Logging prediction method and device applied to source rock geochemical parameters

Abstract

The invention discloses a well logging prediction method and device applied to source rock geochemical parameters. The well logging prediction method applied to the source rock geochemical parameters comprises the steps that rock debris data and well logging data are collected and obtained, the amplitude limit of corresponding lithologic well logging response is divided, and a lithologic recognition standard is established; performing single well depth homing on the organic carbon data, and adjusting the depth position of an organic carbon sampling point; selecting a logging curve sensitive to the content of the organic carbon according to the measured data of the content of the organic carbon, and establishing a calculation model of the corresponding lithology and the content of the organic carbon; establishing a pyrolysis hydrocarbon logging calculation model; and selecting a new drilling well for formula verification. According to the method, the logging curve response characteristics are utilized, corresponding standards are divided for corresponding lithology, and meanwhile, based on the established calculation model of the corresponding lithology and the organic carbon content and the pyrolytic hydrocarbon logging calculation model, the method can be simply and reliably used for geochemical parameter logging prediction of a production well which is not subjected to coring.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas logging evaluation, and particularly to a logging prediction method and device for geochemical parameters of source rocks. Background Art

[0002] Total organic carbon content (TOC) is an important indicator for evaluating source rocks in oil and gas exploration. On the one hand, due to the purity of shale, it is difficult to obtain cores, the samples are limited, and there are problems such as expensive analysis costs and long experimental periods, making it inconvenient to conduct detailed tests on each well position. Therefore, the test data of TOC is limited. On the other hand, source rocks exhibit strong heterogeneity, and there are large errors between the TOC data obtained by traditional methods and the actual TOC data. These errors cannot meet the requirements of source rock evaluation. Therefore, logging information is introduced, linked with geochemical characteristics, a logging calculation model for geochemical parameters of source rocks of different lithologies is established, verified and applied, providing relatively accurate and effective model data for the study area, and further strengthening the evaluation of resource exploration potential.

[0003] Currently, domestic and foreign scholars have carried out research on the calculation of organic carbon content. In 1990, Passey et al. proposed the ΔlogR method for predicting the organic carbon content of carbonate and clastic source rocks at different maturities. This method is based on the superposition of deep acoustic curves and resistivity curves. If the two curves completely coincide, it is the baseline, belonging to non-source rock intervals. If the two curves do not completely coincide but have a certain amplitude difference, it is recorded as ΔlogR, then this interval belongs to the source rock. Generally speaking, ΔlogR and TOC show a positive correlation, that is, the larger ΔlogR is, the higher the organic carbon content of the source rock. This method requires using resistivity, acoustic time difference, and natural gamma to obtain ΔlogR, determine the baseline, then determine the thermal alteration index (LOM) according to TOC and vitrinite reflectance (Ro), and finally through a large number of statistical analyses, an empirical formula is obtained. However, this method does not consider the influence of density on rock formations, has high requirements for the thermal alteration index, and the baseline is significantly different in different wells, different strata, and sedimentary environments. Its determination is greatly affected by human factors, which will cause large errors and is cumbersome to operate.

[0004] The BP neural network model method corrects the weights of existing learning samples, reduces the error between the output data and the sample results, and finally obtains a relatively perfect neural network. The neural network model has significant advantages in solving nonlinear problems that cannot be represented by explicit functions. Its interpretation results not only fit well with laboratory analysis data but also can retain the detailed changes of TOC. Therefore, neural network modeling has become a new method for well logging interpretation of TOC content in source rocks. However, the principle of well logging interpretation of TOC in source rocks based on neural networks is complex and requires professional software programming. Its application threshold is relatively high, and the operation is automatically completed by the software. Therefore, it is difficult to promote the well logging interpretation of TOC in source rocks based on neural networks.

[0005] There is a good relationship between a single well logging curve and TOC. For example, Schmoker, Chen and others pointed out that there is a linear relationship between TOC content and density logging and natural gamma logging. The TOC content of source rocks can be obtained according to the correlation between the two, and good interpretation results have been achieved. However, a single curve is affected by multiple geological factors, and the lithological heterogeneity in the study area is relatively strong, which will cause relatively large estimation errors.

[0006] Although there are many well logging interpretation methods for TOC in source rocks, none of them is generally applicable. The best well logging interpretation method for TOC should be selected according to the actual situation. For the organic carbon content of source rocks under the sedimentary environment of the transitional facies between sea and land and the condition of multi-lithology superimposition, there is no good prediction method, and there is no general formula applicable to each block.

