A method for decoupling primary-secondary cracking hydrocarbon generation conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment

Abstract

The application relates to a method for decoupling primary-secondary cracking hydrocarbon generation conversion rates of organic matter based on a gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment, which comprises the following steps: step 1, kerogen gold tube hydrocarbon generation simulation and solid product rock pyrolysis experiment data acquisition; step 2, parameterized modeling of the kerogen hydrocarbon generation process; step 3, construction of a least square model cooperatively constrained by multi-source experimental information; step 4, optimization solution under the cooperatively constrained condition of multi-source experimental information; and step 5, hydrocarbon generation conversion rate calculation. The application quantitatively characterizes the conversion rate evolution characteristics of each hydrocarbon generation process, and through conversion rate calculation on the cumulative amount of each hydrocarbon generation process obtained by decoupling, the conversion rate evolution law of the kerogen oil generation, gas generation and oil cracking process, the oil cracking gas formation and the unreactive carbon formation process with the change of the maturity can be obtained, and a quantitative basis for fine hydrocarbon generation evaluation is provided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas geochemistry and computational geology, and particularly relates to a method for decoupling primary and secondary cracking of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment. BACKGROUND

[0002] The gold tube hydrocarbon generation thermal simulation experiment is a closed system experimental method commonly used to study the hydrocarbon generation behavior of organic matter thermal evolution. In the present application, kerogen is used as the organic matter sample, which is sealed in a gold tube, and under the conditions of controlled temperature and time, the hydrocarbon generation process of the kerogen as the maturity (represented by EasyRo) increases is simulated. After the experiment, the generated liquid products (denoted as "oil") and gaseous products (denoted as "gas") can be collected and measured respectively.

[0003] However, this experiment can only provide the total amount of oil and gas at different maturities, which reflects the final state results of the continuous superposition of multiple processes such as primary cracking (oil and gas generation) and oil cracking (oil consumption, oil cracking into gas, and unreactive carbon formation) in a closed system. Due to the lack of effective constraints on the mutual relationship between each sub-process, the existing technology cannot decouple the oil and gas generation processes in the primary cracking, nor can it quantitatively separate and characterize each conversion path in the oil cracking process.

[0004] Rock pyrolysis experiment is another type of analytical method widely used in the study of organic matter hydrocarbon generation. By performing programmed temperature pyrolysis on rock or solid organic matter samples, key parameters such as S2 (representing residual hydrocarbon potential) and TOC (reflecting organic carbon content) can be obtained. For the residual solid samples after extraction and oil washing in the gold tube experiment, rock pyrolysis experiment can be used to evaluate their residual hydrocarbon generation capacity and organic carbon preservation state at the corresponding maturity.

[0005] However, at present, rock pyrolysis experiment and gold tube hydrocarbon generation thermal simulation experiment are usually used as independent technical means, and their results are often interpreted separately. The S2, TOC and other parameters obtained by rock pyrolysis essentially describe the final state properties of the solid sample at a certain maturity, which are difficult to directly relate to the specific hydrocarbon generation and cracking kinetics processes involved in the oil and gas generation in the gold tube experiment, and lack a unified interpretation framework.

[0006] Although the prior art can obtain total oil and total gas through gold tube hydrocarbon generation thermal simulation experiment, and can obtain residual hydrocarbon generation potential and organic carbon change information through rock pyrolysis experiment, it is difficult to organically combine the two types of experimental results and establish a unified analysis framework. In the absence of effective constraint relationship, the processes of primary kerogen cracking to generate oil and gas, oil consumption in oil cracking process, oil cracking to generate gas and formation of unreactive carbon in oil cracking process are superimposed in the experimental results, which cannot be effectively decoupled and quantitatively characterized, thereby limiting the in-depth understanding of the overall evolution characteristics of the primary-secondary cracking hydrocarbon generation process of organic matter. SUMMARY

[0007] The present application proposes a method for decoupling the primary-secondary cracking hydrocarbon conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-rock pyrolysis experiment of solid products. The technical problem to be solved is that in the gold tube hydrocarbon generation thermal simulation experiment of kerogen, only total oil and total gas can be obtained, and multiple hydrocarbon generation processes such as primary kerogen cracking and oil cracking are highly superimposed in the results, which are difficult to quantitatively decouple.

[0008] To achieve the above-mentioned purpose, the present application synchronously utilizes two types of experimental data: on the one hand, the final measured total amount of oil and gas under different maturities is obtained through gold tube experiment, which reflects the overall product characteristics after the superposition of hydrocarbon generation and cracking processes; on the other hand, the S2 and TOC parameters of the residual solid sample under the corresponding maturity are determined through rock pyrolysis experiment, which represents the changes of residual hydrocarbon generation potential and organic carbon content in the evolution process of kerogen.

[0009] On this basis, a self-consistent decoupling model is constructed: firstly, the basic equality constraints such as oil conservation, gas conservation and oil consumption conservation are introduced, and the inequality constraints of non-negativity of hydrocarbon generation and cracking conversion and their mutual relationship are applied; further, the rock pyrolysis information is integrated, the consistency constraint of hydrocarbon conversion rate based on S2 parameter is introduced, and the organic carbon conservation constraint based on the evolution characteristics of TOC is constructed. These constraints cooperatively limit the overall behavior of the hydrocarbon generation process in the thermal evolution process of kerogen with the advancement of maturity, ensuring that the decoupling results not only satisfy the oil and gas production observation data, but also maintain consistency with the geochemical law of organic carbon consumption and conversion.

[0010] By integrating the oil and gas product data and the geochemical parameters of solid residues into the same constrained optimization framework, the present application makes the decoupling results internally self-consistent in material balance, carbon evolution path and multi-source experimental measurement, which not only satisfies the total oil and gas of gold tube experiment, but also conforms to the solid organic matter evolution law revealed by rock pyrolysis.

[0011] Finally, the method realizes the quantitative decoupling of the processes such as primary kerogen cracking (oil and gas generation) and oil cracking (oil consumption, oil cracking to generate gas and formation of unreactive carbon), which significantly improves the physical rationality, geochemical consistency and overall self-consistency of the analysis of hydrocarbon generation process.

[0012] The organic matter mentioned in the title of the application takes kerogen as the actual research object. Kerogen is the main substance of hydrocarbon generation in the thermal evolution process. Kerogen refers to the dispersed organic matter in sedimentary rocks which is insoluble in alkali, non-oxidizing acid and non-polar organic solvent. The "primary cracking" and "secondary cracking" are both for the division of the hydrocarbon generation evolution stage of sedimentary organic matter (specifically kerogen). Among them, the primary cracking refers to the process that kerogen decomposes and generates crude oil and natural gas in the thermal evolution process; the secondary cracking refers to the process that the crude oil formed by the primary cracking of kerogen cracks and converts to generate natural gas and non-reactive carbon under further heating conditions. Therefore, the "crude oil cracking" expression used in this paper is a specific description of the cracking behavior in the second stage of kerogen hydrocarbon generation evolution, which emphasizes that the direct reaction object of the secondary cracking is crude oil, and does not change the technical connotation that the primary and secondary crackings are based on the division of the hydrocarbon generation evolution stage of kerogen.

[0013] The specific technical scheme provided by the application is: a method for decoupling the primary and secondary cracking of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment includes the following steps.

[0014] Step 1, kerogen gold tube hydrocarbon generation thermal simulation and solid product rock pyrolysis experiment data acquisition:

[0015] Firstly, the kerogen sample is heated by programmed temperature heating through the gold tube hydrocarbon generation thermal simulation experiment, and the experimental temperature, the corresponding maturity EasyRo, and the measured oil and gas amounts measured at the end of the experiment are recorded synchronously;

[0016] Subsequently, the residual solid sample obtained in the experiment is treated by extraction and oil washing to remove soluble organic matter, and a rock pyrolysis experiment is carried out thereon to obtain S2 and TOC parameters corresponding to the maturity;

[0017] Finally, the temperature, EasyRo, measured oil amount, measured gas amount, hydrocarbon conversion rate and normalized TOC corresponding to each group of experiments are integrated into a complete multi-source experimental data record, which is used as the basic input for subsequent modeling and decoupling calculation;

[0018] Step 2, parameterization modeling of the kerogen hydrocarbon generation process:

[0019] The kerogen process is parameterized into five process quantities, namely kerogen oil generation, kerogen gas generation, oil consumption, oil cracking to gas, and non-reactive carbon formation. In order to ensure the monotonicity of the hydrocarbon generation process with the increase of maturity, and reflect the irreversible characteristics of the pyrolysis process, that is, the cumulative amount of each hydrocarbon generation process increases monotonously with the increase of maturity, each hydrocarbon generation process is expressed in the form of cumulative non-negative increment sequence, thereby naturally introducing the monotonicity constraint at the parameter level;

[0020] At discrete maturity nodes In the above, each to-be-solved hydrocarbon generation process is represented as a corresponding sequence of incremental variables, and cumulative evolution curves of each hydrocarbon generation process with maturity are constructed by accumulating the incremental variables;

