Deep-ultra-deep oil source comparison evaluation method and system

Abstract

The invention relates to a deep layer-ultra-deep layer oil source comparison evaluation method and system, and belongs to the technical field of deep layer-ultra-deep layer oil and gas resource exploration. Comprising the following steps: 1, carrying out a gold tube thermal simulation experiment on a typical crude oil sample, and carrying out GC-MS test on saturated hydrocarbon and aromatic hydrocarbon components in a thermal simulation product to obtain equivalent maturity EqRo in a total thermal simulation temperature section; 2, screening out a relatively stable biomarker compound; relatively stable biomarker compound indexes are screened out; and 3, carrying out GC-MS test on natural crude oil and potential hydrocarbon source rocks, and constructing three types of oil source comparison indexes of an average difference index avgD according to the relatively stable biomarker compound indexes screened in the step 2 to carry out oil source comparison so as to reveal the source of deep-ultra-deep oil. According to the method, stable, effective and reliable deep oil source comparison indexes are found, and proper parameters are constructed for oil source comparison, so that deep-ultra-deep liquid hydrocarbon sources are revealed.

Description

Technical Field

[0001] The present invention relates to the technical field of deep - ultra - deep oil and gas resource exploration, and particularly relates to a method and system for deep - ultra - deep oil source correlation and evaluation, belonging to the category of geochemistry. Background Art

[0002] Deep - ultra - deep oil and gas are important and realistic oil and gas exploration fields globally, and the identification of their oil sources is a hot - spot and difficult point in the current petroleum geochemistry community. With the further deepening of the lower limit of liquid hydrocarbon discovery in China (the buried depth of the oil - producing layer in Well Zhong - Shen 1C in the Tarim Basin is about 7000 m, while that in Well Repu 3 is > 7000 m), it is expected that the maximum buried depth lower limit of liquid hydrocarbons can reach 10000 m. Therefore, the exploration of deep - ultra - deep oil and gas resources has become an inevitable trend in current oil and gas exploration. However, due to the influence of secondary changes such as thermal alteration on deep - ultra - deep crude oil, it has lost its original information related to sedimentary environment and biological composition, and the research on the origin and source of crude oil has been greatly restricted. For example, the oil source problem in the Lower Paleozoic of the Tarim Basin has long been controversial. Whether it is the contribution of the Middle - Upper Ordovician source rock or the Cambrian source rock is still unclear (Zhang Shuichang et al., 2002, 2004; Wang et al., 2008; Cai et al., 2009; Sun Yongge et al., 2014; Song Daofu et al., 2016; Li et al., 2020). The main reason is that the current conventional molecular geochemical evaluation methods (related indicators of isoprenoids, steranes, and terpanes) can no longer meet the current deep - ultra - deep, high - over - maturity oil source correlation. These indicators are greatly affected by the degree of thermal evolution and there is still a lack of stable, effective, and reliable oil source correlation indicators at present, which brings great challenges to oil source identification and further exploration decision - making, and seriously hinders the pace of deep - ultra - deep marine oil and gas exploration in China.

[0003] Therefore, there is an urgent need in the industry for a deep - ultra - deep oil source correlation and evaluation method based on stable, effective, and reliable oil source correlation indicators to guide the later deep - ultra - deep marine oil and gas exploration. Summary of the Invention

[0004] Currently, there is still a lack of a deep - ultra - deep oil source correlation and evaluation method in the industry, which brings great challenges to deep - ultra - deep oil source identification and further exploration decision - making, and seriously hinders the pace of deep - ultra - deep oil and gas exploration in China. In view of this, the technical problem to be solved by the present invention is: to find stable, effective, and reliable deep oil source correlation indicators, and construct appropriate parameters for oil source correlation, so as to reveal the source of deep - ultra - deep liquid hydrocarbons.

[0005] In view of the deficiencies of the prior art, the present invention provides a method and system for deep - ultra - deep oil source correlation and evaluation.

[0006] The technical solution of the present invention is as follows: A method for deep - ultra - deep oil source correlation and evaluation, comprising: Step 1: Conduct a gold tube thermal simulation experiment on typical crude oil samples, perform GC - MS tests on the saturated hydrocarbon and aromatic hydrocarbon components in the thermal simulation products, calculate the equivalent vitrinite reflectance MD - Ro at each temperature point based on the results of methyl - diadamantane, and match it with the Easy - Ro provided by the thermal simulation instrument to obtain the equivalent maturity EqRo for the entire thermal simulation temperature range; Step 2: Compare the evolution characteristics of the GC - MS spectra of saturated hydrocarbon / aromatic hydrocarbon in the products at each temperature point with EqRo, and screen out relatively stable biomarker compounds; According to the relatively stable compounds screened out, compare the evolution characteristics of each biomarker compound index with EqRo, and screen out relatively stable biomarker compound indexes; Step 3: Conduct GC - MS tests on natural crude oil and potential hydrocarbon source rocks, and construct the average difference degree index avg D for three types of oil source correlation indexes to carry out oil source correlation and reveal the source of deep - ultra - deep oil.

