Source rock TOC lower limit and conventional-unconventional resource quantity integrated evaluation method

Abstract

The invention discloses a hydrocarbon source rock TOC lower limit and conventional-unconventional resource quantity integrated evaluation method, which comprises the following steps of: obtaining GR, SP, RD and DT logging curves as prediction data of a convolutional neural network, and removing abnormal data values; performing set division, data normalization and tiling on the data, constructing a network structure and modeling, predicting TOC, S1 and S2 by using a prediction model, and expanding a sample; calculating the difference value between the original hydrocarbon generation potential and the current hydrocarbon generation potential of the hydrocarbon source rock, identifying and removing the micro-migration hydrocarbon content, and identifying the TOC lower limit of the high-quality hydrocarbon source rock by using the S1 / TOC and TOC intersection chart; drawing a contour map of the TOC of the high-quality hydrocarbon source rock, the thickness of the hydrocarbon source rock and the maturity of organic matters, and respectively calculating the hydrocarbon generation amount, the hydrocarbon retention amount and the hydrocarbon discharge amount of conventional and unconventional oil and gas. According to the method, the TOC lower limit of the high-quality hydrocarbon source rock is judged through a small amount of actually measured sample data and logging curve data, and the resource quantity is calculated, so that the cost loss of time, samples and the like caused by a large number of experiments is effectively reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas exploration, and in particular relates to an evaluation method for the TOC lower limit of source rocks and conventional-unconventional resource quantities. Background Art

[0002] my country has vast sea areas, which contain abundant oil and gas resources that are in urgent need of development. At present, oil and gas exploration and development are mainly concentrated on land and offshore. However, with the growth of global energy consumption demand, while increasing the development of existing resources, it is an important task to open up the field of marine oil and gas exploration and development to seek new resources.

[0003] At present, the cost of marine drilling, especially deep-sea drilling, is huge, core and wall core samples are very precious, and experimental test data such as geochemical analysis of source rocks are scarce. Therefore, it brings great challenges to the evaluation of source rocks, the selection and determination of hydrocarbon-rich depressions and favorable exploration blocks, which seriously affects the progress of marine oil and gas exploration. Summary of the invention

[0004] The problem to be solved by the present invention is to provide an integrated evaluation method for source rock TOC lower limit and conventional-unconventional resource volume, which can accurately identify the TOC lower limit of high-quality source rocks and calculate conventional-unconventional resource volume.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for evaluating the TOC lower limit of source rocks and conventional-unconventional resources, comprising the following steps:

[0006] S1: Obtain four logging curves GR, SP, RD, and DT as prediction data for the convolutional neural network, and remove abnormal data values;

[0007] S2: Use convolutional neural networks to perform data set partitioning, data normalization and tiling, network structure construction, and modeling, and use the established prediction model to predict TOC and S 1 and S 2 Make predictions and expand the sample;

[0008] S3: By calculating the difference between the original hydrocarbon generation potential and the current hydrocarbon generation potential of the source rock, the content of micro-migrated hydrocarbons is identified and eliminated, and S 1 / TOC and TOC cross-plot to determine the lower limit of TOC of high-quality source rocks;

[0009] S4: Draw contour maps of TOC, thickness of source rocks and organic matter maturity of high-quality source rocks, and calculate the generated hydrocarbons, retained hydrocarbons and discharged hydrocarbons of conventional and unconventional oil and gas respectively according to the hydrocarbon generation potential method.

[0010] Further, in the step S1, the logging curve data penetrates multiple source rock formations, and the selected wells include four of the logging curves at the same time, and the four logging curve data match each other.

[0011] Further, in the step S1, the predicted data obtained is the logging data measured in the shale interval, and the abnormal data values include the data caused by igneous intrusion and carbonate sedimentation anomalies.

[0012] Further, in the step S2, the fitting rate of the training set and the validation set obtained by the modeling process is greater than or equal to 80%.

[0013] Further, the step S3 includes the following steps

[0014] S31: Correct the S of the source rock, and calculate the current hydrocarbon generation potential of the source rock according to the total organic carbon content TOC, pyrolysis hydrocarbon and light hydrocarbon correction results; 1

[0015] S32: Calculate the hydrogen index and hydrocarbon generation conversion rate of different types of kerogens, and calculate the original hydrocarbon generation potential of the source rock;

[0016] S33: Identify and remove the micro hydrocarbon migration amount S according to the relative size between the original hydrocarbon generation potential of the source rock and the current hydrocarbon generation potential of the source rock; 1 ;

[0017] S34: Use the crossplot of S / TOC and TOC to determine the lower limit of TOC for high-quality source rocks. 1

[0018] Further, in the step S4, the total organic carbon TOC, source rock thickness, and internal boundary and overall size of the map of the organic matter maturity Ro of each formation are kept consistent.

