CN111402969A

CN111402969A - Method, device, equipment and system for predicting terrestrial organic carbon content

Info

Publication number: CN111402969A
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: 2020-07-10
Anticipated expiration: 2040-03-19
Also published as: CN111402969B

Abstract

The embodiment of the specification discloses a method, a device, equipment and a system for predicting the content of terrestrial organic carbon. 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 organic carbon content and analyzing the granularity of the target sample to obtain an analysis result, wherein the analysis result comprises granularity composition and continental source organic carbon content; establishing a prediction model of a terrestrial organic carbon distribution mode according to the correlation information among the granularity composition, the terrestrial organic carbon content and the hydrodynamic condition; and predicting the content of the land-source organic carbon corresponding to different source distances according to the prediction model. By utilizing the embodiment of the specification, the content of the terrestrial organic carbon can be simply and quickly identified, so that the problem of prediction of the terrestrial marine hydrocarbon source rock in the current deep water region is solved, and the exploration efficiency is improved.

Description

Method, device, equipment and system for predicting terrestrial organic carbon content

Technical Field

The embodiment scheme 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 the content of terrestrial organic carbon.

Background

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

The research on the distribution characteristics of land-source organic carbon content of the delta-shallow sea sedimentation system mainly focuses on the research on the modern estuary sedimentation system, and is generally characterized by geochemical parameters such as total carbon stable isotope of organic matter, nitrogen stable isotope ratio, BIT (branched isophenoid tetrather), lignin content and the like. In petroleum geology research, the research on land-source organic matter distribution rules of a delta-shallow sea deposition system is relatively lacked, and the research is mainly focused on the research on land-source organic matter distribution of a three-fold system Mungaroo delta deposition system in a basin of northern Carna, Australia at present, because a large amount of sample actual measurement data is needed during the research through geochemical parameters, the research is very difficult due to the limitation of the number of drilled wells in the actual oil and gas development process.

In addition, △ lgR method, multiple regression method, neural network method and other methods are commonly used for predicting organic carbon, and the methods mainly use correlation modeling of organic carbon content and logging value to predict.

Therefore, there is a need in the art for a solution that can effectively identify the content of terrestrial organic carbon.

Disclosure of Invention

The embodiment of the specification aims to provide a method, a device, equipment and a system for predicting the content of terrestrial organic carbon, which can simply and quickly identify the content of terrestrial organic carbon, so that the problem of predicting the terrestrial marine hydrocarbon source rock in the current deep water area is solved, and the exploration efficiency is improved.

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

a method for predicting the content of terrestrial organic carbon comprises the following steps:

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

establishing a prediction model of a terrestrial organic carbon distribution mode according to the correlation information among the granularity composition, the terrestrial organic carbon content and the hydrodynamic condition;

and predicting the content of the land-source organic carbon 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 a distribution pattern of terrestrial-derived organic carbon according to the correlation information between the particle size composition, the content of terrestrial-derived organic carbon, and the hydrodynamic condition includes:

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

converting the content of each size fraction into a distance from a source based on the change information of the distance between each size fraction and the source, wherein the distance between each size fraction and the source is in a preset rule based on the sortability of hydrodynamic conditions;

and 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 method provided in this specification, the establishing a predictive model of the terrestrial-sourced organic carbon distribution pattern based on the first fitting result and the distance from the source comprises:

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

wherein TOC represents the organic carbon content,C_mdenotes the content of the argillaceous component, C, of the sediment_SRepresents the content of silt component, C_fRepresents the content of the fine sand component of the sediment, C_meRepresenting the content of grit components in the deposit, x representing the distance from the source, C_*Denotes the content of the respective fraction component, x₀Is represented by C_mX value corresponding to 50%, a_i、n_iAnd b are both constants (i ═ 1, 2, 3, … …).

In another embodiment of the method provided in the present specification, the sampling the deposition result to obtain 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 where the deposition is located and the object source, and the deposition phase position information is used for deposition characteristic analysis of the calculation result.

An apparatus for predicting the content of organic carbon in a land source, comprising:

the deposition result acquisition module is used for acquiring a deposition result of deposition simulation, and the deposition simulation comprises simulation based on the 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 determining the organic carbon content and analyzing the granularity of the target sample to obtain an analysis result, and the analysis result comprises a granularity composition and a continental source organic carbon content;

the model establishing module is used for establishing a prediction model of a land-source organic carbon distribution mode according to the correlation information among the granularity composition, the land-source organic carbon content and the hydrodynamic condition;

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

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

the fitting unit is used for fitting the content of each particle size fraction included in the particle size composition and the content of the terrestrial organic carbon to obtain a first fitting result;

the conversion unit is used for converting the content of each size fraction into the distance from the source based on the change information of the carrying distance between each size fraction and the source, wherein the carrying distance between each size fraction and the 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 this specification, the establishing unit includes:

wherein TOC represents the organic carbon content, C_mDenotes the content of the argillaceous component, C, of the sediment_SRepresents the content of silt component, C_fRepresents the content of the fine sand component of the sediment, C_meRepresenting the content of grit components in the deposit, x representing the distance from the source, C_*Denotes the content of the respective fraction component, x₀Is represented by C_mX value corresponding to 50%, a_i、n_iAnd b are both constants (i ═ 1, 2, 3, … …).

