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

Abstract

The invention relates to a method for decoupling organic matter primary-secondary cracking hydrocarbon generation conversion rate based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment. The method comprises the following steps: step 1, kerogen gold tube hydrocarbon generation simulation and solid product rock pyrolysis experiment data acquisition; step 2, parametric modeling of the kerogen hydrocarbon generation process; step 3, constructing a least square model of multi-source experimental information collaborative constraint; 4, carrying out optimization solution under a multi-source experiment information collaborative constraint condition; and 5, calculating the hydrocarbon generation conversion rate. According to the method, conversion rate evolution characteristics of each hydrocarbon generation process are quantitatively represented, conversion rate calculation is performed on cumulant of each hydrocarbon generation process obtained through decoupling, and conversion rate evolution rules of kerogen oil generation, kerogen gas generation, crude oil cracking gas generation, non-reactive carbon formation and other processes along with maturity changes in the crude oil cracking process can be obtained; and a quantitative basis is provided for refined hydrocarbon generation evaluation.

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; therefore, 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 generation amount - crude oil consumption amount = 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 during 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 during optimization solving;

[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 be KTO_inf, then

[0140] Before the inflection point: Implement convexity penalty, that is, for Apply positive residuals to the part:

[0141]

[0142] 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.

[0143] After the inflection point: apply a concave penalty, i.e., apply... Apply positive residuals to the part:

[0144]

[0145] 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.

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

[0147] Before the inflection point: apply a convexity penalty, i.e., apply... Apply positive residuals to the part:

[0148]

[0149] 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;

[0150] After the inflection point: apply a concave penalty, i.e., apply... Apply positive residuals to the part:

[0151]

[0152] 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;

[0153] 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.

[0154] The aforementioned 7) Overall objective function:

[0155] 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:

[0156] .

[0157] In step 4, the TRF algorithm is applicable to least squares problems with variable boundary constraints. It can simultaneously satisfy constraints such as parameter nonnegativity during optimization and exhibits 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 optimization process, the experimental information from the gold tube hydrocarbon generation thermal simulation experiment and the rock pyrolysis experiment is coordinated and constrained within a unified mathematical framework. This ensures that the decoupling results, while satisfying the final oil and gas production constraints, remain consistent with the hydrocarbon generation process and organic carbon change characteristics during kerogen thermal evolution, thereby achieving quantitative decoupling between the initial kerogen cracking and the various processes of crude oil cracking. It should be noted that the optimization solution is not limited to the TRF algorithm. Provided that parameter boundary constraints and inequality constraints are satisfied and the nonlinear least squares problem can be solved, other constraint optimization algorithms functionally equivalent to the TRF algorithm can also be used to implement the technical solution of this invention.

[0158] In step 5:

[0159] Calculate the conversion rate of kerogen to oil.

[0160] The conversion rate of kerogen oil production can be expressed as:

[0161]

[0162] This conversion rate is used to describe the relative progress of kerogen oil production with increasing maturity, where:

[0163] when At this time, it indicates that the kerogen has not yet produced oil;

[0164] when At this point, it indicates that the kerogen oil extraction is complete;

[0165] The conversion rates for other processes can be calculated similarly.

[0166] .

[0167] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0168] (1) This application achieves quantitative decoupling of the primary-secondary cracking hydrocarbon generation process of kerogen: In the thermal simulation experiment of hydrocarbon generation in kerogen gold tube, the hydrocarbon generation processes such as primary cracking of kerogen and cracking of crude oil are highly superimposed in the final state oil and gas data and are difficult to decouple quantitatively. Based on the construction of a parameterized model that includes multiple processes such as kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas and formation of non-reactive carbon, the application introduces the synergistic constraints of two types of experimental data: thermal simulation experiment of hydrocarbon generation in gold tube and rock pyrolysis experiment, so as to achieve decoupling of the primary cracking of kerogen and cracking of crude oil hydrocarbon generation process.

[0169] Specifically, the unified decoupling model simultaneously introduces fundamental equation constraints such as oil conservation, gas conservation, and crude oil consumption conservation during crude oil cracking, as well as inequality constraints such as the non-negativity of each hydrocarbon generation and cracking process and their interrelationships. Furthermore, it incorporates the consistency constraint of hydrocarbon conversion rate reflected by the S2 parameters obtained from rock pyrolysis experiments, and the organic carbon conservation constraint constructed based on TOC. This ensures that the decoupling solution process, while satisfying the final state oil and gas production conditions, remains consistent with the hydrocarbon generation behavior and organic carbon conservation laws during the thermal evolution of kerogen. Through the synergistic constraints of multi-source experimental information, the model achieves quantitative decoupling between the initial cracking of kerogen and the hydrocarbon generation process of crude oil cracking, effectively improving the physical rationality of the decoupling results in terms of material conservation relationships and evolutionary constraints.

[0170] (2) Quantitative characterization of the conversion rate evolution characteristics of each hydrocarbon generation process: By calculating the conversion rate of each hydrocarbon generation and conversion-related process obtained by decoupling, the conversion rate evolution law of processes such as kerogen oil generation, gas generation, crude oil cracking into gas and non-reactive carbon formation during crude oil cracking can be obtained as the maturity changes, providing a quantitative basis for refined hydrocarbon generation evaluation. Attached Figure Description

[0171] Figure 1 This is a graph showing the measured changes in oil and gas volume with EasyRo.

