Method for predicting distribution of shale organic carbon content based on deposition distance

Abstract

The application provides a method for predicting mud shale organic carbon content distribution based on deposition distance, comprising the following steps: step 1, determining the target layer distribution, sedimentary center and boundary based on drilling, basin margin outcrop and seismic data of a research area; step 2, performing lithofacies, element and organic geochemical analysis test; step 3, establishing a model of different offshore distance samples and offshore distance element index, paleo-productivity and oxidation-reduction degree; step 4, establishing a quantitative relationship between multiple parameters and organic matter abundance; step 5, establishing a quantitative prediction model of organic matter abundance and actual distance; and step 6, comprehensively determining the planar distribution of organic matter abundance in combination with sedimentary facies. The method for predicting mud shale organic carbon content distribution based on deposition distance improves the prediction precision of mud shale quality, and lays a solid theoretical basis for strategic selection of oil and gas resources, resource evaluation, shale oil and gas exploration and development.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oilfield exploration and development, and particularly relates to a method for predicting the distribution of organic carbon content of shale based on sedimentary distance. BACKGROUND

[0002] In recent decades, the practice of continental oil and gas exploration in China shows that the distribution of oil and gas is mainly controlled by the effective hydrocarbon supply conditions of source rocks. Under certain burial depth and temperature conditions, only when the organic carbon content of source rocks reaches a certain proportion, oil and gas can be discharged to form effective hydrocarbon supply. The higher the organic carbon content of source rocks, the higher the oil and gas discharge within the hydrocarbon supply range, and the more abundant the oil and gas resources. Therefore, the organic carbon content of source rocks is an important evaluation factor in oil and gas resource evaluation and strategic selection, and the planar prediction of organic carbon content becomes an important research link in exploration deployment decision-making.

[0003] At present, the prediction of organic matter abundance mainly relies on the actual testing of core outcrop samples, supplemented by well logging curve fitting or geophysical attribute prediction methods. This method requires a large amount of actual data support, and it is difficult to carry out and achieve scientific prediction in areas with low exploration degree. Some scholars have proposed that the organic carbon content has a certain correlation with the sedimentary environment, but at present, most of them are qualitative research, and have not formed a prediction method of organic carbon content.

[0004] Therefore, it is urgent to establish a scientific method for predicting the distribution of organic carbon in shale, to provide support for the scientific and reasonable evaluation of the abundance distribution of source rock organic matter, and to lay a foundation for zonal oil and gas potential evaluation.

[0005] In the Chinese patent application with application number CN201811511167.9, a method and device for predicting the abundance of source rock organic matter are involved. The method includes: obtaining the logging curve of the target area; calculating the total organic carbon (TOC) value of the target area according to the logging curve; combining the TOC value of the target area with the logging curve to synthesize seismic records for well-seismic calibration; extracting the seismic attribute features beside the well according to the results of the well-seismic calibration, and establishing a relationship between TOC and the seismic attribute features; and establishing the spatial distribution of TOC in the target area according to the relationship.

[0006] In the Chinese patent application with the application number CN201410727185.6, a shale adsorbed gas content prediction method and device are related, which establishes a first relationship between the Langmuir volume VL, temperature T and the organic matter abundance TOC of the shale sample according to the Langmuir volume VL of each shale sample obtained in the study area at different temperatures T, and establishes a second relationship between the logarithm of the Langmuir pressure PL and the reciprocal of the temperature T according to the Langmuir pressure PL of each shale sample at different temperatures T, and then substitutes the first relationship and the second relationship into the Langmuir equation to obtain a third relationship between the temperature T, the pressure P, the organic matter abundance TOC of the shale sample and the adsorbed gas content V, and finally predicts the adsorbed gas content V of the study area according to the third relationship.

[0007] In the Chinese patent application with the application number CN201910405762.2, a method for predicting the organic matter maturity of source rocks under overpressure is related, which comprises the following steps: step 1, collecting and obtaining high-abundance drilling cores and field samples of unripe and low-mature; step 2, carrying out high-temperature and high-pressure simulation experiment of actual geological samples to obtain laboratory test data; step 3, obtaining the conversion rate parameters of the samples at different thermal evolution degrees through laboratory data analysis of the cores; step 4, obtaining the calculation formula coefficient of the thermal evolution degree of the actual block by using the least square method; step 5, predicting and obtaining the pressure coefficient according to the seismic data and well test data of the oil and gas bearing basin; step 6, predicting the planar distribution characteristics of the thermal evolution degree of organic matter under overpressure.