[0007] Therefore, there is an urgent need for a well logging prediction method for calculating geochemical parameters of source rocks under the condition of multi-lithology superimposition, which can effectively predict the TOC content by selecting the multiple regression analysis method according to the geological characteristics of the study area. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed to provide a well logging prediction method and device for geochemical parameters of source rocks that can overcome the above problems or at least partially solve the above problems.

[0009] Other characteristics and advantages of the present invention will become obvious through the following detailed description, or will be learned partially through the practice of the present invention.

[0010] According to the first aspect of the embodiments of the present invention, there is provided a well logging prediction method for geochemical parameters of source rocks, and the well logging prediction method for geochemical parameters of source rocks includes:

[0011] S1. Collect and obtain cuttings data and logging data, divide the amplitude limits of the corresponding lithologic logging responses, perform depth alignment and lithologic correction on the amplitude limits of the corresponding lithologic logging responses according to the collected core photos and the visual observation results at the logging site, and create logging identification criteria for the corresponding lithologies based on the corrected amplitude limits of the corresponding lithologic logging responses;

[0012] S2. Measure the actual organic carbon content data of the core, perform single-well depth alignment on the core according to the measured organic carbon content data, and adjust the depth position of the organic carbon sampling points;

[0013] S3. Select logging curves sensitive to the organic carbon content according to the measured organic carbon content data, and establish a calculation model for the corresponding lithology and organic carbon content using multiple regression;

[0014] S4. Combine the logging data to establish a pyrolysis hydrocarbon logging calculation model;

[0015] S5. Select adjacent new wells to verify the calculation model for the corresponding lithology and organic carbon content and the pyrolysis hydrocarbon logging calculation model. If the verification results of the adjacent new wells match, then based on the calculation model for the corresponding lithology and organic carbon content and the pyrolysis hydrocarbon logging calculation model, it is used for logging prediction of geochemical parameters in production wells without coring.

[0016] In some embodiments of the present invention, based on the foregoing solution, in step S1, the collecting and obtaining cuttings data and logging data, and dividing the amplitude limits of the corresponding lithologic logging responses specifically include:

[0017] Obtain cuttings data, and divide the corresponding lithologies according to the cuttings data. The lithologies include shale, carbonaceous shale, coal, and sandstone;

[0018] Obtain logging data, and select logging curves sensitive to rocks according to the logging data. The logging curves include natural gamma curve, acoustic time difference curve, compensated neutron curve, and density curve;

[0019] According to the response characteristics of the selected logging curves and the corresponding lithologies, divide the amplitude limits of the corresponding lithologies and the corresponding logging curves.

[0020] In some embodiments of the present invention, based on the foregoing solution, in step S2, the method includes:

[0021] The depth position of the organic carbon sampling points is adjusted based on the measured organic carbon content data and the high or low shale content of the core. Among them, when the organic carbon content increases, the natural gamma in the corresponding natural gamma curve increases, and the density in the corresponding density curve decreases. Thus, the organic carbon sampling points are adjusted and moved to the corresponding depth positions to achieve single-well depth alignment.

[0022] In some embodiments of the present invention, based on the foregoing solution, in step S3, the specific steps of selecting a logging curve sensitive to the organic carbon content according to the measured data of the organic carbon content and establishing a calculation model of the corresponding lithology and the organic carbon content by using multiple regression are as follows:

[0023] For the corresponding lithology, according to the measured data of the organic carbon content and the logging curve corresponding to the depth position after homing, a calculation model of the corresponding lithology and the organic carbon content is fitted, and the lithology includes shale and carbonaceous shale.

[0024] In some embodiments of the present invention, based on the foregoing solution, for shale, the calculation model of the corresponding lithology and the organic carbon content fitted is:

[0025] TOC1 = -0.015 * AC1 + 0.103 * CNL1 + 1.309 * DEN1 + 0.055 * GR1 - 6.781, and the corresponding determination coefficient R 2 = 0.813;

[0026] In the formula, TOC1 is the organic carbon content corresponding to shale, in %; GR1 is the natural gamma corresponding to shale, in API; AC1 is the acoustic time difference corresponding to shale, in μs / m; CNL1 is the compensated neutron corresponding to shale, in %; DEN1 is the density corresponding to shale, in g / cm 3 .

[0027] In some embodiments of the present invention, based on the foregoing solution, for carbonaceous shale, the calculation model of the corresponding lithology and the organic carbon content fitted is:

[0028] TOC2 = -0.136 * GR2 + 0.035 * AC2 - 0.37 * CNL2 + 21.596 * DEN2 - 2.575, and the corresponding determination coefficient R 2 = 0.854;

[0029] In the formula, TOC2 is the organic carbon content corresponding to carbonaceous shale, in %; GR2 is the natural gamma corresponding to carbonaceous shale, in API; AC2 is the acoustic time difference corresponding to carbonaceous shale, in μs / m; CNL2 is the compensated neutron corresponding to carbonaceous shale, in %; DEN2 is the density corresponding to carbonaceous shale, in g / cm 3 .

