CN111402969B

CN111402969B - Method, device, equipment and system for predicting organic carbon content of land source

Info

Publication number: CN111402969B
Application number: CN202010196327.6A
Authority: CN
Inventors: 高岗; 屈童
Original assignee: China University of Petroleum Beijing
Current assignee: China University of Petroleum Beijing
Priority date: 2020-03-19
Filing date: 2020-03-19
Publication date: 2023-06-02
Anticipated expiration: 2040-03-19
Also published as: CN111402969A

Abstract

The embodiment of the specification discloses a method, a device, equipment and a system for predicting land-based organic carbon content. The method comprises the steps of obtaining a deposition result of a deposition simulation, wherein the deposition simulation comprises a simulation based on a land source organic matter distribution state; sampling the deposition result to obtain a target sample; determining the content of organic carbon and analyzing the granularity of the target sample to obtain an analysis result, wherein the analysis result comprises granularity composition and land-source organic carbon content; establishing a prediction model of land organic carbon distribution mode according to the granularity composition, the land organic carbon content and the association information among hydrodynamic conditions; and predicting land organic carbon content corresponding to different source distances according to the prediction model. The embodiment of the specification can simply and quickly identify the content of the land-source organic carbon, so that the problem of prediction of the land-source sea-phase hydrocarbon source rock in the current deepwater area is solved, and the exploration efficiency is improved.

Description

Method, device, equipment and system for predicting organic carbon content of land source

Technical Field

The embodiment of the specification belongs to the technical field of geological exploration, and particularly relates to a method, a device, equipment and a system for predicting land source organic carbon content.

Background

Deposition modeling techniques were first used by Deacon as early as 1894, and so far have been widely used as a "forward" modeling study of reservoir research and sand development, but have been limited to sand development process-related studies for many years.

Research on land-source organic carbon content distribution characteristics of delta-shallow sea deposition systems is mainly focused on research on modern estuary deposition systems, and is generally characterized by geochemical parameters such as organic matter total carbon stable isotope, nitrogen stable isotope ratio, BIT (Branched and Isoprenoid Tetraether), lignin content and the like. In petroleum geology research, research on land source organic matter distribution rules of a delta-shallow sea deposition system is relatively lacking, and the research on land source organic matter distribution of a three-fold system Mungaroo delta deposition system in a North Carnean basin in Australia is mainly focused at present, because a large amount of sample actual measurement data are required when the research is carried out through geochemistry parameters, and the research is very difficult due to the limitation of drilling quantity in the actual oil gas development process.

In addition, the method such as the delta lgR method, the multiple regression method, the neural network method and the like is commonly used at present for predicting the organic carbon, and the methods are mainly used for predicting by using a correlation building model of the organic carbon content and the logging value. However, the result predicted by the method is usually total organic carbon content, and land-source organic carbon content is not identified alone, so that the method is not applicable to the hydrocarbon source rock prediction research of the deep water exploration.

Thus, there is a need in the art for a solution that can effectively identify the organic carbon content of land sources.

Disclosure of Invention

The embodiment of the specification aims to provide a method, a device, equipment and a system for predicting the land source organic carbon content, which can simply and quickly identify the land source organic carbon content, so that the problem of predicting land source sea-phase hydrocarbon source rocks in a current deepwater area is solved, and the exploration efficiency is improved.

The method, the device, the equipment and the system for predicting the land-source organic carbon content provided by the specification are realized in the following modes:

a method for predicting organic carbon content of land source, comprising:

obtaining a deposition result of a deposition simulation, wherein the deposition simulation comprises a simulation based on a land source organic matter distribution state;

sampling the deposition result to obtain a target sample;

determining the content of organic carbon and analyzing the granularity of the target sample to obtain an analysis result, wherein the analysis result comprises granularity composition and land-source organic carbon content;

establishing a prediction model of land organic carbon distribution mode according to the granularity composition, the land organic carbon content and the association information among hydrodynamic conditions;

and predicting land organic carbon content corresponding to different source distances according to the prediction model.

In another embodiment of the method provided in the present specification, the establishing a prediction model of land-based organic carbon distribution pattern according to the correlation information among the particle size composition, land-based organic carbon content and hydrodynamic conditions includes:

fitting the content of each particle fraction included in the particle size composition and the content of the land organic carbon to obtain a first fitting result;

converting the content of each particle fraction into a distance from an object source based on the change information of the carrying distance between each particle fraction and the object source, wherein the carrying distance between each particle fraction and the object source is in a preset rule based on the sorting property of hydrodynamic conditions;

and establishing a prediction model of a land source organic carbon distribution mode based on the first fitting result and the distance from the object source.

In another embodiment of the method provided in the present specification, the establishing a prediction model of land-source organic carbon distribution pattern based on the first fitting result and the distance from the object source includes:

establishing a prediction model of a land-source organic carbon distribution mode according to the following formula:

wherein TOC represents organic carbon content, C _m Representing the content of sediment muddy component C _S Representing the content of sediment powder sand component C _f Represents the content of sediment fine sand component C _me Represents the content of coarse sand component in the sediment, x represents the distance from the object source, C _* Indicating the content of each fraction component, x ₀ Represent C _m Corresponding x value, a when=50% _i 、n _i And b are constants (i=1, 2,3, … …).