[0021] Step 3, constructing a least square model with multi-source experimental information collaborative constraints: a composite least square objective function composed of multiple residual terms is constructed, which is used to decouple and solve each process under the premise of satisfying the mass conservation and non-negativity constraints;

[0022] Step 4, nonlinear least square optimization solution under constraints:

[0023] The Trust Region Reflective algorithm (TRF) is used to solve the above nonlinear least square problem with boundary constraints;

[0024] Taking the incremental parameter vectors corresponding to the five processes of cheese root oil generation KTO, cheese root gas generation KTG, crude oil consumption OC, crude oil cracking gas OTG, and unreactive carbon formation C, and the hydrocarbon conversion coefficient K as optimization variables, the basic equality constraints of oil conservation, gas conservation, and crude oil consumption conservation in the crude oil cracking process provided by the gold tube hydrocarbon generation thermal simulation experiment, and the inequality constraints of non-negativity and mutual relationship of each hydrocarbon and cracking process, the consistency constraints of hydrocarbon conversion rate calculated from the S2 parameter obtained from the rock pyrolysis experiment, and the organic carbon conservation constraints constructed based on the TOC evolution model are unified into the same least square objective function in the form of residuals, and together constitute a multi-source experimental information collaborative constraint system;

[0025] Step 5, hydrocarbon conversion rate calculation:

[0026] After completing the constraint optimization solution, the quantitative results of the cumulative amounts of the five processes of cheese root oil generation KTO, cheese root gas generation KTG, crude oil consumption OC, crude oil cracking gas OTG, and unreactive carbon formation C with maturity can be obtained;

[0027] In order to facilitate comparative analysis between different maturity stages and different samples, the cumulative amounts of each process are dimensionless processed and defined as the corresponding hydrocarbon conversion rate;

[0028] For any process , the conversion rate at its maturity is defined as:

[0029]

[0030] Wherein: is the cumulative amount of the process under the condition of maturity ; the maximum cumulative amount reached by the process throughout the thermal evolution; for characterizing the relative degree of completion of the process.

[0031] The step 1 is to ensure that the data at different maturities are comparable, and S2 and TOC are normalized according to the original kerogen sample mass to establish a unified reference; based on the normalized S2 value, the hydrocarbon conversion rate of kerogen at each maturity is further calculated, which is used to characterize the degree of development of the primary cracking and hydrocarbon generation process with thermal evolution; wherein: S2 is the cracking hydrocarbon content; TOC is the total organic carbon;

[0032] The specific steps of the normalization process are as follows:

[0033] From the results of the gold tube hydrocarbon generation thermal simulation experiment, the total hydrocarbon yield and non-hydrocarbon yield corresponding to the unit mass of kerogen at each maturity condition are obtained, and the unit of measurement is mg / g of kerogen; since 1g of kerogen corresponds to 1000mg, the sum of the total hydrocarbon yield and the non-hydrocarbon yield reflects the mass of organic matter that has been converted and released from the kerogen under the maturity condition;

[0034] Accordingly, the proportion of residual kerogen under the corresponding maturity condition is calculated, which is defined as:

[0035]

[0036] wherein, is the total hydrocarbon yield obtained from the gold tube experiment, is the non-hydrocarbon yield, both units are mg / g of kerogen;

[0037] characterizes the mass proportion of residual kerogen in the original sample under the maturity condition;

[0038] Subsequently, the S2 parameter and the TOC parameter obtained from the rock pyrolysis experiment are multiplied by the proportion of residual kerogen under the corresponding maturity condition , so that the S2 and TOC data are normalized to the original kerogen sample reference, and the normalized S2 and TOC are obtained:

[0039]

[0040] wherein, is the normalized S2 parameter under each maturity condition, is the normalized TOC parameter under each maturity condition;

[0041] Based on the above data, under each maturity condition, the hydrocarbon conversion rate of kerogen is defined as the proportion of the original hydrocarbon generation potential that has been converted and released, and the calculation formula is:

[0042]

[0043] wherein, S2 is the S2 parameter of the original kerogen sample, represents the kerogen hydrocarbon conversion rate.

[0044] In step 2:

[0045] Let the optimization variable be the increment vector which consists of the following five groups of increment variables, corresponding to the five processes respectively:

[0046]

[0047] The above non-negativity constraint is used to ensure that the cumulative amount of each process is monotonically non-decreasing with increasing maturity;

[0048] Based on the above increment parameters, the cumulative amount of each hydrocarbon generation process at the i-th maturity node is reconstructed by the following way:

[0049]

[0050] wherein: KTO: kerogen oil generation; KTG: kerogen gas generation; OTG: oil cracking into gas; OC: oil consumption; C: unreactive carbon; Oil: measured oil amount; Gas: measured gas amount; Oil1: measured oil amount under initial maturity condition; Gas1: measured gas amount under initial maturity condition;

[0051] u1: increment at the 1st maturity node; u j : increment at the jth maturity node; u n : increment at the nth maturity node; KTO i : at the i-th node, the cumulative kerogen oil generation amount = initial oil amount + the sum of increments of oil at each maturity node; KTG: at the i-th node, the cumulative kerogen gas generation amount = initial gas amount + the sum of increments of gas at each maturity node; OTG: at the i-th node, the oil cracking into gas amount = the sum of increments of oil cracking into gas at each maturity node; OC: at the i-th node, the oil consumption amount = the sum of increments of oil consumption at each maturity node; C: at the i-th node, the unreactive carbon amount = the sum of increments of unreactive carbon at each maturity node;

[0052] On the basis of the above parameterized modeling framework, the hydrocarbon conversion carbon coefficient K is introduced as a global parameter to be optimized, which is used to convert the oil and gas amount generated in the hydrocarbon generation process into the corresponding organic carbon consumption amount; this conversion provides a key bridge for subsequent construction of organic carbon conservation constraints using normalized TOC data, thereby realizing the self-consistent coupling of gas, liquid products and solid residual carbon information in a unified material balance system.

[0053] In step 3, the objective function is composed of six types of residual terms:

[0054] 1) Equation constraint residual based on the conservation of mass relationship;

[0055] 2) Inequality constraint residual based on the non-negativity and upper limit conditions;

[0056] 3) Hydrocarbon conversion consistency constraint residual based on rock pyrolysis S2 parameters;

[0057] 4) Organic carbon conservation constraint residual based on rock pyrolysis TOC parameters;

[0058] 5) Smooth regularization residual for suppressing solution oscillation;

[0059] 6) S-shaped feature residual for constraining curve shape.

[0060] In step 3,

[0061] To incorporate the constraints into the least squares solution framework, the equation constraints and inequality constraints are uniformly processed using residual construction. By converting various constraints into residual terms and combining them with the experimental data corresponding residuals to form the objective function, the organic matter hydrocarbon generation and cracking process are co-solved:

[0062] For the equation constraint condition, let the constraint function be:

[0063]

[0064] When the model calculation results satisfy the constraint, the function value tends to zero; when the model calculation results deviate from the conservation relationship, the function value reflects the degree of deviation; therefore, the equation constraint function is directly introduced as a residual term into the least squares objective function, and its mathematical expression is:

[0065]

[0066] where, represents the residual term corresponding to the equation constraint, and the weight coefficient is used to adjust the influence of the conservation constraint in the overall optimization process to ensure effective constraint of the mass conservation relationship in oil and gas generation and conversion processes;

[0067] For the inequality constraint condition, let the constraint function be:

[0068]

[0069] When the constraint condition is satisfied, no residual is generated; when the model calculation results violate the physical constraint, only the violated part is penalized; for this reason, a positive truncated form is introduced to construct the inequality constraint residual, and its mathematical expression is:

[0070]

[0071] wherein, represents the residual term corresponding to the inequality constraint, and the weight coefficient is used to control the punishment strength of the behavior violating the non-negativity or upper limit constraint, so as to ensure that the decoupling result meets the physical rationality of the hydrocarbon generation and cracking process.

[0072] In addition, in order to coordinate the action strength of different types of constraints in the objective function, the present application respectively introduces first-order difference smoothing weight , second-order difference smoothing weight , curve shape constraint weight , hydrocarbon conversion rate consistency constraint weight , and organic carbon conservation constraint weight .

[0073] wherein, represents the weight of the smoothing constraint constructed based on the first-order difference in the objective function, and is used to inhibit the unreasonable mutation of the cumulative amount at adjacent maturity nodes; represents the weight of the curvature constraint constructed based on the second-order difference in the objective function, and is used to limit the excessive fluctuation of the cumulative amount curve in the maturity direction; represents the weight of the curve shape constraint in the objective function, and is used to limit the cumulative amount evolution curve to meet the expected physical shape characteristics.

[0074] Meanwhile, represents the weight of the hydrocarbon conversion rate consistency constraint calculated based on the S2 parameter of rock pyrolysis experiment in the objective function, and is used to constrain the hydrocarbon generation process of the decoupling result in the maturity direction to be consistent with the hydrocarbon conversion characteristics indicated by the experiment; represents the weight of the organic carbon conservation constraint constructed based on the TOC data of rock pyrolysis experiment in the objective function, and is used to constrain the physical rationality of each process in the sense of organic carbon conservation.