[0030] In some embodiments of the present invention, based on the foregoing solution, in step S4, the logging data is combined to establish a pyrolysis hydrocarbon logging calculation model. For shale, the specific pyrolysis hydrocarbon logging calculation model fitted is:

[0031] S2a = 0.078 * TOC1 + 0.001 * GR1 - 0.002 * CNL1 + 0.108, and the corresponding coefficient of determination R 2 = 0.873, where S2a is the pyrolytic hydrocarbon corresponding to shale.

[0032] In some embodiments of the present invention, based on the foregoing solution, in step S4, when combining well logging data to establish a pyrolytic hydrocarbon well logging calculation model, for carbonaceous shale, the specifically fitted pyrolytic hydrocarbon well logging calculation model is:

[0033] S2b = 0.029 * TOC2 + 0.002 * GR2 - 0.001 * AC2 - 0.006 * CNL2 - 0.497 * DEN2 + 1.41, and the

[0034] corresponding coefficient of determination R 2 = 0.84, where S2b is the pyrolytic hydrocarbon corresponding to carbonaceous shale.

[0035] In some embodiments of the present invention, based on the foregoing solution, step S5 specifically includes:

[0036] Collect the organic carbon content and pyrolytic hydrocarbon of adjacent well cores, compare and verify the collected measured data with the simulated values calculated by the calculation model of the corresponding lithology and organic carbon content and the pyrolytic hydrocarbon well logging calculation model. If the verification results match, confirm the reliability of the calculation model of the corresponding lithology and organic carbon content and the pyrolytic hydrocarbon well logging calculation model, and use the calculation model of the corresponding lithology and organic carbon content and the pyrolytic hydrocarbon well logging calculation model for well logging prediction of geochemical parameters of production wells without coring.

[0037] According to the second aspect of the embodiments of the present invention, there is provided a well logging prediction device for hydrocarbon source rock geochemical parameters, which is applied to the well logging prediction method for hydrocarbon source rock geochemical parameters described in any one of the above, and the well logging prediction device for hydrocarbon source rock geochemical parameters includes:

[0038] A lithology identification module, configured to collect and obtain cuttings data and well logging data, divide the amplitude limits of the corresponding lithology well logging responses, perform depth alignment and lithology correction on the amplitude limits of the corresponding lithology well logging responses according to the collected core photos and the visual observation recognition results at the well logging site, and create a well logging identification standard for the corresponding lithology according to the corrected amplitude limits of the corresponding lithology well logging responses;

[0039] A depth alignment module, configured to actually measure the corresponding organic carbon content measured data of the core, perform single-well depth alignment on the core according to the collected organic carbon content measured data, and adjust the depth position of the organic carbon sampling point;

[0040] The first calculation module is used to select logging curves sensitive to the measured organic carbon content according to the measured data of the organic carbon content, and establish a calculation model of the corresponding lithology and the organic carbon content by using multiple regression;

[0041] The second calculation module is used to establish a pyrolysis hydrocarbon logging calculation model in combination with logging data;

[0042] The verification and prediction module is used to select adjacent new wells to verify the calculation model of the corresponding lithology and the organic carbon content and the pyrolysis hydrocarbon logging calculation model. If the verification results of the adjacent new wells match, the calculation model of the corresponding lithology and the organic carbon content and the pyrolysis hydrocarbon logging calculation model are used for logging prediction of geochemical parameters of production wells without coring.

[0043] The technical solution provided in the embodiments of the present invention has at least the following technical effects or advantages:

[0044] A logging prediction method and device for hydrocarbon source rock geochemical parameters provided in the embodiments of the present invention. The logging prediction method for hydrocarbon source rock geochemical parameters provided in the embodiments of the present invention uses the response characteristics of logging curves to divide corresponding standards for the corresponding lithology, and solves the problem of difficult identification caused by the interbedding of shale, carbonaceous shale, coal and sandstone under the condition of multi-lithology combination in the marine-terrestrial transitional facies. At the same time, based on the established calculation model of the corresponding lithology and the organic carbon content and the pyrolysis hydrocarbon logging calculation model, it can be simply and reliably used for logging prediction of geochemical parameters of production wells without coring.