[0172] Figure 2 Hydrocarbon conversion rate, TOC norm Graph showing changes with EasyRo.

[0173] Figure 3 This is a graph showing the cumulative change in kerogen oil production during the iterative process.

[0174] Figure 4 This is a graph showing the cumulative change in the kerogen gasification iterative process.

[0175] Figure 5 This is a graph showing the cumulative change in the iterative process of crude oil cracking into gas.

[0176] Figure 6 This is a graph showing the cumulative change in crude oil consumption over the iterative process.

[0177] Figure 7 This is a graph showing the cumulative change in the non-reactive carbon iteration process.

[0178] Figure 8 This is a graph showing the iterative changes in the hydrocarbon-to-carbon conversion coefficient K.

[0179] Figure 9 It is a graph showing the cumulative changes in residuals during the iteration process.

[0180] Figure 10 This is an error graph showing the difference between the measured and calculated values ​​of oil and gas volume as a function of EasyRo.

[0181] Figure 11 This is an error graph showing the difference between the measured and calculated hydrocarbon conversion rates as a function of EasyRo.

[0182] Figure 12 It is TOC norm Error plot showing the calculated value as a function of EasyRo.

[0183] Figure 13 This is a graph showing the conversion rates of kerogen into oil, kerogen into gas, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation as a function of EasyRo. Detailed Implementation

[0184] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0185] Example: 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 includes the following steps:

[0186] Step 1: Simulation of hydrocarbon generation in kerogen gold tubes and acquisition of experimental data from solid product rock pyrolysis:

[0187] First, a set of deep lacustrine kerogen samples from the Wenchang Formation in the Lufeng Depression of the Pearl River Estuary Basin was selected as the research object, and a thermal simulation experiment of hydrocarbon generation in the gold tube was carried out using its original kerogen. Multiple sets of closed-system pyrolysis experiments were conducted under different heating conditions. After each set of experiments, the corresponding experimental temperature, maturity parameter (EasyRo), final measured oil volume, and final measured gas volume were recorded simultaneously as basic experimental data characterizing the total amount of hydrocarbon generation at that maturity level.

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

[0189] Rock pyrolysis experimental steps:

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

[0191] (2) Weigh approximately 60–100 mg of extraction residue and load it into a nickel boat. Perform programmed temperature pyrolysis in a Rock-Eval6 pyrolysis apparatus under a nitrogen atmosphere. After holding the sample at 300°C for 3 min, heat it to 650°C at a heating rate of 25°C / min. Record the hydrocarbon signals released during the pyrolysis stage and the associated carbon monoxide and carbon dioxide signals. S2 characterizes the residual hydrocarbon generation potential of the sample, and its peak temperature is defined as Tmax. The amount of carbon released during the pyrolysis stage comprehensively characterizes the pyrolytic organic carbon (PC) in the sample.

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

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

[0194] (5) The S2 and TOC parameters obtained from the above rock pyrolysis experiment were normalized according to the mass standard of the original kerogen sample to eliminate the scale bias caused by sample mass loss; and based on the normalized S2 data, the hydrocarbon conversion rate of kerogen under each maturity condition was calculated to quantify the degree of evolution of the initial pyrolysis hydrocarbon generation process.

[0195] (6) The EasyRo, measured oil volume, measured gas volume, hydrocarbon conversion rate, and normalized TOC data corresponding to each maturity node are matched and integrated to form a one-to-one multi-source experimental dataset. This dataset serves as the core input for the subsequent construction of a collaborative constraint decoupling model, used to achieve quantitative decoupling between the primary cracking of kerogen and the hydrocarbon generation process of crude oil cracking. The experimental data results are shown in Table 1. Figure 1 and Figure 2 .from Figure 1 It can be seen that: with the increase of maturity, the measured oil volume first increases and then decreases, reflecting that after the initial cracking of kerogen to generate oil reaches its peak, the crude oil begins to crack into gas; the continuous increase in measured gas volume is due to the combined contribution of kerogen gas and crude oil cracking gas; from Figure 2 It can be seen that as maturity increases, kerogen continuously cracks to generate oil and gas, resulting in a continuous increase in hydrocarbon conversion rate, which gradually approaches 1. As maturity increases, TOC shows a phenomenon of first decreasing and then increasing. The initial decrease is due to the consumption of organic carbon by kerogen cracking, while the subsequent increase is due to the formation of some non-reactive carbon by crude oil cracking.

[0196] Table 1. Simulation data of hydrocarbon generation from kerogen gold tubes and experimental data of solid product rock pyrolysis.

[0197] Step 2: Parameterization of the hydrocarbon generation process. The hydrocarbon generation process is modeled parametrically using a Python program.

[0198] (1) Based on the maturity range of the hydrogene generation thermal simulation experiment in the gold tube, it is discretized into several maturity nodes, and five incremental variables for the hydrogene generation process are defined at each node: kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation. The cumulative amount of each process is obtained by progressively summing the corresponding increments to ensure that each hydrogene generation process changes monotonically with the increase of maturity, which conforms to the irreversible characteristics of the pyrolysis process. Its mathematical expression is as follows:

[0199] The cumulative amount of the process = the initial value + the incremental increase of each maturity stage.