[0075] The above five constraint residuals are specifically:

[0076] The 1) equation constraint residual: mass conservation relationship

[0077] At each maturity node, the following mass conservation relationship is introduced and incorporated into the objective function in the form of a residual, and the weight coefficient is set to :

[0078] Oil conservation: cheese oil production-oil consumption = measured oil amount

[0079] Oil mass conservation residual:

[0080] Gas mass conservation: kerogen-derived gas + oil-cracked gas = measured gas

[0081] Gas mass conservation residual:

[0082] Oil consumption conservation: oil consumption + unreactive carbon = oil-cracked gas

[0083] Oil-cracked material conservation residual:

[0084] The above equation residuals are used to ensure that the primary kerogen cracking and the overall oil cracking process satisfy the material conservation relationship in oil mass, gas mass, and oil consumption.

[0085] The 2) inequality constraint residual: non-negativity and upper limit condition

[0086] To ensure that the solution satisfies physical reasonableness, the following inequality constraints are introduced, with the weight coefficient set as

[0087] Oil consumption ≥ oil-cracked gas:

[0088] Oil-cracked gas upper limit constraint residual:

[0089] Oil consumption ≥ unreactive carbon:

[0090] Unreactive carbon upper limit constraint residual:

[0091] Oil-cracked gas ≥ 0:

[0092] Oil-cracked gas non-negativity constraint residual:

[0093] Unreactive carbon ≥ 0:

[0094] Unreactive carbon non-negativity constraint residual: .

[0095] The 3) hydrocarbon conversion rate constraint residual:

[0096] To constrain the overall evolution degree of the primary kerogen cracking and hydrocarbon generation process, the hydrocarbon conversion rate consistency residual based on the S2 parameter calculated from rock pyrolysis experiments is introduced; the S2 parameter obtained from rock pyrolysis experiments reflects the residual hydrocarbon generation potential of the sample under the corresponding maturity condition, and by normalizing the S2 values under different maturity conditions to the initial state, the change of hydrocarbon conversion rate with maturity during the thermal evolution of kerogen can be obtained;

[0097] At each maturity node, the cumulative hydrocarbon generation amount of the primary cracking stage of kerogen is defined as:

[0098]

[0099] wherein KTO and KTG represent the cumulative amounts of oil and gas generated from kerogen, respectively;

[0100] The maximum hydrocarbon generation of kerogen is further defined as:

[0101]

[0102] Accordingly, the hydrocarbon conversion HC based on the decoupling result is calculated as:

[0103]

[0104] The hydrocarbon conversion HC calculated by the above model is compared with the hydrocarbon conversion HC calculated by the S2 parameter of rock pyrolysis The consistency constraint is performed with the weight coefficient set as , and the consistency residual of hydrocarbon conversion is constructed as:

[0105]

[0106] The 4) organic carbon conservation constraint residual is:

[0107] To ensure that the decoupling result meets the oil and gas production constraint condition while maintaining consistency with the change characteristics of organic carbon in the kerogen thermal evolution process, the organic carbon conservation residual constructed based on the TOC parameter of rock pyrolysis experiment is introduced; the TOC parameter obtained by rock pyrolysis experiment reflects the overall characteristics of the change of organic carbon content with maturity in the kerogen thermal evolution process, which is used to impose constraints on the decoupling result from the perspective of organic carbon conservation, thereby improving the physical reasonableness of the decoupling result;

[0108] Let the total organic carbon content of the original sample be On the basis of considering the consumption of organic carbon by primary cracking of kerogen and the generation of unreactive carbon by cracking of crude oil, the organic carbon conservation model is constructed as:

[0109]

[0110] The theoretical value of TOC is calculated as:

[0111]

[0112] wherein, is the residual TOC of primary cracking of kerogen, represents the cumulative amount of hydrocarbons generated by the primary cracking of kerogen; C represents the cumulative amount of non-reactive carbon formed by the cracking of crude oil; the coefficient 1 / 10 is used to convert the mass of non-reactive carbon into a percentage of TOC; K is the proportionality coefficient of hydrocarbon conversion to carbon; TOC calculated by the constructed organic carbon conservation model;

[0113] Based on the above organic carbon conservation model, an organic carbon conservation residual is constructed, and the weight coefficient is set to :

[0114]

[0115] The 5) smoothing regularization residual: suppresses oscillation

[0116] In order to avoid unreasonable local oscillation of the cumulative amount of each hydrocarbon generation process with respect to maturity in the solving process, a smoothing regularization residual is introduced into the objective function to impose a difference constraint on the change of the cumulative curve in the maturity direction;

[0117] Let the cumulative amount of a certain hydrocarbon generation process at a discrete maturity point be represented by the sequence

[0118]

[0119] is the cumulative amount of the process at the i-th maturity node, and n is the total number of maturity nodes.

[0120] Define the first-order difference operator as:

[0121]

[0122] First-order difference operator is used to represent the incremental change amplitude of the cumulative amount at adjacent maturity nodes, reflecting the local change characteristics of the process in the maturity direction;

[0123] Define the second-order difference operator as the second-order action of the first-order difference:

[0124]

[0125] Second-order difference operator represents the second change of the first-order difference in the maturity direction, and is used to constrain the smoothness of the cumulative amount curve in the maturity direction, preventing local oscillation that does not conform to the actual thermal evolution rule in the optimization solving process;

[0126] Among them, the first-order difference is used to describe the change amplitude of the cumulative amount between adjacent maturity points, and the second-order difference is used to describe the curvature change of the cumulative curve in the maturity direction;

[0127] ① The first-order difference smoothing residual, the weight To suppress drastic jumps between adjacent maturity points, first-order difference smoothing is introduced for each process to smooth the residuals.

[0128]

[0129] in These terms represent the variation range of the cumulative amount of each hydrocarbon generation process between adjacent maturity nodes. The above difference terms are used as smoothing constraint residuals and uniformly incorporated into the objective function to constrain the excessive variation range at adjacent maturity nodes, thereby suppressing unreasonable abrupt changes that may occur in the cumulative curves of each hydrocarbon generation and cracking process, and ensuring the continuity and physical rationality of its evolution with maturity.

[0130] ② Second-order difference curvature smoothed residuals, weighted To avoid unreasonable curvature abrupt changes in the cumulative curve along the maturity direction, a second-order difference is introduced to smooth the residuals related to the primary cleavage of kerogen.

[0131] ;

[0132] in, and These represent the second-order differences of the cumulative amount in the maturity direction, respectively, which are used to constrain the local curvature changes of the cumulative curve and avoid unreasonable bends and fluctuations as maturity progresses;

[0133] The aforementioned 6) S-curve shape constraint residuals, weights :

[0134] The cumulative curves for kerogen oil generation and regeneration should exhibit an S-shaped evolution characteristic of "convex first, then concave"; curve shape constraints are introduced at the preset inflection point positions:

[0135] The maturity sequence corresponding to the experimental data is:

[0136]

[0137] in, Initial maturity level, This represents the maturity level at which the measured oil volume reaches its peak. The maturity level of kerogen when the initial cleavage is basically complete ( (corresponding maturity level); Reaching the highest level of maturity;

[0138] Specifically, the inflection point of KTO is set as follows: and The midpoint of KTG is set as the inflection point. and The midpoint;

[0139] Let the inflection point index of kerogen oil generation be KTO_inf, then

[0140] Before the inflection point: convexity penalty is implemented, i.e. positive residual is imposed on the part of

[0141]

[0142] wherein, represents the convexity constraint residual of kerogen oil generation; represents that the kerogen oil generation presents decelerated growth before the inflection point, which does not conform to the evolution feature that the primary cracking oil generation process should gradually accelerate in the early stage, thus a penalty needs to be imposed; represents that the second-order difference morphology constraint is only imposed in the maturity interval before the inflection point, i.e. the corresponding residual is calculated only for the nodes of , which is used to limit the oil generation evolution morphology in the early stage of the primary cracking of kerogen;

[0143] After the inflection point: concavity penalty is imposed, i.e. positive residual is imposed on the part of

[0144]

[0145] wherein, represents the concavity constraint residual of kerogen oil generation; represents that the kerogen oil generation presents accelerated growth after the inflection point, which does not conform to the evolution feature that the primary cracking oil generation process should gradually decelerate in the later stage, thus a penalty needs to be imposed; represents that the second-order difference morphology constraint is only imposed in the maturity interval after the inflection point, i.e. the corresponding residual is calculated only for the nodes of , which is used to limit the oil generation evolution morphology in the later stage of the primary cracking of kerogen;

[0146] Let the inflection point index of kerogen gas generation be KTG_inf, then

[0147] Before the inflection point: convexity penalty is implemented, i.e. positive residual is imposed on the part of

[0148]

[0149] wherein, represents the convexity constraint residual of kerogen gas generation; represents that the kerogen gas generation presents decelerated growth before the inflection point, which does not conform to the evolution feature that the primary cracking oil generation process should gradually accelerate in the early stage, thus a penalty needs to be imposed; represents that the second-order difference morphology constraint is only imposed in the maturity interval before the inflection point, i.e. the corresponding residual is calculated only for the nodes of ​​​The node calculates a corresponding residual, which is used to limit the evolution pattern of gas generation in the early stage of the primary cracking stage of kerogen;

[0150] After the inflection point: a concave penalty is applied, that is, a positive residual is applied to the part of

[0151]

[0152] wherein, represents a concave constraint residual of kerogen oil generation; represents the phenomenon that the gas generation of kerogen presents accelerated growth after the inflection point, which does not conform to the evolution feature that the primary cracking gas generation process should gradually slow down in the later stage, and therefore a penalty needs to be applied; represents that the second-order difference form constraint is only applied in the maturity interval after the inflection point, that is, only to the part of The node calculates a corresponding residual, which is used to limit the evolution pattern of gas generation in the late stage of the primary cracking stage of kerogen;

[0153] The weight of the curve form constraint in the objective function is used to limit the cumulative amount evolution curve to meet the expected physical form feature;

[0154] The 7) overall objective function:

[0155] All the residual terms above are spliced into a residual vector r in a vector form, and the optimization objective function is R:

[0156] .