[0045] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are given below. Description of the Drawings

[0046] The drawings here are incorporated into the specification and constitute a part of this specification, showing the embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts. In the drawings:

[0047] Figure 1 It is a schematic flow chart of a logging prediction method for hydrocarbon source rock geochemical parameters provided by an embodiment of the present invention;

[0048] Figure 2 It is a reference schematic diagram for lithology division and correction of sampling depth points of organic carbon content and pyrolysis hydrocarbon;

[0049] Figure 3 It is a reference schematic diagram of the correlation between the measured data of organic carbon content and the calculated values of the model;

[0050] Figure 4 It is a reference schematic diagram of the correlation between organic carbon content and pyrolytic hydrocarbons;

[0051] Figure 5 It is a reference schematic diagram of the correlation between the measured data of pyrolytic hydrocarbons and the simulated values calculated by the model;

[0052] Figure 6 It is a reference schematic diagram for verifying the formula with the core samples taken from the newly drilled wells;

[0053] Figure 7 It is a schematic diagram of the principle structure of a logging prediction device for hydrocarbon source rock geochemical parameters provided by an embodiment of the present invention. Detailed implementation manners

[0054] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0055] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to give a full understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present application.

[0056] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0057] The flowcharts shown in the drawings are only illustrative and do not necessarily include all the content and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may change according to the actual situation.

[0058] To enable those skilled in the art to better understand this application, first, in combination with Figure 1 a simple description of the application scenario involved in this application will be given.

[0059] Figure 1 FIG. is a schematic flowchart of a logging prediction method for hydrocarbon source rock geochemical parameters provided by an embodiment of the present invention. As Figure 1 shown, the logging prediction method for hydrocarbon source rock geochemical parameters includes:

[0060] S1. Collect and obtain cuttings data and logging data, divide the amplitude boundaries of the logging responses of corresponding lithologies, perform depth alignment and lithology correction on the amplitude boundaries of the logging responses of corresponding lithologies according to the collected core photos and the visual observation recognition results at the logging site, and create logging identification criteria for corresponding lithologies according to the corrected amplitude boundaries of the logging responses of corresponding lithologies;

[0061] S2. Measure the actual organic carbon content data of the core, perform single-well depth alignment on the core according to the measured organic carbon content data, and adjust the depth position of the organic carbon sampling points;

[0062] S3. Select logging curves sensitive to the organic carbon content according to the measured organic carbon content data, and establish a calculation model for the corresponding lithology and the organic carbon content by using multiple regression;

[0063] S4. Combine the logging data to establish a pyrolysis hydrocarbon logging calculation model;

[0064] S5. Select adjacent new wells to verify the calculation model for the corresponding lithology and the organic carbon content and the pyrolysis hydrocarbon logging calculation model. If the verification results of the adjacent new wells match, then based on the calculation model for the corresponding lithology and the organic carbon content and the pyrolysis hydrocarbon logging calculation model, it is used for logging prediction of geochemical parameters in production wells without coring.

[0065] The hydrocarbon source rock geochemical parameters described in the embodiments of the present invention include organic carbon content and pyrolysis hydrocarbon.

[0066] The logging prediction method for hydrocarbon source rock geochemical parameters described in the embodiments of the present invention uses the response characteristics of logging curves to divide corresponding standards for corresponding lithologies, solves the problem of difficult identification caused by the interbedding of shale, carbonaceous shale, coal and sandstone under the condition of multi-lithology combination in the marine-continental transitional facies, and at the same time, based on the established calculation model for the corresponding lithology and the organic carbon content and the pyrolysis hydrocarbon logging calculation model, it can be simply and reliably used for logging prediction of geochemical parameters in production wells without coring.

[0067] In the embodiment of the present invention, in step S1, the collecting and obtaining cuttings data and logging data, and dividing the amplitude boundaries of the logging responses of corresponding lithologies specifically include:

[0068] Obtain cuttings data, and divide corresponding lithologies according to the cuttings data, where the lithologies include shale, carbonaceous shale, coal, and sandstone;

[0069] Obtain logging data, and select logging curves sensitive to rocks according to the logging data, where the logging curves include natural gamma curve, acoustic travel time curve, compensated neutron curve, and density curve;

[0070] According to the response characteristics of the selected logging curves and the corresponding lithologies, divide the amplitude boundaries of the corresponding lithologies and the corresponding logging curves.

