A deep - ultra - deep oil source correlation and evaluation method and system

Abstract

The present invention relates to a deep - ultra - deep oil source correlation and evaluation method and system, belonging to the technical field of deep - ultra - deep oil and gas resource exploration. It includes: 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, and obtain the equivalent maturity EqRo in the entire thermal simulation temperature range; Step 2: Screen out relatively stable biomarker compounds; and screen out relatively stable biomarker compound indicators; 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 source of deep - ultra - deep oil. The present invention has found stable, effective, and reliable deep oil source correlation indicators, and constructed appropriate parameters for oil source correlation, thereby revealing the source of deep - ultra - deep liquid hydrocarbons

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 evaluating deep - ultra - deep oil source correlation, belonging to the category of geochemistry. Background Art

[0002] Deep - ultra - deep oil and gas are an important and realistic field of oil and gas exploration globally, and the identification of their oil sources is a hot and difficult point in the current petroleum geochemistry community. With the further deepening of the lower limit of liquid hydrocarbon discovery in China, 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, deep - ultra - deep crude oil 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 (isoprenoid, sterane, and terpane - related indicators) can no longer meet the current deep - ultra - deep, high - over - mature 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 evaluation method based on stable, effective, and reliable oil source correlation indicators to guide the later exploration of deep - ultra - deep marine oil and gas. Summary of the Invention

[0004] Currently, there is still a lack of a deep - ultra - deep oil source correlation 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 evaluating deep - ultra - deep oil source correlation.

[0006] The technical solution of the present invention is as follows:

[0007] A method for evaluating deep - ultra - deep oil source correlation includes:

[0008] 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 methyl diadamantane results, match it with the Easy-Ro provided by the thermal simulation instrument, and obtain the equivalent maturity EqRo for the entire thermal simulation temperature range;

[0009] Step 2: 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;

[0010] Based on 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;

[0011] Step 3: Conduct GC-MS tests on natural crude oil and potential hydrocarbon source rocks. According to the relatively stable biomarker compound indexes screened out in Step 2, construct three types of oil-source correlation indexes of the average difference degree index avgD to carry out oil-source correlation and reveal the sources of deep-ultra deep oils.

[0012] Furthermore, the specific implementation process of Step 1 includes:

[0013] After the gold tube thermal simulation experiment on typical crude oil samples, separate the group components by 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 methyl diadamantane;

[0014] 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.

[0015] Furthermore, the specific implementation process of Step 2 includes:

[0016] Extract the GC-MS spectra of saturated hydrocarbons and aromatic hydrocarbons 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:

[0017] (1) The peak shape characteristics of the GC-MS spectra of the compound series are consistent before cracking and destruction in different thermal evolution stages;

[0018] (2) Compounds in different thermal evolution stages can still be detected by GC-MS in the over-mature stage;

[0019] By comparison and screening, relatively stable biomarker compounds are selected, and the evolution sequences of the parameters of relatively stable biomarker compounds at each temperature point are established. Relatively stable geochemical indicators are screened out. The relatively stable geochemical indicators include:

[0020] ① Biomarker compound parameters with a change range of less than 5% in different thermal evolution stages;

[0021] ② Biomarker compound parameters with a change range of less than 10% in different thermal evolution stages;

[0022] The screened biomarker parameters are respectively labeled as P i , where i = 1, 2, 3,..., n.

[0023] Furthermore, the specific implementation process of step three includes:

[0024] According to the biomarker parameters P i screened in step two, the characteristic sequences of potential source rocks and natural crude oils are obtained accordingly, as follows:

[0025] The characteristic sequence of the source rock is: SP = {SP1, SP2, SP3, SP4,..., SP i ,..., SP n}; SP i refers to the biomarker parameters in the source rock;

[0026] The characteristic sequence of natural crude oil is: OP = {OP1, OP2, OP3, OP4,..., OP i ,..., OP n}; OP i refers to the biomarker parameters in the crude oil;

[0027] Then, the average value characteristics of each parameter of the potential source rock are expressed as: avg SP = {avg SP1, avg SP2, avgSP3, avg SP4,..., avg SP i ,..., avg SP n}; where avg SP n is calculated by the arithmetic mean;

[0028] The difference degree d(i) between a single oil sample and the potential source rock is expressed as:

[0029]

[0030] The average value of the difference degree d(i) between a single oil sample and a potential hydrocarbon source rock, i.e., the average difference degree index avgD, is taken as the final difference degree:

[0031]

[0032] Therefore, when avgD ≤ 100%, the potential hydrocarbon source rock corresponding to the minimum difference degree is recommended as the main source of the crude oil; when avgD > 100%, it indicates that the crude oil does not mainly come from this potential hydrocarbon source rock and has a new source.