[0008] The above prior art has great difference from the present application, and cannot solve the technical problem of predicting the organic matter abundance that the exploration personnel want to solve, and therefore we have invented a new method for predicting the organic carbon content distribution of shale based on sedimentary distance. SUMMARY

[0009] The purpose of the present application is to provide a method for predicting the organic carbon content distribution of shale based on sedimentary distance, which provides technical support for high-quality shale distribution prediction and zonal oil and gas potential evaluation.

[0010] The purpose of the present application can be achieved by the following technical measures: a method for predicting the organic carbon content distribution of shale based on sedimentary distance, which comprises:

[0011] Step 1, determining the target layer distribution, sedimentary center and boundary based on the drilling, basin margin outcrop and seismic data of the study area;

[0012] Step 2, carrying out lithofacies, element and organic geochemical analysis test;

[0013] Step 3: Establish a model for elemental indicators, paleoproductivity, and redox degree of samples at different offshore distances;

[0014] Step 4: Establish a quantitative relationship between multiple parameters and organic matter abundance;

[0015] Step 5: Establish a quantitative prediction model of organic matter abundance and actual distance;

[0016] Step 6: Determine the planar distribution of organic matter abundance based on the sedimentary facies.

[0017] The purpose of the present invention can also be achieved by the following technical measures:

[0018] In step 1, the distribution of each stratigraphic system is determined based on the stratigraphic development characteristics and lithologic combination characteristics of the wells and basin margin outcrops in the study area, combined with the interpretation of regional seismic data.

[0019] In step 1, for potential mudstone strata, sedimentary lithofacies studies are conducted on pre-exploration wells and basin-edge outcrop samples. Sedimentary facies characteristics are comprehensively analyzed based on lithology, grain size, roundness, color, and rock component characteristics. The results of outcrop and drilling element analysis are used to predict the boundaries of the original sedimentary water body in the mudstone development concentrated section. The deposition center is determined by the changes in stratum thickness and lithofacies combination.

[0020] In step 2, samples are taken at regular intervals from the mudstone development intervals of each drilling core and outcrop point to determine the major, trace and rare earth elements such as calcium (Ca), iron (Fe), boron (B), barium (Ba), strontium (Sr), manganese (Mn), vanadium (V), nickel (Ni), titanium (Ti), chromium (Cr), gallium (Ga), lanthanum (La), ytterbium (Yb), cerium (Ce) and europium (Eu). At the same time, geochemical analysis tests related to the organic matter abundance of the samples are performed.

[0021] In step 3, the actual distance between the sampling point and the boundary of the original sedimentary water body is determined according to the geographical location of each sampling point. On the basis of drilling and outcrop testing, the offshore distance index I is analyzed using the thickness of the formation and the sedimentary age, Fe / Mn, Mn / Ti, Co / Ti, (La-Yb)n and other elemental indicators. 距离 , establish the offshore distance parameter I for different sample points 距离 The correlation model with the actual distance L between the sample point and the boundary of the sedimentary water body is as follows:

[0022] L=f(I 距离 ) (1).

[0023] In step 3, the paleoproductivity of each sample point was calculated using the barium Ba and aluminum Al element determination results to establish the paleoproductivity P of each sample point. 古生产力 Offshore distance index I 距离 Related model, regression relationship formula:

[0024] P古生产力 = f (I 距离 ) (2).

[0025] In step 3, the redox parameters I 还原 of each sample are established by using the element indicators of V / Ni, U / Th, V / Cr, (Cu+Mo) / Zn 还原 , and the correlation model of I 距离 and the offshore distance indicator I 还原 is established, and the regression relationship formula is:

[0026] I 距离 = f (I 距离 ) (3).

[0027] In step 4, the correlation model of each sedimentary environment indicator and the organic matter abundance TOC is established, and the relationship of the organic matter abundance and the offshore distance indicator I 古生产力 , the paleo-productivity P 还原 , and the redox degree I 古生产力 is analyzed, and the regression single-factor relationship formula is:

[0028] TOC1 = f (P 距离 ) (4)

[0029] TOC2 = f (I 还原 ) (5)

[0030] TOC3 = f (I 古生产力 ) (6).

[0031] In step 4, the multi-factor regression method is used to establish the quantitative prediction model of the multi-sedimentary environment factors and the organic matter abundance in the study area:

[0032] TOC = f (P 距离 , I 还原 ) (7).

