Shale heating exploitation well pattern unit optimization method and device based on thermal field simulation

Abstract

The invention discloses a shale heating exploitation well pattern unit optimization method and device based on thermal field simulation. The method comprises the steps that according to a designed heating well pattern unit, an experimental heating well pattern unit is obtained through equal-proportion reduction; obtaining an experimental data set obtained by performing a thermal simulation experiment by utilizing the shale sample; based on the experimental data set, the corresponding relation among the vertical heating interval, the formation temperature and the actual heating time is established, and the ratio of the horizontal heating well interval to the vertical heating interval is determined according to different temperatures with the same distance from a heat source in the horizontal direction and the vertical direction; according to the minimum kerogen conversion temperature and the optimal heating time of the shale, the optimal vertical heating interval of the shale electric heating well pattern is determined through the corresponding relation; and determining the optimal horizontal heating well spacing according to the optimal vertical heating layer spacing, and reserving the structure of the designed heating well pattern unit to obtain the optimal electric heating well pattern unit. By means of the method, reasonable optimization of the in-situ exploitation electric heating well pattern unit of the shale rich in organic matter can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of shale oil development, and in particular to a method and device for optimizing a shale heating production well pattern unit based on thermal field simulation. Background Art

[0002] The existing well network deployment is mainly aimed at the development of underground oil and gas reservoirs, mainly including the development of conventional conglomerate oil and gas reservoirs, tight conglomerate oil and gas reservoirs and shale oil and gas reservoirs. The well network deployment forms mainly include inverted nine-point, five-point, linear, four-point and seven-point injection and production well networks. Most of the existing well network deployment methods are based on development parameters such as effective half-fracture length, horizontal section length, fracturing section spacing, number of perforation clusters per section and expected mining life. The oil and gas wells and water injection wells are evenly deployed, and the well spacing is relatively large, generally several hundred meters.

[0003] The existing methods for in-situ well network deployment for oil shale mining target the solid organic matter in the shallowly buried oil shale. Usually, a row of injection wells is deployed above the oil shale layer to heat it by injecting high-temperature fluid, and a row of production wells with the same number are deployed below to extract oil and gas. These methods are suitable for in-situ mining of thin layers of oil shale with a burial depth of less than 300 meters, but not for in-situ mining of thick layers of organic-rich shale with a burial depth of more than 1,000 meters. The physical properties of deeply buried shale reservoirs are very poor, and it is difficult to inject high-temperature fluids and achieve uniform heating. Summary of the invention

[0004] In order to enrich the process routes and increase the selection space, the embodiment of the present invention provides a shale heating production well network unit optimization method and device based on thermal field simulation, which can realize the reasonable optimization of the electric heating well network unit for in-situ mining of organic-rich shale based on thermal field simulation experiments.

[0005] In a first aspect, an embodiment of the present invention provides a shale heating production well pattern unit optimization method based on thermal field simulation, comprising:

[0006] According to the designed heating well pattern unit, the scaling ratio is determined in combination with the set experimental heat source distance, and the experimental heating well pattern unit is obtained by scaling down in proportion;

[0007] Acquire an experimental data set obtained by conducting a thermal simulation experiment using a shale sample, wherein the sample is deployed with a heat source group deployed according to the experimental heating well pattern unit, and temperature sensors are deployed in the horizontal and vertical directions respectively;

[0008] Based on the experimental data set, a first correspondence between the vertical heating layer distance, the formation temperature and the actual heating time is established, and the ratio of the horizontal heating well distance to the vertical heating layer distance is determined according to the different temperatures at the same distance from the heat source in the horizontal and vertical directions;

[0009] According to the minimum conversion temperature of kerogen and the optimal heating time of shale, the optimal vertical heating layer distance of the shale electric heating well pattern is determined by using the first corresponding relationship;

[0010] The optimal horizontal heating well spacing is determined according to the optimal vertical heating layer spacing and the ratio, and the optimal electric heating well network unit is obtained on the basis of retaining the designed heating well network unit structure.

[0011] In a second aspect, an embodiment of the present invention provides a method for arranging electric heating wells for shale mining based on thermal field simulation, comprising:

[0012] In the process of deploying the electric heating well network for in-situ shale mining, the heating well network units are deployed according to the optimal electric heating well network units determined by the above method.

