Method and system for predicting in-situ heating time of oil shale

Abstract

This invention provides a method for predicting the in-situ heating time of oil shale, belonging to the field of oil and gas resource extraction technology. The method includes: collecting experimental data on the heating process of the formation; obtaining data on the temperature variation of various pseudo-group products based on preset component fitting rules; constructing a cracking reaction model for each pseudo-group product based on the temperature variation data; predicting the geothermal temperature field under heating conditions based on sample parameters; obtaining the production prediction curve of each pseudo-group product based on the temperature field prediction results and the cracking reaction model of each pseudo-group product; obtaining the net output energy curve by fitting the production prediction curve of each pseudo-group product with a preset energy consumption curve; and obtaining the optimal heating time based on the net output energy curve. This invention comprehensively studies and determines the optimal heating time from the perspective of the entire extraction area, ensuring the accuracy of the in-situ heating time prediction for oil shale, thereby achieving the goal of optimal heating time positioning.

Description

Technical Field

[0001] This invention relates to the field of oil and gas resource extraction technology, specifically to a method and system for predicting the in-situ heating time of oil shale. Background Technology

[0002] In-situ heating extraction of oil shale and heating-based upgrading of low-maturity shale oil are considered promising technologies for the effective utilization of shale oil. Heating the formation promotes the conversion of viscous, heavy liquid hydrocarbons into light oil, thus upgrading the shale oil and improving its fluidity. Simultaneously, high temperatures convert solid organic matter into oil and gas, significantly increasing formation pressure. The thermal stress generated by heating creates new fracture systems, which are beneficial for shale oil production. Heating-based upgrading technology is expected to be a breakthrough technology for the effective utilization of low-maturity shale oil. Currently, this technology is still in the laboratory and pilot testing stages. The economics of in-situ heating extraction of oil and gas is a key focus. On the one hand, heating the formation to a certain temperature produces a large amount of oil and gas, but production gradually decreases in the later stages of heating. On the other hand, heating the formation requires a large amount of energy, and the longer the heating, the greater the energy consumption. Therefore, determining the optimal heating time is crucial for improving economic efficiency. Theoretically, net present value analysis can determine the optimal heating time, but the cost structure of in-situ heating extraction is complex, with numerous variable factors and a wide range of variations. Although extensive mechanistic studies have been conducted and small-scale field trials have been carried out in China, the stability of the experimental processes is poor, making it difficult to provide reliable data on total cost, especially operating cost. Furthermore, there are no industrial-scale field trials for in-situ heated shale oil and gas extraction technology. Therefore, determining the optimal heating time using traditional net present value (NPV) assessment methods at this stage carries significant uncertainty.

[0003] The heating time at a specific point can be determined by monitoring the temperature at that point and comparing it with the organic matter pyrolysis temperature obtained in the laboratory. However, for the entire formation, the temperature distribution is uneven, and laboratory temperatures are insufficient to determine the stopping temperature and corresponding heating time for the entire mining area. To address the problems of poor accuracy in predicting the in-situ heating time of oil shale and the difficulty in determining the optimal heating time, a new method for predicting the in-situ heating time of oil shale is needed. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for predicting the in-situ heating time of oil shale, so as to at least solve the problems of poor accuracy in predicting the in-situ heating time of oil shale and difficulty in determining the optimal heating time.

[0005] To achieve the above objectives, the first aspect of the present invention provides a method for predicting the in-situ heating time of oil shale. The method includes: collecting experimental data on the heating process of the formation; obtaining data on the temperature variation of various pseudo-group products based on a preset component fitting rule; constructing a pyrolysis reaction model for each pseudo-group product based on the temperature variation data of the various pseudo-group products; obtaining sample parameters of core samples from the target layer; predicting the geothermal temperature field under heating conditions based on the sample parameters; obtaining a production prediction curve for each pseudo-group product based on the temperature field prediction result and the pyrolysis reaction model of each pseudo-group product; obtaining a net output energy curve by fitting the production prediction curve of each pseudo-group product with a preset energy consumption curve; and obtaining the optimal heating time based on the net output energy curve.