[0007] Furthermore, the specific implementation process of Step 1 includes: After the gold tube thermal simulation experiment on typical crude oil samples, separate the group components by column chromatography method to obtain saturated hydrocarbon, aromatic hydrocarbon, non - hydrocarbon, and asphaltene components respectively, and conduct GC - MS detection of full scan and selected ion scan on saturated hydrocarbon and aromatic hydrocarbon. Calculate the equivalent vitrinite reflectance MD - Ro at each temperature point according to the GC - MS detection result MD; MD - Ro = 0.0243×[4 - MD / (1 - MD + 3 - MD + 4 - MD)] + 0.4415, where MD refers to methyl - diadamantane; Compare with the Easy - Ro provided by the thermal simulation instrument; when MD - Ro > Easy - Ro, take MD - Ro as the equivalent maturity EqRo of the crude oil; otherwise, take Easy - Ro as EqRo.

[0008] Furthermore, the specific implementation process of Step 2 includes: Extract the GC - MS spectra of saturated hydrocarbon and aromatic hydrocarbon in the products at each temperature point, arrange them in ascending order of EqRo, and compare and screen out relatively stable biomarker compounds. The relatively stable biomarker compounds have the following characteristics: (1) The peak shape characteristics of the compound series in different thermal evolution stages are consistent before cracking and destruction in their GC - MS spectra; (2) The compound series in different thermal evolution stages can still be detected by GC - MS in the over - maturity stage; Compare and screen out relatively stable biomarker compounds, establish the evolution sequence of the parameters of relatively stable biomarker compounds at each temperature point with EqRo, and screen out relatively stable geochemical indicators. The relatively stable geochemical indicators include: a. The biomarker compound parameters with a change amplitude less than 5% in different thermal evolution stages; b. The biomarker compound parameters with a change amplitude less than 10% in different thermal evolution stages; Mark the screened biomarker parameters as P i , where i = 1, 2, 3,…, n.

[0009] Furthermore, the specific implementation process of step three includes: According to the biomarker parameter P i screened in step two, the characteristic sequences of potential source rocks and natural crude oils are obtained accordingly, as follows: The characteristic sequence of the source rock is: SP = { SP 1 , SP 2 , SP 3 , SP 4 , …, SP i , …, SP n}; SP i refers to the biomarker parameter in the source rock; The characteristic sequence of natural crude oil is: OP = { OP 1 , OP 2 , OP 3 , OP 4 , …, OP i , …, OP n}; OP i refers to the biomarker parameter in the crude oil; Then, the average value characteristic of each parameter of the potential source rock is expressed as: avg SP = {avg SP 1 , avg SP 2 ,avg SP 3 , avg SP4 , …, avg SP i , …,avg SP n}; Among them, avg SP n is obtained by calculating the arithmetic mean; The difference degree between a single oil sample and a potential hydrocarbon source rock is expressed as: ; Select the average value of the difference degree between a single oil sample and a potential hydrocarbon source rock i.e., the average difference degree index avg D as the final difference degree: ; Therefore, when avg D ≤100%, it is recommended that the potential hydrocarbon source rock corresponding to the minimum difference degree be the main source of crude oil; when avg D >100%, it indicates that the crude oil does not mainly come from this potential hydrocarbon source rock and has a new source.

[0010] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a deep - ultra - deep oil source contrast and evaluation method.

[0011] A computer - readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of a deep - ultra - deep oil source contrast and evaluation method.