[0013] In a third aspect, an embodiment of the present invention provides a shale heating production well pattern unit optimization device based on thermal field simulation, comprising:

[0014] The experimental heating well network unit design module is used to design the heating well network unit and determine the scaling ratio in combination with the set experimental heat source distance, and obtain the experimental heating well network unit by scaling down in proportion;

[0015] An experimental data set acquisition module is used to acquire an experimental data set obtained by performing a thermal simulation experiment using a shale sample, wherein the sample is deployed with a heat source group deployed according to the experimental heating well pattern unit, and temperature sensors are deployed in the horizontal and vertical directions respectively;

[0016] A corresponding relationship establishing module, used for establishing a first corresponding relationship between the vertical heating layer distance, the formation temperature and the actual heating time based on the experimental data set;

[0017] A module for determining the ratio of horizontal heating well spacing to vertical heating layer spacing, for determining the ratio of horizontal heating well spacing to vertical heating layer spacing based on the experimental data set and according to the different temperatures at the same distance from the heat source in the horizontal and vertical directions;

[0018] A vertical heating layer spacing optimization module is used to determine the optimal vertical heating layer spacing of the shale electric heating well pattern according to the minimum kerogen conversion temperature and the optimal heating time of the shale using the first corresponding relationship;

[0019] The electric heating well network unit optimization module is used to determine the optimal horizontal heating well spacing according to the optimal vertical heating layer spacing and the ratio, and obtain the optimal electric heating well network unit on the basis of retaining the designed heating well network unit structure.

[0020] In a fourth aspect, an embodiment of the present invention provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the above-mentioned shale heating production well network unit optimization method based on thermal field simulation is implemented.

[0021] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0022] The shale heating mining well network unit optimization method based on thermal field simulation provided by the embodiment of the present invention simulates the real heating well network unit through a proportional reduction experiment, obtains multiple groups of data through thermal simulation experiments; establishes a first correspondence between the vertical heating layer distance, the formation temperature and the actual heating time, and determines the ratio of the horizontal heating well distance to the vertical heating layer distance according to the different temperatures at the same distance from the heat source in the horizontal and vertical directions; determines the optimal vertical heating layer distance of the shale electric heating well network according to the minimum kerogen conversion temperature and the optimal heating time of the shale using the first correspondence; and finally obtains the optimal electric heating well network unit. The reasonable optimization of the electric heating well network unit for in-situ mining of organic-rich shale is achieved, which reduces production costs and improves production efficiency.

[0023] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0024] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0026] Figure 1 This is a flow chart of a shale heating production well pattern unit optimization method based on thermal field simulation in Example 1 of the present invention;

[0027] Figure 2 It is a schematic diagram of the sample and the experimental heating well pattern unit in Example 1 of the present invention;

[0028] Figure 3 A single well cross-sectional diagram for determining the thickness of the target layer for shale in-situ heating in Example 2 of the present invention;

[0029] Figure 4 This is a schematic diagram of a heating well pattern unit in Embodiment 2 of the present invention;

[0030] Figure 5 This is a schematic diagram of the deployment of the experimental heating well pattern unit and the temperature sensor in the second embodiment of the present invention;

[0031] Figure 6 is a cross-plot of the actual vertical distance corresponding to each heating time and the test temperature in the second embodiment of the present invention;

[0032] Figure 7 are cross-plots of heating time and coefficients a and b in Example 2 of the present invention;

[0033] Figure 8 A schematic structural diagram of a shale heating production well pattern unit optimization device based on thermal field simulation in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0035] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0036] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which the invention belongs. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0037] In order to solve the problem that the existing technology of in-situ oil shale mining well network deployment method is not suitable for in-situ mining of thick organic-rich shale with a burial depth of more than 1000 meters, the embodiment of the present invention provides a shale heating mining well network unit optimization method and device based on thermal field simulation, focusing on solving the optimization problem of electric heating well network units for in-situ mining of organic-rich shale.

[0038] Embodiment 1

[0039] Embodiment 1 of the present invention provides a shale heating mining well pattern unit optimization method based on thermal field simulation, and its process is as follows Figure 1 As shown, the following steps are included:

[0040] Step S11: Determine the scaling ratio based on the designed heating well network unit and the set experimental heat source distance, and obtain the experimental heating well network unit by scaling it down in proportion.