In another embodiment of the method provided in the present specification, the sampling the deposition result, after obtaining a target sample, includes:

recording characteristic information of the target sample, wherein the characteristic information comprises coordinate information, deposition phase position information and deposition period; the coordinate information is used for calculating the distance between the position of the deposit and the object source, and the deposit phase position information is used for calculating the deposit characteristic analysis of the result.

A land-based organic carbon content prediction apparatus, comprising:

the deposition result acquisition module is used for acquiring a deposition result of a deposition simulation, wherein the deposition simulation comprises a simulation based on a land source organic matter distribution state;

the sampling module is used for sampling the deposition result to obtain a target sample;

the analysis module is used for measuring the content of organic carbon in the target sample and analyzing the granularity to obtain an analysis result, wherein the analysis result comprises granularity composition and land-source organic carbon content;

The model building module is used for building a prediction model of the land organic carbon distribution mode according to the granularity composition, the land organic carbon content and the association information among hydrodynamic conditions;

and the prediction module is used for predicting the land source organic carbon content corresponding to different source distances according to the prediction model.

In another embodiment of the apparatus provided in the present specification, the model building module includes:

the fitting unit is used for carrying out fitting treatment on the particle fraction content and the land organic carbon content included in the particle size composition to obtain a first fitting result;

the conversion unit is used for converting the content of each particle fraction into the distance from the object source based on the change information of the carrying distance between each particle fraction and the object source, wherein the carrying distance between each particle fraction and the object source is in a preset rule based on the sorting property of hydrodynamic conditions;

and the establishing unit is used for establishing a prediction model of the land source organic carbon distribution mode based on the first fitting result and the distance from the object source.

In another embodiment of the apparatus provided in the present specification, the establishing unit includes:

In another embodiment of the apparatus provided in the present specification, the sampling the deposition result, after obtaining a target sample, includes:

the recording module is used for recording characteristic information of the target sample, wherein the characteristic information comprises coordinate information, deposition phase position information and deposition period; the coordinate information is used for calculating the distance between the position of the deposit and the object source, and the deposit phase position information is used for calculating the deposit characteristic analysis of the result.

A land-based organic carbon content prediction apparatus comprising a processor and a memory for storing processor-executable instructions that when executed by the processor perform the steps comprising:

Sampling the deposition result to obtain a target sample;

A land-based organic carbon content prediction system comprising at least one processor and a memory storing computer-executable instructions that, when executed by the processor, perform the steps of any of the method embodiments of the present specification.

The present specification provides a method, apparatus, device and system for predicting land-based organic carbon content. In some embodiments, on the basis of a sediment simulation experiment, a prediction model of land source organic carbon content is established through the relationship between hydrodynamic conditions and land source carried organic carbon content reflected by particle size analysis data, so that the land source organic carbon content distribution characteristics of a deep water oil and gas exploration area with less drilling at present are predicted, and further, the favorable development area of land source sea-phase hydrocarbon source rocks is predicted, so that the next oil and gas exploration can be guided. In addition, as the method has few basic data of the research area, the method can simply and quickly solve the problem of predicting the land-source sea-phase hydrocarbon source rock in the current deepwater area and improve the exploration efficiency. By adopting the implementation scheme provided by the specification, the land organic carbon content can be simply and rapidly identified, so that the problem of prediction of the land source sea phase hydrocarbon source rock in the current deepwater area is solved, and the exploration efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some of the embodiments described in the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart diagram of one embodiment of a method for predicting land-based organic carbon content provided herein;

FIG. 2 is a schematic illustration of one embodiment of an experimental bottom-slope design provided herein;

FIG. 3 is a schematic view of one embodiment of a thickness contour of a floor shape provided herein;

FIG. 4 is a schematic representation of a fit of the TOC content of the experimental deposit to the deposit-related parameters provided in the present specification;

FIG. 5 is a schematic representation of the relationship between different fraction content of the experimental deposit and source distance provided in the present specification;

FIG. 6 is a schematic fit of the results of the experimental terrestrial organic carbon content prediction model provided in the present specification;

FIG. 7 is a graph of the predicted effect of the organic carbon content of the experimental land source provided in the present specification;

FIG. 8 is a schematic fit of results of a three-stage land-source organic carbon content prediction model for a cliff sunk cliff group provided in the present specification;

FIG. 9 is a schematic block diagram showing an embodiment of a device for predicting the organic carbon content of land source according to the present disclosure;

FIG. 10 is a block diagram of the hardware architecture of one embodiment of a terrestrial organic carbon content prediction server provided herein.

Detailed Description

In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments, but not all embodiments in the present specification. All other embodiments, which can be made by one or more embodiments of the present disclosure without inventive faculty, are intended to be within the scope of the embodiments of the present disclosure.

The research of land organic matter distribution rules of the delta-shallow sea deposition system is mainly characterized by geochemical parameters such as organic matter total carbon stable isotope, nitrogen stable isotope ratio, BIT, lignin content and the like and biomarker compounds, and a contour map is drawn according to data to reflect the land organic matter distribution characteristics. However, the method requires a large amount of sample actual measurement data, and the current delta-shallow sea sediment system has little well drilling in the deep water direction, so that the research on the land organic matter distribution rule in the middle-low exploration degree area is very difficult, and the sample amount required by the sampling detection method is large, and the detection cost is high. And the geochemical parameters and biomarker compound parameters are greatly influenced by secondary transformation, so that a large error may exist in the characterization of the land-source organic matter distribution rule. The existing organic carbon prediction method is used for predicting the organic carbon content based on logging response characteristics, but the method cannot effectively distinguish land sources from ocean organic carbon content, and is high in cost and cannot predict drilling-free areas in deep water areas due to the fact that the method relies on a large amount of analysis and test data. Therefore, based on a deposition simulation experiment research method of organic matters, a large number of samples are collected, particle size analysis and organic carbon content measurement are carried out on the samples, the correlation is explored, a prediction model is built to predict the distribution characteristics of the land organic carbon content, and it is very important to search the change rule of the land organic carbon content along with the increase of the object source distance.