[0033] In step 5, the formula (2) and (3) are substituted into the formula (7), the independent variable of the formula is changed into the offshore distance indicator I 距离 , and the conversion formula (1) of the offshore distance indicator and the actual distance is combined to obtain the calculation formula of TOC and the actual distance:

[0034] TOC = f (L 实际距离 ) (8).

[0035] In step 6, the measured sample TOC is taken as a constraint, the sedimentary facies distribution is combined, the calculation formula 8 is used to predict the sediment organic carbon plane distribution of the different distance original sediment water body boundary distance.

[0036] The method for predicting the distribution of the organic carbon content of mud shale based on the sedimentary distance in the application, on the basis of seismic data, through sample analysis and test of outcrop samples of basin margin and typical pre-prospecting wells, proposes to establish the quantitative relationship between the sedimentary distance and the organic carbon (TOC) index, and predict the distribution of the organic carbon of the source rock.

[0037] The innovation points of the application are as follows: first, the method takes the organic matter deposition and enrichment mechanism as the core, comprehensively considers the organic matter supply, deposition and preservation conditions of the source rock under the condition of different distances from the sedimentary boundary, and innovatively establishes the organic matter abundance quantitative prediction method taking the distance from the sedimentary boundary as the key parameter. Second, based on the actual lack of actual geochemical test data in the low exploration degree area, the prediction method is innovatively proposed based on the elemental analysis test results of the system sampling and the test data of individual drilling samples, which does not excessively depend on the actual geochemical test, logging and seismic data.

[0038] The organic carbon content distribution prediction method in the application relates to the theories and methods of sedimentary petrology, element and organic geochemistry, and aims to provide technical support and theoretical basis for the organic matter abundance prediction in the low exploration degree area. Based on the data of system coring wells, seismic and analysis and test, the organic matter abundance quantitative prediction method taking the distance from the sedimentary boundary as the key parameter is first established; the application solves the problems of low prediction precision of the organic matter distribution of the source rock in the continental sedimentary environment and strong dependence on the measured data, improves the precision of the mud shale quality prediction, and lays a solid theoretical basis for the strategic selection of oil and gas resources, resource evaluation, shale oil and gas exploration and development. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The flow chart of a specific embodiment of the method for predicting the distribution of the organic carbon content of mud shale based on the sedimentary distance in the application;

[0040] Figure 2 The P1t sedimentary facies distribution map of the Zhongnanshan area in a specific embodiment of the application;

[0041] Figure 3 The P1t mud shale elemental geochemistry comprehensive profile map of the Zhongnanshan section in a specific embodiment of the application;

[0042] Figure 4 The relationship between the actual distance from the shore and the distance from the shore elemental index of the P1t sample in the Zhongnanshan area in a specific embodiment of the application;

[0043] Figure 5 The relationship between the distance from the shore elemental index and the paleo-productivity of the P1t in the Zhongnanshan area in a specific embodiment of the application;

[0044] Figure 6A P1t off-shore distance element index and a redox degree index relationship graph in a specific embodiment of the present application in the Quandongnan area;

[0045] Figure 7 A P1t off-shore distance element index and a TOC relationship graph in a specific embodiment of the present application in the Quandongnan area;

[0046] Figure 8 A P1t paleoproductivity and TOC relationship graph in a specific embodiment of the present application in the Quandongnan area;

[0047] Figure 9 A redox degree index and TOC relationship graph in a specific embodiment of the present application;

[0048] Figure 10 A P1t shale TOC distribution graph (unit: %) in a specific embodiment of the present application in the Quandongnan area. DETAILED DESCRIPTION

[0049] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0050] It is to be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0051] As shown in Figure 1 , a flow chart of a method for predicting shale organic carbon content distribution based on sedimentary distance according to the present application. Figure 1

[0052] Step 1: Based on the stratigraphic development characteristics and lithologic combination characteristics of the drilling wells and the outcrops of the basin margin in the study area, combined with the regional seismic data interpretation, the distribution of each stratigraphic series is determined.

[0053] Step 2: For the potential shale series, sedimentary facies research is carried out on the pre-exploration wells and the outcrop samples of the basin margin; the sedimentary facies characteristics are comprehensively analyzed according to the characteristics of lithology, particle size, roundness, color and rock composition, and the element analysis results of the outcrops and drilling wells are used to predict the original sedimentary water boundary of the concentrated section of the mudstone development; the sedimentary center is determined through the changes of stratigraphic thickness and lithofacies combination.