[0012] A deep - ultra - deep oil source contrast and evaluation system includes: A full - heat simulation temperature - segment equivalent maturity EqRo calculation module, which is configured to: conduct a gold - tube thermal simulation experiment on typical crude oil samples, perform GC - MS tests on the saturated hydrocarbon and aromatic hydrocarbon components in the thermal simulation products, calculate the equivalent vitrinite reflectance MD - Ro at each temperature point based on the results of methyl - diamantane and match it with the Easy - Ro provided by the thermal simulation instrument to obtain the full - heat simulation temperature - segment equivalent maturity EqRo; A relatively stable biomarker index calculation module, which is configured to: compare the evolution characteristics of the GC - MS spectra of saturated hydrocarbon / aromatic hydrocarbon in the products at each temperature point with EqRo, and screen out relatively stable biomarker compounds; According to the screened - out relatively stable compounds, compare the evolution characteristics of each biomarker index with EqRo, and screen out relatively stable biomarker indices; Deep - ultra - deep oil source determination module, configured to: conduct GC - MS tests on natural crude oil and potential hydrocarbon source rocks, and construct the average difference index avg based on the relatively stable biomarker compound indicators screened in step two. D Carry out oil source correlation using three types of oil source correlation indicators to reveal the source of deep - ultra - deep oil.

[0013] Compared with the prior art, the present invention has the following excellent technical effects: (1) The present invention fully considers the limitations of conventional molecular geochemical analysis methods for deep - ultra - deep oil source correlation research. By calibrating the equivalent maturity of crude oil during the original thermal simulation process through a gold tube experiment, a set of deep - ultra - deep oil source correlation index systems integrating stable biomarker spectra, biomarker parameters, and oil source difference index avg D is proposed for the first time, enriching the oil and gas exploration theory system in China; (2) The present invention reveals that aromatic hydrocarbon compounds such as triaromatic steranes, triaromatic dinosteranes, and aryl isoprenoids and related parameters have high stability through a gold tube experiment, and based on this, the oil source difference index avg D is proposed, which can accurately determine the genetic relationship between a single oil sample and potential hydrocarbon source rocks. This method can be digitized in the future, with simple operation and strong reliability; (3) All the equipment used in the present invention are conventional equipment for organic geochemistry, and the reagents used are easily obtained in actual research. The relevant parameters of potential hydrocarbon source rocks can be obtained from literature - reported data or data accumulated over a long time in the research area (the more data, the more reliable). Moreover, the entire experimental scheme has simple operation and strong feasibility, playing a demonstration effect for the exploration of deep - ultra - deep oil and gas resources in China.

[0014] (4) The analysis method of the present invention solves the current hot issue of oil source correlation for deep - ultra - deep marine crude oil. Compared with conventional analysis methods, it has higher credibility and provides important technical support for the exploration of deep - ultra - deep oil and gas resources in China. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] With the detailed description given below in conjunction with the accompanying drawings, the implementation manner of the present invention will be understood more clearly. In the drawings: Figure 1 is a flow schematic diagram of a deep - ultra - deep oil source correlation evaluation method of the present invention; Figure 2 is a distribution characteristic diagram of aryl isoprenoids during the thermal simulation process of crude oil; Figure 3 is a distribution characteristic diagram of triaromatic steranes and triaromatic dinosteranes during the thermal simulation process of crude oil; Figure 4GC-MS comparison chart of aryl isoprenoid series between Class I crude oil and Class C hydrocarbon source rocks; Figure 5 GC-MS comparison chart of triaromatic steranes and triaromatic dinosteranes series between Class I crude oil and Class C hydrocarbon source rocks; Figure 6 Cross plot of stable biomarker compound index for oil source correlation in deep - ultra - deep layers of Tarim Basin; Specific implementation manners

[0016] The present invention will be further defined below in conjunction with the accompanying drawings of the specification and embodiments, but not limited thereto.

[0017] Term explanation: 1. Room temperature, 25 °C.

[0018] 2. Thermal simulation experiment, using the gold tube thermal simulation experiment. The specifications of the gold tube are 6 cm in length, 0.5 cm in inner diameter, and 0.25 mm in thickness.

[0019] 3. GC - MS detection, gas chromatography - mass spectrometry (GC - MS) combined instrument, using 5975i mass spectrometry and 6890 gas chromatography, and the chromatographic column is HP - PONA (length: 50 m, inner diameter: 200 μm, coating: 0.5 μm).