The present specification provides a method, apparatus, device and system for predicting land-based organic carbon content. On the basis of a sediment simulation experiment, a land source organic carbon content prediction model is established according to the relation between hydrodynamic conditions and land source carrying organic carbon content reflected by the granularity analysis data, so that the land source organic carbon content distribution characteristics of a deep water oil and gas exploration area with less drilling at present are predicted, and further the favorable development area of land source sea phase hydrocarbon source rock is predicted, so that the next oil and gas exploration can be guided. In addition, as the basic data of the research area required by the method is less, the land organic carbon content can be simply and rapidly identified, so that the problem of prediction of the land-source sea-phase hydrocarbon source rock in the current deepwater area is solved, and the exploration efficiency is improved.

The following describes embodiments of the present disclosure by taking a specific application scenario as an example. Specifically, fig. 1 is a schematic flow chart of an embodiment of a method for predicting land-based organic carbon content provided in the present specification. Although the description provides methods and apparatus structures as shown in the examples or figures described below, more or fewer steps or modular units may be included in the methods or apparatus, whether conventionally or without inventive effort. In the steps or the structures where there is no necessary causal relationship logically, the execution order of the steps or the module structure of the apparatus is not limited to the execution order or the module structure shown in the embodiments or the drawings of the present specification. The described methods or module structures may be implemented in a device, server or end product in practice, in a sequential or parallel fashion (e.g., parallel processor or multi-threaded processing environments, or even distributed processing, server cluster implementations) as shown in the embodiments or figures.

It should be noted that the following description of the embodiments does not limit the technical solutions in other scalable application scenarios based on the present description. In one embodiment, as shown in fig. 1, in one embodiment, a method for predicting land-based organic carbon content provided in the present specification may include:

s0: a deposition result of a deposition simulation is obtained, the deposition simulation including a simulation based on a land-based organic matter distribution state.

The deposition experiment simulation technology can be developed based on analysis of lithofacies, paleotopography, hydrocarbon source rock and other characteristics of the research area.

In one embodiment of the present disclosure, the deposition results may be obtained after performing a deposition simulation on a delta-shallow sea deposition system. Delta is understood to be a deposit of a river in a stable body of water or in close proximity to the body of water, partly exposed to the water. Deposition systems are understood to be massive three-dimensional depositions of depositions combined by depositions of intergrowth in origin with a unified source of material, a unified water power system. Delta-shallow sea sediment systems are understood to be sediment bodies controlled by a unified source, unified water power system, which can range from delta sediment bodies to deep water areas where sediment can be swept. In addition, organic matter sources can be classified into marine organic matter and land organic matter, which can be understood as organic matter derived from aquatic organisms in lakes and higher plants. In the embodiments of the present disclosure, land-based organic matter is understood to be organic matter transported from a river into a marine sediment area. The deposition results may be obtained by performing deposition simulation on other deposition systems, which is not limited in this specification.

In one embodiment of the present disclosure, the preparation steps may be as follows before performing the simulation experiment of the land-source organic matter distribution state:

(1) Preparing experimental materials which can comprise gravel, sand, mud, lignite and the like;

in some embodiments, lignite may be used as a modeling material for land-based organic matter. In some embodiments, the gravel may be screened through an 8-10 mesh screen. The sand can be screened by a 18-35 mesh sieve, a 35-65 mesh sieve and a 65-150 mesh sieve respectively, so that coarse sand, medium sand and fine sand can be obtained respectively. The mud can be screened through a 300-mesh screen. The lignite can comprise powdery lignite and granular lignite, the powdery lignite can be screened by a sieve with 200-240 meshes for simulating floating state and dissolved state organic matters, and the granular lignite can be screened by a sieve with 40-150 meshes for simulating granular organic matters.

(2) Basic data such as a research area structure, stratum, paleo-geomorphology, paleo-climate and the like are collected, and experimental parameters such as an experimental scale, an experimental bottom, hydrodynamic strength, water addition amount, proportion of sand with different granularity, proportion of sand to lignite, sand addition amount and the like are designed according to the geological background of the research area;

in some embodiments, the experimental scale may be understood as the ratio of the scale of the experimental setup to the actual scale of the investigation region. The experimental model can be understood as a model of the actual study area ancient landform reduced according to the experimental scale, and the local auxiliary structure can be omitted. The proportion of sand of different particle sizes can be understood as the proportion determined from the actual study area particle size data. The sand to lignite ratio is understood to be a ratio determined from the organic geochemical data of the actual research area. The sanding amount is understood to be the relative sanding amount determined according to the supply amounts of the source of the different deposition phases of the actual investigation region.

(3) Preparing an experimental device according to an experimental scale and an experimental base model, preparing experimental materials according to the proportion of sand with different granularity and the proportion of sand to lignite, and performing experiments.