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

the recording module is used for recording the 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 where the deposition is located and the object source, and the deposition phase position information is used for deposition characteristic analysis of the calculation result.

A device for predicting terrestrial-derived organic carbon content, comprising a processor and a memory for storing processor-executable instructions, which when executed by the processor, implement steps comprising:

sampling the deposition result to obtain a target sample;

A system for predicting terrestrial-derived organic carbon content, comprising at least one processor and a memory storing computer-executable instructions, the instructions, when executed by the processor, performing the steps of the method of any one of the method embodiments of the present specification.

The specification provides a method, a device, equipment and a system for predicting the content of terrestrial organic carbon. In some embodiments, on the basis of a deposition simulation experiment, a prediction model of the land-source organic carbon content is established through the relation between hydrodynamic conditions reflected by particle size analysis data and the land-source organic carbon content in transportation, so that the land-source organic carbon content distribution characteristics of a deepwater oil and gas exploration area with few drilled wells are predicted, the favorable development area of the land-source marine-phase hydrocarbon source rock is predicted, and further the next oil and gas exploration can be guided. In addition, because the method needs less basic data of a research area, the method can simply and quickly solve the prediction problem of the continental source marine hydrocarbon source rock of the current deep water area and improve the exploration efficiency. By adopting the implementation scheme provided by the specification, the content of the terrestrial organic carbon can be simply and quickly identified, so that the prediction problem of the terrestrial marine hydrocarbon source rock in the current deep water area is solved, and the exploration efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is a schematic flow diagram of one embodiment of a method for predicting the content of terrestrial organic carbon provided herein;

FIG. 2 is a schematic diagram of one embodiment of an experimental foot slope design provided herein;

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

FIG. 4 is a graphical illustration of a fit of the TOC content of experimental deposits to deposition related parameters provided herein;

FIG. 5 is a graphical illustration of the relationship between the content of different fractions of experimental sediments and the distance from the source, as provided herein;

FIG. 6 is a schematic fitting of experimental land-derived organic carbon content prediction model results provided herein;

FIG. 7 is a graph of the effect of experimental terrestrial organic carbon content prediction provided in the present specification;

FIG. 8 is a schematic fitting diagram of the results of a three-segment continental source organic carbon content prediction model for a cliff group of a south african cave provided by the present specification;

FIG. 9 is a schematic block diagram of an embodiment of an apparatus for predicting the content of organic carbon in a terrestrial source provided by the present specification;

fig. 10 is a block diagram of a hardware configuration of an embodiment of a prediction server for terrestrial organic carbon content provided by the present specification.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present specification, 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 a part of the embodiments in the present specification, and not all of the embodiments. All other embodiments that can be obtained by a person skilled in the art on the basis of one or more embodiments of the present description without inventive step shall fall within the scope of protection of the embodiments of the present description.

The method is characterized mainly by geochemical parameters such as total carbon stable isotope, nitrogen stable isotope ratio, BIT, lignin content and the like of the organic matter and a biomarker compound, and drawing a contour map according to data to reflect the distribution characteristics of the land-source organic matter. However, the method needs a large amount of measured sample data, and at present, the number of wells drilled in the deep water direction of the delta-shallow sea sediment system is small, so that the research on the land-source organic matter distribution rule of a medium-low exploration degree area is very difficult, and the sampling detection method needs a large amount of samples and is expensive in detection cost. And the geochemical parameters and the parameters of the biomarker compounds are greatly influenced by secondary modification, so that the land source organic matter distribution rule can be represented to have larger errors. The existing organic carbon prediction method is mainly used for predicting the organic carbon content based on logging response characteristics, but the method cannot effectively distinguish the land source organic carbon content from the marine organic carbon content, depends on a large amount of analysis and test data, is high in cost, and cannot predict the area without a drilling well in a deep water area. Therefore, the research method of the deposition simulation experiment based on the organic matter is very important for acquiring a large number of samples, analyzing the granularity of the samples and measuring the organic carbon content, exploring the correlation of the samples, establishing a prediction model to predict the distribution characteristics of the terrestrial organic carbon content and exploring the change rule of the terrestrial organic carbon content along with the increase of the distance between the terrestrial organic carbon and the object source.