[0041] According to the thickness of the in-situ heating target layer in the study area, the structure of the three-dimensional heating well network unit is designed, usually a triangular well network unit consisting of 3 heating wells, a quadrilateral well network unit consisting of 4 heating wells, or a hexagonal well network consisting of 6 heating wells. The horizontal heating well spacing is set, that is, the initial horizontal heating well spacing is generally 5 to 20 meters, and the vertical heating layer spacing is generally 2 to 15 meters.

[0042] According to the heating well network unit structure, the horizontal heating well spacing and vertical heating layer spacing are set to determine the designed heating well network unit.

[0043] The experimental heat source distance can be the horizontal distance between the experimental heat sources or the vertical distance between the experimental heat sources. Generally, the horizontal distance between the experimental heat sources is 0.2 to 1 meter.

[0044] For example, the reduction ratio is determined based on the actual designed heating well pattern unit size and the horizontal distance between the experimental heat source, which is generally reduced to 0.05-0.1 of the actual size. The experimental well pattern unit is obtained by proportionally reducing the vertical and horizontal dimensions, thereby determining the location of the heating source.

[0045] Step S12: Acquire an experimental data set obtained by performing a thermal simulation experiment using a shale sample.

[0046] The heating wells in the designed heating well network unit are multiple horizontal wells deployed in parallel. The size of the sample is determined according to the size of the designed heating well network unit.

[0047] For details, see Figure 2 As shown, the shape of the sample is a cuboid, and the width of the sample is the width of the side parallel to the heat source. The length of the sample is greater than 2 times the length of the experimental heating well network unit, the height of the sample is greater than 2 times the height of the experimental heating well network unit, and the width of the sample is not less than the set experimental heat source distance. Figure 2 The red lines in the diagram indicate heat sources.

[0048] The sample is deployed with a heat source group according to the experimental heating well network unit, and temperature sensors are deployed in the horizontal and vertical directions respectively.

[0049] Furthermore, a plurality of first temperature sensors are deployed between two heat sources in the vertical direction of the sample, and at least one second temperature sensor is deployed between two heat sources in the horizontal direction. The plurality of first temperature sensors are arranged at equal intervals between the center of the heat source group and the first selected heat source, the first selected heat source and the center of the heat source group are on the same vertical line, and the number of the first temperature sensors is at least 6.

[0050] The thermal simulation experiment process is as follows:

[0051] (1) Heat the sample to 270°C using a heat source at a first heating rate.

[0052] The first heating rate is relatively fast, usually 50-100°C / day, and generally requires rapid heating for 3-5 days.

[0053] (2) Heat the sample to 600°C using a heat source at a second heating rate and keep the temperature constant.

[0054] The second heating rate is slower, usually 10-30°C / day.

[0055] After slowly heating the sample to a temperature of 600°C, the heating power is adjusted so that the temperature of the heat source does not change, and constant heating at 600°C is performed.

[0056] The constant temperature heating time is determined according to the distance between the heat sources. The closer the distance between the heat sources, the shorter the heating time. When the distance between the heat sources is within 0.5 meters, the heating time is generally 2 to 3 months; when the distance between the heat sources is within 0.5 to 1 meter, the heating time is generally 4 to 6 months.

[0057] (3) Collect data from each temperature sensor at set time intervals to obtain an experimental data set.

[0058] At least 6 sets of data are collected during the heating cycle. Furthermore, the interval for data collection is usually set to 10 to 30 days.

[0059] Each set of data in the obtained experimental data set includes a heating time and multiple pairs of first data pairs and at least one pair of second data pairs. Each pair of first data pairs includes the vertical distance between the first selected heat source and the first temperature sensor and the corresponding test temperature, and the second data pair includes the horizontal distance between the second selected heat source and the second temperature sensor and the corresponding test temperature.

[0060] The above-mentioned experimental samples can be real rock samples collected from surface outcrops or target layers near the surface.

[0061] The samples are wrapped with thermal insulation materials to prevent heat loss and simulate the heating environment of the formation as much as possible.

[0062] Step S13: Based on the experimental data set, a first correspondence between the vertical heating layer distance, the formation temperature and the actual heating time is established, and the ratio of the horizontal heating well distance to the vertical heating layer distance is determined according to the different temperatures at the same distance from the heat source in the horizontal and vertical directions.

