency-division data after interpolation processing, intuitively showing the planar distribution characteristics of source rocks, significantly improving the efficiency and success rate of oil and gas exploration.

[0065] It can be understood that the above embodiments only express the preferred implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the patent scope of the present invention. It should be pointed out that, for ordinary technicians in this field, the above technical features can be freely combined without departing from the concept of the present invention, and several deformations and improvements can be made, which all belong to the protection scope of the present invention. Therefore, all equivalent changes and modifications made to the scope of the claims of the present invention should belong to the coverage of the claims of the present invention.

Claims

1. A method for predicting source rocks, characterized in that: The method comprises: S1: Obtain post-stack time domain seismic data in the target area and actual drilling data of source rocks encountered; S2: performing well-seismic joint calibration based on the actual drilling data and the time-domain seismic data, determining the top and bottom interfaces of the source rock corresponding to the time-domain seismic data, and performing full-area tracking on the time-domain seismic data based on the top and bottom interfaces to obtain the top and bottom surface data of the source rock; S3: Analyze the time-domain seismic data using time-frequency spectrum analysis based on the actual drilling data after well-seismic joint calibration to obtain the characteristic frequency of the source rock; S4: performing frequency division interpretation on the time domain seismic data according to the characteristic frequency of the source rock to determine the seismic frequency division data corresponding to the characteristic frequency of the source rock; S5: converting the time-domain seismic frequency division data into frequency-domain seismic frequency division data, and performing interpolation processing on the frequency-domain seismic frequency division data using the Shannon interpolation method to determine the frequency-domain seismic frequency division data after the interpolation processing; S6: Based on the top and bottom surface data of the source rock and the interpolated frequency domain seismic frequency division data, waveform clustering is performed between the top and bottom surfaces to obtain a waveform clustering plane map reflecting the plane distribution of the source rock in the target area.

2. The method for predicting source rocks according to claim 1, characterized in that: The actual drilling data includes acoustic wave data and logging density data, and S2 includes: Obtaining velocity data and density data of the source rock according to the acoustic wave data and the well logging density data; Based on the velocity data and density data of the source rock, the time domain seismic data is calibrated with well-seismic joint calibration to determine the top and bottom interfaces of the time domain seismic data corresponding to the source rock, and full-area tracking is carried out on the time domain seismic data to obtain the top and bottom surface data of the source rock.

3. The method for predicting source rocks according to claim 2, characterized in that: The wave resistance characteristics of the source rock include three-peak and two-valley characteristics and two-peak and two-valley characteristics.

4. The method for predicting source rocks according to claim 2, characterized in that: The logging data of the actual drilling includes an acoustic wave time difference curve and a density curve, and the time domain seismic data includes actual seismic records; The performing well-seismic joint calibration on the time-domain seismic data based on the wave resistance characteristics of the source rock to determine the top and bottom interfaces of the time-domain seismic data corresponding to the source rock comprises: synthesizing the acoustic wave time difference curve and the density curve to obtain a synthetic seismic record; By comparing the cross-correlation between the actual seismic record and the synthetic seismic record, the top and bottom interfaces of the time domain seismic data corresponding to the source rock are determined.

5. The method for predicting source rocks according to claim 4, characterized in that: The method of determining the top and bottom interfaces of the time domain seismic data corresponding to the source rock by comparing the cross-correlation between the actual seismic record and the synthetic seismic record includes: Based on the top-bottom interface in the actual drilling data, the time-domain seismic data is tracked using a three-dimensional seismic phase tracking technology to determine the top and bottom interfaces of the time-domain seismic data corresponding to the source rock.

6. The method for predicting source rocks according to claim 1, characterized in that: The S3 includes: Based on the actual drilling data after the well-seismic joint calibration, the time domain seismic data is analyzed using time-frequency spectrum analysis to obtain the characteristic frequency segment of the source rock, and the average value of the characteristic frequency segment of the source rock is calculated to obtain the characteristic frequency of the source rock.

7. The method for predicting source rocks according to claim 1, characterized in that: The S4 includes: The time-domain seismic data is subjected to structural guidance filtering processing, and the time-domain seismic data after structural guidance filtering processing is subjected to frequency division interpretation according to the characteristic frequency of the source rock to determine the seismic frequency division data corresponding to the characteristic frequency of the source rock.

8. The method for predicting source rocks according to claim 1, characterized in that: The converting the time domain seismic frequency division data into frequency domain seismic frequency division data comprises: The time-domain seismic frequency-division data is converted into frequency-domain seismic frequency-division data using the Fourier transform method.

9. A system for predicting hydrocarbon source rocks, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method for predicting hydrocarbon source rocks according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method for predicting source rocks according to any one of claims 1 to 8 are implemented.

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