[0157] In step 4, the TRF algorithm is suitable for a least square problem containing variable boundary constraints, can meet constraint conditions such as non-negativity of parameters in the optimization process, and has good numerical stability and convergence; in each step of the iteration process, the optimization algorithm updates the parameters according to the comprehensive constraint action of various residual terms in the objective function, so that the value of the objective function gradually decreases until the preset convergence condition is met; through the optimization solving process, the two types of experimental information of the gold tube hydrocarbon generation thermal simulation experiment and the rock pyrolysis experiment are cooperatively constrained in a unified mathematical framework, so that the decoupling result meets the oil and gas final state production constraint and is consistent with the hydrocarbon generation process and the organic carbon change feature in the kerogen thermal evolution process, thereby realizing quantitative decoupling of the primary cracking and crude oil cracking processes of kerogen. It should be noted that the TRF algorithm is not limited to the optimization solving method described above, and other constraint optimization algorithms equivalent to the TRF algorithm in function can also be used to realize the technical scheme of the present application, as long as the parameter boundary constraints and inequality constraints are met and the nonlinear least square problem can be solved.

[0158] In step 5,​

[0159] Conversion rate of kerogen oil generation process

[0160] The conversion rate of the kerogen oil generation process can be expressed as:

[0161]

[0162] The conversion rate is used to describe the relative progress of the kerogen oil generation process with maturity, wherein:

[0163] When , it indicates that the kerogen has not yet generated oil;

[0164] When , it indicates that the kerogen oil generation has been completed;

[0165] Other process conversion rate calculations can be calculated respectively

[0166] .

[0167] Compared with the prior art, the present application has the beneficial effects of:

[0168] (1) The present application realizes the quantitative decoupling of the primary and secondary cracking of kerogen hydrocarbon generation process: In view of the problem that the primary cracking of kerogen and the oil cracking hydrocarbon generation process are highly superimposed in the final oil and gas data in the gold tube hydrocarbon generation thermal simulation experiment, and it is difficult to quantitatively decouple, on the basis of building a parameterized model including kerogen oil generation, kerogen gas generation, oil consumption, oil cracking gas and unreactive carbon formation and other processes, the present application introduces the collaborative constraints of gold tube hydrocarbon generation thermal simulation experiment and rock pyrolysis experiment, realizes the decoupling of the primary cracking of kerogen and the oil cracking hydrocarbon generation process.

[0169] Specifically, in the unified decoupling model, basic equality constraints such as oil conservation, gas conservation and oil consumption conservation in the oil cracking process, and inequality constraints such as non-negativity of each hydrocarbon and cracking process and their mutual relationship are introduced at the same time, and further combined with the consistency constraint of hydrocarbon conversion rate reflected by S2 parameter obtained from rock pyrolysis experiment, and the organic carbon conservation constraint constructed based on TOC, so that the decoupling solving process meets the final oil and gas yield condition at the same time, and keeps consistent with the hydrocarbon generation behavior and organic carbon conservation law in the process of kerogen thermal evolution. Through the collaborative constraints of multiple experimental information, the quantitative decoupling of the primary cracking of kerogen and the oil cracking hydrocarbon generation process is realized at the same time, and the physical rationality of the decoupling result in the sense of material conservation relationship and evolution constraint is effectively improved.

[0170] (2) Quantitative characterization of the conversion rate evolution characteristics of each hydrocarbon generation process: By calculating the conversion rate of the cumulative amount of each hydrocarbon generation and transformation related process obtained by decoupling, the conversion rate evolution law of the dry kerogen oil, gas and oil cracking process, and the formation of non-reactive carbon in the oil cracking gas and oil cracking process with maturity can be obtained, providing quantitative basis for fine hydrocarbon evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0171] Figure 1 is the measured oil and gas volume versus EasyRo.

[0172] Figure 2 is the hydrocarbon conversion rate, TOC norm versus EasyRo.

[0173] Figure 3 is the cumulative amount of dry kerogen oil iteration process.

[0174] Figure 4 is the cumulative amount of dry kerogen gas iteration process.

[0175] Figure 5 is the cumulative amount of oil cracking gas iteration process.

[0176] Figure 6 is the cumulative amount of oil consumption iteration process.

[0177] Figure 7 is the cumulative amount of non-reactive carbon iteration process.

[0178] Figure 8 is the hydrocarbon conversion coefficient K iteration process.

[0179] Figure 9 is the cumulative amount of each residual change in the iteration process.

[0180] Figure 10 is the error diagram of the measured oil and gas volume measured value and calculated value versus EasyRo.

[0181] Figure 11 is the error diagram of the measured hydrocarbon conversion rate measured value and calculated value versus EasyRo.

[0182] Figure 12 is the error diagram of TOC norm and calculated value versus EasyRo.

[0183] Figure 13 is the conversion rate of dry kerogen oil, dry kerogen gas, oil consumption, oil cracking gas, and non-reactive carbon formation versus EasyRo. DETAILED DESCRIPTION

[0184] The technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0185] Embodiment: A method for decoupling primary-secondary cracking hydrocarbon generation conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment includes the following steps:

[0186] Step one: dry kerogen gold tube hydrocarbon generation simulation and solid product rock pyrolysis experiment data acquisition:

[0187] First, a set of deep lacustrine facies dry kerogen samples from the Wenchang Formation of the Lufeng Sag in the Pearl River Mouth Basin are selected as the research object, and gold tube hydrocarbon generation thermal simulation experiments are carried out on the original dry kerogen. Under different heating conditions, a plurality of closed system pyrolysis experiments are carried out, and after each experiment, the corresponding experimental temperature, maturity parameter (EasyRo), final measured oil volume and final measured gas volume are recorded synchronously as the basic experimental data for characterizing the total oil and gas generation amount at this maturity.

[0188] Subsequently, the residual solid samples recovered under each experimental condition are subjected to extraction and oil washing treatment to completely remove the soluble organic matter therein; on this basis, rock pyrolysis experiments are carried out on the obtained insoluble solid organic matter (i.e. residual dry kerogen) to obtain S2 parameters and TOC parameters under corresponding maturity conditions, which are used to characterize the geochemical state of the solid product.

[0189] Rock pyrolysis experiment steps:

[0190] (1) Remove the contaminated part that may exist on the surface of the rock sample, and use liquid nitrogen freezing method to crush the rock sample to a particle size of less than 0.15 mm. Place the crushed sample in dichloromethane / methanol mixed solvent (93:7, v / v) and perform Soxhlet extraction at 45°C for 72h to remove soluble organic matter and obtain extraction residue.

[0191] (2) Weigh about 60-100mg of extraction residue into a nickel boat and perform programmed temperature pyrolysis in a Rock-Eval 6 pyrolysis instrument under nitrogen atmosphere. After constant temperature at 300°C for 3min, heat the sample to 650°C at a heating rate of 25°C / min, and record the hydrocarbon signals released during the pyrolysis stage and the associated carbon monoxide and carbon dioxide signals, wherein S2 represents the residual hydrocarbon generation potential of the sample, and the peak temperature is defined as Tmax. The amount of carbon released during the pyrolysis stage comprehensively represents the pyrolyzable organic carbon (PC) in the sample.

[0192] (3) After the pyrolysis is completed, the sample is transferred into the oxidation stage, and the residual organic matter after pyrolysis is completely combusted, and the carbon monoxide and carbon dioxide signals released in the oxidation stage are recorded. The carbon released in the oxidation stage is used to characterize the organic carbon components still remaining in the sample after pyrolysis, corresponding to residual organic carbon (RC).

[0193] (4) Based on the carbon release information obtained in the pyrolysis stage and the oxidation stage, the pyrolyzable organic carbon (PC) and the residual organic carbon (RC) are calculated respectively, and the two are superimposed to obtain the total organic carbon content (TOC = PC + RC) of the sample. All pyrolysis analysis results are blanked and calibrated by standard samples to ensure the accuracy and comparability of the data.

