A method for decoupling the primary and secondary pyrolysis hydrocarbon conversion rate of organic matter based on a gold tube hydrocarbon generation thermal simulation experiment.

Abstract

This application relates to a method for decoupling the primary-secondary pyrolysis hydrocarbon generation conversion rate of organic matter based on a gold tube hydrocarbon generation thermal simulation experiment. The method includes the following steps: Step 1, acquiring thermal simulation data of kerogen gold tube hydrocarbon generation; Step 2, parametric modeling of the kerogen hydrocarbon generation process; Step 3, constructing a least-squares objective model under multiple constraints; Step 4, nonlinear least-squares optimization solution under constraints; and Step 5, calculating the hydrocarbon generation conversion rate. By calculating the conversion rate of the cumulative amounts of each decoupled hydrocarbon generation process, this invention can obtain the evolution law of conversion rate changes with maturity for processes such as kerogen oil generation, gas generation, and crude oil pyrolysis into gas and non-reactive carbon formation during crude oil pyrolysis, providing a quantitative basis for refined hydrocarbon generation evaluation.

Description

Technical Field

[0001] This application relates to the technical field of oil and gas geochemistry and computational geology, and more specifically, to a method for decoupling the primary-secondary cracking hydrocarbon generation conversion rate of organic matter based on a gold tube hydrocarbon generation thermal simulation experiment. Background Technology

[0002] The gold tube hydrocarbon generation thermal simulation experiment is a commonly used experimental technique for studying the oil and gas generation behavior of organic matter under heated conditions. This technique typically involves sealing an organic matter sample in a gold tube and, under conditions without mass exchange, simulating the hydrocarbon generation process of organic matter as its maturity (EasyRo) increases during geological evolution by controlling the heating temperature and time. In this application, the organic matter sample used is a kerogen sample. After the experiment, the liquid and gaseous products generated in the system can be collected and measured. The liquid products are generally statistically analyzed as crude oil (hereinafter referred to as oil), and the gaseous products are statistically analyzed as natural gas (hereinafter referred to as gas). However, this experiment can essentially only obtain the total amount of oil and gas under different temperature or maturity conditions. Its results reflect the final state of hydrocarbon generation and cracking processes occurring continuously and superimposed in the same closed system.

[0003] In the thermal simulation experiment of hydrocarbon generation in the gold tube, the hydrocarbon generation behavior of kerogen is not a single process, but simultaneously includes multiple processes such as the initial cracking of kerogen to produce oil, the initial cracking of kerogen to produce gas, and the continued cracking of crude oil under high temperature conditions. Among them, the crude oil cracking process is further manifested in different conversion pathways such as crude oil consumption, crude oil cracking into gas, and the formation of non-reactive carbon from some crude oil.

[0004] Due to the lack of effective constraints on the interrelationships between processes, existing technologies cannot decouple the oil generation and gas generation processes during the primary cracking of kerogen, nor can they decouple and quantitatively characterize the conversion processes such as crude oil consumption, crude oil cracking into gas, and the formation of non-reactive carbon during crude oil cracking. Therefore, it is difficult to reflect the true evolution characteristics of the hydrocarbon generation process during the primary and secondary cracking of kerogen. Summary of the Invention

[0005] This application proposes a method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on a gold tube hydrocarbon generation thermal simulation experiment. The aim is to address the problem that the gold tube hydrocarbon generation thermal simulation experiment can only obtain the total oil and gas volume under different maturity conditions, while the primary pyrolysis of kerogen and the pyrolysis process of crude oil are highly superimposed in the experimental results and difficult to distinguish.

[0006] The aforementioned kerogen is a major hydrocarbon-generating substance during thermal evolution. Kerogen refers to dispersed organic matter in sedimentary rocks that is insoluble in alkalis, non-oxidizing acids, and non-polar organic solvents. In this application, kerogen is used as the actual research object.

[0007] The terms "primary pyrolysis" and "secondary pyrolysis" refer to the classification of hydrocarbon generation stages in sedimentary organic matter (specifically kerogen). Primary pyrolysis refers to the process by which kerogen decomposes during thermal evolution to generate crude oil and natural gas; secondary pyrolysis refers to the process by which the crude oil formed from the primary pyrolysis of kerogen undergoes further heating and pyrolysis, transforming into natural gas and non-reactive carbon.

[0008] Therefore, the term "crude oil cracking" used in this paper is a specific description of the cracking behavior in the second stage of kerogen hydrocarbon generation, used to emphasize that the direct reaction object of secondary cracking is crude oil, rather than changing the technical connotation that both primary and secondary cracking are based on the division of kerogen hydrocarbon generation stages.

[0009] The technical solution provided by this invention is:

[0010] A method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on a gold tube hydrocarbon generation thermal simulation experiment includes the following steps.

[0011] Step 1: Data acquisition from the thermal simulation experiment of hydrocarbon generation in kerogen gold tubes:

[0012] Based on the thermal simulation experiment of hydrocarbon generation in gold tubes, kerogen samples were heated. In each set of experiments, the corresponding experimental temperature, maturity parameter (EasyRo), and the measured oil and gas volume at the end of the experiment were recorded simultaneously. The temperature-EasyRo-measured oil-measured gas volume corresponding to each set of experiments were used as a complete set of experimental data as the basic input for the decoupled calculation of the hydrocarbon generation process.

[0013] Step 2, Parametric modeling of the kerogen hydrocarbon generation process:

[0014] The kerogen process is parameterized into five process quantities: kerogen hydrocarbon generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation. Each hydrocarbon generation process is represented by an increment at each maturity node. A computer program defines the increment variables for each process, and by progressively accumulating these increments, the cumulative change in each process is calculated, ensuring that each process exhibits a monotonically increasing trend with maturity.

[0015] Step 3: Constructing a least-squares objective model under multiple constraints: Based on the measured oil and gas volumes obtained in Step 1, and combined with the parameterized expressions for the increments and cumulative quantities of each hydrocarbon generation process defined in Step 2, various constraints are introduced, including basic equality constraints such as oil conservation, gas conservation, and crude oil cracking consumption conservation, as well as inequality constraints such as nonnegativity and upper bounds applied to the increments of each hydrocarbon generation process. Empirical constraints are also added to limit the degree of completion of the initial cracking of kerogen, ensuring that the model solution conforms to the material conservation relationship and evolution law of the hydrocarbon generation process. Finally, based on the above data, parameterized expressions, and constraints, a least-squares optimization model is constructed with the deviation between the experimentally measured oil and gas volumes and the cumulative quantities of each process as the objective.