[0054] ​Step 3: For each drilling core and outcrop point, sample the mudstone development section at certain intervals, and measure the calcium Ca, iron Fe, boron B, barium Ba, strontium Sr, manganese Mn, vanadium V, nickel Ni, titanium Ti, chromium Cr, gallium Ga, lanthanum La, ytterbium Yb, cerium Ce, europium Eu, and other constant, trace, and rare earth elements. At the same time, conduct geochemical analysis and testing related to sample organic matter abundance.

[0055] Step 4: According to the geographical location of each sample point, determine the actual distance (L, unit: km) between the sample point and the original sediment water boundary in step 2. Based on drilling and outcrop testing, use stratigraphic thickness, sedimentary age, Fe / Mn, Mn / Ti, Co / Ti, (La-Yb)n, and other element index analysis to determine the offshore distance index (I 距离 ), and establish a correlation model between the offshore distance index (I 距离 ) and the actual distance (L) of the sample point from the sediment water boundary.

[0056] L = f(I 距离 )

[0057] Step 5: Use the barium Ba and aluminum Al element measurement results to calculate the paleo-productivity, and establish a correlation model between the paleo-productivity (P) and the offshore distance index (I 距离 ), and the regression relationship formula.

[0058] P 古生产力 = f(I 距离 )

[0059] Step 6: Use V / Ni, U / Th, V / Cr, (Cu+Mo) / Zn, and other element indexes to establish the redox parameter (I 还原 ) of each sample, and establish a correlation model between I 还原 and the offshore distance index (I 距离 ), and the regression relationship formula.

[0060] I 还原 = f(I 距离 )

[0061] Step 7: Establish a correlation model between each sedimentary environment index and organic matter abundance (TOC), and analyze the relationship between organic matter abundance and offshore distance index, paleo-productivity, and redox degree, and establish a regression relationship formula for single element analysis.

[0062] TOC1 = f(P 古生产力 )

[0063] TOC2 = f(I 距离 )

[0064] TOC3 = f(I 还原 )

[0065] Step 8: using multi-factor regression and other means, the applicable multi-depositional environment factor and organic matter abundance quantitative prediction model of the study area is established.

[0066] TOC=f(P 古生产力 ,I 距离 ,I 还原 )

[0067] Step 9: the related formula in steps 5 and 6 is substituted into the formula in step 8, and the conversion formula of the distance index of the distance from the shore and the actual distance in step 4 is combined to obtain the calculation formula of TOC and the actual distance.

[0068] TOC=f(L 实际距离 )

[0069] Step 10: taking the measured sample TOC as a constraint, combining the sedimentary facies distribution understanding, and using the calculation formula in step 9, the sedimentary organic carbon plane distribution of the distance from the original depositional water body boundary distance is predicted.

[0070] The following are several specific embodiments of the application.

[0071] Embodiment 1:

[0072] In a specific embodiment 1 of the application, a certain medium-low exploration degree block is selected, such as the southeast periphery of the Bogda Mountain in the Junggar Basin as the research area, and the research horizon is the Lower Permian Tashikuang Group (P1t). The drilling in the research area is sporadically distributed in the piedmont structural belt, and it is revealed that the P1t hydrocarbon source rock is less; the outcrop is well exposed, which is convenient for sampling analysis. The specific method is as follows:

[0073] (1) using drilling, outcrop and seismic data, the P1t horizon in the research area is calibrated, then the lateral tracking is performed, the purpose stratum distribution in the research area is clarified, and the stratum boundary is determined.

[0074] (2) according to the drilling lithology and seismic data attribute prediction, the stratum sedimentary center is determined to be the periphery of the Bogda Mountain. According to the comprehensive analysis of the sedimentary facies characteristics such as lithology, particle size, roundness, color and rock composition, the original depositional water body boundary of the mud shale development section is predicted (P1t) according to the outcrop and drilling element analysis results. Figure 2

[0075] ​(3) Systematic sampling (sample interval 0.5m-5m) was carried out for the existing cores and field geological profiles in the P1t layer, and the sampling interval of key layers was increased (interval 0.2m-0.5m) to determine the major, trace and rare earth elements such as Ca, Fe, B, Ba, Sr, Mn, V, Ni, Ti, Cr, Ga, La, Yb, Ce, and Eu. Based on the determination results, the key element index parameters such as Fe / Mn, V / Ni, Sr / Ba, La / Yb were analyzed to compile a comprehensive geochemical profile ( Figure 3 ).