[0033] 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 correlation and evaluation method.

[0034] 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 correlation and evaluation method.

[0035] A deep - ultra - deep oil source correlation and evaluation system includes:

[0036] An equivalent maturity EqRo calculation module for the full - heat simulation temperature range, 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 and match it with the Easy - Ro provided by the thermal simulation instrument to obtain the equivalent maturity EqRo for the full - heat simulation temperature range;

[0037] A relatively stable biomarker index calculation module, 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;

[0038] 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;

[0039] A deep - ultra - deep oil source determination module, configured to: conduct GC - MS tests on natural crude oil and potential hydrocarbon source rocks, and based on the relatively stable biomarker indices screened out in step two, construct three types of oil source correlation indices of the average difference degree index avgD to conduct oil source correlation and reveal the source of deep - ultra - deep oil.

[0040] Compared with the prior art, the present invention has the following excellent technical effects:

[0041] (1) The present invention fully considers the limitations of conventional molecular geochemical analysis methods for the oil-source correlation study of deep and ultra-deep layers, calibrates the equivalent maturity of crude oil during the original thermal simulation process through gold tube experiments, and for the first time proposes a set of oil-source correlation index systems for deep and ultra-deep layers, which integrates stable biomarker spectra, biomarker parameters, and the oil-source difference index avgD, enriching the oil and gas exploration theory system in China;

[0042] (2) The present invention reveals that aromatic hydrocarbon compounds such as triaromatic steranes, triaromatic dinosteranes, and aryl isoprenoids series and related parameters have high stability through gold tube experiments, and based on this, proposes the oil-source difference index avgD, which can accurately determine the genetic relationship between a single oil sample and potential source rocks. This method can be digitized in the future, with simple operation and strong reliability;

[0043] (3) All the equipment used in the present invention are conventional equipment for organic geochemistry, and the reagents used are easily obtainable in actual research. The relevant parameters of potential source rocks can use the data reported in the literature or the data accumulated in the research work area for a long time (the more data, the more reliable), and the entire experimental scheme has simple operation and strong feasibility, playing a demonstration effect for the exploration of deep and ultra-deep oil and gas resources in China.

[0044] (4) The analysis method of the present invention solves the oil-source correlation problem of the current popular deep and ultra-deep marine crude oil. Compared with conventional analysis methods, it has higher credibility and provides important technical support for the exploration of deep and ultra-deep oil and gas resources in China. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The embodiments of the present invention will be more clearly understood with the help of the following detailed description in conjunction with the drawings. In the drawings:

[0046] Figure 1 is a schematic flow chart of a method for evaluating oil-source correlation of deep and ultra-deep layers of the present invention;

[0047] Figure 2 is a distribution characteristic diagram of aryl isoprenoids during the thermal simulation process of crude oil;

[0048] Figure 3 is a distribution characteristic diagram of triaromatic steranes and triaromatic dinosteranes during the thermal simulation process of crude oil;

[0049] Figure 4 is a GC-MS comparison diagram of aryl isoprenoids series between type I crude oil and type C source rocks;

[0050] Figure 5 is a GC-MS comparison diagram of triaromatic steranes and triaromatic dinosteranes series between type I crude oil and type C source rocks;

[0051] Figure 6It is a cross plot of stable biomarker compound indicators for oil source correlation in the deep and ultra-deep layers of the Tarim Basin; Detailed implementation manners

[0052] The present invention will be further defined below in conjunction with the accompanying drawings of the specification and embodiments, but is not limited thereto.

[0053] Term explanations:

[0054] 1. Room temperature, 25 °C.

[0055] 2. Thermal simulation experiment, using a 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.

[0056] 3. GC-MS detection, a gas chromatography-mass spectrometry (GC-MS) combined instrument, using a 5975i mass spectrometer and a 6890 gas chromatography. The chromatographic column is HP-PONA (length: 50 m, inner diameter: 200 μm, coating: 0.5 μm).