[0020] Example 1 A method for oil source correlation and evaluation in deep - ultra - deep layers, which screens relatively stable, effective and reliable GC - MS spectra of biomarker compounds for oil source correlation and related biomarker parameters through the gold tube thermal simulation technology of crude oil; in addition, constructs the oil - source difference index avg D , and studies the genetic relationship between a single oil sample and potential hydrocarbon source rocks. Thus, a set of deep - ultra - deep oil source correlation index systems integrating stable biomarker spectra, biomarker parameters and oil - source difference index is formed. As Figure 1 shown, it includes: Step 1: Conduct the gold tube thermal simulation experiment on typical crude oil samples, perform GC - MS tests on the saturated hydrocarbon and aromatic hydrocarbon components in the thermal simulation products, calculate the equivalent vitrinite reflectance MD - Ro at each temperature point according to the results of methyl - dicyclopentadiene (MD), match it with the Easy - Ro provided by the thermal simulation instrument, and obtain the equivalent maturity EqRo in the whole thermal simulation temperature range; reveal the equivalent maturity EqRo at each temperature point during the thermal simulation process; Step 2: Compare the evolution characteristics of the GC - MS spectra of saturated hydrocarbon / aromatic hydrocarbon in the products at each temperature point with EqRo, and screen out relatively stable biomarker compounds (such as aromatic hydrocarbon series compounds); most biomarker compounds are greatly affected by maturity, and it is necessary to screen out biomarker compounds with little influence of maturity, that is, relatively stable biomarker compounds; According to the relatively stable compounds screened out, compare the evolution characteristics of various biomarker compound indicators (such as the ratio of the peak areas of biomarker compounds) with EqRo, and screen out relatively stable biomarker compound indicators; the relatively stable biomarker compound indicators refer to the ratios between biomarker compounds, usually using the ratio of compound peak areas, and the peak areas of each compound can be obtained through GC-MS experiments; for subsequent oil-source correlation. Step 3: Conduct GC-MS tests on natural crude oil and potential hydrocarbon source rocks, and construct the average difference index avg according to the relatively stable biomarker compound indicators screened out in Step 2. D Carry out oil-source correlation using three types of oil-source correlation indicators to reveal the sources of deep-ultra-deep oils.

[0021] Example 2 A method for evaluating deep-ultra-deep oil-source correlation according to Example 1, which is characterized in that: The specific implementation process of Step 1 includes: Select 40 mg of representative oil samples, choose 11 gold tubes, and set the temperatures as follows: one temperature point is set every 30 °C in the range of 300 °C to 600 °C, the programmed temperature increase is set to 20 °C / h, take out after heating to the specified temperature, cut the gold tube after cooling to room temperature, quickly place it in an 8 ml glass bottle and add 2 ml of dichloromethane, ultrasonically dissolve the organic matter, transfer the solution to a 50 ml pear-shaped flask; then add 2 ml of dichloromethane, ultrasonically dissolve the organic matter, transfer the solution to a 50 ml pear-shaped flask; repeat 6 to 8 times until the solution is colorless to ensure that the organic matter in the gold tube has been completely transferred. Then, naturally dry and concentrate to 0.5 ml at room temperature. After the thermal simulation experiment of the gold tube of the typical crude oil sample, separate the group components according to column chromatography to obtain saturated hydrocarbons, aromatic hydrocarbons, non-hydrocarbons, and asphaltene components respectively, and conduct GC-MS detection of full scan and selected ion scan on saturated hydrocarbons and aromatic hydrocarbons. Calculate the equivalent vitrinite reflectance MD-Ro at each temperature point according to the GC-MS detection result MD; MD-Ro = 0.0243 × [4 - MD / (1 - MD + 3 - MD + 4 - MD)] + 0.4415, where MD refers to methylbiscadinane. Then, compare with Easy-Ro provided by the thermal simulation instrument; when MD-Ro > Easy-Ro, take MD-Ro as the equivalent maturity EqRo of the crude oil; otherwise, take Easy-Ro as EqRo. Thus, EqRo at each temperature point during the thermal simulation process is obtained. Among them, Easy-Ro is obtained based on the empirical formula proposed by predecessors, and this parameter is only related to the heating rate and temperature during the thermal simulation experiment, that is, as long as the heating rate and temperature are set during the thermal simulation experiment, the Easy-Ro value can be obtained.

[0022] The specific implementation process of Step 2 includes: Extract the GC-MS spectra of saturated hydrocarbons and aromatics in the product at each temperature point, arrange them in ascending order of EqRo, and compare and screen out relatively stable biomarker compounds. The relatively stable biomarker compounds have the following characteristics: (1) The peak shape characteristics of the GC-MS spectra of compound series at different thermal evolution stages are consistent before cracking and destruction, indicating that the corresponding compound series change proportionally with the increase in thermal evolution degree before cracking and destruction; (2) Compound series at different thermal evolution stages can still be detected by GC-MS in the over-mature stage, indicating that the corresponding compound series have extremely high thermal stability; Compare and screen out relatively stable biomarker compounds (such as aryl isoprenoids, triaromatic steranes, and triaromatic dinosteranes, etc.), establish the evolution sequence of the parameters of relatively stable biomarker compounds at each temperature point with EqRo, and screen out relatively stable geochemical indicators. The relatively stable geochemical indicators include: a. The parameters of biomarker compounds with a change range of less than 5% at different thermal evolution stages, indicating that this parameter is not affected by the degree of thermal evolution; b. The parameters of biomarker compounds with a change range of less than 10% at different thermal evolution stages, but there are still obvious differences between different oils / sources. It indicates that this biomarker parameter can also be used to assist in oil-source correlation.