For example, in some implementations, geologic structure feature data and organic matter abundance data for a research area may be obtained; determining an analog scale and a base form according to the geological structure characteristic data of the research area; then carrying out layer sequence stratum analysis on the existing drilling well in the research area, and determining a plurality of water inlet periods of sediment simulation according to the layer sequence stratum analysis result; determining the proportion of sand to organic matters according to the abundance data of the organic matters in the research area; determining the proportion of gravels, medium-coarse sand, powder-fine sand and mud according to the granularity analysis data of the research area; and carrying out deposition simulation on organic matter distribution in a plurality of water inlet periods according to the proportion of the simulation scale to the base pattern, the proportion of sand to organic matter, the preset water adding amount and the preset hydrodynamic strength.

In some implementation scenarios, according to the proportion of the analog scale and the base mold, the sand and the organic matter, the preset water adding amount and the hydrodynamic strength, the deposition simulation of the organic matter distribution in a plurality of water inlet periods can include: acquiring object source supply data of a target work area; determining sand adding amount of each water inlet period according to the material source supply data; determining the quantity of the sand with various granularity and the organic matters to be added in each water inlet period according to the sand adding quantity in each water inlet period, the proportion among the sand with various granularity in each water inlet period and the proportion of the sand and the organic matters; according to the determined quantity of the sand with various granularity and the organic matters which are required to be added in each water inlet period, uniformly mixing the sand with various granularity and the organic matters which are required to be added in each water inlet period; and continuously adding water according to the preset water adding amount and hydrodynamic strength in a plurality of water inlet periods of the deposition simulation, continuously adding uniformly mixed sand with various granularity and organic matters according to the quantity of sand with various granularity and organic matters required to be added in each water inlet period, and performing deposition simulation of organic matter distribution. In some embodiments, the multiple water inlet periods of the deposition simulation, according to the preset water adding amount and hydrodynamic strength, may include: and continuously adding water in each water inlet period of the sediment simulation by taking a flood period, a flat water period and a dead water period as a circulation period.

In the embodiment of the specification, a basis can be provided for predicting the variation trend of the organic carbon content of the land source along with the distance of the object source by carrying out the deposition simulation on the deposition system and obtaining the deposition result of the deposition simulation.

S2: and sampling the deposition result to obtain a target sample.

In the embodiment of the present disclosure, after the deposition results of the deposition simulation are obtained, the deposition results may be sampled for subsequent further analysis of the target sample.

The way in which the deposition results are sampled in this specification is not limited.

In one embodiment of the present disclosure, the sampling the deposition result to obtain a target sample may include: recording characteristic information of the target sample, wherein the characteristic information comprises coordinate information, deposition phase position information and deposition period; the coordinate information is used for calculating the distance between the position of the deposit and the object source, and the deposit phase position information is used for calculating the deposit characteristic analysis of the result.

For example, in some implementations, the deposition results are sampled, and after the target sample is obtained, the simulation results of the deposition simulation may be sampled, and the x and y coordinates corresponding to the sampled sample, the approximate deposition phase position (e.g., delta plains, delta leading edges, shallow seas, etc.), and the deposition period times in which the sampled sample is located are recorded. Where the x, y coordinates herein can be used to calculate the distance from the source where the deposit is located later, the deposit phase location can be used for deposit characterization of the calculation, as the analysis then finds that the high value regions of organic carbon content are all near delta front-shallow sea regions.

S4: and measuring the content of organic carbon in the target sample and analyzing the granularity to obtain an analysis result, wherein the analysis result comprises the granularity composition and the land-based organic carbon content.

The organic carbon content is understood to be the weight of organic carbon in the unit rock. In some implementations, the organic carbon content can be used to evaluate organic abundance in general. Land organic carbon content is understood to be the weight of organic carbon in the unit rock in relation to the land organic matter. The particle size composition may include a muddy content, a silt content, a fine sand content, a medium coarse sand content, and the like. The proportion change of each particle grade of the sediment is a visual reflection of the hydrodynamic condition change. The land organic carbon is transported along with water flow, a certain change rule exists in the transportation direction, and the prediction of the organic matter content by hydrodynamic conditions reflected by the granularity characteristics of the analysis corresponds to land organic matters.

In the embodiments of the present disclosure, after a target sample is obtained, the target sample may be analyzed. In some implementations, after the target sample is obtained, the determination of the organic carbon content of the target sample may be performed, thereby obtaining the terrestrial organic carbon content. In other embodiments, after the target sample is obtained, the target sample may be subjected to a particle size analysis to obtain the argillaceous content, the silt content, the fine sand content, and the medium-coarse sand content of the target sample. In some embodiments, the organic carbon content may be determined by a Leco-CS-230 carbon sulfur analyzer. In some embodiments, particle size analysis may be performed by a Malvern Mastersizer model 3000 laser particle sizer. In the examples herein, the organic carbon content may be measured by other means and the particle size analysis may be performed by other means, which is not limited in this specification.

S6: and establishing a prediction model of the land organic carbon distribution mode according to the granularity composition, the land organic carbon content and the association information among hydrodynamic conditions.

Wherein the association information may also be referred to as an intrinsic contact. The intrinsic relation between the particle size composition, the land organic carbon content and the hydrodynamic conditions can be understood as a regular variation of the parameters with increasing object source distance and a correlation with each other. Specifically, as the change of the granularity ratio of the sediment is a visual reflection of the change of hydrodynamic conditions, the hydrodynamic strength is gradually weakened due to energy loss along with the increase of the distance between the object sources, the content of coarse particles of the corresponding sediment is also gradually reduced, the content of fine particles is gradually increased, and the sorting performance of each particle grade due to the hydrodynamic conditions generally has a certain change rule along with the increase of the distance between the object sources. In addition, considering that the land organic matters are carried along with water flow, the embodiment of the specification can analyze and find the relevance between the land organic matters and hydrodynamic conditions, and then establish a prediction model of a land organic carbon distribution mode based on the relevance, so that the prediction of the land organic matter distribution characteristics can be realized.