[0194] (5) The S2 and TOC parameters obtained in the above rock pyrolysis experiment are normalized according to the mass basis of the original kerogen sample to eliminate the scale deviation caused by the loss of sample mass; and based on the normalized S2 data, the hydrocarbon conversion rate of kerogen under each maturity condition is calculated to quantify the evolution degree of the primary cracking hydrocarbon generation process.

[0195] (6) The corresponding EasyRo, measured oil volume, measured gas volume, hydrocarbon conversion rate and normalized TOC data at each maturity node are matched and integrated to form a one-to-one corresponding multi-source experimental data set. This data set is used as the core input for subsequent construction of the collaborative constraint decoupling model to realize the quantitative decoupling of kerogen primary cracking and crude oil cracking hydrocarbon generation processes. The experimental data results are shown in Table 1, Figure 1 and Figure 2 . It can be seen from Figure 1 that with the increase of maturity, the measured oil volume first increases and then decreases, reflecting that after the peak of kerogen primary cracking oil generation, crude oil begins to crack into gas; the measured gas volume continues to rise due to the superimposed contribution of kerogen gas generation and crude oil cracking gas; it can be seen from Figure 2 that with the increase of maturity, kerogen continuously cracks to generate oil and gas, making the hydrocarbon conversion rate continuously increase and tend to 1; with the increase of maturity, TOC presents a phenomenon of first decreasing and then increasing, the former is due to the consumption of organic carbon by kerogen cracking, and the latter is because the cracking of crude oil will form part of the unreactive carbon.

[0196] Table 1: Kerogen gold tube hydrocarbon generation simulation and solid product rock pyrolysis experimental data

[0197] Step two: Parameterization of the hydrocarbon generation process, parameterization modeling of the hydrocarbon generation process by Python program:

[0198] (1) According to the maturity range of the gold tube hydrocarbon generation thermal simulation experiment, it is discretized into several maturity nodes, and five incremental variables of the hydrocarbon generation process are defined at each node: oil generation of kerogen, gas generation of kerogen, oil consumption, oil cracking into gas and non-reactive carbon formation. The cumulative amount of each process is calculated by step-by-step accumulation of the corresponding increment, ensuring that each hydrocarbon generation process changes monotonously with the increase of maturity, which conforms to the irreversible characteristics of pyrolysis. The mathematical expression is:

[0199] Process cumulative amount = initial value + step-by-step accumulation of incremental variables at each maturity stage.

[0200] (2) In the implementation, the cumsum() function of NumPy is used to construct the cumulative process amount. The program takes the increment at each maturity node as the optimization variable, and reconstructs the cumulative process amount by step-by-step accumulation, ensuring that the evolution of each process meets the physical requirements.

[0201] (3) On this basis, the hydrocarbon conversion coefficient K is introduced as a global parameter to be optimized, which is used to convert the cumulative amount of each hydrocarbon generation process at the oil and gas scale into the corresponding organic carbon consumption or conversion amount, so that each hydrocarbon generation process can be uniformly represented at the organic carbon scale.

[0202] (4) During the optimization iteration process, the program adjusts the incremental variables in each maturity interval step by step, and simultaneously calculates the corresponding cumulative process amount. This embodiment selects three representative iteration results: initial iteration, intermediate iteration and final iteration, to show the evolution characteristics of the hydrocarbon generation process parameters in the iteration optimization process. The experimental results are shown in Tables 2-7 and Figures 3-8 The cumulative amount of oil generation of kerogen at different iteration stages is shown in Table 2, which reflects the evolution characteristics of oil generation of kerogen under the constraint of multi-source experimental information, which gradually adjusts and converges with the advancement of iteration. Figure 3 The cumulative amount of gas generation of kerogen at different iteration stages is shown in Table 3, which reflects the evolution characteristics of gas generation of kerogen under the constraint of multi-source experimental information, which gradually adjusts and converges with the advancement of iteration. Figure 4 The cumulative amount of oil cracking into gas at different iteration stages is shown in Table 4, which reflects the evolution characteristics of oil cracking into gas under the constraint of multi-source experimental information, which gradually adjusts and converges with the advancement of iteration. Figure 5 The cumulative amount of oil consumption at different iteration stages is shown in Table 5, which reflects the evolution characteristics of oil consumption under the constraint of multi-source experimental information, which gradually adjusts and converges with the advancement of iteration. Figure 6 The cumulative amount of oil consumption at different iteration stages is shown in Table 5, which reflects the evolution characteristics of oil consumption under the constraint of multi-source experimental information, which gradually adjusts and converges with the advancement of iteration. Figure 7The cumulative amount results of the non-reactive carbon formation process at different iteration stages are shown, reflecting the evolution characteristics of the non-reactive carbon gradually adjusting and converging with the iteration under the constraint of multi-source experimental information. Figure 8 The change of the hydrocarbon conversion carbon coefficient at different iteration stages is shown, reflecting the evolution characteristics of the hydrocarbon conversion carbon coefficient gradually adjusting and converging under the constraint of multi-source experimental information.

[0203] Table 2: Kerogen oil generation increment iteration process and corresponding cumulative amount

[0204]

[0205] Table 3: Kerogen gas generation increment iteration process and corresponding cumulative amount

[0206]

[0207] Table 4: Crude oil cracking gas generation increment iteration process and corresponding cumulative amount

[0208]

[0209] Table 5: Crude oil consumption increment iteration process and corresponding cumulative amount

[0210]

[0211] Table 6: Non-reactive carbon increment iteration process and corresponding cumulative amount

[0212]

[0213] Table 7: Hydrocarbon conversion carbon coefficient iteration process

[0214]

[0215] Step 3: Construct a least squares model with multi-source experimental information collaborative constraint:

[0216] First, through the Python program, according to the measured oil and gas data obtained in step 1, and combined with the parameterized definition of the increment and cumulative amount expressions of each hydrocarbon generation process and the hydrocarbon conversion carbon coefficient K in step 2, the basic data and parameters to be optimized of the least squares optimization problem are determined.

[0217] Subsequently, various constraints are added, including basic equality constraint of oil conservation, gas conservation and crude oil cracking consumption conservation, inequality constraint of non-negativity and upper limit of each hydrocarbon generation process increment, so as to ensure that the solution result conforms to the conservation of matter and evolution law of hydrocarbon generation conversion. At the same time, the consistency constraint of hydrocarbon conversion rate based on S2 parameter calculated by rock pyrolysis experiment and the organic carbon conservation constraint based on TOC parameter constructed by rock pyrolysis experiment are uniformly introduced into the least square model, so that the model meets the end state oil and gas production constraint, and the evolution characteristics of the hydrocarbon generation process are consistent with the hydrocarbon conversion process and the organic carbon conservation law in the process of kerogen thermal evolution, so as to realize the collaborative constraint of multi-source experimental information.

[0218] On this basis, a composite least square objective function is constructed. The objective function is composed of the following six types of residual terms: (1) equality constraint residual based on the conservation of matter; (2) inequality constraint residual based on the non-negativity and upper limit condition; (3) hydrocarbon conversion rate consistency constraint residual based on rock pyrolysis S2 parameter; (4) organic carbon conservation constraint residual based on rock pyrolysis TOC parameter; (5) smoothing regularization residual for inhibiting solution oscillation; (6) S-shaped characteristic residual for constraining curve shape.

[0219] Secondly, various types of residual terms are uniformly constructed in the program through the NumPy library of Python. Among them, the equality constraint residual based on the conservation of matter is calculated by calling the subtract() function to calculate the difference between the model predicted oil and gas and the experimental measured values; the inequality constraint residual based on the non-negativity and upper limit condition is processed by calling the maximum() function to truncate the positive value of the part violating the constraint, and only the part exceeding the constraint range is punished; the hydrocarbon conversion rate consistency constraint residual based on rock pyrolysis S2 parameter is calculated by calling the subtract() function to calculate the difference between the hydrocarbon conversion rate obtained by the model and the hydrocarbon conversion rate calculated from the S2 parameter; the organic carbon conservation constraint residual based on rock pyrolysis TOC parameter is calculated by calling the subtract() function to calculate the deviation between the organic carbon consumption calculated by the model and the measured TOC evolution characteristics; the smoothing regularization residual for inhibiting solution oscillation uses the diff() function to calculate the first or second difference, and the part with large change is punished; the S-shaped characteristic residual for constraining curve shape is based on the second difference and combined with the maximum() function, and the part not conforming to the expected evolution form is constrained.

[0220] Finally, all the residual terms are weighted by the preset weight and summed by calling the sum() function to form a composite least squares objective function. This objective function serves as the evaluation basis for optimization solution, providing a solution basis for the subsequent TRF optimization algorithm, so that the increments of each hydrocarbon generation process gradually converge under the satisfaction of the constraint conditions, and the decoupling results that meet the experimental measurement and physical rationality are finally obtained.