[0071] The logging identification criteria for the corresponding lithologies divided in the embodiments of the present invention are specifically shown in Table 1 below:

[0072] Table 1 Logging identification criteria for corresponding lithologies

[0073]

[0074]

[0075] In the embodiments of the present invention, in step S2, the method includes:

[0076] In the embodiments of the present invention, in step S2, the method includes:

[0077] The depth position of the organic carbon sampling point is adjusted based on the measured data of the collected organic carbon content and the high or low core shale content. Among them, when the organic carbon content increases, the natural gamma in the corresponding natural gamma curve increases, and the density in the corresponding density curve decreases. Therefore, the organic carbon sampling point is adjusted and moved to the corresponding depth position to achieve single-well depth homing; among them, the high or low core shale content follows the general rule of coal > carbonaceous shale > shale > sandstone. The higher the shale content, the higher the natural gamma, and the higher the organic carbon content. Refer to Figure 2 As shown, it is a reference schematic diagram for lithology division and correction of organic carbon content and pyrolysis hydrocarbon sampling depth points.

[0078] Since GR is affected by the environment and mineral types, AC is affected by mineral composition and compaction, Rt (resistivity) is affected by the fluid in the pores, and DEN is affected by the enrichment of heavy minerals, various influencing factors will cause relatively large estimation errors. That is, when analyzing the total organic carbon content TOC using a single logging curve, the correlation is poor. In order to improve the accuracy of calculating TOC from logging data, in the embodiment of the present invention, in step S3, the present invention selects logging curves sensitive to the total organic carbon content according to the measured data of the total organic carbon content, and uses multiple regression to establish a calculation model for the corresponding lithology and the total organic carbon content, which specifically includes: for the corresponding lithology, according to the measured data of the total organic carbon content and the logging curves corresponding to the depth position after homing, a calculation model for the corresponding lithology and the total organic carbon content is fitted. The lithology includes shale and carbonaceous shale. Refer to Figure 3 shown in the figure, which is a reference schematic diagram of the correlation between the measured data of the total organic carbon content and the calculated values of the model.

[0079] Specifically, for shale, the calculation model for the corresponding lithology and the total organic carbon content fitted is:

[0080] TOC1 = -0.015 * AC1 + 0.103 * CNL1 + 1.309 * DEN1 + 0.055 * GR1 - 6.781, and the corresponding determination coefficient R 2 = 0.813;

[0081] In the formula, TOC1 is the total organic carbon content corresponding to shale, in %; GR1 is the natural gamma corresponding to shale, in API; AC1 is the acoustic transit time corresponding to shale, in μs / m; CNL1 is the compensated neutron corresponding to shale, in %; DEN1 is the density corresponding to shale, in g / cm 3 .

[0082] For carbonaceous shale, the calculation model for the corresponding lithology and the total organic carbon content fitted is:

[0083] TOC2 = -0.136 * GR2 + 0.035 * AC2 - 0.37 * CNL2 + 21.596 * DEN2 - 2.575, and the corresponding determination coefficient R 2 = 0.854;

[0084] In the formula, TOC2 is the total organic carbon content corresponding to carbonaceous shale, in %; GR2 is the natural gamma corresponding to carbonaceous shale, in API; AC2 is the acoustic transit time corresponding to carbonaceous shale, in μs / m; CNL2 is the compensated neutron corresponding to carbonaceous shale, in %; DEN2 is the density corresponding to carbonaceous shale, in g / cm 3 .

[0085] In the embodiment of the present invention, refer to Figure 4As shown, it is a reference schematic diagram of the correlation between organic carbon content and pyrolysis hydrocarbons. Correspondingly, in step S4, by combining well logging data, a pyrolysis hydrocarbon well logging calculation model is established. For shale, the specifically fitted pyrolysis hydrocarbon well logging calculation model is as follows:

[0086] S2a = 0.078 * TOC1 + 0.001 * GR1 - 0.002 * CNL1 + 0.108, and the corresponding determination coefficient R 2 = 0.873, where S2a is the pyrolysis hydrocarbon corresponding to shale.

[0087] In an embodiment of the present invention, correspondingly, in step S4, by combining well logging data, a pyrolysis hydrocarbon well logging calculation model is established. For carbonaceous shale, the specifically fitted pyrolysis hydrocarbon well logging calculation model is as follows:

[0088] S2b = 0.029 * TOC2 + 0.002 * GR2 - 0.001 * AC2 - 0.006 * CNL2 - 0.497 * DEN2 + 1.41, and the corresponding

[0089] determination coefficient R 2 = 0.84, where S2b is the pyrolysis hydrocarbon corresponding to carbonaceous shale.