[0063] 1. Establishing the first corresponding relationship between the vertical heating layer distance, the formation temperature and the actual heating time includes the following steps:

[0064] 1. According to the scaling ratio, the heating time and vertical distance in the experimental data set are converted into actual heating time and actual vertical distance, respectively, and the test temperature is taken as the formation temperature.

[0065] The heating time in the experimental data set is converted into the actual heating time according to formula (1):

[0066] ta=te / R (1)

[0067] In formula (1), ta is the actual heating time after conversion. The actual heating time here is different from the experimental heating time in the experimental simulation process. The actual heating time refers to the actual heating time in the in-situ conversion process obtained by proportional conversion; te is the experimental heating time, and R is the scaling ratio of the designed heating well network unit to the experimental heating well network unit.

[0068] The vertical distance in the experimental data set is converted into the actual vertical distance according to formula (2):

[0069] La=Le / R (2)

[0070] In formula (2), La is the actual vertical distance obtained by conversion, and Le is the vertical distance between the first selected heat source and the first temperature sensor in the rock sample.

[0071] The above time units are days and distance units are meters.

[0072] 2. Use the converted data set to establish a second correspondence between actual heating time, actual vertical distance and formation temperature.

[0073] For each actual heating time, according to the corresponding multiple pairs of data including the actual vertical distance and the formation temperature, the first fitting relationship between the actual vertical distance and the formation temperature is obtained by fitting:

[0074] La=a*T b (3)

[0075] In formula (3), T is the formation temperature, and a and b are coefficients determined by fitting.

[0076] According to each actual heating time and its corresponding coefficients a and b, the relationship between the actual heating time ta and the coefficient a and the relationship between the actual heating time ta and the coefficient b are fitted respectively:

[0077] a=a1*e a2*ta (4)

[0078] b=b1*ta+b2 (5)

[0079] In equations (4) and (5), a1, a2, b1 and b2 are coefficients determined by fitting.

[0080] Substitute the coefficients a1, a2, b1 and b2 determined by fitting into the above formula (3) to establish the second corresponding relationship between the actual heating time, the actual vertical distance and the formation temperature:

[0081] La=(a1*e a2*ta )*T (b1*ta+b2) (6).

[0082] 3. Based on the second corresponding relationship, the first corresponding relationship between the vertical heating layer distance, the formation temperature and the actual heating time is obtained.

[0083] According to the second correspondence between the actual heating time, the actual vertical distance and the formation temperature, the first correspondence between the vertical heating interval, the formation temperature and the actual heating time is obtained:

[0084] Lv=(a1*e a2*ta )*T (b1*ta+b2) (7)

[0085] In formula (7), Lv is the vertical heating layer distance.

[0086] Step S14: According to the minimum kerogen conversion temperature and the optimal heating time of the shale, the optimal vertical heating layer distance of the shale electric heating well network is determined using the first corresponding relationship.

[0087] According to the minimum conversion temperature of kerogen and the optimal heating time of shale, the optimal vertical heating layer distance of shale electric heating well pattern is determined using formula (7).

[0088] The minimum conversion temperature of kerogen can be determined by physical simulation experiments on in-situ conversion of shale in the study area, and the optimal heating time can be determined by economic evaluation research in the study area.

[0089] Step S15: Determine the optimal horizontal heating well spacing according to the optimal vertical heating layer spacing and the ratio, and obtain the optimal electric heating well network unit on the basis of retaining the designed heating well network unit structure.

[0090] Second, based on the experimental data set, the ratio of the horizontal heating well distance to the vertical heating layer distance is determined according to the different temperatures at the same distance from the heat source in the horizontal and vertical directions.

[0091] The optimized deployment method of the second temperature sensor in the sample can be that a second temperature sensor is deployed between two heat sources in the horizontal direction, the second temperature sensor is on the same horizontal line as the center of the heat source group and the second selected heat source, and the horizontal distance between the second temperature sensor and the second selected heat source is equal to the vertical distance between one of the first temperature sensors and the first selected heat source.

[0092] If the second temperature sensor is deployed in the above manner, determining the ratio of the horizontal heating well spacing to the vertical heating layer spacing may include:

[0093] For each set of data, the ratio of the test temperature corresponding to the horizontal distance to the test temperature corresponding to the vertical distance is determined, and the average value of the multiple ratios obtained is used as the ratio of the horizontal heating well distance to the vertical heating layer distance.