[0006] Optionally, the experimental data for the formation heating process are: pyrolysis experimental data conducted under multiple pressure gradients at the actual formation heating rate under each pressure condition.

[0007] Optionally, the proposed grouping products include: CH4, moisture, light oil, heavy oil, and non-hydrocarbon components; the preset component fitting rules include: merging hydrocarbon components that satisfy preset property similarity rules and preset carbon number similarity rules to obtain a class of proposed grouping products.

[0008] Optionally, the construction of pyrolysis reaction models for each pseudogroup product based on temperature variation data of multiple pseudogroup products includes: constructing corresponding first-order reaction models for the primary pyrolysis process of kerogen and the secondary pyrolysis process of each fraction produced by the primary pyrolysis of kerogen, based on the pyrolysis process of kerogen heating, with the following construction relationship:

[0009]

[0010] Where, k i X is the rate constant for the cracking reaction of the i-th component; i a is the amount of the i-th component; i,j The stoichiometric coefficients for the pyrolysis of the i-th component to produce the j-th component are given; based on the first-order reaction models of each component, the pyrolysis reaction models of the corresponding pseudo-group products are obtained.

[0011] Optionally, the rule for determining the pyrolysis reaction rate constants of each component is as follows:

[0012]

[0013] Among them, E i Δ is the experimental activation energy; R is the molar gas constant; T is the absolute temperature; A i It is a pre-exponential factor.

[0014] Optionally, the method further includes: fitting a first-order reaction model based on experimental data, including: fitting the process of obtaining the experimental activation energy, the pre-exponential factor, and the stoichiometric coefficients of the i-th component pyrolysis to produce the j-th component, wherein the fitting rule is:

[0015] F(E i A i ,a i,j )=∑(X exp -X cat ) 2

[0016] Among them, X exp These are the experimental values ​​of each component under preset conditions; X cat These are the theoretical values ​​of each component under preset conditions.

[0017] Optionally, the sample parameters of the target layer core sample include: kerogen weight percentage, unit molecular weight, kerogen density, rock density, char density, rock thermal conductivity, kerogen heat capacity, and kerogen porosity.

[0018] Optionally, the prediction of the geothermal temperature field under heating conditions based on the sample parameters includes: superimposing the temperature fields generated by different heaters to obtain a geothermal temperature field prediction model, the model relationship being:

[0019]

[0020] Among them, U j ρ is the internal energy of the fluid; s c is the density of kerogen. s K is the heat capacity of kerogen; K is the thermal conductivity; H is the heat capacity of kerogen. j Let be the enthalpy of the fluid; based on the law of conservation of mass and the geothermal temperature field prediction model, a mass conservation equation is constructed for each group of products, and the equation is:

[0021]

[0022] Where i represents the i-th pseudo-group product; ξ represents the porosity of kerogen; j X is the molar density of the product of phase j pseudogroup; ij The mole fraction of component i in the pseudo-group product of phase j; u j For the product velocity of phase j; r k v is the reaction rate of the k-th chemical reaction; ij,k Let i be the component of phase j in the system of the k-th reaction.

[0023] Optionally, the calculation rule for the molar density of the j-phase pseudogroup product is as follows:

[0024] ξ j =p j / ZRT

[0025] Where, p j Let Z be the pressure of the j-phase pseudogroup product, Z be the deviation between the actual and theoretical states of the j-phase pseudogroup product, R be the molar gas constant, T be the absolute temperature, and the calculation rule for the velocity of the j-phase pseudogroup product is as follows:

[0026]

[0027] Where K is the penetration rate; k rj The relative permeability of the j-phase pseudo-group product; μ j Let be the viscosity of the j-phase pseudogroup product.