[0221] In the model solving process, the computer program calculates and records the total residual formed by the combination of each type of residual and its combination in each iteration, as shown in Table 8, and evaluates the satisfaction of the current parameter update to the mass conservation constraint, form constraint and smoothing constraint by tracking the change of each residual with the number of iterations. With the advancement of iteration, each residual and the total residual gradually decreases, as shown in Figure 9 , indicating that the model solution gradually tends to be stable and consistent under multiple constraint conditions. Figure 9 The figure shows the change of each type of residual and the total residual with the number of iterations in the iterative calculation process. The results in the figure show that with the advancement of iterative calculation, each constraint residual and the overall value of the objective function gradually decrease, indicating that the calculation process can gradually converge under multiple constraint conditions and tend to be stable solution state.

[0222] Table 8 Change of each residual in the iteration process

[0223]

[0224] Step four: optimization solution under constraint conditions

[0225] By calling the scipy.optimize.least_squares() function in the SciPy optimization library through the Python program, the Trust Region Reflective (TRF) algorithm is used to solve the nonlinear least squares model with boundary constraints under the premise of satisfying the oil conservation, gas conservation, crude oil consumption conservation, hydrocarbon conversion consistency constraint, organic carbon conservation constraint, and non-negativity and upper limit constraint of each hydrocarbon and cracking process.

[0226] In the optimization process, the cumulative parameters of each hydrocarbon and cracking process on the discrete maturity sequence are updated by iteration, so that the deviation between the oil and gas production and the organic carbon conservation characteristics calculated by the model and the multiple experimental measured data gradually decreases and converges, thereby obtaining the parameter solution that satisfies the mass conservation relationship and experimental consistency requirements.

[0227] After the optimization calculation, the quantitative results of the cumulative amounts of kerogen oil, kerogen gas, crude oil consumption, crude oil cracking gas and unreactive carbon formation in each process with the change of maturity are obtained, and the hydrocarbon conversion carbon coefficient K used to represent the conversion relationship between the oil and gas generation and the organic carbon consumption is determined. The experimental results are shown in Tables 9, 10 and Figures 10-12 . It can be seen from Figure 10 that the calculated values are highly consistent with the measured values at each EasyRo point, the oil amount error is controlled within 2%, and the gas amount error is controlled within 7%, indicating that the model has high quantitative decoupling accuracy. It can be seen from Figure 11 that due to the difference between samples, the measured and calculated values of the hydrocarbon conversion rate of the first two points have a larger error, and the calculated values of the remaining points are highly consistent with the measured values at each EasyRo point, and the error is controlled within 4%, indicating that the model has high quantitative decoupling accuracy. It can be seen from Figure 12 that the measured and calculated values of TOC are highly consistent at each EasyRo point, and the error is controlled within 14%, indicating that the model has high quantitative decoupling accuracy.

[0228] Table 9 Experimental measured values and optimization solving results

[0229]

[0230] Table 10 Error results of experimental measured values and optimization solving results

[0231]

[0232] Step five: hydrocarbon conversion rate calculation

[0233] The cumulative amount results of each hydrocarbon generation process obtained in step four are normalized by a computer program, and the corresponding hydrocarbon conversion rate is calculated, so as to realize the quantitative characterization of the hydrocarbon generation behavior of kerogen primary cracking and oil cracking,

[0234] Taking the conversion rate of the kerogen oil generation process as an example, the conversion rate of the kerogen oil generation process is represented as:

[0235]

[0236] The conversion rate is used to describe the relative progress of the kerogen oil generation process with the advance of maturity, wherein:

[0237] When , it indicates that the kerogen has not generated oil;

[0238] When , it indicates that the kerogen oil generation has been completed;

[0239] The conversion rates of other processes are calculated in the same way, and the conversion rates of kerogen gas generation, crude oil consumption, crude oil cracking gas and unreactive carbon formation are calculated respectively.

[0240]

[0241] According to the calculation results of step four (Table 9), the cumulative amount of the cheese root oil generation process under each maturity condition can be obtained. Among them, the final cumulative amount max(KTO) of the cheese root oil generation process is 288.82.

[0242] When EasyRo is 0.62, 0.71, 0.80, 0.88, 0.96, 1.15, 1.38, 1.44, 1.66, 1.88, 2.12 and 2.49 respectively, the corresponding cumulative amount KTO (EasyRo of the cheese root oil generation is 50.69, 111.51, 241.27, 280.25, 290.79, 290.97, 291.15, 291.33, 291.51, 291.69, 291.87, 292.05 respectively. i

[0243] According to the above conversion rate definition formula, the conversion rate of the cheese root oil generation process is 0.17, 0.38, 0.83, 0.96, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00 and 1.00 respectively.

[0244] It can be seen that the conversion rate of the cheese root oil generation increases rapidly with the maturity in the low-maturity stage, and tends to 1 after EasyRo is about 0.96, indicating that the cheese root oil generation process is completed in this maturity interval. The experiment is shown in Table 11 and Figure 13 , Figure 13 The results of the conversion rate of the cheese root oil generation, the cheese root oil generation, the crude oil consumption, the crude oil cracking into gas and the formation of unreactive carbon are shown. With the gradual increase of maturity, the conversion rate of each process shows a continuous increasing evolution trend, and gradually tends to 1 in the high maturity stage, indicating that the corresponding process is completed.

[0245] Table 11 Conversion rate calculation results

[0246]

[0247] The present application is applicable to simultaneously carry out cheese root gold tube hydrocarbon generation thermal simulation experiment and corresponding cheese root solid product rock pyrolysis experiment, and is applied to the application scene of quantitative decoupling analysis of hydrocarbon generation process under the condition of multi-source experimental data.

[0248] ​The oil and gas production data under different maturity conditions were obtained by gold tube hydrocarbon generation thermal simulation experiment, and the rock pyrolysis experiment was carried out on the corresponding maturity conditions of dry kerogen solid products to obtain the measured parameters reflecting the state of organic matter evolution such as S2 and TOC. On this basis, the parameterized model of hydrocarbon generation process was constructed, including dry kerogen oil generation, dry kerogen gas generation, oil consumption, oil cracking gas and non-reactive carbon generation. In the process of model solving, the basic equation constraints such as oil conservation, gas conservation and oil consumption conservation were introduced, as well as the inequality constraints of non-negativity of each hydrocarbon generation and cracking conversion and their mutual relationship. At the same time, the rock pyrolysis information was integrated, the hydrocarbon conversion consistency constraint was established by the hydrocarbon conversion calculated from S2 parameter, and the organic carbon conservation constraint was constructed combined with the evolution characteristics of TOC, so as to automatically determine the hydrocarbon generation process and the maturity of the initial cracking completion of dry kerogen without setting the empirical constraint of the initial cracking completion of dry kerogen.

[0249] Under the framework of multi-source experimental information collaborative constraint, the least square objective function of multi-source experimental information collaborative constraint was constructed and optimized to realize the decoupling of the initial cracking and secondary cracking of dry kerogen, which is suitable for fine hydrocarbon evaluation and mechanism research with high requirements for the consistency of hydrocarbon generation mechanism and the rationality of mass conservation.

Claims

1. A method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation and solid product pyrolysis experiment, comprising the following steps: Step 1: Acquisition of experimental data from kerogen gold tube hydrocarbon generation thermal simulation and solid product rock pyrolysis: First, the kerogen sample was heated by programmed temperature rise through a gold tube hydrocarbon generation thermal simulation experiment. The experimental temperature, the corresponding maturity EasyRo, and the measured oil and gas volume at the end of the experiment were recorded simultaneously. Subsequently, the residual solid sample obtained from the experiment was subjected to extraction and oil washing to remove soluble organic matter, and rock pyrolysis experiments were carried out on it to obtain the S2 and TOC parameters at the corresponding maturity level. Finally, the temperature, EasyRo, measured oil volume, measured gas volume, hydrocarbon conversion rate and normalized TOC corresponding to each group of experiments are integrated into a complete multi-source experimental data record, which serves as the basic input for subsequent modeling and decoupling calculations. Step 2, Parametric modeling of the kerogen hydrocarbon generation process: The kerogen process is parameterized into five process quantities: kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation. To ensure the monotonicity of the hydrocarbon generation process with maturity and to reflect the irreversible characteristics of the pyrolysis process, i.e., the cumulative amount of each hydrocarbon generation process remains monotonically constant with increasing maturity, each hydrocarbon generation process is represented as a cumulative form of a non-negative incremental sequence, thus naturally introducing monotonicity constraints at the parameter level. At discrete maturity nodes The above method represents each hydrocarbon generation process to be solved as a corresponding sequence of incremental variables, and by accumulating the incremental variables, a cumulative evolution curve of each hydrocarbon generation process as maturity changes is constructed. Step 3: Construct a least squares model with coordinated constraints of multi-source experimental information: Construct a composite least squares objective function composed of multiple types of residual terms to decouple and solve each process under the premise of satisfying the constraints of material conservation and non-negativity; Step 4: Solving the nonlinear least squares optimization under constraints: The Trust Region Reflective (TRF) algorithm is used to solve the above least squares problem; Using the incremental parameter vectors corresponding to five processes—kerogen oil generation (KTO), kerogen gas generation (KTG), crude oil consumption (OC), crude oil cracking into gas (OTG), and non-reactive carbon formation (C)—as optimization variables, the basic equality constraints provided by the gold tube hydrocarbon generation thermal simulation experiment, including oil quantity conservation, gas quantity conservation, and crude oil consumption conservation during crude oil cracking, as well as the inequality constraints, including the non-negativity of each hydrocarbon generation and cracking process and their interrelationships, along with the consistency constraints of hydrocarbon conversion rate calculated from the S2 parameters obtained from rock pyrolysis experiments and the organic carbon conservation constraints constructed based on the TOC evolution model, are uniformly incorporated into the same least squares objective function in the form of residuals, together forming a multi-source experimental information collaborative constraint system. Step 5, Calculation of hydrocarbon generation conversion rate: After completing the constraint optimization solution, quantitative results can be obtained on the cumulative changes of five processes as maturity: Kerogen-derived oil (KTO), Kerogen-generated gas (KTG), crude oil consumption (OC), crude oil cracking into gas (OTG), and non-reactive carbon formation (C). To facilitate comparative analysis between different maturity stages and different samples, the cumulative amount of each process was dimensionless and defined as the corresponding hydrocarbon generation conversion rate. For any process In its maturity The conversion rate is defined as follows: ; in: For this process at maturity Cumulative amount under certain conditions; This represents the maximum cumulative amount achieved by the process throughout the entire thermal evolution process; This is used to characterize the relative degree of completion of the process.

2. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment as described in claim 1, characterized in that: In step 1, to ensure the comparability of data at different maturity levels, S2 and TOC were normalized according to the original kerogen sample quality to establish a unified benchmark. Based on the normalized S2 value, the hydrocarbon conversion rate of kerogen at each maturity level was further calculated to characterize the degree of development of the initial pyrolysis hydrocarbon generation process with thermal evolution. Wherein: S2 is the pyrolysis hydrocarbon content; TOC is the total organic carbon. The specific steps of normalization are as follows: From the results of the thermal simulation experiment of hydrocarbon generation in the gold tube, the total hydrocarbon yield and non-hydrocarbon yield per unit mass of kerogen under each maturity condition were obtained. The unit of measurement is mg / g kerogen. Since 1g kerogen corresponds to 1000mg, the sum of the total hydrocarbon yield and the non-hydrocarbon yield reflects the mass of organic matter that has been converted and released from kerogen under the maturity condition. Based on this, the proportion of residual kerogen under the corresponding maturity condition is calculated and defined as: ; in, The total hydrocarbon yield obtained from the gold tube experiment. Both figures represent non-hydrocarbon yields and are in mg / g kerogen. Characterizes the mass proportion of residual kerogen in the original sample under this maturity condition; Subsequently, the S2 and TOC parameters obtained from the rock pyrolysis experiment were multiplied by the residual kerogen proportion under the corresponding maturity conditions. This normalizes the S2 and TOC data to the original kerogen sample baseline, resulting in normalized S2 and TOC: ; ; in, The S2 parameters are normalized for each maturity condition. The TOC parameters are normalized for each maturity condition; Based on the above data, under each maturity condition, the hydrocarbon conversion rate of kerogen is defined as the proportion of the original hydrocarbon generation potential that has been converted and released, and its calculation formula is as follows: ; in, The S2 parameters of the original kerogen sample. This indicates the hydrocarbon conversion rate of kerogen.

3. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment as described in claim 1, characterized in that: In step 2: Let the optimization variable be an increment vector. It consists of the following five groups of incremental variables, corresponding to five processes: kerogen derivative (KTO), kerogen gas (KTG), crude oil cracking into gas (OTG), crude oil consumption (OC), and non-reactive carbon (C): ; The aforementioned nonnegativity constraints are used to ensure that the cumulative amount of each process remains monotonically constant as maturity increases; Based on the above incremental parameters, the cumulative amount of each hydrocarbon generation process is reconstructed at the i-th maturity node in the following way: ; Wherein, KTO: Kerogen crude oil; KTG: Kerogen gas; OTG: Crude oil cracking into gas; OC: Crude oil consumption; C: Non-reactive carbon; Oil: Measured oil volume; Gas: Measured gas volume; Oil1: Measured oil volume under initial maturity conditions; Gas1: Measured gas volume under initial maturity conditions; u1: The increment at the first maturity node; j : The increment at the j-th maturity node; u n Increment at the nth maturity node; KTO i At node i, the cumulative oil production from kerogen = initial oil production + sum of oil increments at each maturity node; KTGi: At node i, the cumulative gas production from kerogen = initial gas production + sum of gas increments at each maturity node; OTGi: At node i, the amount of gas produced from crude oil cracking = sum of gas increments from crude oil cracking at each maturity node; OCI: At node i, the amount of crude oil consumed = sum of crude oil consumption increments at each maturity node; Ci: At node i, the amount of non-reactive carbon = sum of non-reactive carbon increments at each maturity node. Based on the above parametric modeling framework, the hydrocarbon-to-carbon coefficient K is introduced as a global parameter to be optimized, which is used to convert the amount of oil and gas generated in the hydrocarbon generation process into the corresponding organic carbon consumption. This conversion provides a key bridge for the subsequent construction of organic carbon conservation constraints using normalized TOC data, thereby realizing the self-consistent coupling of gaseous, liquid products and solid residual carbon information under a unified material balance system.

4. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment as described in claim 1, characterized in that: In step 3: the objective function consists of the following six types of residual terms: 1) Residuals constrained by equations based on the law of conservation of matter; 2) Inequality-constrained residuals based on nonnegativity and upper limit conditions; 3) Residual constrained by consistency of hydrocarbon conversion rate based on S2 parameter of rock pyrolysis; 4) Organic carbon conservation constraint residuals based on rock pyrolysis TOC parameters; 5) Smoothing and regularizing residuals used to suppress deoscillations; 6) S-shaped feature residuals used to constrain the shape of curves.

5. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment according to claim 1, characterized in that: In step 3: To unify the constraints within the least squares solution framework, a residual construction method is used to uniformly handle equality and inequality constraints. By transforming various constraints into residual terms and combining them with the residuals corresponding to experimental data to form the objective function, a synergistic solution for the hydrocarbon generation and cracking processes of organic matter is achieved. For equality constraints, let their constraint functions be: ; When the model calculation results satisfy this constraint, the function value approaches 0; when the model calculation results deviate from this equality relationship, the function value reflects the degree of deviation; therefore, the equality constraint function is directly introduced as a residual term into the least squares objective function, and its mathematical expression is: ; in, This represents the residual term corresponding to the equality constraint, with weighting coefficients. This is used to adjust the degree of influence of the conservation constraint in the overall optimization process, so as to ensure the effective constraint of the equation relationship in the process of oil and gas generation and conversion; For inequality constraints, let their constraint functions be: ; When the constraint is satisfied, no residual is generated; when the model calculation result violates the constraint, only the violated part is penalized; therefore, a positive truncation form is introduced to construct the inequality constraint residual, the mathematical expression of which is: ; in, This represents the residual term corresponding to the inequality constraint, with weighting coefficients. Used to control the intensity of penalties for violations of nonnegativity or upper bound constraints, thereby ensuring that the decoupling results satisfy the physical rationality of hydrocarbon generation and cracking processes.

6. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment according to claim 4, characterized in that: The aforementioned 1) Equality constraint residual: mass conservation relation At each maturity node, the following material conservation relationship is introduced and incorporated into the objective function as a residual, with weighting coefficients set to... : Oil conservation principle: Kerogen oil production - Crude oil consumption = Actual oil production Oil quantity conservation residual: ; Gas conservation: Gas production from kerogen + Gas production from crude oil cracking = Measured gas production Gas volume conservation residual: ; Oil consumption conservation: Crude oil consumption + Non-reactive carbon content = Crude oil cracking into gas content Crude oil cracking material conservation residual: The above equation constraint residuals are used to ensure that the initial cracking of kerogen and the overall cracking process of crude oil satisfy the material conservation relationship in the process of oil quantity, gas quantity and crude oil consumption. The aforementioned 2) Inequality-constrained residuals: nonnegativity and upper limit conditions To ensure the solution satisfies physical validity, the following inequality constraint is introduced, with the weighting coefficients set as follows: Crude oil consumption ≥ Crude oil cracking gas production: Crude oil cracking to gas upper limit constraint residual: ; Crude oil consumption ≥ non-reactive carbon content: Non-reactive carbon upper limit constraint residuals: ; Crude oil cracking gas production ≥ 0: Non-negativity constraint residuals of crude oil cracking into gas: ; Non-reactive carbon ≥ 0: Non-reactive carbon non-negativity constraint residuals: ; The aforementioned 3) hydrocarbon conversion rate constraint residual: To constrain the overall evolution of the initial pyrolysis hydrocarbon generation process of kerogen, a consistency residual of hydrocarbon conversion rate calculated based on the S2 parameter of rock pyrolysis experiment is introduced. The S2 parameter obtained from the rock pyrolysis experiment reflects the residual hydrocarbon generation potential of the sample under the corresponding maturity conditions. By normalizing the S2 values ​​under different maturity conditions to the initial state, the change of hydrocarbon conversion rate with maturity during the thermal evolution of kerogen can be obtained. At each maturity node, the cumulative hydrocarbon generation during the initial cleavage stage of kerogen is defined as: ; Wherein, KTO and KTG represent the cumulative amounts of kerogen oil and gas, respectively; Further define the maximum hydrocarbon generation of kerogen as: ; Based on this, the hydrocarbon conversion rate HC based on the decoupling results is calculated: ; The hydrocarbon conversion rate HC calculated by the above model is compared with the hydrocarbon conversion rate calculated by the rock pyrolysis S2 parameter. Apply consistency constraints, with weighting coefficients set to... Constructing a hydrocarbon conversion consistency residual: ; The aforementioned 4) Organic carbon conservation constraint residual: To ensure that the decoupling results meet the constraints on oil and gas production while maintaining consistency with the characteristics of organic carbon changes during the thermal evolution of kerogen, an organic carbon conservation residual based on the TOC parameters obtained from rock pyrolysis experiments is introduced. The TOC parameters obtained from rock pyrolysis experiments reflect the overall characteristics of organic carbon content changes with maturity during the thermal evolution of kerogen, and are used to impose constraints on the decoupling results from the perspective of organic carbon conservation, thereby improving the physical rationality of the decoupling results. Let the total organic carbon content of the original experimental sample be... Based on the consideration of the organic carbon consumed in the initial cracking of kerogen and the non-reactive carbon generated by the cracking of crude oil, an organic carbon conservation model is constructed: ; Calculate the theoretical value of TOC: ; in, TOC is the residual amount from the initial cleavage of kerogen. This represents the cumulative amount of hydrocarbons generated during the initial cracking of kerogen; C represents the cumulative amount of non-reactive carbon formed during crude oil cracking; the coefficient 1 / 10 is used to convert the mass of non-reactive carbon into a percentage of TOC; K is the hydrocarbon-to-carbon conversion ratio coefficient. The TOC value is calculated using the constructed organic carbon conservation model. Based on the above organic carbon conservation model, an organic carbon conservation residual is constructed, with weighting coefficients set as follows: : ; The fifth point mentioned is to smooth and regularize the residuals to suppress oscillations. To avoid unreasonable local oscillations in the cumulative amount of each hydrocarbon generation process as maturity changes during the solution process, a smoothing regularized residual is introduced into the objective function to apply differential constraints to the change of the cumulative curve in the maturity direction. Let the cumulative amount of a certain hydrocarbon generation process at discrete maturity points be represented as a sequence. ; Let n be the cumulative amount corresponding to the i-th maturity node of the process, and n be the total number of maturity nodes. Define the first-order difference operator as: ; First-order difference operator It is used to characterize the incremental change of the cumulative amount at adjacent maturity nodes, reflecting the local change characteristics of the process in the direction of maturity; Define the second-order difference operator as a further action of the first-order difference: ; Second-order difference operator This represents the further change of the first-order difference in the maturity direction, used to constrain the smoothness of the cumulative curve in the maturity direction and prevent local oscillations that do not conform to the actual thermal evolution law during the optimization solution process. The first-order difference is used to characterize the magnitude of the cumulative change between adjacent maturity points, while the second-order difference is used to characterize the curvature change of the cumulative curve in the maturity direction. ① First-order difference smoothing residuals, weights To suppress drastic jumps between adjacent maturity points, first-order difference smoothing is introduced for each process to smooth the residuals. ; in These represent the variation range of the cumulative amount of each hydrocarbon generation process between adjacent maturity nodes. These variations are obtained through first-order difference operations, forming difference terms, and are uniformly incorporated into the objective function as smoothing constraint residuals. They are used to impose constraints on cases where the variation range at adjacent maturity nodes is too large, thereby suppressing unreasonable abrupt changes that may occur in the cumulative curves of each hydrocarbon generation and cracking process, and ensuring the continuity and physical rationality of its evolution with maturity. ② Second-order difference curvature smoothed residuals, weighted To avoid unreasonable curvature abrupt changes in the cumulative curve along the maturity direction, a second-order difference is introduced to smooth the residuals related to the primary cleavage of kerogen. ； in, and These represent the second-order differences of the cumulative amount in the maturity direction, respectively, which are used to constrain the local curvature changes of the cumulative curve and avoid unreasonable bends and fluctuations as maturity progresses; The aforementioned 6) S-curve shape constraint residuals, weights : From the perspective of hydrocarbon generation kinetics, the cumulative amount of oil and gas generated by kerogen should exhibit an S-shaped evolution characteristic of initial acceleration followed by deceleration with increasing maturity; curve shape constraints are introduced at the predetermined inflection point: The maturity sequence corresponding to the experimental data is: ; in, Initial maturity level, This represents the maturity level at which the measured oil volume reaches its peak. This represents the maturity level of kerogen when its initial cleavage is essentially complete. Corresponding maturity level; Reaching the highest level of maturity; Specifically, the inflection point of KTO is set as follows: and The midpoint of KTG is set as the inflection point. and The midpoint; Let the inflection point index of kerogen oil be KTO_inf, then Before the inflection point: Implement convexity penalty, that is, for Apply positive residuals to the part: ; in, This represents the residual due to the convexity constraint of kerogen oil production; This indicates that the growth of kerogen oil production slows down before the inflection point, which does not conform to the evolutionary characteristic that the initial cracking oil production process should gradually accelerate in the early stage. Therefore, a penalty needs to be imposed. This indicates that the second-order difference morphological constraint is applied only within the maturity interval before the inflection point, i.e., only to... The node calculation corresponds to the residual, which is used to limit the oil generation evolution morphology in the early stage of the initial kerogen pyrolysis. After the inflection point: apply a concave penalty, i.e., apply... Apply positive residuals to the part: ; in, This represents the concave constraint residual of kerogen oil; This indicates that kerogen oil production accelerates after the inflection point, which does not conform to the evolutionary characteristic that the initial cracking oil production process should gradually slow down in the later stages. Therefore, a penalty needs to be imposed. This indicates that the second-order difference morphological constraint is applied only within the maturity interval after the inflection point, i.e., only to... The node calculation corresponds to the residual, which is used to limit the oil generation evolution morphology in the later stage of the primary cleavage of kerogen. Let the inflection point index of kerogen regeneration be KTG_inf, then Before the inflection point: apply a convexity penalty, i.e., apply... Apply positive residuals to the part: ; in, This represents the residual due to the convexity constraint of kerogen gas generation; This indicates that the growth of kerogen gasification slows down before the inflection point, which does not conform to the evolutionary characteristic that the initial cracking and oil generation process should gradually accelerate in the early stage. Therefore, a penalty needs to be imposed. This indicates that the second-order difference morphological constraint is applied only within the maturity interval before the inflection point, i.e., only to... The node calculation corresponds to the residual, which is used to limit the life evolution morphology in the early stage of the initial cleavage of kerogen; After the inflection point: apply a concave penalty, i.e., apply... Apply positive residuals to the part: ; in, This represents the concave constraint residual of kerogen oil; This indicates that kerogen gasification accelerates after the inflection point, which does not conform to the evolutionary characteristic that the gasification process of the initial pyrolysis should gradually slow down in the later stages. Therefore, a penalty needs to be imposed. This indicates that the second-order difference morphological constraint is applied only within the maturity interval after the inflection point, i.e., only to... The node calculation corresponds to the residual, which is used to limit the life evolution morphology in the later stage of the primary cleavage of kerogen; The weight of the curve shape constraint in the objective function is used to limit the cumulative quantity evolution curve to meet the expected physical shape characteristics. 7) Overall objective function: Concatenate all the above residual terms into a vector form to form a single residual vector r. The objective function is then optimized as R: 。 7. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment according to claim 1, characterized in that: In step 4: The TRF algorithm is applicable to least squares problems with variable boundary constraints. It can simultaneously satisfy constraints, including parameter nonnegativity, during the optimization process and has good numerical stability and convergence. In each iteration, the optimization algorithm updates the parameters based on the comprehensive constraints of various residual terms in the objective function, gradually reducing the objective function value until the preset convergence condition is met. Through this solution process, the experimental information of the two types of experiments, namely the gold tube hydrocarbon generation thermal simulation experiment and the rock pyrolysis experiment, is synergistically constrained under a unified mathematical framework. This ensures that the decoupling results, while satisfying the final state production constraints of oil and gas, are consistent with the hydrocarbon generation process and organic carbon change characteristics during the thermal evolution of kerogen, thereby achieving quantitative decoupling between the initial cracking of kerogen and the various processes of crude oil cracking.

8. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment according to claim 1, characterized in that: In step 5: Calculate the conversion rate of kerogen to oil. The conversion rate of kerogen oil can be expressed as: ; This conversion rate is used to describe the relative progress of kerogen oil production with increasing maturity, where: when At this time, it indicates that the kerogen has not yet produced oil; when At this point, it indicates that the kerogen oil extraction is complete; The conversion rates for other processes can be calculated similarly. 。

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