[0076] (4) According to the geographical location of field system sampling, Figure 2 The boundary between the mid-delta sedimentary facies and the shallow lake sedimentary facies was used as the boundary to determine the distance between the sampling point and the original sedimentary water body (L, in km), with a maximum distance of 39 km. The element indices (I) reflecting the offshore distance, such as Fe / Mn, Mn / Ti, La / Yb, and Co / Ti, were used to determine the distance from the shore. 距离 ), this section uses the La / Yb ratio as the I 距离 Parameter index. Establish the actual distance L and element index I 距离 Correlation ( Figure 4 ).

[0077] L=-16.6ln(I 距离 )+13.08 (R 2 =0.718)

[0078] (5) Using sedimentation rate (Rs), sediment dry weight (P DB ) Calculate the mass accumulation rate (R Ma ), and calculate the terrigenous barium, biogenic barium, and paleoproductivity (P) using the test results of elements such as Ba and Al. The paleoproductivity calculation process is as follows:

[0079] Ba 陆源 =Al×0.0075

[0080] Ba 生源 =Ba-Ba 陆源

[0081] R Ma =Rs×P DB

[0082] Ba 理论生源 =Ba 生源 / (0.209LgR MA -0.213)

[0083] P = 0.195 × (Ba 理论生源 ) 1.41

[0084] The correlation model between the ancient productivity and the distance from the coast is established. Figure 5 ).

[0085] P = - 531.56 x I 距离 + 1122.5 (R 2 = 0.653)

[0086] (6) The element parameter (I 还原 ) reflecting the sedimentary redox environment is used, and the U / Th ratio is selected as the redox parameter in this profile. The correlation model between the redox parameter and the distance from the coast is established. Figure 6 ), and the specific formula is:

[0087] I 还原 = - 0.116 ln(I 距离 ) + 0.3912 (R 2 = 0.796)

[0088] (7) The relationship between the organic matter abundance and the distance from the coast, productivity, and redox condition is established.

[0089] The sedimentary distance from the coast is significantly negatively correlated with the organic matter abundance, that is, the farther from the coast, the higher the organic matter abundance. Accordingly, the quantitative relationship between the element parameter (I 距离 ) of the distance from the coast and TOC is established. Figure 7 ).

[0090] TOC1 = 1.6024 x (I 距离 ) 2 - 5.7771 x I 距离 + 6.4308 (R 2 = 0.664)

[0091] The correlation diagram between the ancient productivity (P) and TOC is prepared, showing a good positive correlation between them, and the quantitative relationship between the ancient productivity and TOC is established. Figure 8 ).

[0092] TOC2 = 0.0047 x P + 0.599 (R 2 = 0.681)

[0093] The correlation between the element parameter (I 还原 ) of the redox environment and TOC is analyzed, and the quantitative relationship between the key redox parameter and TOC is established. Figure 9 ).

[0094] TOC3 = 9.1789 x ln(I 还原 ) + 10.517 (R 2 = 0.655)

[0095] (8) According to the above quantitative relationship formula, a multivariate regression quantitative evaluation model of the redox environment, paleoproductivity and TOC at different offshore distances is established.

[0096] TOC = f(P 古生产力 , I 距离 , I 还原 )

[0097] Through multivariate regression, a quantitative prediction formula of the organic matter abundance (TOC) under the constraint of multiple factors is obtained:

[0098] TOC = 1.09 x e I距离 + 0.001 x P 古生产力 + 21.03 x I 还原 - 8.81 (R 2 = 0.872)

[0099] (9) In the control area with measured data points, the offshore distance, redox degree and paleoproductivity test results of the actual samples are used to constrain the TOC data of the measured samples, and the multivariate quantitative prediction formula established in step 8 is used to calculate the organic matter abundance.

[0100] (10) For the low exploration degree area without measured data, the L and I 距离 related formula established in steps (4), (5) and (6) of the embodiment is substituted into step (8) to establish a distance related prediction model suitable for the area:

[0101] TOC = 1.09 x e EXP((13.8-L) / 16.6) + 0.001 x (-531.56 x e (13.8-L) / 16.6 + 1122.5) + 21.03 x (-0.116 x ((13.8-L) / 16.6) + 0.3912) - 8.81

[0102] On the basis of the distribution of sedimentary facies, the P1t organic matter abundance plane distribution is predicted. Figure 10 ).

[0103] Embodiment 2

[0104] In the specific embodiment 2 of the application, a low exploration degree block, such as the western section of the southern margin of Junggar Basin, is selected as the research area, and the research horizon is Paleogene Anjihaihe Formation (E 2-3 a). The research area has scattered drilling, and due to the large burial depth, only part of the drilling reveals E 2-3 a source rock; the outcrop is well exposed and is convenient for sampling and analysis. The specific method is as follows:

[0105] (1) The drilling, outcrop and seismic data are used to calibrate the E 2-3a layer, and then trace it horizontally to clarify the distribution of the target layer in the study area; based on the drilling lithology and seismic data attribute prediction, the stratigraphic deposition center was determined to be the southwest of Shawan Sag, and it was distributed in an east-west belt.