[0028] Optionally, obtaining the yield prediction curves of each pseudo-group product based on the temperature field prediction results and the pyrolysis reaction model of each pseudo-group product includes: solving the mass conservation equation of each pseudo-group product based on the pyrolysis reaction model of each pseudo-group product to obtain the predicted relationship between the yield and time of each pseudo-group product, and obtaining the yield prediction curve of each pseudo-group product based on the predicted relationship; obtaining the net output energy curve by fitting the yield prediction curve of each pseudo-group product with a preset energy consumption curve, and obtaining the optimal heating time based on the net output energy curve includes: obtaining the cumulative heating value of each pseudo-group product based on the yield prediction curve of each pseudo-group product; obtaining the energy consumption value based on the preset energy consumption curve; obtaining the curve of net output energy changing with temperature based on the cumulative heating value and the energy consumption value, as the net output energy curve; expressed as:

[0029] E net =E oil -E heat

[0030]

[0031] Among them, E net E represents the net energy value at time t. oil E represents the cumulative heating value at time t. heat Let be the energy consumption value at time t; n be the number of components; Q i E represents the cumulative yield of the i-th component at time t; oi Let be the calorific value of the i-th component.

[0032] A second aspect of the present invention provides an in-situ heating time prediction system for oil shale, the system comprising: a data acquisition unit for acquiring experimental data on the heating process of the formation; a processing unit for obtaining temperature variation data of various pseudo-group products obtained based on a preset component fitting rule, and constructing a pyrolysis reaction model for each pseudo-group product based on the temperature variation data of the various pseudo-group products; the data acquisition unit is further configured to acquire sample parameters of core samples from the target layer; the processing unit is further configured to: predict the geothermal temperature field under heating conditions based on the sample parameters; obtain the production prediction curve of each pseudo-group product based on the temperature field prediction result and the pyrolysis reaction model of each pseudo-group product; and a solution unit for fitting a net output energy curve based on the production prediction curve of each pseudo-group product and a preset energy consumption curve, and obtaining the optimal heating time based on the net output energy curve.

[0033] On the other hand, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method for predicting the in-situ heating time of oil shale.

[0034] Through the above technical solution, the method of this invention can predict the yield of different components, the energy output and production of the entire mining area, and determine the optimal heating time range for in-situ oil shale mining or low-maturity shale oil refining mining areas, providing a basis for on-site mining plan preparation and engineering implementation. Comprehensive research determines the optimal heating time from the perspective of the entire mining area, ensuring the accuracy of in-situ oil shale heating time prediction, thereby achieving the goal of optimal heating time positioning.

[0035] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0037] Figure 1 This is a flowchart of the steps of an embodiment of the oil shale in-situ heating time prediction method provided by the present invention;

[0038] Figure 2 This is a system structure diagram of an oil shale in-situ heating time prediction system provided in one embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the net output energy curve provided by one embodiment of the present invention. Detailed Implementation

[0040] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0041] In-situ heating extraction of oil shale and heating-modification extraction of low-maturity shale oil are considered promising technologies for the effective utilization of shale oil. Heating the formation promotes the conversion of viscous, heavy liquid hydrocarbons into light oil, thus modifying the shale oil and improving its fluidity. Simultaneously, the high temperature converts solid organic matter into oil and gas, significantly increasing formation pressure. The thermal stress generated by heating creates new fracture systems, which are beneficial for shale oil production. Heating-modification technology holds promise as a breakthrough for the effective utilization of low-maturity shale oil. Currently, this technology is still in the laboratory and pilot testing stages.