[0023] Mark the screened biomarker parameters as P i , where i = 1, 2, 3,…, n.

[0024] The specific implementation process of Step 3 includes: According to the biomarker parameter P i screened in Step 2, the characteristic sequences of potential source rocks and natural crude oils are obtained accordingly, as follows: The characteristic sequence of the source rock is: SP = { SP 1 , SP 2 , SP 3 , SP 4 , …, SP i , …, SP n}; SP i refers to the biomarker parameter in the source rock; The characteristic sequence of natural crude oil is: OP = { OP 1 , OP2 , OP 3 , OP 4 , …, OP i , …, OP n}; OP i refers to the biomarker parameters in crude oil; Then, the average value characteristics of each parameter of the potential source rock are expressed as: avg SP = {avg SP 1 , avg SP 2 ,avg SP 3 , avg SP 4 , …, avg SP i , …,avg SP n}; where avg SP n is obtained by calculating the arithmetic mean; Obviously, the smaller the ratio of the difference between the parameter value of the stable biomarker compound in the oil sample and the average value of the corresponding parameter of the source rock to the variation range of the corresponding parameter of the source rock, that is, the difference degree between a single oil sample and the potential source rock is expressed as: ; To avoid the contingency of a single parameter, the average value of the difference degree between a single oil sample and the potential source rock, that is, the average difference degree index avg D is used as the final difference degree: ; Therefore, when avg D ≤100%, all stable and effective oil-source correlation indicators of the crude oil fall within the distribution range of the corresponding parameters of the potential source rock. The smaller the value, the stronger the genetic relationship; on the contrary, the larger the value, the smaller the possibility that the crude oil comes from the potential source rock. At this time, the potential source rock corresponding to the minimum recommended difference degree is recommended as the main source of the crude oil; when avg D >100%, it indicates that the crude oil does not mainly come from the potential source rock and has a new source.

[0025] Example 3 The deep-ultra-deep oil source comparison and evaluation method according to Example 2 is different in that: This embodiment takes the comparison of oil sources in the Lower Paleozoic of the Tarim Basin as an example, and illustrates the specific implementation plan and application effect in combination with the accompanying drawings and specific implementation methods.

[0026] The following is combined with the technical roadmap ( Figure 1 ) and specific examples are further described to further describe the technical method of the present invention.

[0027] The gold tube thermal simulation oil sample selected for this study is the brown-yellow liquid hydrocarbon of the Silurian system in the TZ62 well, which was previously considered to be a typical Cambrian oil source. Other crude oil samples mainly come from natural crude oil in different strata (Cambrian-Carboniferous) in the southwest and north of Tarim Basin, which are marked as Type I and Type II crude oil respectively; and from the three major source rocks of the Lianglitage Formation mud limestone in the Middle and Upper Ordovician, the Sargan Formation mud shale in the Middle and Upper Ordovician, and the Heituo Formation mud shale in the Lower Cambrian and Lower Ordovician in the Tarim Basin, which are marked as Type A, B and C source rocks respectively. Among them, the selected source rock samples are mainly TOC>0.50%, and the organic matter type is II 1 -II 2 type, are all in the high overmaturity stage (EqRo>1.30%), and S 1 +S 2 <0.20mg / g, indicating that the source rocks have generated and discharged a large amount of oil and gas, and are an important source of rich oil and gas resources in the Lower Paleozoic in the Tarim Basin.

[0028] Step 1: Carry out a gold tube thermal simulation experiment on a typical crude oil sample and calibrate each thermal simulation temperature point EqRo.

[0029] A gold tube thermal simulation experiment of typical crude oil samples was carried out, and MD-Ro was calculated by calculating the parameters of methyl diadamantane. Since methyl diadamantane cracked and disappeared when the simulation temperature exceeded 450℃, but MD-Ro was equal to Easy-Ro at 450℃, the equivalent maturity EqRo of crude oil with a simulation temperature>450℃ can be represented by Easy-Ro, while the equivalent maturity EqRo of crude oil with a simulation temperature≤450℃ is represented by MD-Ro. The EqRo of each temperature point during the thermal simulation of crude oil is shown in Table 1.