[0090] In an embodiment of the present invention, step S5 specifically includes:

[0091] Collect the organic carbon content and pyrolysis hydrocarbons of adjacent well cores. Compare and verify the collected measured data with the simulated values calculated by the corresponding lithology and organic carbon content calculation model and the pyrolysis hydrocarbon well logging calculation model. If the verification results match, confirm the reliability of the corresponding lithology and organic carbon content calculation model and the pyrolysis hydrocarbon well logging calculation model, and use them to predict the geochemical parameters of production wells without coring.

[0092] In an embodiment of the present invention, single-well calibration is carried out through adjacent wells to explain the development of organic-rich shale in the interpreted horizons, and TOC and S2 sample analysis and test data are collected. The continuous values of the geochemical parameters of each well are calculated using the aforementioned corresponding lithology and organic carbon content calculation model and the pyrolysis hydrocarbon well logging calculation model, and the calculated simulated values are corresponding to the measured data to verify the applicability of the model. Refer to Figures 5 - 6 as shown Figure 5 which is a reference schematic diagram of the correlation between the measured pyrolysis hydrocarbon data and the simulated values calculated by the model Figure 6A reference schematic diagram for formula verification of the selected new drilling core, and a schematic diagram of the measured data corresponding to the new drilling and the simulated data curve calculated by the calculation model. The results show that the continuous curves of TOC and S2 have a high degree of coincidence with the measured data, and can better reflect the continuous change of the geochemical parameters of the organic-rich shale in this area. Therefore, the calculation model based on the corresponding lithology and organic carbon content and the pyrolysis hydrocarbon logging calculation model can be used for logging prediction of geochemical parameters in production wells without coring.

[0093] The logging prediction method for hydrocarbon source rock geochemical parameters described in the embodiments of the present invention has the following advantages compared with the prior art:

[0094] 1. Using the response characteristics of logging curves, corresponding standards are defined for the corresponding lithologies, solving the problem of difficult identification caused by the interbedding of shale, carbonaceous shale, coal and sandstone under the condition of multi-lithology combination in the transitional facies between sea and land.

[0095] 2. Compared with the existing analysis and testing methods for organic carbon content and pyrolysis hydrocarbon evaluation, it solves the problems of expensive analysis and testing costs, long time consumption, difficult coring and limited samples. Based on the established calculation model of corresponding lithology and organic carbon content and the pyrolysis hydrocarbon logging calculation model, the present invention can be simply and reliably used for logging prediction of geochemical parameters in production wells without coring. Using rich logging data, prediction can be carried out, which is simple and reliable, and has strong operability and applicability.

[0096] Based on the above embodiments, the present invention also provides a logging prediction device for hydrocarbon source rock geochemical parameters, which is applied to the logging prediction method for hydrocarbon source rock geochemical parameters described in any of the above embodiments. Refer to Figure 7 as shown Figure 7 is a schematic diagram of the principle structure of a logging prediction device for hydrocarbon source rock geochemical parameters provided by an embodiment of the present invention. The logging prediction method for hydrocarbon source rock geochemical parameters includes:

[0097] A lithology identification module 100, which is used to collect and obtain cuttings data and logging data, divide the amplitude limits of the corresponding lithology logging responses, perform depth alignment and lithology correction on the amplitude limits of the corresponding lithology logging responses according to the collected core photos and the visual observation identification results at the logging site, and create a logging identification standard for the corresponding lithology according to the corrected amplitude limits of the corresponding lithology logging responses;

[0098] A depth alignment module 200, which is used to actually measure the corresponding organic carbon content measured data from the core, perform single-well depth alignment on the core according to the collected organic carbon content measured data, and adjust the depth position of the organic carbon sampling point;

[0099] The first calculation module 300 is configured to select logging curves sensitive to the measured organic carbon content based on the measured data of the organic carbon content, and establish a calculation model of the corresponding lithology and the organic carbon content by using multiple regression.

[0100] The second calculation module 400 is configured to establish a logging calculation model for pyrolysis hydrocarbons in combination with logging data.

[0101] The verification and prediction module 500 is configured to select adjacent new wells to verify the calculation model of the corresponding lithology and the organic carbon content and the logging calculation model for pyrolysis hydrocarbons. If the verification results of the adjacent new wells match, then based on the calculation model of the corresponding lithology and the organic carbon content and the logging calculation model for pyrolysis hydrocarbons, it is used for logging prediction of geochemical parameters of production wells without coring.

[0102] The logging prediction device for geochemical parameters of source rocks according to the embodiments of the present invention can execute the logging prediction method for geochemical parameters of source rocks provided in the above embodiments. The logging prediction device for geochemical parameters of source rocks has the corresponding functional steps and beneficial effects of the logging prediction method for geochemical parameters of source rocks described in the above embodiments. For details, please refer to the embodiments of the logging prediction method for geochemical parameters of source rocks. The embodiments of the present invention will not be described in detail here.