[0042] The economics of in-situ heated shale oil and gas extraction is a key concern. On the one hand, heating the formation to a certain temperature produces a large amount of oil and gas, with production gradually decreasing in the later stages of heating. On the other hand, heating the formation requires a significant amount of energy, and the longer the heating period, the greater the energy consumption. Therefore, determining the optimal heating time is crucial for improving economic efficiency. Theoretically, net present value (NPV) analysis can determine the optimal heating time, but the cost structure of in-situ heated extraction is complex, with numerous variable factors and a wide range of variations. Although extensive mechanistic studies have been conducted, and small-scale field trials have been carried out in China, the stability of the experimental processes is poor, making it difficult to provide reliable data on total costs, especially operating costs. Furthermore, there are no industrial-scale field trials for in-situ heated shale oil and gas extraction technology. Therefore, at this stage, using traditional NPV assessment methods to determine the optimal heating time carries considerable uncertainty.

[0043] The heating time at a specific point can be determined by monitoring the temperature at that point and comparing it with the organic matter pyrolysis temperature obtained in the laboratory. However, for the entire formation, the temperature distribution is uneven, and laboratory temperatures are insufficient to determine the stopping temperature and corresponding heating time for the entire mining area. To address the problems of poor accuracy in predicting the in-situ heating time of oil shale and the difficulty in determining the optimal heating time, this invention proposes a new method for predicting the in-situ heating time of oil shale. The proposed method establishes the relationship between heating time, energy consumption, and the produced oil and gas and the calorific value of the produced products, thus determining an economically viable heating time range. By comprehensively studying and determining the optimal heating time for the entire mining area, the accuracy of the in-situ heating time prediction for oil shale is ensured, thereby achieving the goal of locating the optimal heating time.

[0044] Figure 1 This is a flowchart of a method for predicting the in-situ heating time of oil shale according to one embodiment of the present invention. Figure 1As shown, this invention provides a method for predicting the in-situ heating time of oil shale, the method comprising:

[0045] Step S10: Collect experimental data on the heating process of the formation, obtain data on the temperature change of various pseudo-group products based on the preset component fitting rules, and construct the cracking reaction model of each pseudo-group product based on the temperature change data of various pseudo-group products.

[0046] Specifically, the present invention addresses the issue that during the in-situ heating of oil shale, kerogen decomposes into various gaseous or liquid products. These products, under varying temperatures and pressures, generate heat and flow conditions that affect the formation temperature field. By comprehensively considering this interrelationship, the invention achieves a full simulation of the changes in the formation temperature field caused by kerogen decomposition during the heating process, and then finds the moment with the maximum net energy output, thereby determining the optimal heating time.

[0047] Based on this, the present invention first requires obtaining corresponding experimental data to understand the pyrolysis process of kerogen under different pressure conditions with temperature. Preferably, the experimental data of the formation heating process are pyrolysis experimental data conducted under multiple pressure gradients at the actual formation heating rates under each pressure condition. The proposed grouping products include: CH4, moisture, light oil, heavy oil, and non-hydrocarbon components; the preset component fitting rules include: merging hydrocarbon components that satisfy preset property similarity rules and preset carbon number similarity rules to obtain a class of proposed grouping products.

[0048] In the kerogen pyrolysis process, the process of directly obtaining multiple fractions from kerogen pyrolysis is called primary pyrolysis, and the result of obtaining multiple components based on subsequent distillation is called secondary pyrolysis. To facilitate the characterization of each pyrolysis process, this invention constructs corresponding first-order reaction models for both the primary kerogen pyrolysis process and the secondary pyrolysis process of each fraction produced by the primary kerogen pyrolysis, thereby accurately characterizing the pyrolysis process of each intermediate component. The relationship is as follows:

[0049]

[0050] Where, k i X is the rate constant for the cracking reaction of the i-th component; i a is the amount of the i-th component; i,j The stoichiometric coefficients for the pyrolysis of component i to produce component j are given. Based on the first-order reaction models of each component, the pyrolysis reaction models for each proposed product are obtained. The arrows above indicate the component to be pyrolyzed, and the arrows below indicate the compositional relationship of the pyrolyzed components. The pyrolysis process described above follows the law of conservation of mass.