[0094] Optionally, when more than one second temperature sensor is deployed between two heat sources in the horizontal direction, the ratio of the horizontal heating well distance to the vertical heating layer distance can be obtained for the test temperature of each second temperature sensor through the corresponding test temperature of the first temperature sensor, and finally the optimal ratio of the horizontal heating well distance to the vertical heating layer distance can be obtained by averaging.

[0095] Furthermore, strictly speaking, the difference between the test temperature and the original temperature corresponding to the horizontal distance and the difference between the test temperature and the original temperature corresponding to the vertical distance should be determined first, and then the ratio of the two differences should be determined. Since the test temperature, that is, the heating temperature in the simulation experiment is very high, generally heated to 600°C, and the original temperature is room temperature, usually 20°C, there is little difference between directly determining the ratio of the two test temperatures and determining the ratio of the two temperature differences.

[0096] The shale heating mining well network unit optimization method based on thermal field simulation provided in the first embodiment of the present invention simulates the real heating well network unit through a proportionally reduced experimental method, obtains multiple sets of data through thermal simulation experiments; establishes a first correspondence between the vertical heating layer distance, the formation temperature and the actual heating time, and determines the ratio of the horizontal heating well distance to the vertical heating layer distance according to the different temperatures at the same distance from the heat source in the horizontal and vertical directions; determines the optimal vertical heating layer distance of the shale electric heating well network according to the minimum kerogen conversion temperature and the optimal heating time of the shale using the first correspondence; and finally obtains the optimal electric heating well network unit. The reasonable optimization of the electric heating well network unit for in-situ mining of organic-rich shale is achieved, which reduces production costs and improves production efficiency.

[0097] Embodiment 2

[0098] Embodiment 2 of the present invention provides a specific application of a shale heating production well pattern unit optimization method based on thermal field simulation, comprising the following steps:

[0099] 1. Obtain parameters such as TOC, S1, S2, etc. that characterize the heated geological body in the study area, and determine that the thickness of the heating target layer in the embodiment is 20 meters.

[0100] See also Figure 3 As shown, this is a single well profile diagram for determining the thickness of the target layer for shale in-situ heating.

[0101] 2. The designed heating well network unit is a quadrilateral consisting of 4 wells, with an initial vertical heating layer spacing of 3 meters and a horizontal heating well spacing of 6 meters.

[0102] See also Figure 4 shown.

[0103] 3. The reduction ratio is 0.1. The horizontal spacing of the heat sources in the experimental well network is 0.6 meters, and the vertical spacing of the heat source layers is 0.3 meters. Six temperature sensors are arranged at intervals of 0.05 meters from the center of the heating well network unit to the heat sources in the vertical direction; one temperature sensor is arranged from the center of the heating well network unit to the middle of the heat source in the horizontal direction, and the position is 0.05 meters horizontally to the right of the left heat source.

[0104] See also Figure 5 shown.

[0105] 4. The dimensions of the prepared sample are 1.2 meters in length and height and 0.6 meters in width.

[0106] 5. Heating procedure: The heat source is first quickly heated to 270°C for 3 days, and then slowly heated up, generally 30°C / day. When the temperature is heated to 600°C, the heating power is adjusted so that the heat source temperature does not change, and the constant temperature of 600°C is heated. The heating time is 162 days.

[0107] 6. Experimental data collection: Each temperature sensor collects temperature data once every 18 days, and the data is collected 9 times in total during the 162-day heating cycle (Table 1, Table 2).

[0108] Table 1 Experimental temperature data of 6 sensors in the vertical direction at different heating times

[0109]

[0110] Table 2 Experimental temperature data of a sensor in the vertical direction at different heating times

[0111]

[0112] 7. Data processing: R is 0.1. According to formulas (1) and (2), the geological extrapolation of the heating time and the distance between the temperature measuring point and the vertical heat source is performed (Table 3, Table 4).