[0200] (2) In the implementation, the NumPy cumsum() function is used to construct the cumulative process quantity. The program uses the increment of each maturity node as the optimization variable and reconstructs the cumulative process quantity by gradually accumulating it to ensure that the evolution of each process meets the physical requirements.

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

[0202] (4) During the optimization iteration process, the program gradually adjusts the incremental variables within each maturity interval and simultaneously calculates the corresponding cumulative process quantities. This embodiment selects three representative iteration results—initial iteration, intermediate iteration, and final iteration—to demonstrate the evolution characteristics of hydrocarbon generation process parameters during the iterative optimization process. Experimental results are shown in Tables 2-7. Figures 3-8 As shown. By Figure 3 The cumulative results of the kerogen oil generation process with maturity at different iteration stages are shown, reflecting the evolutionary characteristics of the process under the constraint of multi-source experimental information, in which the kerogen oil generation gradually adjusts and tends to converge with the progress of iteration. Figure 4 The cumulative results of the kerogen gasification process at different iteration stages with maturity are shown, reflecting the evolutionary characteristics of the process under the constraint of multi-source experimental information, in which kerogen gasification gradually adjusts and tends to converge with the progress of iteration. Figure 5 The cumulative results of the crude oil cracking to gas process with maturity at different iteration stages are shown, reflecting the evolutionary characteristics of the crude oil cracking to gas process gradually adjusting and tending to converge as the iteration progresses under the constraint of multi-source experimental information. Figure 6 The cumulative results of crude oil consumption at different iteration stages with maturity are shown, reflecting the evolutionary characteristics of crude oil consumption gradually adjusting and tending to converge as the iteration progresses under the constraint of multi-source experimental information. Figure 7The cumulative results of the non-reactive carbon formation process at different iteration stages with maturity are shown, reflecting the evolutionary characteristics of non-reactive carbon gradually adjusting and tending to converge as the iteration progresses under the constraint of multi-source experimental information. Figure 8 The changes in the hydrocarbon-to-carbon conversion coefficient at different iteration stages are shown, reflecting the evolutionary characteristics of the hydrocarbon-to-carbon conversion coefficient gradually adjusting and tending to converge under the constraints of multi-source experimental information.

[0203] Table 2. Iterative process of kerogen oil increment and corresponding cumulative amount

[0204]

[0205] Table 3. Iterative process of kerogen regeneration increment and corresponding cumulative amount

[0206]

[0207] Table 4. Iterative process of crude oil cracking into gas increment and corresponding cumulative amount

[0208]

[0209] Table 5. Iterative Process and Corresponding Cumulative Amount of Crude Oil Consumption Increment

[0210]

[0211] Table 6 Iterative Process of Non-Reactive Carbon Increment and Corresponding Cumulative Amount

[0212]

[0213] Table 7 Iterative Process of Hydrocarbon-to-Carbon Conversion Coefficient

[0214]

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

[0216] First, using a Python program, based on the measured oil and gas volume data obtained in step 1, and combined with the parameterized expressions for the increments and cumulative amounts of each hydrocarbon generation process and the hydrocarbon-to-carbon coefficient K defined in step 2, the basic data and parameters to be optimized for the least squares optimization problem are determined.

[0217] Subsequently, various constraints were incorporated, including basic equation constraints such as oil conservation, gas conservation, and crude oil cracking consumption conservation, as well as inequality constraints such as the non-negativity of increments in each hydrocarbon generation process and upper bound restrictions, to ensure that the solution results conform to the material conservation relationships and evolution laws of hydrocarbon generation and transformation. Simultaneously, the consistency constraint of hydrocarbon conversion rate calculated based on the S2 parameters of rock pyrolysis experiments, and the organic carbon conservation constraint constructed based on the TOC parameters of rock pyrolysis experiments, were uniformly introduced into the least squares model. This ensures that while satisfying the final state oil and gas production constraints, the evolution characteristics of the hydrocarbon generation process remain consistent with the hydrocarbon conversion process and organic carbon conservation laws during the thermal evolution of kerogen, thereby achieving synergistic constraints from multi-source experimental information.

[0218] Based on this, a composite least squares objective function is constructed. The objective function consists of the following six types of residual terms: (1) residuals constrained by equality based on the conservation of matter; (2) residuals constrained by inequality based on nonnegativity and upper limit conditions; (3) residuals constrained by consistency of hydrocarbon conversion rate based on S2 parameters of rock pyrolysis; (4) residuals constrained by conservation of organic carbon based on TOC parameters of rock pyrolysis; (5) smoothing and regularization residuals used to suppress solution oscillations; and (6) residuals constrained by S-shaped features for curve morphology.