[0016] Step 4: Solving the nonlinear least squares optimization under constraints:

[0017] The Trust Region Reflective (TRF) algorithm is used to solve the aforementioned nonlinear least squares problem with boundary constraints. The TRF algorithm is applicable to least squares problems with variable boundary constraints, and can simultaneously satisfy constraints such as parameter nonnegativity during the optimization process, and has good numerical stability and convergence.

[0018] In the model, five incremental parameter vectors are used as optimization variables, and the various constraints defined in step 3 are uniformly incorporated into the least squares objective function through residuals. In each iteration, the TRF algorithm updates the optimization variables according to the current parameters, so that the objective function value gradually decreases until convergence, thereby obtaining an optimized solution that conforms to experimental data and satisfies the laws of matter conservation and hydrocarbon generation evolution.

[0019] Step 5, Calculation of hydrocarbon generation conversion rate:

[0020] After completing the constrained optimization solution, quantitative results can be obtained on the cumulative changes of the five processes of kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation with maturity.

[0021] To facilitate observation of hydrocarbon generation processes at different maturity stages, the cumulative amounts of each hydrocarbon generation process are dimensionless and defined as the corresponding hydrocarbon generation conversion rates. The general definition of conversion rate is as follows:

[0022] For any process In its maturity The conversion rate is defined as follows:

[0023]

[0024] in: For this process at maturity Cumulative amount under certain conditions; This represents the maximum cumulative amount achieved by the process throughout the entire thermal evolution process; This is used to characterize the relative degree of completion of the process.

[0025] In step 2 above, in order to ensure the monotonicity of the hydrocarbon generation process as maturity evolves and to reflect the irreversible characteristics of the pyrolysis process, that is, the cumulative amount of each hydrocarbon generation process remains monotonically constant as maturity increases, each hydrocarbon generation process is represented as a cumulative form of a non-negative incremental sequence, thereby naturally introducing monotonicity constraints at the parameter level.

[0026] At discrete maturity nodes The above method represents each hydrocarbon generation process to be solved as a corresponding sequence of incremental variables, and by accumulating the incremental variables, a cumulative evolution curve of each hydrocarbon generation process as maturity changes is constructed.

[0027] Let the optimization variable be an increment vector. It consists of the following five groups of incremental variables, each corresponding to one of the five processes:

[0028]

[0029] The aforementioned nonnegativity constraint is used to ensure that the cumulative amount of each hydrocarbon generation process remains monotonically constant as maturity increases;

[0030] Based on the above incremental parameters, the cumulative amount of each hydrocarbon generation process is reconstructed at the i-th maturity node in the following way:

[0031]

[0032] Where: KTO: Kerogen crude oil; KTG: Kerogen gas; OTG: Crude oil cracking into gas; OC: Crude oil consumption; C: Non-reactive carbon; Oil: Measured oil volume; Gas: Measured gas volume; Oil1: Measured oil volume under initial maturity conditions; Gas1: Measured gas volume under initial maturity conditions; u1: Increment at the first maturity node; u j : The increment at the j-th maturity node; u n Increment at the nth maturity node; KTO i At node i, the cumulative oil production from kerogen = initial oil production + sum of oil increments at each maturity node; KTG i At node i, the cumulative gas production of kerogen = initial gas production + sum of gas increments at each maturity node; OTG i At node i, the amount of crude oil cracking into gas equals the sum of the increments in crude oil cracking into gas at each maturity node; OC i At node i, crude oil consumption equals the sum of the incremental crude oil consumption at each maturity node; C iAt node i, the amount of non-reactive carbon is equal to the sum of the increments of non-reactive carbon at each maturity node.

[0033] The objective function construction described in step 3 above involves constructing a least-squares objective model under multiple constraints, and building a composite least-squares objective function composed of multiple types of residual terms. This function is used to decouple and solve each hydrocarbon generation process while satisfying all constraints.

[0034] The objective function consists of the following five types of residual terms:

[0035] (1) Residuals constrained by the equation based on the law of conservation of matter;

[0036] (2) Inequality-constrained residuals based on nonnegativity and upper limit conditions;

[0037] (3) Smoothing and regularizing residuals used to suppress deoscillations;

[0038] (4) S-shaped feature residuals used to constrain the shape of curves;

[0039] (5) The a priori constraint residuals of the initial cleavage of kerogen.

[0040] To unify the constraints within the least squares solution framework, a residual construction method is used to uniformly handle equality and inequality constraints. By transforming various constraints into residual terms and combining them with the residuals corresponding to experimental data to form the objective function, a synergistic solution for the hydrocarbon generation and cracking processes of organic matter is achieved.

[0041] For equality constraints, let their constraint functions be:

[0042]

[0043] When the model calculation results satisfy this constraint, the function value approaches 0; when the model calculation results deviate from this conservation relationship, the function value reflects the degree of deviation. Therefore, the equality constraint function is directly introduced as a residual term into the least squares objective function, and its mathematical expression is:

[0044]

[0045] in, This represents the residual term corresponding to the equality constraint, with weighting coefficients. This is used to adjust the degree of influence of the conservation constraint in the overall optimization process, so as to ensure the effective constraint of the mass conservation relationship in the process of oil and gas generation and conversion.

[0046] For inequality constraints describing nonnegativity and upper bound relationships, let their constraint functions be:

[0047]

[0048] When the constraint is satisfied, no residual is generated; when the model calculation result violates the constraint, only the violated part is penalized; therefore, a positive truncation form is introduced to construct the inequality constraint residual, the mathematical expression of which is:

[0049]

[0050] in, This represents the residual term corresponding to the inequality constraint, with weighting coefficients. Used to control the intensity of penalties for violations of nonnegativity or upper bound constraints, thereby ensuring that the decoupling results satisfy the physical rationality of hydrocarbon generation and cracking processes.

[0051] The five types of residual terms mentioned above are as follows:

[0052] (1) Equality constraint residual: mass conservation relation

[0053] At each maturity node, the following material conservation relationship is introduced and incorporated into the objective function as a residual, with weighting coefficients set to... :

[0054] Oil conservation principle: Oil produced by kerogen - Oil consumed = Actual oil volume

[0055] Oil quantity conservation residual:

[0056] Gas conservation: Gas production from kerogen + Gas production from crude oil cracking = Measured gas production

[0057] Gas volume conservation residual:

[0058] Oil consumption conservation: Crude oil consumption + Non-reactive carbon content = Crude oil cracking into gas content

[0059] Crude oil cracking material conservation residual:

[0060] The above equation residuals are used to ensure that the initial cracking of kerogen and the overall cracking process of crude oil satisfy the material conservation relationship in terms of oil quantity, gas quantity and crude oil consumption.