[0051] Based on the constructed theoretical model, theoretical calculation values ​​for each component are obtained. However, these calculation values ​​often deviate from the actual values ​​of experimental data. This deviation may be influenced by various accidental factors and other implicit factors. To account for these influencing factors, after obtaining the corresponding pyrolysis reaction model, this invention will also fit the pyrolysis reaction model, that is, fit it based on the deviation between experimental data and theoretical data, taking into account the interfering factors. Based on this, the reaction model and kinetic parameter E... i A i and stoichiometric coefficient a i,j The process of obtaining is to solve for the experimental value (X) under certain conditions. exp ) and the theoretical value (X) under the corresponding conditions cal The error function is formed by the sum of the squares of the differences between E and E'. Where E' is... i Δ is the experimental activation energy; R is the molar gas constant; T is the absolute temperature; A i Let be a pre-exponential factor. It satisfies the following relation:

[0052]

[0053] The corresponding error function is:

[0054] F(E i A i ,a i,j )=∑(X exp -X cat ) 2

[0055] Among them, X exp These are the experimental values ​​of each component under preset conditions; X cat These are the theoretical values ​​of each component under preset conditions.

[0056] In one possible implementation, the coefficients of the multi-step reaction equation and the reaction kinetic parameters are obtained by solving for the minimum value under certain constraints. The calculation results are filled into the multi-step reaction equation coefficients and reaction kinetic parameter calculation results table as shown in Table 1.

[0057]

[0058] Table 1. Calculation of coefficients and reaction kinetic parameters for multi-step reaction equations.

[0059] Step S20: Obtain the sample parameters of the core sample of the target layer, and predict the geothermal temperature field under heating conditions based on the sample parameters.

[0060] Specifically, the temperature field distribution characteristics of strata differ after heating in different geological environments. This is related to the type of strata rocks and the parameters of kerogen. To accurately predict the in-situ heating time of oil shale in a target stratum area, it is necessary to predict the corresponding temperature field for that target area. Therefore, before constructing the temperature field, it is necessary to understand the geological parameters of the corresponding target strata. This requires obtaining core samples from the target layer to obtain the geological parameters of the target strata. Preferably, the sample parameters of the target layer core samples include: kerogen weight percentage, unit molecular weight, kerogen density, rock density, char density, rock thermal conductivity, kerogen heat capacity, and kerogen porosity.

[0061] Specifically, the first step is to establish a method for predicting the formation temperature field under well network heating conditions, involving the superposition calculation of temperature fields generated by different heaters. When fluids seep into oil shale formations, the high-temperature fluids carry heat and transfer and exchange heat with the rock mass system through conduction and convection, directly affecting the distribution of the temperature field within the rock mass. By superimposing the temperature fields generated by different heaters, a geothermal temperature field prediction model is obtained, with the following relationship:

[0062]

[0063] Among them, U j ρ is the internal energy of the fluid; s c is the density of kerogen. s K is the heat capacity of kerogen; K is the thermal conductivity; H is the heat capacity of kerogen. j Enthalpy of the fluid.

[0064] Furthermore, after obtaining the geothermal temperature field prediction model, it is necessary to solve for the formation temperature field and the yield of each component. Based on the principle of mass conservation, mass conservation equations are constructed for each component's pseudo-products. The equations are as follows:

[0065]

[0066] Where i represents the i-th pseudo-group product; ξ represents the porosity of kerogen; j X is the molar density of the product of phase j pseudogroup; ij The mole fraction of component i in the pseudo-group product of phase j; u j For the product velocity of phase j; r k v is the reaction rate of the k-th chemical reaction; ij,k Let i be the component of phase j in the system of the k-th reaction.

[0067] The calculation rule for the molar density of the j-phase pseudogroup product is as follows:

[0068] ξ j =pj / ZRT

[0069] Where, p j Let Z be the pressure of the j-phase pseudogroup product; Z be the deviation between the actual and theoretical states of the j-phase pseudogroup product; R be the molar gas constant; T be the absolute temperature; the calculation rule for the velocity of the j-phase pseudogroup product is as follows:

[0070]

[0071] Where K is the penetration rate; k rj The relative permeability of the j-phase pseudo-group product; μ j Let be the viscosity of the j-phase pseudogroup product.