[0219] Secondly, all types of residual terms are uniformly constructed in the program using Python's NumPy library. Among them, the residuals of equality constraints based on the conservation of matter calculate the difference between the model's predicted oil and gas volumes and the experimentally measured values ​​by calling the subtract() function; the residuals of inequality constraints based on nonnegativity and upper limit conditions truncate the parts that violate the constraints by calling the maximum() function, and only penalize the parts that exceed the constraint range; the residuals of hydrocarbon conversion rate consistency constraints based on rock pyrolysis S2 parameters calculate the difference between the hydrocarbon conversion rate obtained by the model and the hydrocarbon conversion rate estimated by the S2 parameters by calling the subtract() function; the residuals of organic carbon conservation constraints based on rock pyrolysis TOC parameters calculate the deviation between the organic carbon consumption calculated by the model and the measured TOC evolution characteristics by calling the subtract() function; the smoothing regularization residuals used to suppress solution oscillations calculate the first or second order difference using the diff() function and penalize the parts that change too much; and the S-shaped feature residuals used to constrain the curve shape are based on the second order difference and combined with the maximum() function to constrain the segments that do not conform to the expected evolution shape.

[0220] Finally, all residual terms are weighted according to preset weights and summed using the sum() function to form a composite least squares objective function. This objective function serves as the evaluation criterion for the optimization solution, providing a solution basis for the subsequent TRF optimization algorithm, enabling the increments of each hydrocarbon generation process to gradually converge under the constraints, ultimately yielding decoupled results that conform to experimental measurements and physical rationality.

[0221] During the model solution process, the computer program calculates and records the various residual terms and their combinations to form the total residual in each iteration, as shown in Table 8. By tracking the changes in each residual term with the number of iterations, the program evaluates the degree to which the current parameter update satisfies constraints such as matter conservation, morphological constraints, and smoothness constraints. As the iteration progresses, each residual and the total residual gradually decrease, as shown in Table 8. Figure 9 As shown, the model solution gradually tends to be stable and consistent under multiple constraints. Figure 9 The figure shows the changes in various residual terms and the total residual with the number of iterations during the iterative calculation. The results in the figure show that as the iterative calculation progresses, the residuals of each constraint and the overall value of the objective function all show a gradual decreasing trend, indicating that the calculation process can gradually converge under multi-source constraints and tend to a stable solution state.

[0222] Table 8. Changes in residuals during the iteration process.

[0223]

[0224] Step 4: Optimization solution under constraints:

[0225] The Python program calls the scipy.optimize.least_squares() function in the SciPy optimization library and uses the Trust Region Reflective (TRF) algorithm to solve the nonlinear least squares model with boundary constraints, under the premise of satisfying the constraints of oil conservation, gas conservation, crude oil consumption conservation, hydrocarbon conversion rate consistency constraint, organic carbon conservation constraint, and nonnegativity and upper bound of each hydrocarbon generation and cracking process.

[0226] During the optimization process, the cumulative parameters of each hydrocarbon generation and cracking process on the discrete maturity sequence are updated iteratively. This gradually reduces the deviation between the oil and gas production and organic carbon conservation characteristics calculated by the model and the experimental data from multiple sources, and eventually leads to convergence. Thus, a parameter solution that satisfies the requirements of material conservation relationship and experimental consistency is obtained.

[0227] After optimization calculations, quantitative results were obtained showing the cumulative changes in the amounts of each process—kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation—as a function of maturity. Simultaneously, the hydrocarbon-to-carbon conversion coefficient K, used to characterize the conversion relationship between oil and gas generation and organic carbon consumption, was determined. Experimental results are shown in Tables 9 and 10. Figures 10-12 .Depend on Figure 10 It can be seen that the calculated values ​​and measured values ​​are in high agreement at all EasyRo points, with oil quantity errors controlled within 2% and gas quantity errors controlled within 7%, indicating that the model has high quantitative decoupling accuracy. Figure 11 It can be seen that, due to sample variability, the measured and calculated values ​​of hydrocarbon conversion rates at the first two points have slightly larger errors. However, the calculated and measured values ​​at the remaining points are in high agreement at all EasyRo points, with errors controlled within 4%, indicating that the model has high quantitative decoupling accuracy. Figure 12 It can be seen that the measured and calculated values ​​of TOC are in high agreement at each EasyRo point, with the error controlled within 14%, indicating that the model has high quantitative decoupling accuracy.

[0228] Table 9. Experimental Measured Values ​​and Optimized Solution Results

[0229]

[0230] Table 10 shows the error results between the experimentally measured values ​​and the optimized solution.

[0231]

[0232] Step 5: Calculation of hydrocarbon generation conversion rate

[0233] The cumulative hydrocarbon generation results obtained in step four are normalized using a computer program, and the corresponding hydrocarbon generation conversion rates are calculated, thereby achieving a quantitative characterization of the hydrocarbon generation behavior of primary kerogen cracking and crude oil cracking.

[0234] Taking the conversion rate of kerogen to oil as an example, the conversion rate of kerogen to oil is expressed as follows:

[0235]

[0236] This conversion rate is used to describe the relative progress of kerogen oil production with increasing maturity, where:

[0237] when At this time, it indicates that the kerogen has not yet produced oil;

[0238] when At this point, it indicates that the kerogen oil extraction is complete;

[0239] The conversion rates for other processes are calculated similarly.