In one embodiment of the present disclosure, the establishing a prediction model of the land-source organic carbon distribution mode according to the correlation information among the particle size composition, the land-source organic carbon content and the hydrodynamic condition may include: fitting the content of each particle fraction included in the particle size composition and the content of the land organic carbon to obtain a first fitting result; converting the content of each particle fraction into a distance from an object source based on the change information of the carrying distance between each particle fraction and the object source, wherein the carrying distance between each particle fraction and the object source is in a preset rule based on the sorting property of hydrodynamic conditions; and establishing a prediction model of a land source organic carbon distribution mode based on the first fitting result and the distance from the object source. For example, in some implementation scenarios, multiple regression is used to fit the clay content, the silt content, the fine sand content, the medium coarse sand content and the land organic carbon content, and then the change relation of granularity along with the carrying distance of the object source is used to convert the particle content into the distance from the object source, so as to realize the establishment of a prediction model of the land organic carbon distribution mode.

In one embodiment of the present disclosure, the establishing a prediction model of land-source organic carbon distribution mode based on the first fitting result and the distance from the object source may include: establishing a prediction model of a land-source organic carbon distribution mode according to the following formula:

wherein TOC represents organic carbon content, C _m Representing the content of sediment muddy component C _S Representing the depositPowder sand component content, C _f Represents the content of sediment fine sand component C _me Represents the content of coarse sand component in the sediment, x represents the distance from the object source, C _* Indicating the content of each fraction component, x ₀ Represent C _m Corresponding x value, a when=50% _i 、n _i And b are constants (i=1, 2,3, … …).

In the embodiment of the specification, the quantitative prediction of the land-source organic carbon content distribution mode can be realized more rapidly and accurately by establishing the prediction model of the land-source organic carbon distribution mode by utilizing the formula.

S8: and predicting land organic carbon content corresponding to different source distances according to the prediction model.

In the embodiment of the present disclosure, since the prediction model of the land organic carbon distribution mode includes the variation rules of each parameter, the object source distance, and each other, after the prediction model of the land organic carbon distribution mode is established, the land organic carbon content corresponding to the different object source distances may be predicted according to the prediction model.

In the embodiment of the specification, as the granularity data can intuitively reflect the change of hydrodynamic conditions, the land source organic carbon content can be predicted by utilizing the granularity data, and the land source organic matter distribution characteristics of the non-drilling area can be predicted only by the distance from the object source in the actual exploration of the deepwater area, so that the cost is reduced and the prediction efficiency is improved.

The above method is described below in connection with a specific embodiment, however, it should be noted that this specific embodiment is only for better illustrating the present application and is not meant to be a undue limitation on the present application.

In the specific embodiment of the specification, a field cliff group of a Qiongtong basin cliff concave cliff 13-1 is taken as a prototype geological model, a work area deposition system takes a north material source as a main material source, and an actual material is combined to design a material source which is consistent with the actual material.

Specifically, an experimental area of 3m multiplied by 5m is designed, the transverse effective use range is 0-3.0 m, and the specific ruler is 1:10000; the longitudinal use range is 0-5.0 m, and the specific ruler is 1:8000; the thickness ratio in the height direction is 1:1000.

designing experiments according to the characteristics of the bottom shape of the lake basin in the research area and the practical conditions of the experiments, wherein Y=0-0.35 m is a fixed river channel area, namely a river channel provided with a supply source, and the effective measurement range is not counted; y=0.35 to 3.5m is the delta deposition area, which is the final deposition range of the deposition experiment; y=3.5 to 5.0m is shallow sea. The gradient of the near-object source of the research area is about 2 degrees, and generally the gradient of the near-object source is steeper, and gradually decreases towards the ocean. The experimental bottom slope design is shown in fig. 2, where near source y=0.35-2 m designs slope about 3-5 °, y=2-4 m slope 1-3 °, y=4-5 m slope 1 °. The thickness contour diagram of the bottom shape is shown in fig. 3, wherein the values of 38, 30, 20 and the like are expressed in centimeters, and the thickness between the bottom surface after the gradient is paved and the bottom surface before the thickness is not paved, and the white frame in the diagram is the position of a river channel which is artificially arranged and is used for an additive source.

As can be seen from the analysis of the granularity of the cliff group in the research area, the sediment area of the braided river delta is mainly made of fine sand, a small amount of gravels are seen, the lithology of each sediment period is different, the experimental design mainly considers the granularity characteristics of sediment, the carrying capacity of water flow and the change of sand content in flood period, middle water period and dead water period, and the design material source mainly consists of fine sand, silt, mud and lignite, as shown in the table 1. In addition, as can be seen from basic data of the research area, the research area cliff group is an integral water inlet period, so that a braided river delta deposition simulation experiment is firstly carried out before water inlet, and is marked as Run1, and cliff three-section delta deposition is divided into three-period water inlet periods, which are respectively marked as Run2-1, run2-2 and Run2-3.