[0057] Example 1

[0058] A method for evaluating oil source correlation in the deep and ultra-deep layers. This method uses the gold tube thermal simulation technology for crude oil to screen relatively stable, effective, and reliable GC-MS spectra of biomarker compounds for oil source correlation and related biomarker parameters. In addition, an oil-source difference index avgD is constructed to study the genetic relationship between a single oil sample and potential hydrocarbon source rocks. Thus, a set of deep and 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:

[0059] 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 (MD) and match it with the Easy-Ro provided by the thermal simulation instrument to obtain the equivalent maturity EqRo in the entire thermal simulation temperature range; reveal the equivalent maturity EqRo at each temperature point during the thermal simulation process;

[0060] Step 2: Compare the evolution characteristics of the GC-MS spectra of saturated hydrocarbons / aromatic hydrocarbons of 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 biomarker compounds with little influence of maturity, that is, relatively stable biomarker compounds;

[0061] According to the relatively stable compounds screened out, compare the evolution characteristics of each biomarker compound index (such as the ratio of the peak areas of biomarker compounds) with EqRo, and screen out relatively stable biomarker compound indexes; the relatively stable biomarker compound indexes refer to the ratios between biomarker compounds, usually expressed as the ratio of compound peak areas, and the peak areas of each compound can be obtained through GC-MS experiments; it is used for subsequent oil source comparison;

[0062] Step 3: Conduct GC-MS tests on natural crude oil and potential source rocks. According to the relatively stable biomarker compound indexes screened out in Step 2, construct the average difference index avgD and three types of oil source comparison indexes to carry out oil source comparison and reveal the sources of deep and ultra-deep oils.

[0063] Example 2

[0064] A method for evaluating deep and ultra-deep oil source comparison according to Example 1, characterized in that:

[0065] The specific implementation process of Step 1 includes:

[0066] Select 40 mg of representative oil samples, choose 11 gold tubes, and set the temperatures at: one temperature point is set every 30 °C from 300 °C to 600 °C, the programmed temperature increase is set at 20 °C / h, take out after heating to the specified temperature, and after cooling to room temperature, cut the gold tube, quickly place it in an 8 ml glass bottle and add 2 ml of dichloromethane, ultrasonically dissolve the organic matter, and transfer the solution to a 50 ml pear-shaped flask; then add 2 ml of dichloromethane, ultrasonically dissolve the organic matter, and transfer the solution to a 50 ml pear-shaped flask; repeat 6 - 8 times until the solution is colorless to ensure that the organic matter in the gold tube has been completely transferred;

[0067] Then, naturally dry and concentrate to 0.5 ml at room temperature. After the gold tube thermal simulation experiment of typical crude oil samples, 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 methylbisdiamantane;

[0068] 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, once the heating rate and temperature are set during the thermal simulation experiment, the value of Easy-Ro can be obtained.

[0069] The specific implementation process of step two includes:

[0070] Extract the GC-MS spectra of saturated hydrocarbons and aromatics 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:

[0071] (1) The peak shape characteristics of the compound series in different thermal evolution stages are consistent before cracking and destruction; it indicates that the corresponding compound series changes proportionally with the increase of thermal evolution degree before cracking and destruction;

[0072] (2) The compound series in different thermal evolution stages can still be detected by GC-MS in the over-mature stage; it indicates that the corresponding compound series has extremely high thermal stability;

[0073] Compare and screen out relatively stable biomarker compounds (such as aryl isoprenoids, triaromatic steranes, and triaromatic dinosteranes and other series of 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:

[0074] ① Biomarker compound parameters with a change range less than 5% in different thermal evolution stages; it indicates that this parameter is not affected by the degree of thermal evolution;

[0075] ② Biomarker compound parameters with a change range less than 10% in different thermal evolution stages; however, there are still obvious differences between different oils / sources. It indicates that this biomarker parameter can also be used as an auxiliary for oil-source correlation.

[0076] Mark the screened biomarker parameters as P i , where i = 1, 2, 3, …, n.

[0077] The specific implementation process of step three includes:

[0078] According to the biomarker parameters P i screened in step two, the characteristic sequences of potential hydrocarbon source rocks and natural crude oils are obtained accordingly, as follows:

[0079] The characteristic sequence of the source rock is: SP = {SP1, SP2, SP3, SP4, …, SP i , …, SP n}; SP i refers to the biomarker parameters in the source rock;

[0080] The characteristic sequence of natural crude oil is: OP = {OP1, OP2, OP3, OP4, …, OP i , …, OP n}; OP i refers to the biomarker parameters in the crude oil;

[0081] Then, the average characteristic of each parameter of the potential source rock is expressed as: avg SP = {avg SP1, avg SP2, avgSP3, avg SP4, …, avg SP i , …, avg SP n}; where avg SP n is obtained by calculating the arithmetic mean;

[0082] Obviously, the smaller the proportion 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 smaller the difference degree d(i) between a single oil sample and the potential source rock, the more likely it is to come from this set of source rocks. Among them, the difference degree d(i) between a single oil sample and the potential source rock is expressed as:

[0083]

[0084] To avoid the contingency of a single parameter, the average value of the difference degree d(i) between a single oil sample and the potential source rock, that is, the average difference degree index avgD, is selected as the final difference degree:

[0085]

[0086] Therefore, when avgD ≤ 100%, all stable and effective oil-source correlation indexes 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 this potential source rock. At this time, the potential source rock corresponding to the minimum difference degree is recommended as the main source of the crude oil; when avgD > 100%, it indicates that the crude oil does not mainly come from this potential source rock and has a new source.