[0240]

[0241] Based on the calculation results of step four (Table 9), the cumulative amount of kerogen oil production under various maturity conditions can be obtained. Among them, the final cumulative amount of kerogen oil production, max(KTO), is 288.82.

[0242] When EasyRo was 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, the corresponding cumulative kerogen oil content KTO (EasyRo) was... i The values ​​are: 50.69, 111.51, 241.27, 280.25, 290.79, 290.97, 291.15, 291.33, 291.51, 291.69, 291.87, and 292.05.

[0243] Based on the above definition of conversion rate, the conversion rates of kerogen oil production are calculated by normalizing the results for each maturity point: 0.17, 0.38, 0.83, 0.96, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, and 1.00.

[0244] This shows that the kerogen oil conversion rate increases rapidly with maturity in the low-to-medium maturity stage, and approaches 1 after EasyRo reaches approximately 0.96, indicating that the kerogen oil conversion process is completed within this maturity range. Experimental results are shown in Table 11 and... Figure 13 , Figure 13 The results show the conversion rates of processes such as kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation as a function of EasyRo. With increasing maturity, the conversion rates of each process generally show a continuously increasing trend, gradually approaching 1 at high maturity, indicating that the corresponding process has been completed.

[0245] Table 11 Conversion Rate Calculation Results

[0246]

[0247] This application is applicable to scenarios where thermal simulation experiments of hydrocarbon generation in kerogen gold tubes and pyrolysis experiments of corresponding kerogen solid product rocks are carried out simultaneously, and quantitative decoupling analysis of the hydrocarbon generation process is performed under multi-source experimental data conditions.

[0248] Oil and gas production data under different maturity conditions were obtained through thermal simulation experiments of hydrocarbon generation using gold tubes. Simultaneously, rock pyrolysis experiments were conducted on kerogen solid products under corresponding maturity conditions to obtain measured parameters such as S2 and TOC reflecting the evolution state of organic matter. Based on this, a parameterized model was constructed to represent the hydrocarbon generation processes, including kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and the formation of non-reactive carbon. During the model solution process, basic equation constraints such as oil conservation, gas conservation, and crude oil consumption conservation were introduced, along with inequality constraints regarding the non-negativity of each hydrocarbon generation and cracking conversion rate and their interrelationships. Furthermore, rock pyrolysis information was integrated, and a consistency constraint on the hydrocarbon generation conversion rate was established using the hydrocarbon conversion rate calculated from the S2 parameter. An organic carbon conservation constraint was also constructed by combining TOC evolution characteristics. This allows for the automatic determination of the hydrocarbon generation process and the maturity level of the initial cracking of kerogen without the need for manually setting empirical constraints on the completion of the initial cracking.

[0249] Within the framework of multi-source experimental information collaborative constraints, a least-squares objective function for multi-source experimental information collaborative constraints is constructed and optimized to achieve decoupling of hydrocarbon generation conversion rates between primary and secondary kerogen pyrolysis. This approach is suitable for refined hydrocarbon generation evaluation and mechanism research with high requirements for consistency of hydrocarbon generation mechanisms and rationality of material conservation.

Claims

1. 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, comprising the following steps: Step 1, kerogen gold tube hydrocarbon generation thermal simulation and solid product rock pyrolysis experiment data acquisition: First, the kerogen sample is heated by temperature programmed heating through the gold tube hydrocarbon generation thermal simulation experiment, and the experimental temperature, the corresponding maturity EasyRo, and the measured oil and gas volume at the end of the experiment are recorded synchronously; Then, the residual solid sample obtained in the experiment is treated by extraction and washing oil to remove soluble organic matter, and a rock pyrolysis experiment is carried out to obtain S2 and TOC parameters corresponding to the maturity; Finally, the temperature, EasyRo, measured oil volume, measured gas volume, hydrocarbon generation 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 basis input for subsequent modeling and decoupling calculation; Step 2, parameterization modeling of kerogen hydrocarbon generation process: The kerogen process is parameterized into five process quantities, namely kerogen oil generation, kerogen gas generation, oil consumption, oil cracking into gas, and non-reactive carbon formation; To ensure the monotonicity of the hydrocarbon generation process with maturity evolution, and reflect the irreversible characteristics of the pyrolysis process, that is, the cumulative amount of each hydrocarbon generation process increases monotonously with maturity, each hydrocarbon generation process is represented as the cumulative form of a non-negative incremental sequence, thereby naturally introducing the monotonicity constraint at the parameter level; At discrete maturity nodes In the foregoing, each hydrocarbon generation process to be solved is represented as a corresponding sequence of delta variables, and a cumulative evolution curve of each hydrocarbon generation process with respect to maturity is constructed by accumulating the delta variables. Step 3, building a least squares model with multi-source experimental information collaborative constraints: a composite least squares objective function composed of multiple residual terms is constructed to decouple and solve each process under the premise of satisfying the conservation of matter and non-negativity constraints; Step 4, nonlinear least squares optimization solution under constraints: The Trust Region Reflective algorithm, abbreviated as TRF, is used to solve the above least squares problem; The incremental parameter vectors corresponding to the five processes of kerogen oil generation KTO, kerogen gas generation KTG, oil consumption OC, oil cracking into gas OTG, and non-reactive carbon formation C are used as optimization variables, and the basic equality constraints such as oil conservation, gas conservation and oil consumption conservation in the oil cracking process provided by the gold tube hydrocarbon generation thermal simulation experiment, as well as the inequality constraints of the non-negativity and mutual relationship of each hydrocarbon and cracking process, the consistency constraints of the hydrocarbon conversion rate calculated from the S2 parameter obtained by the rock pyrolysis experiment, and the organic carbon conservation constraints based on the TOC evolution model are unified into the same least squares objective function in the form of residuals, and together form a multi-source experimental information collaborative constraint system; Step 5, hydrocarbon conversion rate calculation: After completing the constrained optimization solution, the cumulative amounts of the five processes of kerogen oil generation KTO, kerogen gas generation KTG, oil consumption OC, oil cracking into gas OTG, and non-reactive carbon formation C with respect to maturity can be obtained; 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. For any process The conversion rate at its maturity is defined as: ; wherein: is the cumulative amount of the process at maturity under the given conditions; is the maximum cumulative amount reached by the process during the entire thermal evolution; is used to characterize the relative degree of completion of the process.