[0019] Further, in the step S4, the hydrocarbon generation amount, retained hydrocarbon amount, and expelled hydrocarbon amount of conventional and unconventional oil and gas are calculated by the hydrocarbon generation potential method.

[0020] Further, the present invention provides a device for running the above data processing method.

[0021] Further, the present invention provides a device including a memory, a processor, and an algorithm stored in the memory and executable on the processor, and when the processor executes the computer program, the above data processing method is implemented.

[0022] Further, the present invention provides a computer-readable storage medium storing a computer algorithm, and when the computer algorithm is executed by a processor, the above data processing is implemented.

[0023] The advantages and positive effects of the present invention are:

[0024] The present invention only requires a small amount of measured sample data and logging curve data to identify the TOC lower limit of high-quality source rocks and calculate the resource volume, effectively reducing the time, sample and other cost losses caused by a large number of experiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present invention.

[0026] Figure 2 In the embodiment of the present invention, TOC, S 1 and S 2 Prediction pattern diagram.

[0027] Figure 3 It is a scatter plot before and after the micro-migrated hydrocarbons are removed in the embodiment of the present invention.

[0028] Figure 4 It is a schematic diagram of the lower limit identification principle of high-quality hydrocarbon source rocks according to an embodiment of the present invention.

[0029] Figure 5 It is a TOC contour map of high-quality source rocks in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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.

[0031] The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

[0032] like Figure 1 As shown, a method for evaluating the TOC lower limit of source rocks and conventional and unconventional resources includes the following steps:

[0033] S1: Obtain four logging curves GR, SP, RD, and DT related to organic matter as the prediction data of the convolutional neural network, and remove abnormal data values ​​based on the 3Sigma criterion. Specifically, the selected logging curve data must run through multiple source rock formations, and the four logging curve data should match each other, and the selected wells should include the four curves at the same time. Among them, the screened data should be the logging data measured in the shale layer; abnormal data points are identified based on the 3Sigma criterion and lithology differences. Specifically, the abnormal data values ​​include abnormal data points of each curve caused by non-shale deposition such as igneous rock intrusion and carbonate rock deposition.

[0034] S2: Use a convolutional neural network to perform set partitioning, data normalization and tiling, network structure construction, and modeling on the data, and use the established prediction model to predict TOC, S 1 and S 2 respectively, while maintaining the prediction accuracy, effectively expanding the samples. Among them, the fitting rate of the training set and validation set obtained from the modeling process should reach more than 80%.

[0035] S3: By calculating the difference △Q between the original hydrocarbon generation potential and the current hydrocarbon generation potential of the source rock, identify and eliminate the content of micro-migrated hydrocarbons, and use the S 1 / TOC and TOC crossplot to determine the lower limit of TOC for high-quality source rocks. Among them, the data sample points used to determine the lower limit of TOC for high-quality source rocks should avoid the influence of micro-migrated hydrocarbons S 1 . Specifically, S3 includes the following steps

[0036] S31: Correct the S 1 of the source rock according to the light hydrocarbon compensation correction plot of shale cores proposed by Xue Haitao et al. (2015), and calculate the current hydrocarbon generation potential of the source rock based on the total organic carbon content TOC, pyrolysis hydrocarbon, and light hydrocarbon correction results

[0037] S32: Calculate the hydrogen index and hydrocarbon generation conversion rate of different types of kerogens, and calculate the original hydrocarbon generation potential of the source rock

[0038] S33: Identify and remove the micro-migrated hydrocarbon amount S 1 according to the relative magnitude between the original hydrocarbon generation potential and the current hydrocarbon generation potential of the source rock

[0039] S34: Use the crossplot of S 1 / TOC and TOC to determine the lower limit of TOC for high-quality source rocks. Specifically, the TOC value corresponding to the inflection point of the decrease of S 1 / TOC is the critical point of a large amount of hydrocarbon expulsion, that is, the critical TOC lower limit of high-quality source rocks

[0040] S4: Draw the isopach maps of TOC, source rock thickness, and organic matter maturity of high-quality source rocks, and calculate the hydrocarbon generation amount, retained hydrocarbon amount, and expelled hydrocarbon amount of conventional and unconventional oils and gases respectively according to the hydrocarbon generation potential method. Among them, the sizes of the planar isopach maps of each layer should be kept consistent. Specifically, the internal boundaries and the overall size of the maps of the total organic carbon TOC, source rock thickness, and organic matter maturity Ro of each formation system should be consistent. Calculate the hydrocarbon generation amount, retained hydrocarbon amount, and expelled hydrocarbon amount of conventional and unconventional oils and gases through the hydrocarbon generation potential method

[0041] The marginal sag on the southeastern margin of the Bohai Bay Basin is located in the offshore oil drilling area of China. Ocean drilling and sampling are costly, and there is a lack of geochemical analysis and test data for source rocks. Identifying high-quality source rocks, selecting hydrocarbon-rich sags, and calculating conventional and unconventional resource volumes in the study area are of great significance for oil and gas exploration in the entire study area. The present invention will be further elaborated below in combination with specific embodiments, specifically including the following steps.