[0072] Step S30: Based on the temperature field prediction results and the cracking reaction model of each pseudogroup product, obtain the yield prediction curve of each pseudogroup product.

[0073] Specifically, after obtaining the geothermal temperature field prediction results, it is necessary to solve the problem based on the actual situation to obtain the correspondence between production and time. The production-time correspondence formula has been obtained above, and the production calculation rules follow the constructed pyrolysis reaction model. Under the set heating and pressure conditions, the production value of each pseudo-group product can be predicted. Based on the temperature field prediction results and the pyrolysis reaction model, the constructed mass conservation equation can be solved to obtain the time-production correspondence of each pseudo-group product under given mining area, well pattern, and heating parameter conditions. Based on the timeline, the production prediction curve can be obtained.

[0074] Step S40: Based on the predicted output curves of each proposed product group and the preset energy consumption curve, a net output energy curve is obtained, and the optimal heating time is obtained based on the net output energy curve.

[0075] Specifically, the energy consumption versus time curve is calculated based on heating power or injection flow rate and temperature; the cumulative heating value of each product is calculated based on its composition, and the net output energy is obtained by subtracting the energy consumption, thus obtaining the net output energy versus temperature curve and the optimal heating time range for the entire block. This invention determines the optimal heating time based on net present value (NPV). NPV refers to the difference between the present value of future cash inflows (revenue) and the present value of future cash outflows (expenditure), and is a fundamental indicator in project evaluation using the NPV method. Future cash inflows and outflows are converted to present value using the present value factor of the expected discount rate for each period before determining their NPV. This expected discount rate is determined based on the company's minimum rate of return on investment, representing the lowest acceptable limit for the company's investment. In the application embodiment of this invention, NPV is applied to the net output energy; the higher the net output energy, the greater the output revenue; a net output energy of 0 indicates that the investment return is just balanced. Therefore, the optimal heating time is the time when the maximum net output energy occurs, while the longest heating time is when the net output energy is 0.

[0076] Based on this, the mass conservation equations for each pseudo-group product are solved using the pyrolysis reaction model of each pseudo-group product to obtain the predicted relationship between the yield and time of each pseudo-group product. Based on this predicted relationship, the output prediction curves for each pseudo-group product are obtained. The process of fitting the output prediction curves of each pseudo-group product with a preset energy consumption curve to obtain a net output energy curve, and obtaining the optimal heating time based on the net output energy curve, includes: obtaining the cumulative heating value of each pseudo-group product based on the output prediction curves of each pseudo-group product.

[0077] The energy consumption value is obtained based on the preset energy consumption curve; the curve of net output energy changing with temperature is obtained based on the accumulated heating value and the energy consumption value, which serves as the net output energy curve; expressed as:

[0078] E net =E oil -E heat

[0079]

[0080] Among them, E net E represents the net energy value at time t. oil E represents the cumulative heating value at time t. heat Let be the energy consumption value at time t; n be the number of components; Q i E represents the cumulative yield of the i-th component at time t; oi Let be the calorific value of the i-th component. After obtaining the net output energy curve, the time corresponding to the highest point of the curve is the end time of the optimal heating time, and the time corresponding to the far end point of the intersection of the curve and the horizontal axis is the end time of the longest heating time.

[0081] In this embodiment of the invention, within a given mining area and with basic geological parameters, the method of this invention can predict the production of different components, the energy output and output of the entire mining area, and determine the optimal heating time interval for in-situ mining of oil shale or heating and upgrading of low-maturity shale oil, providing a basis for the preparation of on-site mining plans and engineering implementation.