TABLE 1 composition Source Sand and mud composition of the Innovative area cliff

The changes of flood period, water period and dead water period in the nature have a certain rule, and flood can be designed by considering the formation conditions of the jawbone in the cliff group: reclaimed water: the proportion of time spent water is 1:3:6. according to the characteristics of the formation of the delta in the cliff group of the research area and the flow ratio of the natural boundary river flood, reclaimed water and dead water, the method designs the water for entering the sea river by the braided river delta: reclaimed water: the flow ratio of the dead water is 6:3:1. in the experiment, the flow rate in flood period is 1.0-1.2L/S, the flow rate in medium water period is 0.5-0.6L/S, and the flow rate in dead water period is 0.2-0.3L/S. Sand, coal and shale in the ratio of 8:1:1 in flood period, 12:1:1 in reclaimed water period and 18:1:1 in dead water period. The ratio of the powdery coal to the granular coal is 1:3. As shown in table 2.

Table 2 study area parameter design table

The final sediment of the experiment is sampled 149 times, information such as coordinates, positions, characteristics and the like of each sample is recorded, and then the sampled samples are subjected to determination of organic carbon content and particle size analysis.

The experimental results show that: because the land source organic carbon content is related to the hydrodynamic strength, the hydrodynamic type, the topography and the object source conditions, the change of the hydrodynamic strength is related to the topography and the topography, and the interactions with different hydrodynamic types can be expressed on the change of the granularity composition of the sediment, the control of the land source organic matter deposition by the conditions of the hydrodynamic strength, the hydrodynamic type, the topography, the object source and the like can be reflected through the relation between the granularity composition and the TOC. As shown in fig. 4, fig. 4 is a schematic fit diagram of the TOC content of the experimental sediment and the deposition related parameters provided in the present specification, wherein the deposition related parameters include a argillaceous content, a silt content, a fine sand content and a medium coarse sand content, and the argillaceous content, the silt content, the fine sand content, the medium coarse sand content and the TOC have good correlation according to the fit. Therefore, the relationship between the content of each particle and the TOC can be fitted by multiple regression, and then the relationship between the particle size and the carrying distance of the object source is utilized to convert the content of each particle into the distance from the object source, so as to obtain the formula (2), as shown in fig. 5, fig. 5 is a schematic diagram of the relationship between the content of different particle and the distance from the object source of the experimental sediment provided in the specification, wherein the mutual conversion is realized by utilizing the correlation relationship among the particle in the implementation scene because the correlation between the powder sand, the fine sand and the distance from the object source is not high. It should be noted that, as the distance between the sources increases, the hydrodynamic strength changes regularly as a whole, and the corresponding proportion of the granularity and the composition of the sediment changes gradually regularly, so that a certain quantitative relationship may exist between the particles through analysis. The fitting shows that the correlation between the silt content and the muddy content is higher, and the correlation between the fine sand content and the medium coarse sand content is higher. In order to simplify the problem, in the implementation scene, the mutual correlation relation among all particle fractions is utilized to indirectly convert the distance between the powder sand and the fine sand component and the material source.

The result shown in fig. 6 can be obtained by solving the prediction model, and fig. 6 is a schematic diagram showing the fitting of the result of the experimental land-based organic carbon content prediction model provided in the specification, and it can be seen that the land-based organic matter can be carried for 6.66m furthest under the experimental condition, and the maximum value appears near the delta front-shallow sea range. Fig. 7 is a graph showing the effect of predicting the organic carbon content of the experimental land source provided in the present specification, and as can be seen from fig. 7, the prediction correlation is high.

The model is applied to practical researches of the arborvitae sunk arborvitae group in the Qionsoutheast basin, and is built to obtain the formula (3), and the formula (4) is obtained after simplification. The result shown in fig. 8 can be obtained by solving the prediction model, and fig. 8 is a schematic fitting diagram of the result of the prediction model of the three-section land-source organic carbon content of the arborvitae recessed arborvitae group provided by the specification, and it can be seen that the organic matters of the arborvitae recessed arborvitae can be carried for 47.70km furthest, and the maximum value appears near the front edge of delta-shallow sea and is consistent with the experimental result.

TOC＝-0.01e ^0.101x +0.23e ^0.0505x -2.61×10 ^-11 x ⁴ -6.46×10 ^-7 x ³ -3.99×10 ^-3 x ² +0.21x-2.19 (4)

From the above description, it can be seen that, in the embodiment of the present application, the parameters reflecting the correlation between the hydrodynamic condition and the land organic carbon content are found through a large number of experimental data points, so that quantitative prediction of the land organic carbon content changing trend along with the object source distance, maximum land organic matter carrying distance, and prediction of the land organic carbon high-value region can be realized. According to experimental results, the maximum value of the land-source organic carbon content appears near the front edge-shallow sea range of the delta, and then the maximum value is continuously reduced, so that the land-source sea-phase hydrocarbon source rock of the deepwater area without well drilling can be effectively predicted.

According to the land organic carbon content prediction method provided by the specification, on the basis of a sediment simulation experiment, a land organic carbon content prediction model is established according to the relationship between hydrodynamic conditions reflected by particle size analysis data and land transport organic carbon content, so that land organic carbon content distribution characteristics of a deep water oil and gas exploration area with less drilling at present are predicted, and further a favorable development area of land sea hydrocarbon source rocks is predicted, so that the next oil and gas exploration can be guided. In addition, as the basic data of the research area required by the method is less, the land organic carbon content can be simply and rapidly identified, so that the problem of prediction of the land-source sea-phase hydrocarbon source rock in the current deepwater area is solved, and the exploration efficiency is improved.