The specification provides a method, a device, equipment and a system for predicting the content of terrestrial organic carbon. On the basis of a deposition simulation experiment, a prediction model of the land-source organic carbon content is established according to the relation between hydrodynamic conditions reflected by particle size analysis data and the land-source organic carbon content in transportation, so that the land-source organic carbon content distribution characteristics of the deepwater oil and gas exploration area with few drilled wells are predicted, the favorable development area of the land-source marine-phase hydrocarbon source rock is predicted, and further the next oil and gas exploration can be guided. In addition, the method needs less basic data of a research area, and can simply and quickly identify the content of the terrestrial organic carbon, so that the prediction problem of the terrestrial marine hydrocarbon source rock of the current deep water area is solved, and the exploration efficiency is improved.

The following describes an embodiment of the present disclosure with a specific application scenario as an example. Specifically, fig. 1 is a schematic flow chart of an embodiment of the method for predicting the content of the terrestrial organic carbon provided in the present specification. Although the present specification provides the method steps or apparatus structures as shown in the following examples or figures, more or less steps or modules may be included in the method or apparatus structures based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution order of the steps or the block structure of the apparatus is not limited to the execution order or the block structure shown in the embodiments or the drawings of the present specification. When the described method or module structure is applied to a device, a server or an end product in practice, the method or module structure according to the embodiment or the figures may be executed sequentially or in parallel (for example, in a parallel processor or multi-thread processing environment, or even in an implementation environment including distributed processing and server clustering).

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

s0: and obtaining a deposition result of a deposition simulation, wherein the deposition simulation comprises a simulation based on the land source organic matter distribution state.

Development of the sedimentary experiment simulation technique may be based on analysis of characteristics of facies, paleotopographic, source rocks, etc. in the research area.

In one embodiment of the present description, the deposition results may be results obtained after performing a deposition simulation on a delta-shallow sea deposition system. Delta is understood to mean, among other things, a sediment formed in a stable body of water or in the immediate vicinity of a river, partly emerging from the surface of the water. The deposition system can be understood as a huge three-dimensional deposition body which is formed by deposition and combination in a symbiotic relationship on formation and has a uniform substance source and a uniform water dynamic fluid. An delta-shallow sea sediment system is understood to be a sediment body controlled by a uniform source, a uniform water dynamic fluid, which may range from a delta sediment body to a deep water region where sediment can reach. In addition, the organic matter can be divided into marine organic matter and land-source organic matter according to the organic matter source, and the land-source organic matter can be understood as organic matter derived from lake aquatic organisms and from higher plants. In the embodiment of the specification, the land-source organic matter is mainly understood as organic matter carried into the ocean deposition range from river flow. It should be noted that the deposition result may also be obtained by performing deposition simulation on other deposition systems, and this specification does not limit this.

In an embodiment of the present specification, before performing the simulation experiment of the land-source organic matter distribution state, the method may include the following preparation steps:

(1) preparing experimental materials which can comprise gravel, sand, mud, lignite and other materials;

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

(2) Collecting basic data of the research area structure, stratum, ancient landform, ancient climate and the like, and designing experiment parameters such as an experiment scale, an experiment bottom form, hydrodynamic strength, water adding quantity, proportion of sand with different particle sizes, sand-lignite ratio, sand adding quantity and the like according to the geological background of the research area;

in some embodiments, the experimental scale may be understood as the ratio of the dimensions of the experimental device to the actual dimensions of the study area. The experimental base model can be understood as a base model after the ancient landform of the actual research area is reduced according to the experimental scale, and the local auxiliary structure can not be considered. The proportion of sand of different particle sizes is understood to be the proportion determined on the basis of the particle size data of the actual study area. The ratio of sand to lignite is understood to be a ratio determined from organic geochemical data of a real research area. The sand adding amount can be understood as relative sand adding amount determined according to the supply amount of the material sources in different deposition periods of the actual research area.

(3) And preparing an experimental device according to an experimental scale and an experimental bottom model, preparing experimental materials according to the proportion of the sand with different particle sizes and the proportion of the sand and the lignite, and carrying out experiments.

For example, in some implementations, geological structure characteristic data and organic matter abundance data may be obtained for a region of interest; determining a simulation scale and a bottom model according to geological structure characteristic data of a research area; then, performing sequence stratum analysis on the existing drilled wells in the research area, and determining a plurality of water intake periods of the deposition simulation according to the sequence stratum analysis result; determining the proportion of sand and organic matters according to the abundance data of the organic matters in the research area; determining the proportions of gravel, medium-coarse sand, powder-fine sand and mud according to the particle size analysis data of the research area; and performing deposition simulation of organic matter distribution in multiple water intake periods according to the simulation scale and the bottom model, the ratio of sand to organic matter, and preset water addition amount and hydrodynamic strength.