[0030] Table 1 EqRo calibration results at various temperature points of the gold tube thermal simulation experiment of typical oil samples;

[0031] In Table 1, MD-Ro = 0.0243 × [4-MD / (1-MD+3-MD+4-MD]+0.4415; -, indicating that MD has been cleaved and no data were detected.

[0032] Step 2: Screen relatively stable biomarker compounds and relatively stable biomarker compound indicators.

[0033] Screen relatively stable biomarker compounds, Figure 2 and Figure 3 respectively show the distribution characteristics of aryl isoprenoid series, triaromatic sterane series and triaromatic dinosterane series in crude oil with increasing thermal evolution degree. Figure 2 In, C 14 , C 15 and C 16 are aryl isoprenoids corresponding to the carbon numbers, respectively, and can still be effectively preserved at EqRo = 3.39%, indicating that this compound series has high stability and can be used for oil-source correlation of high-overmature (generally corresponding to deep-ultra-deep) oils; while Figure 3 in, the dotted line indicates the distribution fingerprints of triaromatic sterane and triaromatic dinosterane compounds. Before disappearing by thermal cracking at EqRo = 1.38%, this fingerprint hardly changes, indicating that these two compound series also have high stability. Moreover, if the above compound series can be detected in deep-ultra-deep crude oils or source rocks, it can be effectively used for oil-source correlation.

[0034] Screen relatively stable biomarker compound indicators: Based on the stable biomarker compounds screened in Step 2, construct the relevant parameter set {Pi}. After comparison and verification with EqRo, in {Pi}, such as C 26 TAS(%), C 27 TAS(%), C 28 TAS(%), C 26 S / C 28 S TAS, (C 26 R + C 27 S) / C 28 S TAS and TDSI parameters change little with the increase of EqRo, indicating that this group of {Pi} also has high stability and can be used for oil-source correlation.

[0035] Note: C 26 TAS(%), C 27 TAS(%), C 28 TAS(%) successively indicate the percentage contents of C 26 , C 27 and C 28 triaromatic steranes in C 26 , C 27 and C 28 triaromatic steranes respectively, reflecting the relative contributions of different biological sources; C 26 S / C 28 S TAS, is C 26 S triaromatic sterane / C 28S triaromatic steranes reflect the salinity of the sedimentary water column; (C 26 R + C 27 S) / C 28 S TAS, (C 26 S triaromatic steranes / C 27 (S triaromatic steranes) / C 28 S triaromatic steranes reflect the relative contribution of red algae; TDSI, the ratio of triaromatic dinosteranes to 3-methyl-24-ethyltriaromatic steranes, reflects the relative contribution of dinoflagellate origin.

[0036] Step 3: Calculate the average oil-source difference index avg D to determine the origin of deep and ultra-deep crude oils.

[0037] 1) Average oil-source difference index avg D Calculation and oil-source correlation. Based on the stable biomarker parameters established in Step 2, the average oil-source difference index avg D is calculated. The results show that the average oil-source difference index avg of Class I crude oils relative to Class C potential hydrocarbon source rocks D ≤100%, while relative to Class A and Class C potential hydrocarbon source rocks avg D > 100%; for Class II crude oils, the average oil-source difference index avg relative to Class A potential hydrocarbon source rocks D ≤100%, while relative to Class B and Class C potential hydrocarbon source rocks avg D > 100%. This indicates that Class I crude oils mainly come from Class C potential hydrocarbon source rocks, while Class II crude oils mainly come from Class A potential hydrocarbon source rocks.

[0038] 2) Stable biomarker GC-MS spectra are used for oil-source correlation. Taking the study of the origin of Class I crude oils as an example, Figure 4 and Figure 5 respectively show that Class I crude oils have a high correlation with Class C potential hydrocarbon source rocks in the GC-MS spectra of aryl isoprenoid series, triaromatic sterane series, and triaromatic dinosterane series, indicating that Class I crude oils mainly come from Class C potential hydrocarbon source rocks.

[0039] 3) Stable biomarker parameters are used for oil-source correlation. Figure 6 Respectively show the oil-source correlation cross plots of Class I and Class II crude oils with potential hydrocarbon source rocks of Classes A, B, and C. It can be clearly seen that all the sample points of Class I crude oils fall within the distribution range of Class C potential hydrocarbon source rocks, while all the sample points of Class II crude oils fall within the distribution range of Class A potential hydrocarbon source rocks.