[0113] Table 3 Geological extrapolation results of heating time

[0114]

[0115] Table 4 Geological extrapolation results of the vertical distance between the temperature measurement point and the heat source

[0116]

[0117] 8. Establishment of vertical heating layer distance optimization model:

[0118] 8.1 Make a cross plot of the test temperature and the distance of the temperature measuring point from the vertical heat source after geological extrapolation ( Figure 6 ), and using the power function model, the model coefficients a and b under different heating time conditions were calculated (Table 5).

[0119] Table 5 Coefficients a and b for different heating times

[0120] ta(day) 180 360 540 720 900 1080 1260 1440 1620 a 224147 4.00E+07 2.00E+12 6.00E+15 3.00E+18 3.00E+20 1.00E+22 1.00E+24 1.00E+27 b -2.329 -3.168 -4.977 -6.27 -7.234 -8.009 -8.558 -9.323 -10.39

[0121] 8.2 Based on the calculated coefficients a and b of different heating times and the heating time data after geological extrapolation, make a cross plot ( Figure 7 ), calculate the model coefficients a1=6714, a2=0.0297, b1=-0.0048, b2=-1.6804.

[0122] 8.3 Based on the coefficients a1, a2, b1, and b2 determined in 8.2, the following vertical heating layer distance model applicable to the study area is established:

[0123] L=4724.9*e 0.0341ta *T (-0.0055*ta-1.7278) (8)

[0124] 9. The lowest kerogen conversion temperature determined by the in-situ conversion physical simulation experiment in the study area is 330 degrees, the optimal heating time determined by the economic evaluation study is 4 years, and the optimal vertical heating layer distance is calculated to be 5.26 meters.

[0125] 10. Determine the ratio of vertical heating layer distance to horizontal heating well distance: Based on the temperature data of the monitoring points 0.05 m vertically downward from the top heat source and horizontally to the right from the left heat source in Tables 1 and 2, calculate the horizontal and vertical temperature ratios at different heating times, which is the ratio r of horizontal heating well distance to vertical heating layer distance (Table 6).

[0126] Table 6 Ratio of vertical heating layer spacing to horizontal heating well spacing at different monitoring times

[0127] Heating time after geological extrapolation ta (days) 180 360 540 720 900 1080 1260 1440 1620 Ratio of vertical heating layer distance to horizontal heating well distance r 1.33 1.26 1.16 1.13 1.11 1.10 1.10 1.09 1.09

[0128] 11. Optimization of horizontal heating well spacing: The average value ra of the ratio of horizontal heating well spacing to vertical heating layer spacing at different heating times determined in 10 is 1.15. Based on the vertical heating layer spacing of 5.26 meters determined in 9, the horizontal heating well spacing is calculated to be 12.10 meters.

[0129] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a method for arranging electric heating wells for shale mining based on thermal field simulation, comprising:

[0130] In the process of deploying the electric heating well network for in-situ shale mining, the heating well network units are deployed according to the optimal electric heating well network units determined by the above method.

[0131] Based on the inventive concept of the present invention, the embodiment of the present invention also provides a shale heating production well pattern unit optimization device based on thermal field simulation, the structure of the device is as follows: Figure 8 As shown, including:

[0132] The experimental heating well pattern unit design module 81 is used to design the heating well pattern unit and determine the scaling ratio in combination with the set experimental heat source distance, and obtain the experimental heating well pattern unit by scaling down in proportion;

[0133] The experimental data set acquisition module 82 is used to acquire an experimental data set obtained by performing a thermal simulation experiment using a shale sample, wherein the sample is deployed with a heat source group deployed according to the experimental heating well pattern unit, and temperature sensors are deployed in the horizontal and vertical directions respectively;

[0134] A corresponding relationship establishing module 83, used to establish a first corresponding relationship between the vertical heating layer distance, the formation temperature and the actual heating time based on the experimental data set;

[0135] A module 84 for determining the ratio of the horizontal heating well spacing to the vertical heating layer spacing, for determining the ratio of the horizontal heating well spacing to the vertical heating layer spacing based on the experimental data set and according to the difference in temperature at the same distance from the heat source in the horizontal and vertical directions;

[0136] A vertical heating layer spacing optimization module 85 is used to determine the optimal vertical heating layer spacing of the shale electric heating well pattern according to the minimum kerogen conversion temperature and the optimal heating time of the shale using the first corresponding relationship;

[0137] The electric heating well network unit optimization module 86 is used to determine the optimal horizontal heating well spacing according to the optimal vertical heating layer spacing and the ratio, and obtain the optimal electric heating well network unit on the basis of retaining the designed heating well network unit structure.