[0042] S1: Obtain four logging curves related to organic matter, namely GR, SP, RD, and DT, as the prediction data for the convolutional neural network. Based on the 3Sigma criterion, eliminate abnormal data points of each curve caused by non-shale sedimentation such as igneous rock intrusion and carbonate sedimentation. Specifically, the selected logging curve data must penetrate multiple source rock formations, and the four logging curve data should match each other. The selected wells should include at least the four logging curves of GR, SP, RD, and DT. And a part of the logging data should have corresponding measured geochemical data of source rocks. The lithologies corresponding to the four logging curves of GR, SP, RD, and DT are all shale sedimentation. In specific implementation, first select the well positions in the study area that commonly have the four logging curves of GR, SP, RD, and DT. Use Resform to open each well column, and import all the four logging curves of GR, SP, RD, and DT, formation, lithology, and depth information into Excel. Screen and eliminate the data of non-shale intervals. At the same time, use the 3Sigma principle to delete the maximum and minimum values within a certain range of data.

[0043] S2: Use the convolutional neural network to perform set partitioning, data normalization and tiling, network structure construction, and modeling processing on the sorted data. Predict TOC, S 1 and S 2 respectively according to the established model, and effectively expand the samples while maintaining the prediction accuracy. Specifically, in specific implementation, divide the selected data into a training set and a validation set in a ratio of 3:7, and establish a prediction model. As Figure 2 shown, then predict TOC, S 1 and S 2 respectively using the established model to expand the sample data points.

[0044] S3: Based on the theory and concept of hydrocarbon micro-migration identification, judge and eliminate the content of micro-migrated hydrocarbons by calculating the difference (△Q) between the original hydrocarbon generation potential and the current hydrocarbon generation potential of the source rock. As Figure 3 shown, according to the data after finally eliminating the abnormal points, use the S 1 / TOC and TOC crossplot to determine the lower limit of TOC for high-quality source rocks.

[0045] In specific implementation, using the light hydrocarbon compensation correction chart of shale core, the light hydrocarbon correction is performed on the obtained hydrothermal data of the source rock to obtain the actual soluble hydrocarbon content (S 1c ) of the source rock;

[0046] Preferably, using the total organic carbon TOC, the actual soluble hydrocarbon content S 1c of the source rock, and the pyrolysis hydrocarbon S 2 of the source rock, calculate the current and present hydrocarbon generation potential I HGP of the source rock through formula (1).

[0047] I HGP = (S 1C + S 2 ) / TOC × 100 (1)

[0048] Preferably, using the hydrogen index HI, the highest pyrolysis peak temperature T max , and the organic matter type division chart, divide the organic matter type of the source rock for the predicted data, and establish the kerogen evolution model of different organic matter types:

[0049]

[0050] According to the established kerogen evolution model and the hydrogen index HI, determine the hydrocarbon generation conversion rate TR corresponding to different thermal evolution degrees.

[0051] According to the preset range where the hue is located in the average value of the HSL value, determine the maturity level of the hydrocarbon fluid.

[0052] Then, use formula (2) to calculate the original hydrogen index HIo of the source rock, and calculate the difference ΔQ between the original hydrocarbon generation potential and the current and present hydrocarbon generation potential of the source rock according to formula (3).

[0053] HI o = HI(1 - TR) (2)

[0054] ΔQ = HIo - I HGP (3)

[0055] If the calculated ΔQ < 0, there is an injection of external migrated hydrocarbons, and delete this data point; if ΔQ > 0 and there is no injection of external migrated hydrocarbons, then retain the data point.

[0056] During the implementation process, using S 1 , total organic carbon TOC data, through the lower limit discrimination chart of high-quality source rock, as Figure 4 shown, determine the lower limit of high-quality source rock; draw the intersection diagram of S 1 / TOC and TOC. As TOC increases, the point where S 1 / TOC begins to decline is the TOC lower limit of the high-quality source rock.

[0057] S4: As Figure 5 shown, draw the isopach maps of TOC of high-quality source rocks, source rock thickness, and organic matter maturity. Calculate the hydrocarbon generation amount, retained hydrocarbon amount, and expelled hydrocarbon amount of conventional and unconventional oil and gas respectively according to the hydrocarbon generation potential method.