In some implementation scenarios, the simulating the deposition of organic matter distribution in multiple water intake periods according to the simulation scale and the bottom model, the ratio of sand to organic matter, the preset water addition amount and hydrodynamic strength may include: acquiring source supply data of a target work area; determining the sand adding amount of each water inlet period according to the material source supply data; determining the quantity of the sand with various granularities and the organic matters which need to be added in each water intake period according to the sand adding quantity of each water intake period, the proportion among the sand with various granularities in each water intake period and the proportion of the sand and the organic matters; uniformly mixing the sand with various particle sizes and the organic matters which are required to be added in each water inflow period according to the determined quantity of the sand with various particle sizes and the organic matters which are required to be added in each water inflow period; and continuously adding water according to the preset water adding amount and hydrodynamic strength in a plurality of water inflow periods of the deposition simulation, and continuously adding the uniformly mixed sand with various granularities and organic matters according to the quantity of the sand with various granularities and the organic matters which need to be added in each water inflow period to perform the deposition simulation of the distribution of the organic matters. In some embodiments, the continuously adding water according to the preset water adding amount and the hydrodynamic strength for the plurality of water intake periods of the sedimentation simulation may include: and in each water inflow period of the deposition simulation, continuously adding water by taking a flood period, a water leveling period and a dry period as a circulation period.

In the embodiment of the specification, a deposition simulation is performed on a deposition system and a deposition result of the deposition simulation is obtained, so that a basis can be provided for predicting the change trend of the content of the terrestrial organic carbon along with the distance of the source.

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

In the embodiment of the present description, after obtaining the deposition result of the deposition simulation, the deposition result may be sampled, so as to further analyze the target sample subsequently.

The present specification does not limit the manner in which the deposition result is sampled.

In an 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 where the deposition is located and the object source, and the deposition phase position information is used for deposition characteristic analysis of the calculation result.

For example, in some implementations, the deposition result is sampled, and after the target sample is obtained, the simulation result of the deposition simulation may be sampled, and the corresponding x and y coordinates of the sampled sample, the approximate deposition phase position (e.g., delta plain, delta leading edge, shallow sea, etc.), and the deposition period may be recorded. The x and y coordinates can be used for calculating the distance between the position of the deposit and the source, and the position of the deposit phase can be used for deposition characteristic analysis of the calculated result, for example, the analysis result shows that the organic carbon content high value regions are all located near the delta front edge-shallow sea region.

S4: and (3) determining the organic carbon content and analyzing the granularity of the target sample to obtain an analysis result, wherein the analysis result comprises the granularity composition and the continental source organic carbon content.

The organic carbon content is understood to be the weight of organic carbon per rock. In some implementations, organic carbon content can generally be used to assess organic matter abundance. Land-derived organic carbon content is understood to be the weight of organic carbon per unit of rock associated with land-derived organic matter. The granularity composition can comprise a mud content, a silt content, a fine sand content, a medium-coarse sand content and the like. The proportion change of each grain size of the sediment is the visual reflection of the hydrodynamic condition change. It should be noted that, the land-source organic carbon is transported with water flow, and there is a certain change rule in the transporting direction, and the land-source organic matter is corresponding to the prediction of the organic matter content by the hydrodynamic condition reflected by the grain size characteristics of the analysis.

In some implementation scenarios, after the target sample is obtained, the organic carbon content of the target sample may be measured, so as to obtain the continental source organic carbon content, in other implementation scenarios, after the target sample is obtained, the target sample may be subjected to particle size analysis, so as to obtain the shale content, the silt content, the fine sand content, and the medium coarse sand content of the target sample.

S6: and establishing a prediction model of the terrestrial organic carbon distribution mode according to the correlation information among the granularity composition, the terrestrial organic carbon content and the hydrodynamic condition.

The associated information may also be referred to as an intrinsic contact. The internal link between the particle size composition, the content of terrestrial organic carbon and the hydrodynamic conditions is understood to be the regular variation of the parameters with increasing distance from the source and the correlation with each other. Specifically, the change of the sediment particle size ratio is an intuitive reflection of the change of hydrodynamic conditions, the hydrodynamic strength gradually weakens due to energy loss along with the increase of the source distance, the corresponding sediment coarse particle content also gradually decreases, the fine particle content gradually increases, and each particle size fraction generally has a certain change rule along with the increase of the source distance due to the sorting property of the hydrodynamic conditions. In addition, considering that the land-source organic matter is carried along with the water flow, the embodiment of the present disclosure may analyze and search the correlation between the land-source organic matter and the hydrodynamic condition, and then establish a prediction model of a land-source organic carbon distribution model based on the correlation, so as to predict the land-source organic matter distribution characteristics.