[0103] The embodiments of the present invention further provide an electronic device, which may include a processor and a memory, and the processor and the memory may be connected through a bus or other means. The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or combinations of the above types of chips. The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the logging prediction method for geochemical parameters of source rocks in the embodiments of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, that is, to implement the logging prediction method for geochemical parameters of source rocks in the above method embodiments.

[0104] The memory may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created by the processor and the like. In addition, the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. The one or more modules are stored in the memory and, when executed by the processor, execute the logging prediction method applied to the geochemical parameters of the hydrocarbon source rock as shown in Figure 1 the embodiment shown. Specific details of the above electronic device can be understood by referring to the corresponding relevant descriptions and effects in the embodiment shown in Figure 1 and will not be elaborated here. Those skilled in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Among them, the storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory, a Hard Disk Drive (HDD), or a Solid-State Drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0105] In the specification provided here, a large number of specific details are described. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies are not shown in detail so as not to obscure the understanding of this specification.

[0106] It should be noted that the above embodiments illustrate the present invention rather than limit the present invention, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims.

Claims

1. A logging prediction method applied to geochemical parameters of source rocks, characterized in that, The logging prediction method applied to geochemical parameters of source rocks includes: S1. Collect and obtain cuttings data and logging data, divide the amplitude boundaries of the logging responses of corresponding lithologies, perform depth alignment and lithology correction on the amplitude boundaries of the logging responses of corresponding lithologies according to the collected core photos and the visual observation results at the logging site, and create logging identification criteria for corresponding lithologies based on the corrected amplitude boundaries of the logging responses of corresponding lithologies; S2. Measure the actual data of the organic carbon content of the core, perform single-well depth alignment on the core according to the measured data of the organic carbon content collected, and adjust the depth position of the organic carbon sampling points; S3. Select logging curves sensitive to the organic carbon content according to the measured data of the organic carbon content, and establish a calculation model for the corresponding lithology and the organic carbon content using multiple regression; S4. Combine the logging data to establish a pyrolysis hydrocarbon logging calculation model; S5. Select adjacent new wells to verify the calculation model for the corresponding lithology and the organic carbon content and the pyrolysis hydrocarbon logging calculation model. If the verification results of the adjacent new wells match, then based on the calculation model for the corresponding lithology and the organic carbon content and the pyrolysis hydrocarbon logging calculation model, it is used for logging prediction of geochemical parameters of production wells without coring.

2. The logging prediction method applied to geochemical parameters of source rocks according to claim 1, characterized in that, In step S1, the collection and acquisition of cuttings data and logging data, and the division of the amplitude boundaries of the logging responses of corresponding lithologies specifically include: Obtain cuttings data, and divide the corresponding lithologies according to the cuttings data. The lithologies include shale, carbonaceous shale, coal, and sandstone; Obtain logging data, and select logging curves sensitive to rocks according to the logging data. The logging curves include natural gamma curve, acoustic time difference curve, compensated neutron curve, and density curve; According to the response characteristics of the selected logging curves and the corresponding lithologies, divide the amplitude boundaries of the corresponding lithologies and the corresponding logging curves.

3. The logging prediction method applied to geochemical parameters of source rocks according to claim 2, characterized in that, In step S2, the method includes: The depth position of the organic carbon sampling points is adjusted based on the measured data of the organic carbon content collected and the high or low shale content of the core. Among them, when the organic carbon content increases, the natural gamma in the corresponding natural gamma curve increases, and the density in the corresponding density curve decreases. Thus, the organic carbon sampling points are adjusted and moved to the corresponding depth positions to achieve single-well depth alignment.

4. The logging prediction method applied to geochemical parameters of source rocks according to claim 3, characterized in that, In step S3, the selection of logging curves sensitive to the organic carbon content according to the measured data of the organic carbon content, and the establishment of a calculation model for the corresponding lithology and the organic carbon content using multiple regression specifically include: For the corresponding lithologies, according to the measured data of the organic carbon content and the logging curves corresponding to the aligned depth positions, a calculation model for the corresponding lithology and the organic carbon content is fitted. The lithologies include shale and carbonaceous shale.