[0058] Specifically, according to the determined lower limit of high-quality source rocks and the screened data points, use double-arc software to draw the planar isopach maps of TOC, source rock thickness, and Ro for each layer section respectively.

[0059] During the implementation process, for the data of TOC, source rock thickness, and Ro of the same well in the same layer section, take their average values respectively. According to the well positions, mark the data points on the map. Subsequently, according to the characteristics such as sedimentary facies changes and cross-well section changes in the study area, outline the change trends of TOC, source rock thickness, and Ro in the double-arc. Finally, draw the isopach maps of different layer sections.

[0060] During the drawing process, the boundaries and sizes of the three types of isopach maps should be kept consistent. Subsequently, use the software to integrate the obtained isopach maps to calculate the hydrocarbon generation amount, expelled hydrocarbon amount, and retained hydrocarbon amount of source rocks in different layer sections and different sags.

[0061] The advantages and positive effects of the present invention are as follows:

[0062] The present invention only needs a small amount of measured sample data and logging curve data to identify the lower limit of TOC of high-quality source rocks and calculate the resource amount, effectively reducing the cost losses of time, samples, etc. brought by a large number of experiments.

[0063] The above has described in detail an embodiment of the present invention, but the content described is only the preferred embodiment of the present invention and cannot be considered as used to limit the implementation scope of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the patent coverage scope of the present invention.

Claims

1. A method for evaluating the TOC lower limit of source rocks and conventional and unconventional resources, characterized by: The following steps are included: S1: Obtain four logging curves GR, SP, RD, and DT as prediction data for the convolutional neural network, and remove abnormal data values; S2: Use convolutional neural networks to perform data set partitioning, data normalization and tiling, network structure construction, and modeling. Use the established prediction model to predict TOC, S1, and S2 respectively, and expand the sample. S3: By calculating the difference between the original hydrocarbon generation potential and the current hydrocarbon generation potential of the source rock, the content of micro-migrated hydrocarbons is identified and eliminated, and the TOC lower limit of high-quality source rocks is determined using the S1 / TOC and TOC intersection chart; S4: Draw contour maps of TOC, thickness of source rocks and organic matter maturity of high-quality source rocks, and calculate the generated hydrocarbons, retained hydrocarbons and discharged hydrocarbons of conventional and unconventional oil and gas respectively according to the hydrocarbon generation potential method.

2. The method for evaluating the TOC lower limit of source rocks and conventional and unconventional resources according to claim 1, characterized in that: In S1, the logging curve data runs through multiple source rock formations, and the selected well includes four logging curves at the same time, and the four logging curve data match each other.

3. A method for evaluating the TOC lower limit of source rocks and conventional and unconventional resources according to claim 1 or 2, characterized in that: In S1, the prediction data obtained are logging data measured in the shale layer, and the abnormal data values ​​include data caused by abnormal phenomena of igneous rock intrusion and carbonate rock deposition.

4. A method for evaluating the TOC lower limit of source rocks and conventional and unconventional resources according to claim 1 or 2, characterized in that: In S2, the fitting rates of the training set and the validation set obtained by the modeling process are greater than or equal to 80%.

5. The method for evaluating the TOC lower limit of source rocks and conventional and unconventional resources according to claim 1 or 2, characterized in that: The S3 comprises the following steps, S31: Correct the S1 of the source rock and calculate the current hydrocarbon generation potential of the source rock based on the correction results of total organic carbon content TOC, pyrolysis hydrocarbons and light hydrocarbons; S32: Calculate the hydrogen index and hydrocarbon conversion rate of different types of kerogen, and calculate the original hydrocarbon generation potential of source rocks; S33: Identify and remove the trace hydrocarbon amount S1 based on the relative size between the original hydrocarbon generation potential of the source rock and the current hydrocarbon generation potential of the source rock; S34: Use the intersection chart of S1 / TOC and TOC to identify the lower limit of TOC of high-quality source rocks.

6. A method for evaluating the TOC lower limit of source rocks and conventional and unconventional resources according to claim 1 or 2, characterized in that: In the S4, the internal boundaries and the overall size of the planar graph of total organic carbon TOC, source rock thickness and organic matter maturity Ro of each stratum remain consistent.

7. A method for evaluating the TOC lower limit of source rocks and conventional and unconventional resources according to claim 1 or 2, characterized in that: In S4, the amount of hydrocarbons generated, retained, and discharged from conventional and unconventional oil and gas is calculated using the hydrocarbon generation potential method.

8. A device, characterized in that: Run the data processing method according to any one of claims 1 to 7.

9. A device comprising a memory, a processor, and an algorithm stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data processing method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer algorithm, characterized in that: When the computer algorithm is executed by a processor, the data processing according to any one of claims 1 to 7 is implemented.

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