[0106] (2) Based on the existing drilling core, Sikeshu River, Anjihai River and other field geological profiles E 2-3 Systematic sampling was conducted in the a-layer (with sampling intervals of 0.5m-2m), with intensified sampling at sampling intervals of 0.2m in key dark mudstone-developed layers. Major and trace elements, as well as rare earth elements, such as Ca, Fe, B, Ba, Sr, Mn, V, Ni, Ti, Cr, Ga, La, Yb, Ce, and Eu were determined. Based on the determination results, key element parameter analysis, such as Fe / Mn, V / Ni, Sr / Ba, and La / Yb, was conducted to compile a comprehensive geochemical profile.

[0107] (3) Comprehensively analyze the sedimentary facies characteristics based on lithology, grain size, roundness, color, and rock composition, as well as the outcrop and drilling element analysis results to determine the boundaries of the original sedimentary water body.

[0108] (4) The ratio of Fe and Mn content, Fe / Mn, is used as an element index reflecting the offshore distance (I 距离 ). Establish each sample according to the actual distance L of the water body boundary and the element index I 距离 Correlation.

[0109] L=5.23×ln(I 距离 )-2.76 (R 2 =0.891)

[0110] (5) Calculate the paleoproductivity of different sample points using the same calculation method as in Example 1; establish the paleoproductivity and offshore distance element index (I 距离 )’s correlation.

[0111] P=-280.5×I 距离 +1337.6 (R 2 =0.772)

[0112] (6) Using the V / Ni element content ratio to reflect the element parameters of the sedimentary redox environment (I 还原 ), analysis and offshore distance element index (I 距离 )’s correlation.

[0113] I 还原 =-0.116ln(I 距离 )+0.3912 (R 2 =0.796)

[0114] (7) Establish the relationship between organic matter abundance and offshore indicators, productivity, and redox conditions.

[0115] Establish the quantitative relationship between the off-shore depositional distance element parameter (I 距离 ) and TOC.

[0116] TOC1=5.2375×e -2.0335(Fe / Mn) (R 2 =0.805)

[0117] Draw the paleoproductivity and TOC correlation diagram, and establish the quantitative relationship between the paleoproductivity and TOC.

[0118] TOC2=0.0062×P-0.7281 (R 2 =0.751)

[0119] Analyze the correlation between the element parameter (I 还原 ) of the redox environment and TOC, and establish the quantitative relationship between the key redox parameter and TOC.

[0120] TOC3=9.4273×(I 还原 )+0.1523 (R 2 =0.812)

[0121] (8) According to the aforementioned quantitative relationship formula, establish a multivariate regression quantitative evaluation model of the redox environment, paleoproductivity and TOC at different off-shore distances.

[0122] TOC=f(P 古生产力 , I 距离 , I 还原 )

[0123] Through multivariate regression, obtain the quantitative prediction formula of the organic matter abundance (TOC) under the constraint of multiple factors:

[0124] TOC=2.32×e I距离 +0.02×P 古生产力 +13.87×I 还原 -25.81 (R 2 =0.861)

[0125] (9) In the measured data point control area, use the off-shore distance, redox degree and paleoproductivity test results of the actual sample, and constrain the TOC data of the measured sample, and calculate the organic matter abundance by using the multivariate quantitative prediction formula established in step 8.

[0126] (10) For the low exploration degree area without measured data, use the L and I 距离The formula is substituted into step (8) to establish an organic matter abundance prediction model of the area with the actual distance as a key parameter. On the basis of the understanding of the sedimentary facies, the planar distribution of the organic matter abundance of the Anjihaihe Formation in the western segment of the southern margin of the Junggar Basin can be predicted.

[0127] TOC = 2.32 x e EXP((L+2.76) / 5.23) + 0.02 x (-280.5 x e (L+2.76) / 5.23 + 1337.6) + 13.87 x e (L +2.76) / 5.23 - 25.81

[0128] Example 3

[0129] In the specific embodiment 3 of the application, the northern margin of the Tuha Basin is selected as the research area, and the research horizon is the Permian Middle Series Talang Formation (P2t). The drilling wells in the research area are sporadically distributed, and only some of the drilling wells reveal P2t source rocks; the outcrops are well exposed, and sampling analysis is convenient. The specific method is as follows:

[0130] (1) The P2t horizon of the research area is calibrated by using drilling, outcrop and seismic data, and then lateral tracking is performed to determine the distribution of the target strata in the research area; the strata sedimentary center is determined to be the northern part of the Tuha Basin, which is distributed in the east-west direction according to the lithology of the drilling wells and the attribute prediction of the seismic data.