[0040] Therefore, through oil-source correlation using the oil-source correlation parameter system constructed by integrating the above-mentioned stable biomarker spectra, biomarker parameters, and oil-source difference index, it can be seen that Class I crude oils mainly come from Class C potential hydrocarbon source rocks, while Class II crude oils mainly come from Class A potential hydrocarbon source rocks.

[0041] Example 4 A computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of a deep-ultra-deep oil source comparison and evaluation method described in any one of Embodiments 1-3 are implemented.

[0042] Example 5 A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of a deep-ultra-deep oil source comparison and evaluation method described in any one of Embodiments 1-3 are implemented.

[0043] Example 6 A deep-ultra-deep oil source comparison and evaluation system includes: A module for obtaining the equivalent maturity EqRo in the full thermal simulation temperature range, which is configured to: conduct a gold tube thermal simulation experiment on typical crude oil samples, perform GC-MS tests on the saturated hydrocarbon and aromatic hydrocarbon components in the thermal simulation products, calculate the equivalent vitrinite reflectance MD-Ro at each temperature point based on the results of methyl diadamantane (MD) and match it with the Easy-Ro provided by the thermal simulation instrument to obtain the equivalent maturity EqRo in the full thermal simulation temperature range; reveal the equivalent maturity EqRo at each temperature point during the thermal simulation process; A module for obtaining relatively stable biomarker compound indicators, which is configured to: compare the evolution characteristics of the GC-MS spectra of saturated hydrocarbons / aromatic hydrocarbons in the products at each temperature point with EqRo, and screen out relatively stable biomarker compounds (such as aromatic hydrocarbon series compounds); most biomarker compounds are greatly affected by maturity, and it is necessary to screen out biomarker compounds with little influence from maturity, that is, relatively stable biomarker compounds; According to the selected relatively stable compounds, compare the evolution characteristics of each biomarker compound indicator (such as the ratio of the peak areas of biomarker compounds) with EqRo, and screen out relatively stable biomarker compound indicators; relatively stable biomarker compound indicators refer to the ratios between biomarker compounds, usually using the ratio of compound peak areas, and the peak areas of each compound can be obtained through GC-MS experiments; for subsequent oil source comparison; A module for obtaining the source of deep-ultra-deep oil, which is configured to: perform GC-MS tests on natural crude oil and potential hydrocarbon source rocks, and construct an average difference index avg according to the relatively stable biomarker compound indicators screened in Step 2 D Carry out oil source comparison using three types of oil source comparison indicators to reveal the source of deep-ultra-deep oil.

[0044] The above-described examples are only partial content of the application of the present invention and do not limit the concept and the entire scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made by those of ordinary skill in the art to the technical solution of the present invention (for example, changing the thermal simulation oil sample to kerogen or bitumen; changing the verification spectrum to the GC-MS spectrum of other special and stable compounds; using other special and stable aromatic hydrocarbon indicators for the verification cross plot, etc.) shall fall within the protection scope of the present invention.

Claims

1. A deep-ultra-deep oil source comparison and evaluation method, characterized in that: include: Step 1: Conduct a gold tube thermal simulation experiment on a typical crude oil sample, perform GC-MS tests on the saturated hydrocarbon and aromatic components in the thermal simulation product, calculate the equivalent vitrinite reflectance MD-Ro at each temperature point based on the methyl diadamantane result, match it with the Easy-Ro provided by the thermal simulation instrument, and obtain the equivalent maturity EqRo of the full thermal simulation temperature section; Step 2: Compare the GC-MS spectra of saturated hydrocarbons / aromatic hydrocarbons of the products at each temperature point with the evolution characteristics of EqRo to screen out relatively stable biomarker compounds; Based on the relatively stable compounds screened out, the evolution characteristics of each biomarker compound index along with EqRo were compared to screen out relatively stable biomarker compound indexes; Step 3: Perform GC-MS tests on natural crude oil and potential source rocks, and construct an average difference index avg based on the relatively stable biomarker compound indicators screened in step 2 D Three types of oil source comparison indicators are used to carry out oil source comparison and reveal the origin of deep and ultra-deep oil.