[0061] (2) Inequality-constrained residuals: nonnegativity and upper limit conditions

[0062] To ensure the solution satisfies physical validity, the following inequality constraint is introduced, with the weighting coefficients set as follows: ;

[0063] Crude oil consumption ≥ Crude oil cracking gas production:

[0064]

[0065] : Upper limit constraint residual for crude oil cracking into gas;

[0066] Crude oil consumption ≥ non-reactive carbon content:

[0067]

[0068] : Unreactive carbon upper limit constraint residual;

[0069] Crude oil cracking gas production ≥ 0:

[0070]

[0071] : Non-negativity constraint residuals of crude oil cracking into gas;

[0072] Non-reactive carbon ≥ 0:

[0073]

[0074] Non-reactive carbon non-negativity constraint residuals;

[0075] (3) Smoothing regularized residuals: suppressing oscillations

[0076] To avoid unreasonable local oscillations in the cumulative amount of each hydrocarbon generation process as maturity changes during the solution process, a smoothing regularized residual is introduced into the objective function to apply differential constraints to the change of the cumulative curve in the maturity direction.

[0077] Let the cumulative amount of a certain hydrocarbon generation process at discrete maturity points be represented as a sequence.

[0078]

[0079] Let n be the cumulative amount corresponding to the i-th maturity node of the process, and n be the total number of maturity nodes.

[0080] Define the first-order difference operator as:

[0081]

[0082] First-order difference operator It is used to characterize the incremental change of the cumulative amount at adjacent maturity nodes, reflecting the local change characteristics of the process in the direction of maturity;

[0083] Define the second-order difference operator as a further action of the first-order difference:

[0084]

[0085] Second-order difference operator This represents the further change of the first-order difference in the maturity direction, used to constrain the smoothness of the cumulative curve in the maturity direction and prevent local oscillations that do not conform to the actual thermal evolution law during the optimization solution process.

[0086] The first-order difference is used to characterize the magnitude of the cumulative change between adjacent maturity points, while the second-order difference is used to characterize the curvature change of the cumulative curve in the maturity direction.

[0087] (a) First-order difference smoothed residuals, weighted To suppress drastic jumps between adjacent maturity points, a first-order difference smoothing method is introduced for each hydrocarbon generation process to smooth the residuals.

[0088]

[0089] 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.

[0090] (b) Second-order difference 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.

[0091] ;

[0092] 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;

[0093] The curvature constraint, constructed based on second-order difference, is used to characterize the weight of the curvature constraint in the objective function, thereby limiting excessive fluctuations in the cumulative curve in the maturity direction. The weight of the smoothing constraint constructed based on the first-order difference in the objective function is used to suppress unreasonable abrupt changes in the cumulative amount at adjacent maturity nodes.

[0094] (4) S-curve shape constraint residuals, weights :

[0095] From the perspective of hydrocarbon generation kinetics, the cumulative amount of oil and gas generated by kerogen should exhibit an S-shaped evolution characteristic of initial acceleration followed by deceleration with increasing maturity; curve shape constraints are introduced at the predetermined inflection point:

[0096] The maturity sequence corresponding to the experimental data is:

[0097]

[0098] in, Initial maturity level, This represents the maturity level at which the measured oil volume reaches its peak. The maturity level at the end of the initial cleavage of kerogen. Reaching the highest level of maturity;

[0099] Specifically, the inflection point of KTO is set as follows: and The midpoint of KTG is set as the inflection point. and The midpoint;

[0100] Let the inflection point index of kerogen oil be KTO_inf, then

[0101] Before the inflection point: apply a convexity penalty, i.e., apply... Apply positive residuals to the part:

[0102]

[0103] 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.

[0104] After the inflection point: apply a concave penalty, i.e., apply... Apply positive residuals to the part:

[0105]

[0106] 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.

[0107] Let the inflection point index of kerogen regeneration be KTG_inf, then

[0108] Before the inflection point: apply a convexity penalty, i.e., apply... Apply positive residuals to the part:

[0109]

[0110] 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;

[0111] After the inflection point: apply a concave penalty, i.e., apply... Apply positive residuals to the part:

[0112]

[0113] 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;

[0114] 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.

[0115] (5) Prior constraint residuals for the initial cleavage of kerogen:

[0116] Based on the general evolutionary pattern of "oil generation precedes gas generation" in kerogen hydrocarbon generation, the kerogen oil generation process should end before the kerogen gas generation process after reaching a certain maturity level. To reflect this empirical understanding, the initial pyrolysis completion maturity is predetermined. At this point, completion state constraints are introduced for the kerogen oil generation and gas generation processes, respectively;

[0117] exist At this point, the kerogen oil generation process has completely ceased, meaning its cumulative amount no longer increases; simultaneously, the kerogen oil generation process must be essentially complete, reaching 99%.

[0118] The corresponding residual is defined as:

[0119]

[0120] in, and These represent the final cumulative amounts for the corresponding processes; To constrain the residuals for the degree of completion of kerogen oil production, The residuals are constrained by the completion of kerogen generation.

[0121] (6) Overall objective function:

[0122] The objective function is used to uniformly characterize the overall deviation of each hydrocarbon generation process parameter under the constraints. The TRF optimization algorithm uses this objective function as the evaluation basis. Under the premise of satisfying the constraints, it iteratively adjusts each hydrocarbon generation process parameter to gradually reduce the objective function value, and finally obtains the decoupling results of each hydrocarbon generation process consistent with the experimental information.

[0123] Concatenate all the above residual terms into a vector r, and the optimization objective is R:

[0124]

[0125] In step 4 above: Through the above optimization solution process, without introducing additional experimental information, relying only on the temperature, maturity, measured oil volume and measured gas volume data obtained from the gold tube hydrocarbon generation thermal simulation experiment, we can obtain quantitative results on the cumulative changes of the five processes of kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas and non-reactive carbon formation with maturity, thus achieving quantitative decoupling of the primary cracking of kerogen and the cracking process of crude oil.