[0087] Example 3

[0088] According to the deep - ultra - deep oil - source correlation and evaluation method described in Example 2, the difference is that:

[0089] 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.

[0090] Below is the technical roadmap ( Figure 1 ) and specific examples are further described to further describe the technical method of the present invention.

[0091] 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 Class I and Class II crude oil respectively; and from the three sets of main source rocks of the mudstone limestone of the Lianglitage Formation in the Middle and Upper Ordovician, the mud shale of the Sargan Formation in the Middle and Upper Ordovician, and the mud shale of the Heituo Formation in the Lower Cambrian and Lower Ordovician, which are marked as Class A, B and C source rocks respectively. Among them, the selected source rock samples are mainly TOC>0.50%, the organic matter type is II1-II2, all are in the high overmaturity stage (EqRo>1.30%), and S1+S2<0.20mg / g, indicating that the source rock has generated and discharged a large amount of oil and gas, which is an important source of rich oil and gas resources in the Lower Paleozoic of the Tarim Basin.

[0092] Step 1: Carry out a gold tube thermal simulation experiment on a typical crude oil sample and calibrate each thermal simulation temperature point EqRo.

[0093] 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.

[0094] Table 1 EqRo calibration results at various temperature points in the gold tube thermal simulation experiment of typical oil samples;

[0095]

[0096] 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.

[0097] Step 2: Screening relatively stable biomarker compounds and relatively stable biomarker compound indicators.

[0098] Screening relatively stable biomarker compounds, Figure 2 and Figure 3Respectively show the distribution characteristics of aryl isoprenoid series, triaromatic sterane series, and triaromatic dinosterane series in crude oil with the increase of 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 when EqRo = 3.39%, reflecting 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, reflecting 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.

[0099] Screen relatively stable biomarker compound indicators: Based on the stable biomarker compounds screened in step two, construct the relevant parameter set {Pi}. After verification by comparison 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 {Pi} also has high stability and can be used for oil-source correlation.

[0100] 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 respectively accounting for the sum of C 26 , C 27 and C 28 triaromatic steranes, reflecting the relative contributions from different biological sources; C 26 S / C 28 S TAS, is C 26 S triaromatic sterane / C 28 S triaromatic sterane, reflecting the salinity of the sedimentary water body; (C 26 R + C 27 S) / C 28 S TAS, (C 26 S triaromatic sterane / C 27S triaromatic sterane) / C 28 S triaromatic sterane reflects the relative contribution of red algae; TDSI, i.e., triaromatic dinosterane / 3-methyl-24-ethyl triaromatic sterane, reflects the relative contribution of dinoflagellate origin.

[0101] Step 3: Calculate the average oil-source difference index avgD to determine the source of deep and ultra-deep crude oils.

[0102] 1) Calculation of the average oil-source difference index avgD and oil-source correlation. Based on the stable biomarker parameters established in Step 2, the average oil-source difference index avgD is calculated. The results show that for Class I crude oils, the average difference index avgD ≤ 100% relative to Class C potential hydrocarbon source rocks, while avgD > 100% relative to Class A and Class C potential hydrocarbon source rocks; for Class II crude oils, the average difference index avgD ≤ 100% relative to Class A potential hydrocarbon source rocks, while avgD > 100% relative to Class B and Class C potential hydrocarbon source rocks. 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.

[0103] 2) GC-MS spectra of stable biomarker compounds are used for oil-source correlation. Taking the study of the source of Class I crude oils as an example, Figure 4 and Figure 5 respectively show that there is a high correlation between Class I crude oils and 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.

[0104] 3) Stable biomarker compound 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 Class A, Class B, and Class C hydrocarbon source rocks. 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.

[0105] Therefore, through the oil-source correlation based on 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.

[0106] Example 4

[0107] 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 method for evaluating deep and ultra-deep oil-source correlation as described in any one of Examples 1 - 3.

[0108] Example 5

[0109] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the steps of a deep-ultra-deep oil source comparison and evaluation method according to any one of Embodiments 1-3.