[0131] (2) The P2t layer of the drilling wells, Talanggou, and Donggou field geological sections is systematically sampled (sample interval 0.5m-2m), and the sample interval is 0.3m in the key dark mudstone development layer, and the constant, trace and rare earth elements such as Ca, Fe, B, Ba, Sr, Mn, V, Ni, Ti, Cr, Ga, La, Yb, Ce and Eu are determined. On the basis of the determination results, the key element index parameters such as Fe / Mn, V / (V+Ni), Sr / Ba and La / Yb are analyzed, and the comprehensive geochemical section is prepared.

[0132] (3) The sedimentary facies characteristics are comprehensively analyzed according to the characteristics such as lithology, particle size, roundness, color and rock composition, and the original sedimentary water boundary is determined according to the outcrop and drilling element analysis results.

[0133] (4) The ratio of Fe and Mn element contents Fe / Mn is used as an element index (I 距离 ) reflecting the distance from the shore. The correlation between the actual distance L of each sample from the water boundary and the element index I 距离 is established.

[0134] L = 7.67 x ln(I 距离 ) - 5.91 (R 2 = 0.827)

[0135] (5) Calculate the paleo-productivity of different sample points, and the calculation method is the same as that in Example 1; and establish the correlation between the paleo-productivity and the offshore distance element index (I 距离 ).

[0136] P = -177.9 x I 距离 + 932.0 (R 2 = 0.794)

[0137] (6) Use the element content ratio of V / (V+Ni) to reflect the element parameter (I 还原 ) of the sedimentary redox environment, and analyze the correlation with the offshore distance element index (I 距离 ).

[0138] I 还原 = -0.098ln(I 距离 )+0.2752 (R 2 = 0.826)

[0139] (7) Establish the relationship between the abundance of organic matter and the offshore distance index, productivity, and redox condition, respectively.

[0140] Establish the quantitative relationship between the offshore sedimentary distance element parameter (I 距离 ) and TOC.

[0141] TOC1 = 3.2862 x e -3.1105(Fe / Mn) (R 2 = 0.811)

[0142] Draw a correlation diagram of paleo-productivity (P) and TOC, and establish the quantitative relationship between the paleo-productivity and TOC.

[0143] TOC2 = 0.0058 x P + 0.2337 (R 2 = 0.790)

[0144] Analyze the correlation between the element parameter (I 还原 ) of the redox environment and TOC, and establish the quantitative relationship between the key redox parameter and TOC.

[0145] TOC3 = 7.8009 x (I 还原 )+0.2544 (R 2 = 0.753)

[0146] (8) According to the aforementioned quantitative relationship formula, establish a multiple regression quantitative evaluation model of the redox environment, paleo-productivity, and TOC at different offshore distances.

[0147] TOC = f(P 古生产力 , I 距离 , I 还原 )

[0148] Through multiple regression, the quantitative prediction formula of the organic matter abundance (TOC) under the constraint of multiple factors is obtained:

[0149] TOC = 2.77 x e I离岸距离 + 0.11 x P 古生产力 + 12.39 x I 还原 - 9.707 (R 2 = 0.903)

[0150] (9) In the measured data point control area, the off-shore distance, the redox degree and the paleo-productivity test results of the actual sample are used to constrain the TOC data of the measured sample, and the multi-factor quantitative prediction formula established in step 8 is used to calculate the organic matter abundance.

[0151] (10) For the low exploration degree area without measured data, the L and I 距离 related formula established in steps (4), (5) and (6) of the embodiment is substituted into step (8) to establish an organic matter abundance prediction model of the area taking the actual distance as a key parameter, and on the basis of the sedimentary facies recognition, the organic matter abundance plane distribution of the Tarlang group in the northern margin of the Tuha basin can be predicted.

[0152] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for the limitation of the present application, although the present application is described in detail with reference to the foregoing embodiments, for those skilled in the art, it still can be modified, or part of the technical features of the equivalent replacement. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the protection scope of the present application.

[0153] In addition to the technical features described in the specification, they are known to those skilled in the art.