In an embodiment of the present disclosure, the establishing a prediction model of a distribution pattern of the terrestrial organic carbon according to the correlation information between the particle size composition, the content of the terrestrial organic carbon, and the hydrodynamic condition may include: fitting each size fraction content included in the particle size composition and the content of the terrestrial organic carbon to obtain a first fitting result; converting the content of each size fraction into a distance from a source based on the change information of the distance between each size fraction and the source, wherein the distance between each size fraction and the source is in a preset rule based on the sortability of hydrodynamic conditions; and 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. For example, in some implementation scenarios, the shale content, the silt content, the fine sand content, the medium coarse sand content and the land-source organic carbon content may be fitted by using a multiple regression, and then the particle size content is converted into a distance from the object source by using a variation relation of particle size along with the transportation distance of the object source, so as to establish a prediction model of a land-source organic carbon distribution mode.

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

In the embodiment of the description, the formula is used for establishing the prediction model of the terrestrial organic carbon distribution mode, so that the quantitative prediction of the terrestrial organic carbon content distribution mode can be realized more quickly and accurately.

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

In the embodiment of the present specification, since the prediction model of the land-source organic carbon distribution pattern includes each parameter, the source distance, and the change rule between the parameters and the source distance, after the prediction model of the land-source organic carbon distribution pattern is established, the land-source organic carbon content corresponding to different source distances can be predicted according to the prediction model.

In the embodiment of the specification, the change of hydrodynamic conditions can be intuitively reflected by the granularity data, so that the land source organic carbon content can be predicted by utilizing the granularity data, and the land source organic matter distribution characteristics can be predicted only by the distance from a source in actual exploration of a deep water area, so that the cost is reduced, and the prediction efficiency is improved.

The above method is described below with reference to a specific example, however, it should be noted that the specific example is only for better describing the present application and is not to be construed as limiting the present application.

In the specific embodiment of the specification, a cliff city group of a depressed cliff 13-1 gas field region of the south cliff of the southeast basin of Qinan is taken as a prototype geological model, a deposition system of a work area is mainly based on a north material source, and a material source which is consistent with the reality is designed by combining practical data.

Specifically, an experiment area of 3m × 5m is designed, the transverse effective application range is 0-3.0 m, the ratio ruler is 1:10000, the longitudinal application range is 0-5.0 m, the ratio ruler is 1:8000, and the height direction thickness ratio ruler is 1: 1000.

Designing an experiment according to the bottom shape characteristics of the lake basin in the research area and the actual experimental condition, wherein Y is 0-0.35 m and is a fixed river channel area, namely a river channel arranged for a supply source, and the effective measurement range is not counted; y is 0.35-3.5 m and is an Delta deposition area, namely the final deposition range of the deposition experiment; y is 3.5-5.0 m in shallow sea area. The gradient of the research area near the object source is about 2 degrees, generally the gradient of the research area near the object source is steeper, and the gradient gradually decreases towards the ocean. The design of the experimental bottom slope is shown in fig. 2, wherein the design slope of Y is about 3-5 degrees at the position close to the source, Y is about 0.35-2 m, Y is 1-3 degrees at the slope of 2-4 m, and Y is 1 degree at the slope of 4-5 m. The thickness contour map of the bottom profile is shown in fig. 3, where the unit of the

values

38, 30, 20, etc. is cm, which is the thickness between the bottom surface after the slope is laid and the bottom surface before the thickness is not laid, and the white frame in the figure is the position of the artificially set river channel for the additive source.

Through particle size analysis of a cliff city group in a research area, a braided river delta sedimentation area mainly contains fine sand, a small amount of gravel is seen, lithology of each sedimentation period is different, experimental design mainly considers the particle size characteristics of sediments, the carrying capacity of water flow and the change of sand content in a flood period, a middle water period and a dry water period, and a design material source mainly comprises fine sand, silt, mud and lignite, and is specifically shown in table 1. In addition, the basic data of the research area show that the cliff group in the research area is the whole water inflow period, therefore, a braided river delta sedimentation simulation experiment is firstly carried out before water inflow as a bottom model, which is marked as Run1, and the three-stage delta sedimentation of the cliff is divided into three periods of water inflow which are marked as Run2-1, Run2-2 and Run2-3 respectively.