2. A deep-ultra-deep oil source comparison and evaluation method according to claim 1, characterized in that: The specific implementation process of step one includes: After the gold tube thermal simulation experiment of typical crude oil samples, the group components were separated according to column chromatography to obtain saturated hydrocarbons, aromatic hydrocarbons, non-hydrocarbons and asphaltene components, and the saturated hydrocarbons and aromatic hydrocarbons were detected by full scan and selected ion scan GC-MS. The equivalent vitrinite reflectance MD-Ro at each temperature point was calculated according to the GC-MS detection results MD; MD-Ro=0.0243×[4-MD / (1-MD+3-MD+4-MD]+0.4415, MD refers to methyl diadamantane; Compared with Easy-Ro; when MD-Ro>Easy-Ro, MD-Ro is taken as the equivalent maturity of crude oil EqRo; otherwise, Easy-Ro is taken as EqRo.

3. The deep-ultra-deep oil source comparative evaluation method according to claim 1, characterized in that: In step 2, the GC-MS spectra of saturated hydrocarbons and aromatic hydrocarbons in the product at each temperature point are extracted and arranged in ascending order according to EqRo, and relatively stable biomarker compounds are screened out by comparison. Relatively stable biomarker compounds have the following characteristics: (1) The peak characteristics of the GC-MS spectra of the compound series at different thermal evolution stages before pyrolysis and destruction are consistent; (2) The compound series at different thermal evolution stages can still be detected by GC-MS in the over-mature stage.

4. A deep-ultra-deep oil source comparison and evaluation method according to claim 1, characterized in that: In step 2, relatively stable biomarker compounds are screened out by comparison, and the evolution sequence of parameters of relatively stable biomarker compounds at each temperature point along with EqRo is established to screen out relatively stable geochemical indicators. Relatively stable geochemical indicators include: a. Parameters of biomarker compounds with a variation of less than 5% at different thermal evolution stages; b. Parameters of biomarker compounds with a variation of less than 10% at different thermal evolution stages; The selected biomarker parameters are marked as P i , i=1, 2, 3,…, n.

5. A deep-ultra-deep oil source comparison and evaluation method according to claim 4, characterized in that: The specific implementation process of step three includes: According to the biomarker parameter P screened in step 2 i , and the characteristic sequences of potential source rocks and natural crude oil are obtained accordingly, as follows: The characteristic sequence of source rocks is: SP = { SP 1, SP 2, SP 3, SP 4, …, SP i , …, SP n }; SP i It refers to the biomarker parameters in source rocks; The characteristic sequence of natural crude oil is: OP = { OP 1, OP 2, OP 3, OP 4, …, OP i , …, OP n }; OP i It refers to the biomarker parameters in crude oil; Then, the average value characteristic of each parameter of potential source rock is expressed as: avg SP = {avg SP 1, avg SP 2, avg SP 3, avg SP 4, …, avg SP i , …,avg SP n }; where avg SP n Calculated by arithmetic mean; The difference between a single oil sample and potential source rock It is expressed as: ； Select the difference between a single oil sample and potential source rock The average value is the average difference index avg D As the final difference: ； Therefore, when avg D ≤100%, the potential source rock corresponding to the minimum difference value is recommended as the main source of crude oil; when avg D When it is >100%, it indicates that the crude oil does not mainly come from the potential source rock, but has a new source.

6. A deep-ultra-deep oil source comparison and evaluation system, used to implement a deep-ultra-deep oil source comparison and evaluation method according to any one of claims 1 to 5, characterized in that: include: The module for obtaining the equivalent maturity EqRo of the full thermal simulation temperature section is configured as follows: conduct a gold tube thermal simulation experiment on a typical crude oil sample, perform GC-MS tests on the saturated hydrocarbon and aromatic components in the thermal simulation product, calculate the equivalent vitrinite reflectance MD-Ro at each temperature point based on the result of methyldiamondane, match it with the Easy-Ro provided by the thermal simulation instrument, and obtain the equivalent maturity EqRo of the full thermal simulation temperature section; The module for obtaining relatively stable biomarker compound indexes is configured as follows: comparing the evolution characteristics of the GC-MS spectra of saturated hydrocarbons / aromatic hydrocarbons of the products at each temperature point along with EqRo, and screening out relatively stable biomarker compounds; based on the screened relatively stable compounds, comparing the evolution characteristics of the biomarker compound indexes along with EqRo, and screening out relatively stable biomarker compound indexes; The deep-ultra-deep oil source determination module is configured to: perform GC-MS tests on natural crude oil and potential source rocks, and construct an average difference index avg based on the relatively stable biomarker compound indicators screened in step 2 D Three types of oil source comparison indicators are used to carry out oil source comparison and reveal the origin of deep and ultra-deep oil.

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