[0082] Figure 2 This is a system structure diagram of an oil shale in-situ heating time prediction system provided in one embodiment of the present invention. Figure 2 As shown, this invention provides an in-situ heating time prediction system for oil shale, the system comprising: a data acquisition unit for acquiring experimental data on the heating process of the formation;

[0083] The processing unit is used to obtain temperature variation data of various pseudo-group products obtained based on preset component fitting rules, and to construct a pyrolysis reaction model for each pseudo-group product based on the temperature variation data of various pseudo-group products; the acquisition unit is also used to obtain sample parameters of core samples from the target layer; the processing unit is also used to: predict the geothermal temperature field under heating conditions based on the sample parameters; obtain the production prediction curve of each pseudo-group product based on the temperature field prediction results and the pyrolysis reaction model of each pseudo-group product; the solution unit is used to obtain the net output energy curve by fitting the production prediction curve of each pseudo-group product and a preset energy consumption curve, and obtain the optimal heating time based on the net output energy curve.

[0084] In one possible implementation, taking a mining area of ​​100ft x 100ft as an example, the oil shale thickness is 60ft, the molecular weight of the kerogen unit is 4800, and the density of the kerogen is 1.2g / cm³. 3 Shale density 1.8 g / cm³ 3 Kerogen concentration 0.026 bblmole / ft 3 Medium temperature 450℃, injection gas displacement 30000Nm 3 / d.

[0085] Based on component analysis and carbon distribution, the wet gas, light oil, and heavy oil are classified as C2-C5, C6-C14, and C14+, respectively, thus obtaining data on the variation of each component with temperature. Specific variation data are shown in Table 2.

[0086]

[0087]

[0088] Table 2 Data on the variation of each component with temperature

[0089] Solve for the output-time curve and calculate the net energy change. The results are as follows: Figure 3 As shown, the shortest heating time is 295 days and the longest heating time is 436 days.

[0090] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method for predicting the in-situ heating time of oil shale.

[0091] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0092] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0093] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. An in-situ heating time prediction method for oil shale, characterized by, The method includes: Experimental data on the heating process of the formation were collected. Based on the preset component fitting rules, data on the temperature change of various pseudo-group products were obtained. Based on the temperature change data of various pseudo-group products, a cracking reaction model of each pseudo-group product was constructed. Obtain the sample parameters of the core sample of the target layer, and predict the geothermal temperature field under heating conditions based on the sample parameters; Based on the temperature field prediction results and the pyrolysis reaction model of each pseudogroup product, the yield prediction curves of each pseudogroup product are obtained, including: Based on the pyrolysis reaction model of each pseudogroup product, the mass conservation equation of each pseudogroup product is solved to obtain the predicted relationship between the yield and time of each pseudogroup product. Based on this predicted relationship, the output prediction curve of each pseudogroup product is obtained. The net output energy curve is obtained by fitting the output prediction curve of each pseudogroup product with a preset energy consumption curve, and the optimal heating time is obtained based on the net output energy curve. This includes: obtaining the cumulative heating value of each pseudogroup product based on the output prediction curve of each pseudogroup product; obtaining the energy consumption value based on the preset energy consumption curve; obtaining the curve of net output energy changing with temperature based on the cumulative heating value and the energy consumption value, as the net output energy curve; expressed as: in, The net energy value at time t; The accumulated heating value at time t; Let t be the energy consumption value at time t; n be the number of components; Let be the cumulative output of the i-th component at time t; Let i be the calorific value of the i-th component; The net output energy curve is obtained by fitting the output prediction curve and the preset energy consumption curve of each proposed product group, and the optimal heating time is obtained based on the net output energy curve.

2. The method of claim 1, wherein, The experimental data for the formation heating process are: pyrolysis experimental data conducted under multiple pressure gradients at the actual formation heating rates under each pressure condition.

3. The method of claim 1, wherein, The pseudo-group products include: CH4, moisture, light oil, heavy oil, non-hydrocarbon components; The preset component fitting rules include: Hydrocarbon components that satisfy the preset property similarity rules and preset carbon number similarity rules are merged to obtain a class of pseudo-group products.