TABLE 1 composition of cliff city composition source with sand and mud

The change of flood period, middle flood period and dry flood period in nature has a certain rule, considering the forming conditions of cliff group delta, the time ratio of flood to reclaimed water to dry water is designed to be 1: 3: 6, according to the forming characteristics of cliff group delta in a research area and the flow ratio of natural river flood to reclaimed water to dry water, the flow ratio of braided river delta to river flood to reclaimed water to dry water is designed to be 6: 3: 1, in an experiment, the flow rate of flood period is 1.0-1.2L/S, the flow rate of medium flood period is 0.5-0.6L/S, the flow rate of dry period is 0.2-0.3L/S, sand, coal and shale are proportioned according to the ratio of flood period 8:1:1, the medium flood period 12:1:1, the dry period 18:1:1, and the ratio of powdered coal and granular coal is proportioned according to 1:3, and the proportion is specifically shown in table 2.

TABLE 2 study area parameter design Table

Sampling the final sediment of the experiment for 149 times, recording the information of coordinates, positions, characteristics and the like of each sample, and then measuring the organic carbon content and analyzing the granularity of the collected samples.

The experimental results show that: since the content of the land-source organic carbon is related to the hydrodynamic strength, hydrodynamic type, terrain, topography and matter source conditions, the change of the hydrodynamic strength is closely related to the terrain and topography, and the interaction between the hydrodynamic strength and different hydrodynamic types can be reflected on the change of the granularity composition of the deposit, so that the control of the land-source organic matter deposition under the conditions of the hydrodynamic strength, the hydrodynamic type, the terrain, the topography, the matter 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 fitting schematic diagram of the TOC content of the experimental sediment and the deposition related parameters provided in this specification, where the deposition related parameters include a argillaceous content, a silt content, a fine sand content, and a medium coarse sand content, and the fitting shows that the argillaceous content, the silt content, the fine sand content, and the medium coarse sand content all have a good correlation with the TOC. Therefore, the relation between the content of each fraction and TOC may be fitted by using multivariate regression, and then the content of each fraction is converted into the distance from the source by using the variation relation of the particle size along with the source conveying distance to obtain the formula (2), as shown in fig. 5, fig. 5 is a schematic diagram of the relation between the content of different fractions of the experimental sediment and the source distance provided by the present specification, wherein, since the correlation between the silt, the fine sand and the distance from the source is not high, the mutual conversion is realized by using the correlation between the fractions in the present implementation scenario. It should be noted that, as the distance between the sources increases, the hydrodynamic strength changes regularly as a whole, and the corresponding particle size composition ratio of the sediment also changes regularly, so that a certain quantitative relationship may exist between the particle sizes through analysis. Fitting finds that the correlation between the silt content and the argillaceous content is high, and the correlation between the fine sand content and the medium-coarse sand content is high. In order to simplify the problem, the implementation scenario utilizes the correlation among the particle sizes to indirectly convert the distances between the silt and the fine sand components and the source.

Solving the prediction model can obtain the result shown in fig. 6, where fig. 6 is a fitting diagram of the result of the experimental land-source organic carbon content prediction model provided in this specification, it can be seen that, under the experimental condition, the land-source organic matter can be transported by 6.66m at the farthest, and a maximum value appears near the front edge of the delta-shallow sea range. Fig. 7 is a graph showing the effect of predicting the content of the experimental terrestrial organic carbon provided in the present specification, and it can be seen from fig. 7 that the prediction correlation is high.

The model is applied to the practical research of the cliff city group depressed in south of the south basin of the Qiongnan, the model is established to obtain a 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 fitting schematic diagram of the results of the prediction model for the content of organic carbon in three continental sources in the cliff city group provided by the present specification, and it can be seen that the continental source organic matter of the cliff can be transported for 47.70km farthest, and the maximum value appears near the front edge of the delta-shallow sea, which is consistent with the experimental result.

TOC＝-0.01e^0.101x+0.23e^0.0505x-2.61×10^-11x⁴-6.46×10^-7x³-3.99×10^-3x²+0.21x-2.19 (4)

From the above description, it can be seen that, in the embodiment of the present application, a parameter that reflects a correlation between hydrodynamic conditions and terrestrial organic carbon content is found through a large number of experimental data points, so that quantitative prediction of a change trend of terrestrial organic carbon content along with a distance from an object source, a maximum distance for transporting terrestrial organic matter, and prediction of a terrestrial organic carbon high-value area can be achieved. According to experimental results, the maximum value of the land-source organic carbon content appears near the range from the front edge of the delta to the shallow sea, and then the maximum value is continuously reduced, so that the land-source marine hydrocarbon source rock in the deep water area without a drilled well can be effectively predicted.