5. The logging prediction method applied to geochemical parameters of source rocks according to claim 4, characterized in that, For shale, the fitted calculation model for the corresponding lithology and the organic carbon content is: TOC1 = -0.015 * AC1 + 0.103 * CNL1 + 1.309 * DEN1 + 0.055 * GR1 - 6.781, and the corresponding coefficient of determination R 2 = 0.813; Wherein, TOC1 is the organic carbon content corresponding to the shale, with the unit of %; GR1 is the natural gamma corresponding to the shale, with the unit of API; AC1 is the acoustic time difference corresponding to the shale, with the unit of μs / m; CNL1 is the compensated neutron corresponding to the shale, with the unit of %; DEN1 is the density corresponding to the shale, with the unit of g / cm 3 .

6. The logging prediction method applied to geochemical parameters of source rocks according to claim 4, characterized in that, For carbonaceous shale, the fitted calculation model for the corresponding lithology and the organic carbon content is: TOC2 = -0.136 * GR2 + 0.035 * AC2 - 0.37 * CNL2 + 21.596 * DEN2 - 2.575, and the corresponding coefficient of determination R 2 = 0.854; In the formula, TOC2 is the organic carbon content corresponding to carbonaceous shale, with the unit of %; GR2 is the natural gamma corresponding to carbonaceous shale, with the unit of API; AC2 is the acoustic time difference corresponding to carbonaceous shale, with the unit of μs / m; CNL2 is the compensated neutron corresponding to carbonaceous shale, with the unit of %; DEN2 is the density corresponding to carbonaceous shale, with the unit of g / cm 3 .

7. The logging prediction method applied to geochemical parameters of source rocks according to claim 5, characterized in that, In step S4, when combining the logging data to establish a pyrolysis hydrocarbon logging calculation model, for shale, the specifically fitted pyrolysis hydrocarbon logging calculation model is: S2a = 0.078 * TOC1 + 0.001 * GR1 - 0.002 * CNL1 + 0.108, and the corresponding coefficient of determination R 2 = 0.873, where S2a is the pyrolysis hydrocarbon corresponding to the shale.

8. The logging prediction method applied to geochemical parameters of source rocks according to claim 6, characterized in that, In step S4, when combining the logging data to establish a pyrolysis hydrocarbon logging calculation model, for carbonaceous shale, the specifically fitted pyrolysis hydrocarbon logging calculation model is: S2b = 0.029 * TOC2 + 0.002 * GR2 - 0.001 * AC2 - 0.006 * CNL2 - 0.497 * DEN2 + 1.41, for The corresponding determination coefficient R 2 = 0.84, and S2b is the pyrolytic hydrocarbon corresponding to carbonaceous shale.

9. The logging prediction method applied to geochemical parameters of source rocks according to claim 1, characterized in that, Step S5 specifically includes: Collect the organic carbon content and pyrolysis hydrocarbons of adjacent well cores, compare and verify the measured data collected with the simulated values calculated by the calculation models of the corresponding lithology and organic carbon content and the pyrolysis hydrocarbon logging calculation model. If the verification results match, confirm the reliability of the calculation models of the corresponding lithology and organic carbon content and the pyrolysis hydrocarbon logging calculation model, and be used for logging prediction of geochemical parameters of production wells without coring based on the calculation models of the corresponding lithology and organic carbon content and the pyrolysis hydrocarbon logging calculation model.

10. A logging prediction device applied to geochemical parameters of source rocks, which is applied to the logging prediction method for geochemical parameters of source rocks according to any one of claims 1-9, characterized in that, The logging prediction device applied to geochemical parameters of source rocks includes: A lithology identification module, which is used to collect and obtain cuttings data and logging data, divide the amplitude boundaries of the corresponding lithology logging responses, perform depth alignment and lithology correction on the amplitude boundaries of the corresponding lithology logging responses according to the collected core photos and the visual observation identification results at the logging site, and create a logging identification standard for the corresponding lithology according to the corrected amplitude boundaries of the corresponding lithology logging responses; A depth alignment module, which is used to actually measure the corresponding organic carbon content measured data from the core, perform single-well depth alignment on the core according to the collected organic carbon content measured data, and adjust the depth position of the organic carbon sampling point; A first calculation module, which is used to select logging curves sensitive to the organic carbon content according to the measured organic carbon content data, and establish a calculation model of the corresponding lithology and organic carbon content using multiple regression; A second calculation module, which is used to establish a pyrolysis hydrocarbon logging calculation model in combination with logging data; A verification and prediction module, which is used to select adjacent new wells to verify the calculation models of the corresponding lithology and organic carbon content and the pyrolysis hydrocarbon logging calculation model. If the verification results of the adjacent new wells match, then based on the calculation models of the corresponding lithology and organic carbon content and the pyrolysis hydrocarbon logging calculation model, be used for logging prediction of geochemical parameters of production wells without coring.

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