[0126] In step 5 above, the conversion rate of kerogen oil can be expressed as:

[0127]

[0128] This conversion rate is used to describe the relative progress of kerogen oil production with increasing maturity, where:

[0129] when At this time, it indicates that the kerogen has not yet produced oil;

[0130] when At this point, it indicates that the kerogen oil extraction is complete;

[0131] The conversion rates for other processes are calculated similarly.

[0132]

[0133] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0134] (1) Achieve quantitative decoupling of the primary and secondary pyrolysis hydrocarbon generation processes of kerogen:

[0135] This application addresses the problem that thermal simulation experiments of hydrocarbon generation in kerogen gold tubes can only obtain measured oil and gas volumes under different maturity conditions, and it is difficult to decouple the hydrocarbon generation processes, such as primary kerogen cracking and crude oil cracking. Without introducing any additional experimental tests or characterization methods, this application constructs a parameterized model that includes multiple processes such as kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation. It introduces basic equation constraints such as oil volume conservation, gas volume conservation, and crude oil consumption conservation, as well as inequality constraints such as process non-negativity and upper bound restrictions. At the same time, it introduces empirical constraints to limit the completion of primary kerogen cracking. This application solves the hydrocarbon generation process of primary kerogen cracking and crude oil cracking, and achieves quantitative decoupling of primary and secondary cracking in the hydrocarbon generation process of kerogen. This overcomes the technical limitation of traditional experimental analysis methods, which can only obtain final state results and cannot characterize process evolution.

[0136] (2) Quantitative characterization of the conversion rate evolution characteristics of each hydrocarbon generation process:

[0137] By calculating the conversion rate of the cumulative amounts of each hydrocarbon generation process obtained from decoupling, we can obtain the evolution law of conversion rate of processes such as kerogen oil generation, gas generation, crude oil cracking into gas and non-reactive carbon formation during crude oil cracking as they change with maturity, providing a quantitative basis for refined hydrocarbon generation evaluation. Attached Figure Description

[0138] Figure 1 It is a graph showing the actual changes in oil and gas volume with temperature.

[0139] Figure 2 This is a graph showing the measured changes in oil and gas volume with EasyRo.

[0140] Figure 3 This is a graph showing the cumulative change in kerogen oil production during the iterative process.

[0141] Figure 4 This is a graph showing the cumulative change in the kerogen gasification iterative process.

[0142] Figure 5 This is a graph showing the cumulative change in the iterative process of crude oil cracking into gas.

[0143] Figure 6 This is a graph showing the cumulative change in crude oil consumption over the iterative process.

[0144] Figure 7This is a graph showing the cumulative change in the non-reactive carbon iteration process.

[0145] Figure 8 It is a graph showing the cumulative changes in residuals during the iteration process.

[0146] Figure 9 This is an error graph showing the difference between the measured and calculated values ​​of oil and gas volume as a function of EasyRo.

[0147] Figure 10 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

[0148] 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.

[0149] Example: A method for decoupling the primary and secondary cracking hydrocarbon generation conversion rate of kerogen based on thermal simulation experimental data of gold tube hydrocarbon generation includes the following steps:

[0150] Step 1: Data acquisition from the thermal simulation experiment of hydrocarbon generation in kerogen gold tubes:

[0151] This embodiment selects a set of deep lacustrine kerogen samples from the Wenchang Formation in the Lufeng Depression of the Pearl River Estuary Basin as the experimental subject, and conducts a gold tube hydrocarbon generation thermal simulation experiment on the samples. The kerogen-gold tube hydrocarbon generation thermal simulation experiment process is as follows: The kerogen sample is placed into a gold tube (length: 40 mm, outer diameter: 4.2 mm, wall thickness: 0.25 mm), and both ends are welded and sealed under an argon atmosphere. Each pyrolysis series has 12 temperature points, and two parallel samples are set at each temperature point. The gold tubes containing the samples are placed in 12 high-pressure reactors, and then the entire assembly is placed into the pyrolysis furnace. The high-pressure reactors are pressurized using a pressure control system, and the pressure is maintained at 50 MPa (±0.2 MPa). The pyrolysis furnace heats according to a preset temperature rise program.

[0152] Experimental heating rate: Two heating rates were used in the experiment: rapid (20°C / h) and slow (2°C / h). This application uses the experimental results under the slow heating (2°C / h) condition. The specific heating procedure is as follows: The pyrolysis furnace was first heated from room temperature to 250°C over 10 hours, and then continued to heat to 600°C at a rate of 2°C / h. Twelve sampling temperature points were set within the range of 300°C to 600°C.

[0153] During the thermal simulation experiment, the thermal maturity of each sampling point (corresponding to a specific temperature or time) was calculated based on the EasyRo model proposed by Sweeney and Burnham (1990) and converted into the corresponding equivalent vitrinite reflectance (EasyRo) to characterize the thermal maturity of the sample.

[0154] The experimental results were obtained under heating conditions, and the temperature, maturity (EasyRo), measured oil volume, and measured gas volume data for each experimental point were extracted from the results, as shown in Table 1. This data serves as the basic input data for subsequent decoupled calculations. The measured oil and gas volumes as a function of temperature are shown in the graph below. Figure 1 As shown; the measured oil and gas volumes vary with EasyRo as shown in the graph. Figure 2 As shown.

[0155] Table 1. Experimental data obtained in step 1

[0156]

[0157] Step 2, Parametric modeling of the kerogen hydrocarbon generation process:

[0158] The hydrocarbon generation process was parametrically modeled using a Python program. First, based on the maturity range of the gold tube hydrocarbon generation thermal simulation experiment, it was discretized into several maturity nodes. At each node, five incremental variables for the hydrocarbon generation process were defined: kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation. The cumulative amount for each process was calculated by progressively summing the corresponding increments, ensuring that each hydrocarbon generation process monotonically changes with increasing maturity, consistent with the irreversible nature of pyrolysis. Its mathematical expression is as follows:

[0159] The cumulative amount of the process = the initial value + the incremental increase of each maturity stage.

[0160] In the implementation, NumPy's cumsum() function is used to construct the cumulative process variables. The program uses the increment of each maturity node as the optimization variable and reconstructs the cumulative process variables by gradually accumulating them, ensuring that the evolution of each process conforms to physical requirements.