2. The method for decoupling primary-secondary cracking hydrocarbon generation conversion ratio of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment of claim 1, characterized in that: In step 1, to ensure the comparability of data at different maturities, S2 and TOC are normalized by the original kerogen sample mass to establish a unified benchmark; based on the normalized S2 value, the hydrocarbon conversion rate of kerogen at each maturity is further calculated to represent the development degree of primary cracking and hydrocarbon generation process with thermal evolution; wherein: S2 is the cracking hydrocarbon content; TOC is the total organic carbon; The specific steps of normalization are as follows: From the results of gold tube hydrocarbon generation thermal simulation experiment, the total hydrocarbon yield and non-hydrocarbon yield corresponding to 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 total hydrocarbon yield and non-hydrocarbon yield reflects the mass of organic matter that has been converted and released from kerogen at this maturity condition; Accordingly, the proportion of residual kerogen at the corresponding maturity condition is calculated, defined as: ; wherein, total hydrocarbon production obtained for the gold tube experiment, non-hydrocarbon production, both in mg / g of dry kerogen; characterizing the proportion by mass of residual kerogen in the original sample under this maturity condition; Subsequently, the S2 parameter and the TOC parameter obtained from the rock pyrolysis experiment were multiplied by the residual kerogen proportion under the corresponding maturity condition Thus, the S2 and TOC data were unified and normalized to the original kerogen sample benchmark, and the normalized S2 and TOC were obtained: ; ; wherein, S2norm is the normalized S2 parameter for each maturity condition, TOCnorm is the normalized TOC parameter for each maturity condition. Based on the above data, at 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: ; wherein, S2 parameter for the original kerogen sample, represents the kerogen hydrocarbon conversion.

3. The method for decoupling primary-secondary cracking hydrocarbon generation conversion ratio of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment of claim 1, characterized in that: In step 2: Let the optimization variables be the delta vector which consists of the following five sets of delta variables, corresponding to the five processes: ; The above non-negativity constraint is used to ensure that the cumulative amount of each process is monotonically non-decreasing with increasing maturity; Based on the above incremental parameters, the cumulative amount of each hydrocarbon generation process at the i-th maturity node is reconstructed by the following method: ; Where, KTO: kerogen oil generation; KTG: kerogen gas generation; OTG: oil cracking into gas; OC: oil consumption; C: non-reactive carbon; Oil: measured oil volume; Gas: measured gas volume; Oil1: measured oil volume at the initial maturity condition; Gas1: measured gas volume at the initial maturity condition; u1: increment at the 1st maturity node; u j u1: increment at the 1st maturity node; u n u1: increment at the 1st maturity node; u i : at the i th node, kerogen cumulative oil generation = initial oil + sum of increments of oil at each maturity node; KTGi: at the i th node, kerogen cumulative gas generation = initial gas + sum of increments of gas at each maturity node; OTGi: at the i th node, oil cracking gas = sum of increments of oil cracking gas at each maturity node; OCi: at the i th node, oil consumption = sum of increments of oil consumption at each maturity node; Ci: at the i th node, unreactive carbon = sum of increments of unreactive carbon at each maturity node; On the basis of the above parameterized modeling framework, the hydrocarbon conversion coefficient K is introduced as a global parameter to be optimized, which is used to convert the oil and gas volume generated in the hydrocarbon generation process into the corresponding organic carbon consumption; 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.

4. The method for decoupling primary-secondary cracking hydrocarbon generation conversion ratio of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment of claim 1, characterized in that: In step 3: the objective function is composed of the following six types of residual terms: 1) equality constraint residual based on the conservation of mass relationship; 2) inequality constraint residual based on non-negativity and upper limit conditions; 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 suppressing solution oscillation; 6) S-shaped feature residual for constraining curve shape.