In the present specification, each embodiment of the method is described in a progressive manner, and the same and similar parts of each embodiment participate in each other, and each embodiment mainly describes differences from other embodiments. For relevance, see the description of the method embodiments.

Based on the above-mentioned method for predicting the content of organic carbon in land source, one or more embodiments of the present disclosure further provide a device for predicting the content of organic carbon in land source. The apparatus may include a system (including a distributed system), software (applications), modules, components, servers, clients, etc. that employ the methods described in the embodiments of the present specification in combination with the necessary apparatus to implement the hardware. Based on the same innovative concepts, the embodiments of the present description provide means in one or more embodiments as described in the following embodiments. Because the implementation scheme and the method for solving the problem by the device are similar, the implementation of the device in the embodiment of the present disclosure may refer to the implementation of the foregoing method, and the repetition is not repeated. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

Specifically, fig. 9 is a schematic block diagram of an embodiment of a device for predicting land-based organic carbon content according to the present disclosure, and as shown in fig. 9, the device for predicting land-based organic carbon content according to the present disclosure may include: a deposition result acquisition module 120, a sampling module 122, an analysis module 124, a model creation module 126, a prediction module 128.

A deposition result acquisition module 120 that may be used to acquire deposition results of a deposition simulation including a simulation based on a land-based organic matter distribution state;

the sampling module 122 may be configured to sample the deposition result to obtain a target sample;

the analysis module 124 can be used for measuring the content of organic carbon and analyzing the granularity of the target sample to obtain analysis results, wherein the analysis results comprise granularity composition and land-source organic carbon content;

the model building module 126 may be configured to build a prediction model of a land-source organic carbon distribution mode according to the particle size composition, the land-source organic carbon content, and the correlation information between hydrodynamic conditions;

the prediction module 128 may be configured to predict land-source organic carbon content corresponding to different source distances according to the prediction model.

Based on the foregoing description of the embodiments of the method, in another embodiment of the apparatus described herein, the model building module 126 may include:

a fitting unit 1260, configured to perform a fitting process on each fraction content included in the particle size composition and the land-source organic carbon content, to obtain a first fitting result;

a conversion unit 1262, configured to convert the content of each fraction into a distance from the object source based on the change information of the carrying distance between each fraction and the object source, where the carrying distance between each fraction and the object source is in a preset rule based on the sortability of the hydrodynamic condition;

a building unit 1264 may be configured to build a prediction model of the land-source organic carbon distribution pattern based on the first fitting result and the distance from the object source.

Based on the description of the foregoing embodiments of the method, in another embodiment of the apparatus described in this specification, the establishing unit 1264 may include:

In another embodiment of the apparatus described in the present specification, after the sampling the deposition result to obtain the target sample, the method may include:

the recording module can be used for recording characteristic information of the target sample, wherein the characteristic information comprises coordinate information, deposition phase position information and deposition period; the coordinate information is used for calculating the distance between the position of the deposit and the object source, and the deposit phase position information is used for calculating the deposit characteristic analysis of the result.

According to the land organic carbon content prediction device provided by the specification, on the basis of a sediment simulation experiment, a land organic carbon content prediction model is established according to the relationship between hydrodynamic conditions reflected by particle size analysis data and land transport organic carbon content, so that land organic carbon content distribution characteristics of a deep water oil and gas exploration area with less drilling at present are predicted, and further a favorable development area of land sea hydrocarbon source rocks is predicted, so that the next oil and gas exploration can be guided. In addition, as the basic data of the research area required by the method is less, the land organic carbon content can be simply and rapidly identified, so that the problem of prediction of the land-source sea-phase hydrocarbon source rock in the current deepwater area is solved, and the exploration efficiency is improved.

It should be noted that the description of the above apparatus according to the method embodiment may further include other embodiments, and specific implementation manner may refer to the description of the related method embodiment, which is not described herein in detail.

The present specification also provides an embodiment of a land-based organic carbon content prediction apparatus comprising a processor and a memory for storing processor-executable instructions that when executed by the processor implement steps comprising:

sampling the deposition result to obtain a target sample;

It should be noted that the description of the apparatus according to the embodiment of the method or the apparatus described above may further include other implementations, such as determining the well spacing information of adjacent wells, determining the well spacing according to the well spacing information of the reserve split and the well spacing information of the production split, and so on. Specific implementation may refer to descriptions of related method embodiments, which are not described herein in detail.

The present specification also provides an embodiment of a terrestrial organic carbon content prediction system comprising at least one processor and a memory storing computer-executable instructions that when executed by the processor perform the steps of the method of any one or more of the embodiments described above, for example comprising: obtaining a deposition result of a deposition simulation, wherein the deposition simulation comprises a simulation based on a land source organic matter distribution state; sampling the deposition result to obtain a target sample; determining the content of organic carbon and analyzing the granularity of the target sample to obtain an analysis result, wherein the analysis result comprises granularity composition and land-source organic carbon content; establishing a prediction model of land organic carbon distribution mode according to the granularity composition, the land organic carbon content and the association information among hydrodynamic conditions; and predicting land organic carbon content corresponding to different source distances according to the prediction model. The system may be a stand-alone server or may include a server cluster, a system (including a distributed system), software (applications), an actual operating device, a logic gate device, a quantum computer, etc., using one or more of the methods or one or more of the embodiment devices of the present specification in combination with a terminal device that implements the necessary hardware.