[0161] 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 iterations—the initial iteration, intermediate iterations, and the final iteration—for demonstration. The evolution characteristics of the hydrocarbon generation process parameters during the iterative optimization process are shown in Tables 2-6. Figures 3-7 As shown. Figure 3 This is a graph showing the cumulative change in kerogen oil production during the iterative process. Figure 3The cumulative results of the kerogen oil generation process at different iteration stages with maturity are shown, reflecting the evolutionary characteristics of the process under the constraint of oil conservation, where the kerogen oil generation gradually adjusts and tends to converge as the iteration progresses. Figure 4 This is a graph showing the cumulative change in the kerogen gasification iterative process. 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 gas conservation, where kerogen gasification gradually adjusts and tends to converge as the iteration progresses. Figure 5 This is a graph showing the cumulative change in the crude oil cracking and gasification process. 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 crude oil consumption conservation. Figure 6 This is a graph showing the cumulative change in crude oil consumption over the iterative process. Figure 6 The cumulative results of the crude oil consumption process at different iteration stages with the change of 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 crude oil consumption conservation. Figure 7 This is a graph showing the cumulative change in the non-reactive carbon iteration process. Figure 7 The 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 crude oil consumption conservation.

[0162] Table 2. Iterative process of kerogen oil increment and corresponding cumulative amount

[0163]

[0164] Table 3. Iterative process of kerogen regeneration increment and corresponding cumulative amount

[0165]

[0166] Table 4. Iterative process of crude oil cracking into gas increment and corresponding cumulative amount

[0167]

[0168] Table 5. Iterative Process and Corresponding Cumulative Amount of Crude Oil Consumption Increment

[0169]

[0170] Table 6 Iterative Process of Non-Reactive Carbon Increment and Corresponding Cumulative Amount

[0171]

[0172] Step 3: Construct a least squares objective model under multiple constraints:

[0173] Using a Python program, based on the measured oil and gas volumes obtained in step 1, and combined with the parameterized expressions for the increments and cumulative quantities of each hydrocarbon generation process defined in step 2, the basic data and the increment parameters to be determined for the least squares optimization problem are determined. Subsequently, various constraints are added, including basic equality constraints such as oil conservation, gas conservation, and crude oil cracking consumption conservation; inequality constraints such as the non-negativity and upper bound limits of the increments of each hydrocarbon generation process; and empirical constraints to limit the degree of completion of the initial cracking of kerogen. This ensures that the solution results conform to the material conservation relationships and evolutionary laws of hydrocarbon generation and transformation.

[0174] Based on this, a composite least squares objective function is constructed. The objective function consists of the following five 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) smoothing regularization residuals used to suppress solution oscillations; (4) S-shaped characteristic residuals used to constrain the shape of the curve; and (5) prior constraint residuals for the initial cleavage of kerogen.

[0175] Secondly, various residual terms are constructed in the program using the NumPy library. Equality constraint residuals use the `subtract()` function to calculate the difference between measured and model predicted values; inequality constraint residuals use the `maximum()` function to penalize violations of constraints, ensuring the residuals are non-negative; smoothing regularization residuals use the `diff()` function to calculate first or second-order differences and penalize excessive fluctuations; S-curve shape constraint residuals use the second-order difference distribution combined with the `maximum()` function to define the curve's evolutionary characteristics of being convex then concave; and kerogen initial cleavage completion constraint residuals are calculated based on a set completion level and are also processed using the `maximum()` function.

[0176] 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 obtaining decoupled results that conform to experimental observations and physical rationality.

[0177] 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 7. 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 the constraints of matter conservation, morphological constraints, and smoothness constraints. As the iteration progresses, the individual residuals and the total residual gradually decrease, as shown in Table 7. Figure 8 As shown, Figure 8The changes of various residual terms and the total residual with the number of iterations are shown during the iterative calculation process. Figure 8 The results 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. This demonstrates that the model solution gradually tends to be stable and consistent under multiple constraints.

[0178] Table 7. Changes in residuals during the iteration process.

[0179]

[0180] Step 4: Solving the nonlinear least squares optimization under constraints:

[0181] The nonlinear least squares model with boundary constraints is solved by calling the scipy.optimize.least_squares() function in the SciPy optimization library through a Python program and employing the Trust Region Reflective (TRF) algorithm. The TRF algorithm is applicable to least squares problems with variable boundary constraints, can simultaneously satisfy constraints such as parameter nonnegativity during the optimization process, and has good numerical stability and convergence.

[0182] In the model, five incremental parameter vectors are used as optimization variables, and the various constraints defined in step 3 are uniformly incorporated into the least squares objective function through residuals. In each iteration, the TRF algorithm updates the optimization variables according to the current parameters, so that the objective function value gradually decreases until convergence, thereby obtaining an optimized solution that conforms to experimental data and satisfies the laws of matter conservation and hydrocarbon generation evolution.

[0183] Through the above optimized solution process, without introducing additional experimental information, and relying solely on the temperature, maturity, measured oil volume, and measured gas volume data obtained from the gold tube hydrocarbon generation thermal simulation experiment, quantitative results were obtained on the cumulative changes in maturity of five processes: kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation. This achieves quantitative decoupling of the primary kerogen cracking process from the crude oil cracking process. The results are shown in Tables 8 and 9. Figure 9 This is an error graph showing the difference between the measured and calculated values ​​of oil and gas volume as a function of EasyRo. Figure 9 The results show that the calculated and measured values ​​are in high agreement at each EasyRo point, with the oil quantity error controlled within 2% and the gas quantity error controlled within 6%, indicating that the model has high quantitative decoupling accuracy.

[0184] It should be noted that this application is not limited to using the Trust Region Reflective (TRF) algorithm to achieve optimization. Under the premise of satisfying the parameter boundary constraints and inequality constraints and being able to complete the solution of the nonlinear least squares problem, other constraint optimization algorithms that are functionally equivalent to the TRF algorithm can also be used to implement the technical solution of this application.

[0185] Table 8. Experimental Measured Values ​​and Optimized Solution Results

[0186]

[0187] Table 9. Error results between experimentally measured values ​​and optimized solutions.

[0188]

[0189] Step 5, Calculation of hydrocarbon generation conversion rate:

[0190] 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.

[0191] Taking the conversion rate of kerogen to oil as an example, the conversion rate of kerogen to oil is expressed as follows:

[0192]

[0193] This conversion rate is used to describe the relative progress of kerogen oil production with increasing maturity, where:

[0194] when At this time, it indicates that the kerogen has not yet produced oil;

[0195] when At this point, it indicates that the kerogen oil extraction is complete;

[0196] The conversion rates for other processes are calculated similarly.

[0197]

[0198] Based on the calculation results of step four (Table 8), 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.