5. The method for decoupling primary-secondary cracking hydrocarbon generation conversion ratio of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment of claim 1, characterized in that: In step 3: To unify the constraint conditions into the least squares solution framework, the equality constraints and inequality constraints are uniformly processed by residual construction; by converting various constraint conditions into residual terms and combining them with the residual of experimental data to form the objective function, the collaborative solution of organic matter hydrocarbon generation and cracking process is realized: For the equality constraint condition, let its constraint function be: ; When the model calculation result satisfies the constraint, the function value tends to 0; when the model calculation result deviates from the equality relationship, the function value reflects the deviation degree; therefore, the equality constraint function is directly introduced as a residual term into the least squares objective function, and its mathematical expression is: ; wherein, represents the residual term corresponding to the equality constraint, the weight coefficient for adjusting the degree of influence of the conservation constraint in the overall optimization process to ensure the effective constraint of the equality relationship in the oil and gas generation and conversion process; For inequality constraints, let the constraint function be: ; When the constraint is satisfied, no residual is generated; when the model calculation result violates the constraint, only the violated part is penalized; for this purpose, a positive value is introduced to construct the inequality constraint residual, and its mathematical expression is: ; wherein, denotes the residual term corresponding to the inequality constraint, the weight coefficient is used to control the penalty strength for the behavior violating the non-negativity or upper bound constraint, so as to ensure that the decoupling result meets the physical rationality of the hydrocarbon generation and cracking process.