According to the land-source organic carbon content prediction method provided by the specification, on the basis of a deposition simulation experiment, a land-source organic carbon content prediction model is established according to the relation between hydrodynamic conditions reflected by particle size analysis data and land-source organic carbon carrying content, so that the land-source organic carbon content distribution characteristics of a deepwater oil and gas exploration area with few drilled wells at present are predicted, the favorable development area of a land-source marine hydrocarbon source rock is predicted, and further the next oil and gas exploration can be guided. In addition, the method needs less basic data of a research area, and can simply and quickly identify the content of the terrestrial organic carbon, so that the prediction problem of the terrestrial marine hydrocarbon source rock of the current deep water 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 in each embodiment may be joined together, and each embodiment focuses on the differences from the other embodiments. Reference is made to the description of the method embodiments.

Based on the method for predicting the content of the terrestrial organic carbon, one or more embodiments of the present disclosure further provide a device for predicting the content of the terrestrial organic carbon. The apparatus may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that use the methods described in the embodiments of the present specification in conjunction with any necessary apparatus to implement the hardware. Based on the same innovative conception, embodiments of the present specification provide an apparatus as described in the following embodiments. Since the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific implementation of the apparatus in the embodiment of the present specification may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an 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 the device for predicting the content of the terrestrial organic carbon provided by the present specification, and as shown in fig. 9, the device for predicting the content of the terrestrial organic carbon provided by the present specification may include: a deposition result acquisition module 120, a sampling module 122, an analysis module 124, a model building module 126, and a prediction module 128.

A deposition result obtaining module 120, configured to obtain a deposition result of a deposition simulation, where the deposition simulation includes a simulation based on a land-source organic matter distribution state;

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

an analysis module 124, which may be configured to perform organic carbon content determination and particle size analysis on the target sample to obtain an analysis result, where the analysis result includes a particle size composition and a terrestrial organic carbon content;

the model establishing module 126 may be configured to establish a prediction model of a terrestrial organic carbon distribution model according to the correlation information among the particle size composition, the terrestrial organic carbon content, and the hydrodynamic condition;

and the prediction module 128 can be used for predicting the content of the terrestrial organic carbon corresponding to different source distances according to the prediction model.

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

a fitting unit 1260, which may be configured to perform a fitting process on each fraction content included in the particle size composition and the terrestrial 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 a source based on information about a change in a distance from the source, wherein the distance from the source is a preset rule based on a sortability of hydrodynamic conditions;

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

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

Based on the description of the embodiment of the foregoing method, in another embodiment of the apparatus described in this specification, the sampling the deposition result to obtain a target sample may include:

the recording module can be used for recording the 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 where the deposition is located and the object source, and the deposition phase position information is used for deposition characteristic analysis of the calculation result.

According to the land-source organic carbon content prediction device provided by the specification, on the basis of a deposition simulation experiment, a land-source organic carbon content prediction model is established according to the relation between hydrodynamic conditions reflected by particle size analysis data and land-source organic carbon carrying content, so that the land-source organic carbon content distribution characteristics of a deepwater oil and gas exploration area with few drilled wells at present are predicted, a favorable development area of a land-source marine hydrocarbon source rock is predicted, and further the next oil and gas exploration can be guided. In addition, the method needs less basic data of a research area, and can simply and quickly identify the content of the terrestrial organic carbon, so that the prediction problem of the terrestrial marine hydrocarbon source rock of the current deep water area is solved, and the exploration efficiency is improved.

It should be noted that the above-mentioned description of the apparatus according to the method embodiment may also include other embodiments, and specific implementation manners may refer to the description of the related method embodiment, which is not described herein again.

The present specification also provides an embodiment of a device for predicting terrestrial organic carbon content, comprising a processor and a memory for storing processor-executable instructions, which when executed by the processor, implement steps comprising:

sampling the deposition result to obtain a target sample;

It should be noted that the above-mentioned apparatus may also include other implementation manners according to the description of the method or device embodiment, such as an implementation manner of determining the well spacing information of adjacent wells, determining the well spacing according to the well spacing information of the stored volume splits and the well spacing information of the production volume splits, and the like. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.

The present specification also provides an embodiment of a system for predicting terrestrial organic carbon content, comprising at least one processor and a memory storing computer-executable instructions, the processor implementing the steps of the method described in any one or more of the above embodiments when executing the instructions, 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 organic carbon content and analyzing the granularity of the target sample to obtain an analysis result, wherein the analysis result comprises granularity composition and continental source organic carbon content; establishing a prediction model of a terrestrial organic carbon distribution mode according to the correlation information among the granularity composition, the terrestrial organic carbon content and the hydrodynamic condition; and predicting the content of the land-source organic carbon corresponding to different source distances according to the prediction model. The system may be a single 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 example devices of the present specification, in combination with a terminal device implementing hardware as necessary.