4. The method according to claim 1, characterized in that, The construction of pyrolysis reaction models for each pseudogroup product based on temperature variation data of multiple pseudogroup products includes: Based on the pyrolysis process of kerogen, first-order reaction models were constructed for the primary pyrolysis of kerogen and the secondary pyrolysis processes of each fraction produced by the primary pyrolysis of kerogen. The relationships are as follows: wherein k i is the rate constant for the cleavage reaction of the i-th component; Let i be the amount of the i-th component; Stoichiometric coefficient for the i-th component to yield the j-th component; Based on the first-order reaction model of each component, the pyrolysis reaction model of the corresponding pseudo-group product is obtained.

5. The method of claim 4, wherein, The rule for determining the pyrolysis reaction rate constants of each component is as follows: wherein, Ea is the experimental activation energy; R = 0.0821 L-atm / mol-K T is the absolute temperature; For the forward factor.

6. The method of claim 5, wherein, The method further includes: Fitting a first-order reaction model based on experimental data includes: The process of obtaining the experimental activation energy, the pre-exponential factor, and the stoichiometric coefficient of the i-th component to produce the j-th component is fitted, and the fitting rule is as follows: wherein, is the experimental value of each component under the predetermined condition; Theoretical values for each component under the preset conditions.

7. The method of claim 1, wherein, The sample parameters of the core sample from the target layer include: Kerogen weight percentage, unit molecular weight, kerogen density, rock density, char density, rock thermal conductivity, kerogen heat capacity, and kerogen porosity.

8. The method of claim 1, wherein, The prediction of the geothermal temperature field under heating conditions based on the sample parameters includes: By superimposing the temperature fields generated by different heaters, a geothermal temperature field prediction model is obtained. The model relationship is as follows: wherein is the internal energy of the fluid; The density of kerogen; The heat capacity of kerogen; k is the thermal conductivity; Enthalpy of the fluid; Based on the law of conservation of mass and the geothermal temperature field prediction model, a mass conservation equation is constructed for each group of products. The equation is as follows: Where i represents the i-th pseudo-group product; porosity of kerogen; Molar density of the j-phase pseudo-group product; Molar fraction of component i in the pseudo-ternary product of phase j; Vj is the jth phase analog product velocity; Rk is the reaction rate for the kth chemical reaction; System for the i component in the j phase pseudo-group product in the kth reaction.

9. The method of claim 8, wherein, The calculation rule for the molar density of the j-phase pseudogroup product is as follows: wherein, Pj is the j-phase pseudo-group product pressure; Z represents the deviation between the actual state and the theoretical state of the product of phase j; R = 0.0821 L-atm / mol-K T is the absolute temperature; The calculation rule for the product velocity of phase j is as follows: Where K is the penetration rate; Rj is the relative permeability of the jth pseudo-component; Viscosity of j-phase pseudo-group product.

10. An oil shale in-situ heating time prediction system, characterized by, The system is used to execute the oil shale in-situ heating time prediction method according to any one of claims 1-9, the system comprising: The data acquisition unit is used to collect experimental data on the heating process of the formation. The processing unit is used to obtain temperature variation data of various pseudo-group products obtained based on preset component fitting rules, and to construct a cracking reaction model of each pseudo-group product based on the temperature variation data of various pseudo-group products. The acquisition unit is also used to acquire sample parameters of the core sample from the target layer; The processing unit is also used for: Based on the sample parameters, predict the geothermal temperature field under heating conditions; Based on the temperature field prediction results and the pyrolysis reaction model of each pseudogroup product, the yield prediction curve of each pseudogroup product is obtained. The solution unit is used to obtain the net output energy curve by fitting the output prediction curve and the preset energy consumption curve of each proposed group of products, and to obtain the optimal heating time based on the net output energy curve.

11. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the oil shale in-situ heating time prediction method according to any one of claims 1-9.

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