[0199] 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... iThe values ​​are: 50.33, 110.96, 241.09, 279.77, 288.82, 288.82, 288.82, 288.82, 288.82, 288.82, 288.82, and 288.82.

[0200] Based on the above definition of conversion rate, normalized calculations were performed for each maturity point, yielding the following conversion rates for the kerogen oil extraction process:

[0201] 0.17, 0.38, 0.83, 0.97, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00 and 1.00.

[0202] This shows that the kerogen oil conversion rate increases rapidly with maturity in the low-to-medium maturity stage, and approaches 1 after EasyRo is about 0.96, indicating that the kerogen oil conversion process is basically completed in this maturity range.

[0203] The conversion rate calculation results are shown in Table 10. The graphs showing the changes in conversion rates of kerogen oil, kerogen gas, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation with EasyRo are shown below. Figure 10 As shown. Figure 10 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 hydrocarbon generation process generally show a continuously increasing trend, gradually approaching 1 at high maturity, indicating that the corresponding processes are essentially complete.

[0204] Table 10 Conversion Rate Calculation Results

[0205] This method uses the conservation of oil volume, gas volume, and crude oil consumption during crude oil cracking as basic equality constraints within the same constrained optimization model. It also sets a termination constraint for the initial cracking of kerogen, using maturity (EasyRo) as a criterion to ensure that the initial cracking of kerogen has been completed under specified maturity conditions. Under these equality constraints, five processes—kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation—are simultaneously incorporated into the solution.

[0206] Furthermore, to ensure that the decoupling results meet the physical rationality of actual geochemical processes, this invention introduces inequality constraints on the basis of equality constraints, limiting the non-negativity of each conversion quantity and their interrelationship during crude oil cracking. That is, the amount of crude oil cracking into gas satisfies: "crude oil consumption ≥ crude oil cracking into gas ≥ 0", and the amount of non-reactive carbon formation satisfies: "crude oil consumption ≥ non-reactive carbon ≥ 0", thereby ensuring that crude oil consumption is reasonably allocated between crude oil cracking into gas and non-reactive carbon formation.

[0207] Under the combined influence of the aforementioned equality and inequality constraints, the TRF (Trust Region Reflective) optimization algorithm is used to solve the model, enabling coordinated decoupling of each hydrocarbon generation process while satisfying the conditions of mass conservation and nonnegativity. Through this method, without adding experimental observation information, and using only the final-state oil and gas data obtained from the gold tube hydrocarbon generation thermal simulation experiment, quantitative decoupling can be achieved for the initial cracking of kerogen to generate oil and gas, as well as the crude oil consumption, crude oil cracking to gas, and non-reactive carbon formation behaviors during crude oil cracking.

[0208] This application is applicable to scenarios where the main experimental method is the thermal simulation experiment of hydrocarbon generation in the Kerogen gold tube, and quantitative decoupling analysis of the hydrocarbon generation process is performed under the condition that only the final state production data of oil and gas are available.

[0209] Hydrogen generation data under different maturity conditions were obtained through thermal simulation experiments of hydrocarbon generation in gold tubes. Parametric models of hydrocarbon generation processes such as kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon generation were constructed. Based on this, basic equation constraints such as oil quantity conservation, gas quantity conservation, and crude oil cracking consumption conservation were introduced, as well as inequality constraints such as non-negativity and upper bound restrictions applied to the increment of each hydrocarbon generation process. At the same time, empirical constraints were added to limit the degree of completion of the initial cracking of kerogen.

[0210] Under the above constraints, a least-squares objective function is constructed, and through optimization, the conversion rate of the primary pyrolysis and secondary pyrolysis hydrocarbon generation processes of kerogen is quantitatively decoupled while satisfying the material conservation relationship and empirical constraints.

Claims

1. A method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on a gold tube hydrocarbon generation thermal simulation experiment, comprising the following steps: Step 1: Data acquisition from the thermal simulation experiment of hydrocarbon generation in kerogen gold tubes: Based on the thermal simulation experiment of hydrocarbon generation in gold tube, kerogen samples were heated. In each set of experiments, the corresponding experimental temperature, maturity parameter EasyRo, and the measured oil and gas volume at the end of the experiment were recorded simultaneously. The temperature-EasyRo-measured oil-measured gas volume corresponding to each set of experiments were used as a complete set of experimental data as the basic input for the decoupled calculation of the hydrocarbon generation process. Step 2, Parametric modeling of the kerogen hydrocarbon generation process: The kerogen process is parameterized into five process quantities: kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas, and non-reactive carbon formation. Each hydrocarbon generation process is represented by an increment at each maturity node. The incremental variables of each process are defined by a computer program, and the cumulative change of each process is calculated by gradually accumulating these increments to ensure that each process shows a monotonically increasing trend with the increase of maturity. Step 3: Construct a least squares objective model under multiple constraints: Based on the measured oil and gas volumes obtained in Step 1, and combined with the parameterized expressions for the increments and cumulative quantities of each hydrocarbon generation process defined in Step 2, various constraints are introduced, including basic equality constraints such as oil conservation, gas conservation, and crude oil cracking consumption conservation, as well as inequality constraints including nonnegativity and upper bounds applied to the increments of each hydrocarbon generation process. At the same time, empirical constraints are added to limit the degree of completion of the initial cracking of kerogen, so as to ensure that the model solution results conform to the material conservation relationship and evolution law of the hydrocarbon generation process. Finally, based on the above data, parameterized expressions, and constraints, a least squares optimization model is constructed with the deviation between the experimentally measured oil and gas volumes and the cumulative quantities of each process as the objective. Step 4: Solving the nonlinear least squares optimization under constraints: The Trust Region Reflective (TRF) algorithm is used to solve the above least squares problem. The TRF algorithm is applicable to least squares problems with variable boundary constraints. It can satisfy the constraints, including parameter non-negativity, during the optimization process and has good numerical stability and convergence. In the model, five incremental parameter vectors are used as optimization variables, and the various constraints defined in step 3 are uniformly incorporated into the least squares objective function through residual form. In each iteration, the TRF algorithm updates the optimization variables according to the current parameters, so that the objective function value gradually decreases until convergence, thereby obtaining an optimized solution that conforms to experimental data and satisfies the laws of material conservation and hydrocarbon generation evolution. Step 5, Calculation of hydrocarbon generation conversion rate: After completing the constraint optimization solution, quantitative results can be obtained on the cumulative changes of five processes as maturity: Kerogen-derived oil (KTO), Kerogen-generated gas (KTG), crude oil consumption (OC), crude oil cracking into gas (OTG), and non-reactive carbon (Oil) formation. To facilitate observation of hydrocarbon generation processes at different maturity stages, the cumulative amounts of each hydrocarbon generation process are dimensionless and defined as the corresponding hydrocarbon generation conversion rates. The general definition of conversion rate is as follows: For any process In its maturity The conversion rate is defined as follows: ; in: For this process at maturity Cumulative amount under certain conditions; This represents the maximum cumulative amount achieved by the process throughout the entire thermal evolution process; This is used to characterize the relative degree of completion of the process.

2. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on the thermal simulation experiment of hydrocarbon generation in a gold tube according to claim 1, characterized in that: In step 2, to ensure the monotonicity of the hydrocarbon generation process as maturity evolves and to reflect the irreversible characteristics of the pyrolysis process, i.e. the cumulative amount of each hydrocarbon generation process remains monotonically constant as maturity increases, each hydrocarbon generation process is represented as a cumulative form of a non-negative incremental sequence, thus naturally introducing monotonicity constraints at the parameter level. At discrete maturity nodes The above method represents each hydrocarbon generation process to be solved as a corresponding sequence of incremental variables, and by accumulating the incremental variables, a cumulative evolution curve of each hydrocarbon generation process as maturity changes is constructed. Let the optimization variable be an increment vector. It consists of the following five groups of incremental variables, corresponding to five processes: kerogen derivative (KTO), kerogen gas (KTG), crude oil cracking into gas (OTG), crude oil consumption (OC), and non-reactive carbon (C): ； in This represents the amount of new oil generated from the kerogen at the j-th maturity node. This represents the amount of new gas generated by the kerogen at the j-th maturity node. This represents the amount of newly generated gas from the cracking of crude oil at the j-th maturity node. This represents the amount of crude oil consumed at the j-th maturity node; This represents the amount of non-reactive carbon formed by crude oil consumption at the j-th maturity node; The aforementioned nonnegativity constraint is used to ensure that the cumulative amount of each hydrocarbon generation process remains monotonically constant as maturity increases; Based on the above incremental parameters, the cumulative amount of each hydrocarbon generation process is reconstructed at the i-th maturity node in the following way: ; Wherein, KTO: Kerogen crude oil; KTG: Kerogen gas; OTG: Crude oil cracking into gas; OC: Crude oil consumption; C: Non-reactive carbon; Oil: Measured oil volume; Gas: Measured gas volume; Oil1: Measured oil volume under initial maturity conditions; Gas1: Measured gas volume under initial maturity conditions; u1: Increment at the first maturity node; u j : The increment at the j-th maturity node; u n Increment at the nth maturity node; KTO i At node i, the cumulative oil production from kerogen = initial oil production + sum of oil increments at each maturity node; KTG i At node i, the cumulative gas production of kerogen = initial gas production + sum of gas increments at each maturity node; OTG i At node i, the amount of crude oil cracking into gas equals the sum of the increments in crude oil cracking into gas at each maturity node; OC i At node i, crude oil consumption equals the sum of the incremental crude oil consumption at each maturity node; C i At node i, the amount of non-reactive carbon is equal to the sum of the increments of non-reactive carbon at each maturity node.

3. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on the thermal simulation experiment of hydrocarbon generation in a gold tube according to claim 1, characterized in that: The objective function construction described in step 3 involves a least-squares objective model under multiple constraints. A composite least-squares objective function, composed of multiple types of residual terms, is constructed to decouple and solve each hydrocarbon generation process while satisfying all constraints. The objective function consists of the following five types of residual terms: (1) Equation-constrained residuals based on the law of conservation of matter; (2) Inequality-constrained residuals based on nonnegativity and upper limit conditions; (3) Smoothing and regularizing residuals used to suppress deoscillations; (4) S-shaped feature residuals used to constrain the shape of curves; (5) The a priori constraint residuals of the initial cleavage of kerogen.