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

The memory 200 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the method for predicting the content of the terrestrial organic carbon in the embodiment of the present specification, and the processor 100 executes various functional applications and data processing by executing the software programs and modules stored in the memory 200. 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 a computer terminal through 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 for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission module 300 includes a Network adapter (NIC) that can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission module 300 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may 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 may also be possible or may be advantageous.

The method or apparatus provided by the present specification and described in the foregoing embodiments may implement service logic through a computer program and record the service logic on a storage medium, where the storage medium may be read and executed by a computer, so as to implement the effect of the solution described in the embodiments of the present specification.

The storage medium may include a physical device for storing information, and typically, the information is digitized and then stored using an electrical, magnetic, or optical media. The storage medium may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.

The embodiment of the method or the apparatus for predicting the content of the terrestrial organic carbon provided in this specification may be implemented in a computer by executing corresponding program instructions by a processor, for example, implemented in a PC end using a c + + language of a windows operating system, implemented in a linux system, or implemented in an intelligent terminal using, for example, android, an iOS system programming language, implemented in processing logic based on a quantum computer, and the like.

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

The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.

The embodiments of this specification are not limited to what must be in compliance with industry communication standards, standard computer data processing and data storage rules, or the description of one or more embodiments of this specification. Certain industry standards, or implementations modified slightly from those described using custom modes or examples, may also achieve the same, equivalent, or similar, or other, contemplated implementations of the above-described examples. The embodiments using the modified or transformed data acquisition, storage, judgment, processing and the like can still fall within the scope of the alternative embodiments of the embodiments in this specification.

In the 90 th generation of 20 th century, it is obvious that improvements in Hardware (for example, improvements in Circuit structures such as diodes, transistors and switches) or software (for improvement in method flow) can be distinguished for a technical improvement, however, as technology develops, many of the improvements in method flow today can be regarded as direct improvements in Hardware Circuit structures, designers almost all obtain corresponding Hardware Circuit structures by Programming the improved method flow into Hardware circuits, and therefore, it cannot be said that an improvement in method flow cannot be realized by Hardware entity modules, for example, Programmable logic devices (Programmable logic devices L organic devices, P L D) (for example, Field Programmable Gate Arrays (FPGAs) are integrated circuits whose logic functions are determined by user Programming of devices), and a digital system is "integrated" on a P L D "by self Programming of designers without requiring many kinds of integrated circuits manufactured and manufactured by special chip manufacturers to design and manufacture, and only a Hardware software is written in Hardware programs such as Hardware programs, software programs, such as Hardware programs, software, Hardware programs, software programs, Hardware programs, software, Hardware programs, software, Hardware programs, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software, Hardware, software.

A controller may be implemented in any suitable manner, e.g., in 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 (ASICs), programmable logic controllers (PLC's) and embedded microcontrollers, examples of which include, but are not limited to, microcontrollers 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone L abs C8051F320, which may also be implemented as part of the control logic of a memory.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-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 approaches. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or 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, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. The terms first, second, etc. are used to denote names, but not any particular order.

For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, when implementing one or more of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, etc. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

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

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 computer storage media 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, 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 that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

As will be appreciated by one skilled in the art, 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. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," 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 specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer 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, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The above description is merely exemplary of one or more embodiments of the present disclosure and is not intended to limit the scope of one or more embodiments of the present disclosure. Various modifications and alterations to one or more embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims.

Claims

1. A method for predicting the content of terrestrial organic carbon is characterized by comprising the following steps:

sampling the deposition result to obtain a target sample;

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

3. The method of claim 2, wherein establishing a predictive model of the land-source organic carbon distribution pattern based on the first fit and the distance from the source comprises:

wherein TOC represents the organic carbon content, C_mDenotes the content of the argillaceous component, C, of the sediment_SRepresents the content of silt component, C_fRepresents the content of the fine sand component of the sediment, C_meIndicates coarseness in depositSand component content, x represents the distance from the source, C_*Denotes the content of the respective fraction component, x₀Is represented by C_mX value corresponding to 50%, a_i、n_iAnd b are both constants (i ═ 1, 2, 3, … …).

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

5. An apparatus for predicting the content of organic carbon in a land source, comprising:

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

7. The apparatus of claim 6, wherein the establishing unit comprises:

8. The apparatus of claim 5, wherein said sampling said deposition results to obtain a target sample comprises:

9. A device for predicting terrestrial organic carbon content, comprising a processor and a memory for storing processor-executable instructions, which when executed by the processor, implement steps comprising:

sampling the deposition result to obtain a target sample;

10. A system for predicting terrestrial organic carbon content, 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 of claims 1-4.