Claims

1. A method for predicting the distribution of organic carbon content in shale based on the distance of deposition, characterized in that, The method for predicting the distribution of shale organic carbon content based on sedimentary distance comprises the following steps: Step 1, determining the distribution of target layer series, sedimentary center and boundary based on drilling, basin margin outcrop and seismic data in the study area; Step 2, performing lithofacies, element and organic geochemical analysis test; Step 3, establishing a model of element index, paleo-productivity and redox degree of samples at different sedimentary distances and off-shore distances; Step 4, establishing a quantitative relationship between multiple parameters and organic matter abundance; Step 5, establishing a quantitative prediction model of organic matter abundance and sedimentary distance; Step 6, comprehensively determining the planar distribution of organic matter abundance in combination with sedimentary facies; In step 3, according to the geographical location of each sample point, the sedimentary distance of the sampling point from the boundary of the original sedimentary water body is determined, and on the basis of drilling and outcrop testing, the off-shore distance parameter I is analyzed by using the element indexes of stratum thickness, sedimentary age, Fe / Mn, Mn / Ti, Co / Ti and (La-Yb)n 距离 The off-shore distance parameter I of different sample points is established 距离 The correlation model of the off-shore distance parameter I of different sample points and the sedimentary distance L of the sample point from the boundary of the sedimentary water body is established, and the regression relationship formula is: L = f(I 距离 ) (1) In step 3, the ancient productivity is calculated by using the results of barium Ba and aluminum Al element determination, and the ancient productivity P of each sample point is established 古生产力 The correlation model of the offshore distance index I 距离 The regression relationship formula: P 古生产力 = f(I 距离 ) (2) In step 3, the element indexes of V / Ni, U / Th, V / Cr, (Cu+Mo) / Zn are used to establish the redox parameter I of each sample 还原 , and the correlation model of I 还原 and the offshore distance index I 距离 is established, and the regression relationship formula is: I 还原 = f(I 距离 ) (3) In step 4, the correlation model between each depositional environment index and the organic matter abundance TOC is established, and the relationship between the organic matter abundance and the off-shore distance index I is analyzed 距离 , the paleo-productivity P 古生产力 , and the redox degree I 还原 parameters is regressed to obtain a single-element relationship formula: TOC1 = f(P 古生产力 ) (4) TOC2 = f(I 距离 ) (5) TOC3= f(I 还原 ) (6) In step 4, a quantitative prediction model of multiple sedimentary environmental factors and organic matter abundance is established by using multi-factor regression in the study area: TOC = f(P 古生产力 , I 距离 , I 还原 ) (7); In step 5, formula (2), (3) is substituted into formula (7), and the formula independent variable is changed into the offshore distance index I 距离 Combined with the conversion formula (1) of the offshore distance index and the sedimentation distance, the calculation formula of TOC and the sedimentation distance is obtained: TOC=f(L) (8).

2. The method for predicting a shale organic carbon content distribution based on a deposition distance according to claim 1, characterized in that, In step 1, the distribution of each stratigraphic layer series is determined based on the stratigraphic development characteristics and lithologic combination characteristics of drilling and basin margin outcrop in the study area, combined with regional seismic data interpretation.

3. The method for predicting a shale organic carbon content distribution based on a deposition distance according to claim 1, characterized in that, In step 1, sedimentary facies research is carried out on the samples of pre-exploration wells and basin margin outcrops for potential shale layer series; the sedimentary facies characteristics are comprehensively analyzed according to the characteristics of lithology, particle size, roundness, color and rock composition, and the results of element analysis of outcrops and drilling are used to predict the original sedimentary water boundary of the section where mudstone is developed intensively; the sedimentary center is determined through the changes of stratigraphic thickness and lithofacies combination.

4. The method for predicting a shale organic carbon content distribution based on a deposition distance according to claim 1, characterized in that, In step 2, for the mudstone development layer of each drilling core and outcrop point, samples are taken at certain intervals, and the constant, trace and rare earth elements such as calcium Ca, iron Fe, boron B, barium Ba, strontium Sr, manganese Mn, vanadium V, nickel Ni, titanium Ti, chromium Cr, gallium Ga, lanthanum La, ytterbium Yb, cerium Ce and europium Eu are measured, and the related geochemical analysis test of sample organic matter abundance is also performed.

5. The method for predicting shale organic carbon content distribution based on deposition distance according to claim 1, wherein, In step 6, the measured sample TOC is used as a constraint, combined with the understanding of sedimentary facies distribution, and the formula (8) is used to predict the planar distribution of sediment organic carbon at different distances from the original sedimentary water boundary.

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