[0110] Embodiment 6

[0111] A deep-ultra-deep oil source comparison and evaluation system includes:

[0112] 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 methyl diadamantane (MD) results 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;

[0113] 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 of 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;

[0114] According to the screened 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 compound peak area ratio, and the peak areas of each compound can be obtained through GC-MS experiments; for subsequent oil source comparison;

[0115] 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 three types of oil source comparison indicators of the average difference index avgD according to the relatively stable biomarker compound indicators screened in Step 2 to conduct oil source comparison and reveal the source of deep-ultra-deep oil.

[0116] The above examples are only partial contents 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 variations and improvements made by those of ordinary skill in the art to the technical solution of the present invention (such as 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.) should all fall within the protection scope of the present invention.

Claims

1. A deep - ultra - deep oil source correlation and evaluation method, characterized in that, Including: 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 methyl diadamantane results, match it with the Easy-Ro provided by the thermal simulation instrument, and 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 hydrocarbons / aromatic hydrocarbons in the products at each temperature point with EqRo, and screen out relatively stable biomarker compounds. Based on 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. According to the relatively stable biomarker compound indexes screened out in Step 2, construct three types of oil-source correlation indexes of the average difference degree index avgD to carry out oil-source correlation and reveal the sources of deep-ultra-deep oils.

2. The method for deep - ultra - deep oil source correlation and evaluation according to claim 1, characterized in that 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 to obtain saturated hydrocarbons, aromatic hydrocarbons, non-hydrocarbons, and asphaltene components respectively, and conduct GC-MS detection with 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 methyl diadamantane. Compare with Easy-Ro; when MD-Ro > Easy-Ro, take MD-Ro as the equivalent maturity EqRo of the crude oil; otherwise, take Easy-Ro as EqRo.

3. A deep - ultra - deep oil source correlation and evaluation method according to claim 1, characterized in that, In Step 2, extract the GC-MS spectra of saturated hydrocarbons and aromatic hydrocarbons 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-mature stage.

4. A deep - ultra - deep oil source correlation and evaluation method according to claim 1, characterized in that In Step 2, 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 indexes. The relatively stable geochemical indexes include: ① Biomarker compound parameters with a change amplitude less than 5% in different thermal evolution stages. ② Biomarker compound parameters with a change amplitude less than 10% in different thermal evolution stages. The selected biomarker parameters are respectively labeled as P i , where i = 1, 2, 3, …, n.

5. A deep - ultra - deep oil source correlation and evaluation method according to claim 4, characterized in that, The specific implementation process of Step 3 includes: Biomarker parameter P selected according to Step 2 i , and 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 = {SP1, SP2, SP3, SP4, …, SP i , …, SP n}; SP i refers to the biomarker parameter in the source rock; The characteristic sequence of natural crude oil is: OP = {OP1, OP2, OP3, OP4, …, 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 hydrocarbon source rock are expressed as: avg SP = {avg SP1, avg SP2, avg SP3, avg SP4, …, avg SP i , …, avg SP n}; where avg SP n is calculated by the arithmetic mean; The difference degree d(i) between a single oil sample and a potential hydrocarbon source rock is expressed as: Select the average value of the difference degree d(i) between a single oil sample and a potential hydrocarbon source rock, that is, the average difference degree index avgD, as the final difference degree. Therefore, when avgD ≤ 100%, the potential source rock corresponding to the minimum recommended difference is recommended as the main source of crude oil; when avgD > 100%, it indicates that the crude oil does not mainly come from this potential source rock and has a new source.

6. A deep-ultra-deep oil source comparison and evaluation system for implementing the deep-ultra-deep oil source comparison and evaluation method according to any one of claims 1-5, comprising: A full thermal simulation temperature section equivalent maturity EqRo calculation module, 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 methyl diadamantane results and match it with the Easy-Ro provided by the thermal simulation instrument to obtain the full thermal simulation temperature section equivalent maturity EqRo; A relatively stable biomarker index calculation module, configured to: compare the evolution characteristics of the GC-MS spectra of saturated hydrocarbons / aromatic hydrocarbons of the products at each temperature point with EqRo, and screen out relatively stable biomarker compounds; according to the screened relatively stable compounds, compare the evolution characteristics of each biomarker index with EqRo, and screen out relatively stable biomarker indices; A deep-ultra-deep oil source determination module, configured to: perform GC-MS tests on natural crude oil and potential source rocks, and conduct oil source comparison by constructing three oil source comparison indices of the average difference index avgD based on the relatively stable biomarker indices screened in step two to reveal the source of deep-ultra-deep oil.

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