6. The method for decoupling primary-secondary cracking of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment according to claim 4, characterized in that: The 1) equation constraint residual: mass conservation relationship At each maturity node, the following mass conservation relation is introduced and incorporated into the objective function in residual form with a weight coefficient set as : Oil conservation: dry kerogen oil yield-crude oil consumption = measured oil yield Oil mass conservation residual: ; Gas conservation: dry kerogen gas yield + crude oil cracking gas yield = measured gas yield Gas volume conservation residual: ; Oil consumption conservation: crude oil consumption + unreactive carbon = crude oil cracking gas yield Crude cracking material conservation residual: ; The above equation constraint residual is used to ensure that the overall dry kerogen primary cracking and crude oil cracking process satisfies the mass conservation relationship in oil, gas and crude oil consumption process; The 2) inequality constraint residual: non-negativity and upper limit condition To ensure that the solution meets the physical rationality, the following inequality constraints are introduced, and the weight coefficient is set as ; Crude oil consumption ≥ crude oil cracking gas yield: Upper bound constraint on the residue of crude oil cracking into gas: ; Crude oil consumption ≥ unreactive carbon: Upper limit on unreactive carbon constraint residual: ; Crude oil cracking gas yield ≥ 0: Non-negativity constraint residual for cracking of crude oil into gas: ; Unreactive carbon ≥ 0: Non-reactive carbon non-negativity constraint residual: ; The 3) hydrocarbon conversion rate constraint residual: In order to constrain the overall evolution degree of dry kerogen primary cracking hydrocarbon generation process, the hydrocarbon conversion rate consistency residual calculated based on the S2 parameter of rock pyrolysis experiment is introduced; the S2 parameter obtained by rock pyrolysis experiment 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 process of dry kerogen can be obtained; At each maturity node, the cumulative hydrocarbon generation amount of the dry kerogen primary cracking stage is defined as: ; Wherein, KTO and KTG represent the cumulative amount of dry kerogen oil and gas, respectively; Further define the maximum hydrocarbon generation of dry kerogen as: ; Accordingly, the hydrocarbon conversion rate HC based on the decoupling result 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 The consistency constraint is performed, and the weight coefficient is set as The hydrocarbon conversion rate consistency residual is constructed: ; The 4) organic carbon conservation constraint residual: In order to ensure that the decoupling result satisfies the oil and gas yield constraint condition at the same time, and is consistent with the change characteristics of organic carbon in the thermal evolution process of dry kerogen, 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 during the thermal evolution process of dry kerogen, which is used to constrain the decoupling result from the perspective of organic carbon conservation, thereby improving the physical reasonableness of the decoupling result; The total organic carbon content of the experimental original sample is On the basis of considering the consumption of organic carbon by primary kerogen cracking and the generation of unreactive carbon by oil cracking, an organic carbon conservation model is constructed: ; The theoretical value of TOC is calculated: ; wherein, TOCresidual is the residual TOC of the primary kerogen cracking, 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; TOCcalculated is the TOC value calculated by the constructed organic carbon conservation model; Based on the above organic carbon conservation model, the organic carbon conservation residual is constructed, and the weight coefficient is set as : ; The 5) smoothing regularization residual: suppress oscillation In order to avoid unreasonable local oscillation of the cumulative amount of each hydrocarbon generation process with maturity during 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; Let the cumulative amount of a hydrocarbon generation process at discrete maturity points be represented as a sequence ; ni is the cumulative amount of the process at the i-th maturity node, and n is the total number of maturity nodes. Define the first-order difference operator as: ; First-order difference operator The increment change amplitude of the cumulative quantity at adjacent maturity nodes is used to characterize the local change characteristics of the process in the maturity direction. Define the second-order difference operator as the second action of the first-order difference: ; Second-order difference operator represents the second change of the first-order difference in the maturity direction, is used to constrain the smoothness of the accumulation curve in the maturity direction, and prevent local oscillation that does not conform to the actual thermal evolution rule in the optimization solving process; Wherein, 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; first-order difference smoothing residual, weight To suppress the sharp jump between adjacent maturity points, a first-order difference smoothing residual is introduced for each process: ; wherein respectively represent the variation range of the cumulative amount of each hydrocarbon generation process between adjacent maturity nodes, the difference terms are uniformly incorporated into the objective function as smoothing constraint residuals, which are used to impose constraints on the case where the variation range at adjacent maturity nodes is too large, so as to inhibit the unreasonable mutation that may occur in the cumulative curve of each hydrocarbon generation and cracking process, and ensure the continuity and physical reasonableness of the evolution with maturity. (2) Second-order difference curvature smoothing residual, weight To avoid unreasonable curvature mutation of the cumulative curve in the maturity direction, the second-order difference smoothing residual is introduced for the primary cracking related process of kerogen: ； wherein, with respectively represent the second order difference of the corresponding process cumulative quantity in the direction of maturity, for constraining the local curvature variation of the cumulative curve, to avoid unreasonable bending and undulating with the advancement of maturity. The 6) S-shaped curve form constraint residual error, weight : From the perspective of hydrocarbon generation kinetics, the cumulative amount of oil and gas generated from kerogen should show an S-shaped evolution characteristic of first acceleration and then deceleration with increasing maturity; the curve shape constraint is introduced at the preset inflection point position: The maturity sequence corresponding to the experimental data is: ; wherein, is the initial maturity, is the maturity at which the measured oil volume peaks, is the maturity at which the initial kerogen cracking is substantially complete, is the corresponding maturity; is the maximum maturity; In particular, the inflection point of KTO is set to the midpoint of the inflection point of KTG is set to the midpoint of the midpoint of Let the inflection point index of kerogen oil generation be KTO_inf, then Pre-kink: Implement convex penalty, i.e. impose positive residual on fraction of ; wherein, represents a kerogen oil convexity constraint residual; represents a phenomenon that kerogen oil presents a decelerated growth before the inflection point, which does not conform to the evolution characteristics that the primary cracking oil process should gradually accelerate in the early stage, and therefore a penalty needs to be applied; represents that the second-order difference form constraint is only applied in the maturity interval before the inflection point, i.e., only to calculates the corresponding residual for the node of, which is used to limit the oil evolution form in the early stage of the primary cracking stage of kerogen. Post-kink: Apply concave penalty, i.e. positive residual on parts of ; wherein, represents a concave constraint residual of kerogen oil generation; represents a phenomenon that kerogen oil generation presents accelerated growth after the inflection point, which does not conform to the evolution characteristics that the primary cracking oil 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, i.e., only to The corresponding residual is calculated for the node of, which is used to limit the oil generation evolution form in the later stage of the primary cracking stage of kerogen. Let the inflection point index of kerogen gas generation be KTG_inf, then Pre-kink: Apply convex penalty, i.e. positive residuals on parts of ; wherein, represents a gas generation from kerogen convexity constraint residual error; represents a phenomenon that gas generation from kerogen presents a 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, and 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., only for calculates the corresponding residual error for the nodes of the second-order difference morphology constraint, for limiting the gas evolution morphology in the early stage of the primary cracking stage of kerogen. Post-kink: Apply concave penalty, i.e. positive residual on parts of ; wherein, represents a concave constraint residual for kerogen oil; represents a phenomenon that kerogen gas presents accelerated growth after the inflection point, which does not conform to the evolution characteristics that the primary cracking gas process should gradually decelerate 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, i.e., only to The corresponding residual is calculated for the node of, which is used to limit the evolution form of the gas in the later stage of the primary cracking stage of kerogen. The weight of the curve shape constraint in the objective function is used to limit the cumulative amount evolution curve to meet the expected physical shape characteristics; 7) Overall objective function: All the residual terms above are spliced into a residual vector r in vector form, and the optimization objective function is R: 。 7. The method for decoupling primary-secondary cracking hydrocarbon generation conversion ratio of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment of claim 1, characterized in that: In step 4: The TRF algorithm is suitable for least squares problems with variable boundary constraints, can meet the constraints such as non-negativity of parameters during optimization, and has good numerical stability and convergence; in each 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 objective function value gradually decreases until the preset convergence condition is met; through this solving process, the co-constraint of the two types of experimental information, i.e. gold tube hydrocarbon generation thermal simulation experiment and rock pyrolysis experiment, in a unified mathematical framework is realized, so that the decoupling result meets the constraint of oil and gas final state production while keeping consistent with the characteristics of hydrocarbon generation process and organic carbon change during kerogen thermal evolution, thus realizing the quantitative decoupling of kerogen primary cracking and crude oil cracking processes.

8. The method for decoupling primary-secondary cracking hydrocarbon generation conversion ratio of organic matter based on gold tube hydrocarbon generation thermal simulation-solid product pyrolysis experiment of claim 1, characterized in that: In step 5: For the process of kerogen oil generation, the conversion rate is The conversion rate of the process of kerogen oil generation can be expressed as: ; This conversion rate is used to describe the relative progress of the process of kerogen oil generation with the advancement of maturity, where: When kerogen has not yet reached the oil window; When kerogen oil generation is complete; Similarly, the conversion rates of other processes can be calculated respectively 。

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