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

5. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on the thermal simulation experiment of hydrocarbon generation in a gold tube according to claim 3, characterized in that: The five types of residual terms are as follows: (1) Equality constraint residual: mass conservation relation At each maturity node, the following material conservation relationship is introduced and incorporated into the objective function as a residual, with weighting coefficients set to... : Oil conservation principle: Oil produced by kerogen - Oil consumed = Actual oil volume Oil quantity conservation residual: ; Gas conservation: Gas production from kerogen + Gas production from crude oil cracking = Measured gas production Gas volume conservation residual: ; Oil consumption conservation: Crude oil consumption + Non-reactive carbon content = Crude oil cracking into gas content Crude oil cracking material conservation residual: ; The above equation residuals are used to ensure that the initial cracking of kerogen and the overall cracking process of crude oil satisfy the material conservation relationship in terms of oil quantity, gas quantity and crude oil consumption. (2) Inequality-constrained residuals: nonnegativity and upper limit conditions To ensure the solution satisfies physical validity, the following inequality constraint is introduced, with the weighting coefficients set as follows: ; Crude oil consumption ≥ Crude oil cracking gas production: ; : Upper limit constraint residual for crude oil cracking into gas; Crude oil consumption ≥ non-reactive carbon content: ; : Unreactive carbon upper limit constraint residual; Crude oil cracking gas production ≥ 0: ; : Non-negativity constraint residuals of crude oil cracking into gas; Non-reactive carbon ≥ 0: ; Non-reactive carbon non-negativity constraint residuals; (3) Smoothing regularized residuals: suppressing oscillations To avoid unreasonable local oscillations in the cumulative amount of each hydrocarbon generation process as maturity changes during the solution process, a smoothing regularized residual is introduced into the objective function to apply differential constraints to the change of the cumulative curve in the maturity direction. Let the cumulative amount of a certain hydrocarbon generation process at discrete maturity points be represented as a sequence. ; Let be the cumulative amount corresponding to the process at the i-th maturity node, i=1,…,n, where n is the total number of maturity nodes; Define the first-order difference operator as: ; First-order difference operator It is used to characterize the incremental change of the cumulative amount at adjacent maturity nodes, reflecting the local change characteristics of the process in the direction of maturity; Define the second-order difference operator as a further action of the first-order difference: ; Second-order difference operator This represents the further change of the first-order difference in the maturity direction, used to constrain the smoothness of the cumulative curve in the maturity direction and prevent local oscillations that do not conform to the actual thermal evolution law during the optimization solution process; The first-order difference is used to characterize the magnitude of the cumulative change between adjacent maturity points, while the second-order difference is used to characterize the curvature change of the cumulative curve in the maturity direction. (a) First-order difference smoothed residuals, weighted To suppress drastic jumps between adjacent maturity points, a first-order difference smoothing method is introduced for each hydrocarbon generation process to smooth the residuals. ; in These represent the changes in the cumulative amount of each hydrocarbon generation process between adjacent maturity nodes. These changes are obtained through first-order difference operations, forming difference terms, and are incorporated into the objective function as smoothing constraint residuals. This is used to constrain cases where the changes at adjacent maturity nodes are too large, thereby suppressing unreasonable abrupt changes that may occur in the cumulative curves of each hydrocarbon generation and cracking process, and ensuring the continuity and physical rationality of its evolution with maturity. (b) Second-order difference smoothed residuals, weighted To avoid unreasonable curvature abrupt changes in the cumulative curve along the maturity direction, a second-order difference is introduced to smooth the residuals related to the primary cleavage of kerogen. ; in, and These represent the second-order differences of the cumulative amount in the maturity direction, respectively, which are used to constrain the local curvature changes of the cumulative curve and avoid unreasonable bends and fluctuations as maturity progresses; The curvature constraint, constructed based on second-order difference, is used to characterize the weight of the curvature constraint in the objective function, thereby limiting excessive fluctuations in the cumulative curve in the maturity direction. The weight of the smoothing constraint constructed based on the first-order difference in the objective function is used to suppress unreasonable abrupt changes in the cumulative amount at adjacent maturity nodes; (4) S-curve shape constraint residuals, weights : From the perspective of hydrocarbon generation kinetics, the cumulative amount of oil and gas generated by kerogen should exhibit an S-shaped evolution characteristic of initial acceleration followed by deceleration with increasing maturity; curve shape constraints are introduced at the predetermined inflection point: The maturity sequence corresponding to the experimental data is: ; in, Initial maturity level, This represents the maturity level at which the measured oil volume reaches its peak. The maturity level at the end of the initial cleavage of kerogen. Reaching the highest level of maturity; Specifically, the inflection point of KTO is set as follows: and The midpoint of KTG is set as the inflection point. and The midpoint; Let the inflection point index of kerogen oil be KTO_inf, then Before the inflection point: apply a convexity penalty, i.e., apply... Apply positive residuals to the part: ; in, This represents the residual due to the convexity constraint of kerogen 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 primary cleavage of kerogen; After the inflection point: apply a concave penalty, i.e., apply... Apply positive residuals to the part: ; in, This represents the concave constraint residual of kerogen oil; This indicates that kerogen oil production accelerates after the inflection point, which does not conform to the evolutionary characteristic that the initial cracking oil production process should gradually slow down in the later stages. Therefore, a penalty needs to be imposed. This indicates that the second-order difference morphological constraint is applied only within the maturity interval after the inflection point, i.e., only to... The node calculation corresponds to the residual, which is used to limit the oil generation evolution morphology in the later stage of the primary cleavage of kerogen. Let the inflection point index of kerogen regeneration be KTG_inf, then Before the inflection point: apply a convexity penalty, i.e., apply... Apply positive residuals to the part: ; in, This represents the residual due to the convexity constraint of kerogen gas generation; This indicates that the growth of kerogen gasification slows down before the inflection point, which does not conform to the evolutionary characteristic that the initial cracking and oil generation process should gradually accelerate in the early stage. Therefore, a penalty needs to be imposed. This indicates that the second-order difference morphological constraint is applied only within the maturity interval before the inflection point, i.e., only to... The node calculation corresponds to the residual, which is used to limit the life evolution morphology in the early stage of the initial cleavage of kerogen; After the inflection point: apply a concave penalty, i.e., apply... Apply positive residuals to the part: ; in, This represents the concave constraint residual of kerogen oil; This indicates that kerogen gasification accelerates after the inflection point, which does not conform to the evolutionary characteristic that the gasification process of the initial pyrolysis should gradually slow down in the later stages. Therefore, a penalty needs to be imposed. This indicates that the second-order difference morphological constraint is applied only within the maturity interval after the inflection point, i.e., only to... The node calculation corresponds to the residual, which is used to limit the life evolution morphology in the later stage of the primary cleavage of kerogen; The weight of the curve shape constraint in the objective function is used to limit the cumulative quantity evolution curve to meet the expected physical shape characteristics. (5) Prior constraint residuals for the initial cleavage of kerogen: Based on the general evolutionary pattern of "oil generation precedes gas generation" in kerogen hydrocarbon generation, the kerogen oil generation process should end before the kerogen gas generation process after reaching a certain maturity level. To reflect this empirical understanding, the initial pyrolysis completion maturity is predetermined. At this point, completion state constraints are introduced for the kerogen oil generation and gas generation processes, respectively; exist At this point, the kerogen oil generation process has completely ceased, meaning its cumulative amount no longer increases; simultaneously, the kerogen oil generation process must be essentially complete, reaching 99%. The corresponding residual is defined as: ; in, and These represent the final cumulative amounts for the corresponding processes; To constrain the residuals for the degree of completion of kerogen oil production, The residuals are constrained by the completion of kerogen generation. (6) Overall objective function: The objective function is used to uniformly characterize the overall deviation of each hydrocarbon generation process parameter under the constraints. The TRF optimization algorithm uses this objective function as the evaluation basis. Under the premise of satisfying the constraints, it iteratively adjusts each hydrocarbon generation process parameter to gradually reduce the objective function and finally obtains the decoupling results of each hydrocarbon generation process consistent with the experimental information. Concatenate all the above residual terms into a vector r, and the optimization objective is R: 。 6. The method for decoupling the primary-secondary pyrolysis hydrocarbon conversion rate of organic matter based on the thermal simulation experiment of hydrocarbon generation in a gold tube according to claim 1, characterized in that: In step 4: Through the above optimization solution process, without introducing additional experimental information, relying only on the temperature, maturity, measured oil volume and measured gas volume data obtained from the gold tube hydrocarbon generation thermal simulation experiment, we can obtain quantitative results on the cumulative changes of the five processes of kerogen oil generation, kerogen gas generation, crude oil consumption, crude oil cracking into gas and non-reactive carbon formation with maturity, thus achieving quantitative decoupling of the primary cracking of kerogen and the cracking process of crude oil.

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

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