The method embodiments provided in the present specification may be performed in a mobile terminal, a computer terminal, a server, or similar computing device. Taking the example of running on a server, fig. 10 is a hardware structure block diagram of an embodiment of a land-source organic carbon content prediction server provided in the present specification, where the server may be the land-source organic carbon content prediction device or the land-source organic carbon content prediction system in the above embodiment. As shown in fig. 10, the server 10 may include one or more (only one is shown in the figure) processors 100 (the processors 100 may include, but are not limited to, a microprocessor MCU, a processing device such as a programmable logic device FPGA), a memory 200 for storing data, and a transmission module 300 for communication functions. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 10 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the server 10 may also include more or fewer components than shown in FIG. 10, for example, may also include other processing hardware such as a database or multi-level cache, a GPU, or have a different configuration than that shown in FIG. 10.

The memory 200 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the method of predicting land-based organic carbon content in the embodiments of the present disclosure, and the processor 100 executes the software programs and modules stored in the memory 200 to perform various functional applications and data processing. Memory 200 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 200 may further include memory located remotely from processor 100, which may be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission module 300 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission module 300 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission module 300 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The method or apparatus according to the above embodiments provided in the present specification may implement service logic by a computer program and be recorded on a storage medium, where the storage medium may be read and executed by a computer, to implement the effects of the schemes described in the embodiments of the present specification.

The storage medium may include physical means for storing information, typically by digitizing the information before storing it in an electronic, magnetic, or optical medium. The storage medium may include: means for storing information using electrical energy such as various memories, e.g., RAM, ROM, etc.; devices for storing information using magnetic energy such as hard disk, floppy disk, magnetic tape, magnetic core memory, bubble memory, and USB flash disk; devices for optically storing information, such as CDs or DVDs. Of course, there are other ways of readable storage medium, such as quantum memory, graphene memory, etc.

The embodiments of the method and the device for predicting the land organic carbon content provided in the present disclosure may be implemented in a computer by executing corresponding program instructions by a processor, for example, implemented on a PC side using the c++ language of a windows operating system, implemented by a linux system, or implemented on an intelligent terminal using, for example, android, iOS system programming languages, and implemented based on processing logic of a quantum computer.

It should be noted that, the descriptions of the apparatus, the computer storage medium, and the system according to the related method embodiments described in the foregoing description may further include other implementations, and specific implementation manners may refer to descriptions of corresponding method embodiments, which are not described herein in detail.

All embodiments in the application are described in a progressive manner, and identical and similar parts of all embodiments are mutually referred, so that each embodiment mainly describes differences from other embodiments. In particular, for a hardware+program class embodiment, the description is relatively simple, as it is substantially similar to the method embodiment, as relevant see the partial description of the method embodiment.

Embodiments of the present description are not limited to situations in which industry communication standards, standard computer data processing and data storage rules are required or described in one or more embodiments of the present description. Some industry standards or embodiments modified slightly based on the implementation described by the custom manner or examples can also realize the same, equivalent or similar or predictable implementation effect after modification of the above examples. Examples of data acquisition, storage, judgment, processing, etc., using these modifications or variations may still fall within the scope of alternative implementations of the examples of this specification.

In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a car-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Although one or more embodiments of the present description provide method operational steps as described in the embodiments or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When implemented in an actual device or end product, the instructions may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment) as illustrated by the embodiments or by the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, it is not excluded that additional identical or equivalent elements may be present in a process, method, article, or apparatus that comprises a described element. The terms first, second, etc. are used to denote a name, but not any particular order.

For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, when one or more of the present description is implemented, the functions of each module may be implemented in the same piece or pieces of software and/or hardware, or a module that implements the same function may be implemented by a plurality of sub-modules or a combination of sub-units, or the like. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage, graphene storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

One skilled in the relevant art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, one or more embodiments of the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments. In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

The foregoing is merely an example of one or more embodiments of the present specification and is not intended to limit the one or more embodiments of the present specification. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims.

Claims

1. A method for predicting organic carbon content of land source, comprising:

sampling the deposition result to obtain a target sample;

predicting land organic carbon content corresponding to different object source distances according to the prediction model;

the land organic carbon distribution model is obtained by establishing the following formula:

Wherein TOC represents organic carbon content, C _m Representing the content of sediment muddy component C _S Representing the content of sediment powder sand component C _f Represents the content of sediment fine sand component C _me Represents the content of coarse sand component in the sediment, x represents the distance from the object source, C _* Indicating the content of each fraction component, x ₀ Represent C _m Corresponding x value, a when=50% _i 、n _i And b is a constant.

2. The method of claim 1, wherein said establishing a predictive model of land-based organic carbon distribution patterns based on the correlation information between the particle size composition, land-based organic carbon content, and hydrodynamic conditions comprises:

3. The method of claim 1, wherein sampling the deposition results to obtain a target sample comprises:

4. A land-based organic carbon content prediction apparatus, comprising:

the prediction module is used for predicting land source organic carbon content corresponding to different object source distances according to the prediction model;

5. The apparatus of claim 4, wherein the model building module comprises:

6. The apparatus of claim 4, wherein sampling the deposition results to obtain a target sample comprises:

7. A land-based organic carbon content prediction apparatus comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor implementing steps comprising:

sampling the deposition result to obtain a target sample;

8. A land-based organic carbon content prediction system comprising at least one processor and a memory storing computer-executable instructions that when executed by the processor implement the steps of the method of any one of claims 1-3.