[0138] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0139] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the above-mentioned shale heating production well network unit optimization method based on thermal field simulation is implemented.

[0140] Unless otherwise specifically stated, terms such as processing, computing, calculating, determining, displaying, etc. may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, which operate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0141] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0142] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that an embodiment of the claimed subject matter requires more features than those set forth in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0143] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

[0144] The steps of the method or algorithm described in conjunction with the embodiments herein may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the storage medium may also be present in a user terminal as discrete components.

[0145] For software implementation, the techniques described in this application can be implemented with modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor. In the latter case, it is coupled to the processor in a communication manner via various means, which are well known in the art.

[0146] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments can be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to represent "non-exclusive or". The terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance.

Claims

1. A shale heating mining well pattern unit optimization method based on thermal field simulation, characterized in that: include: According to the designed heating well pattern unit, the scaling ratio is determined in combination with the set experimental heat source distance, and the experimental heating well pattern unit is obtained by scaling down in proportion; Acquire an experimental data set obtained by conducting a thermal simulation experiment using a shale sample, wherein the sample is deployed with a heat source group deployed according to the experimental heating well pattern unit, and temperature sensors are deployed in the horizontal and vertical directions respectively; Based on the experimental data set, a first correspondence between the vertical heating layer distance, the formation temperature and the actual heating time is established, and the ratio of the horizontal heating well distance to the vertical heating layer distance is determined according to the different temperatures at the same distance from the heat source in the horizontal and vertical directions; According to the minimum conversion temperature of kerogen and the optimal heating time of shale, the optimal vertical heating layer distance of the shale electric heating well pattern is determined by using the first corresponding relationship; The optimal horizontal heating well spacing is determined according to the optimal vertical heating layer spacing and the ratio, and the optimal electric heating well network unit is obtained on the basis of retaining the designed heating well network unit structure.

2. The method according to claim 1, characterized in that The designed heating well pattern unit is obtained by the following steps: According to the heating well network unit structure, the horizontal heating well spacing and vertical heating layer spacing are set to determine the designed heating well network unit.

3. The method according to claim 2, characterized in that The heating well pattern unit structure is a triangular well pattern unit composed of three heating wells, a quadrilateral well pattern unit composed of four heating wells, or a hexagonal well pattern unit composed of six heating wells; The horizontal heating well spacing is set to be 5 to 20 meters, and the vertical heating layer spacing is set to be 2 to 15 meters.

4. The method according to claim 3, characterized in that The set experimental heat source distance is the horizontal distance of the experimental heat source, which is 0.2 to 1 meter.

5. The method according to claim 1, characterized in that The heating wells in the designed heating well pattern unit are multiple horizontal wells deployed in parallel; The shape of the sample is a cuboid, and the width of the sample is the width of the side parallel to the heat source; The length of the sample is greater than 2 times the length of the experimental heating well network unit, the height of the sample is greater than 2 times the height of the set experimental heating well network unit, and the width of the sample is not less than the set experimental heat source distance.

6. The method according to claim 1, characterized in that The experimental data set is obtained in the following way: heating the sample to 270° C. using the heat source at a first heating rate; At a second heating rate, heating the sample to 600° C. using the heat source, and maintaining the temperature constant; The data of each temperature sensor is collected at set intervals to obtain the experimental data set.

7. The method according to claim 6, characterized in that The first heating rate is 50-100°C / day, and the second heating rate is 10-30°C / day; The setting interval is 10 to 30 days.

8. The method according to claim 1, characterized in that A plurality of first temperature sensors are arranged between two heat sources in the vertical direction of the sample, and at least one second temperature sensor is arranged between two heat sources in the horizontal direction; Each set of data in the experimental data set includes a heating time and multiple pairs of first data pairs and at least one pair of second data pairs, each pair of first data pairs includes the vertical distance between the first selected heat source and the first temperature sensor and the corresponding test temperature, and the second data pair includes the horizontal distance between the second selected heat source and the second temperature sensor and the corresponding test temperature.

9. The method according to claim 8, characterized in that Based on the experimental data set, a first correspondence between the vertical heating layer distance, the formation temperature and the actual heating time is established, including: According to the scaling ratio, the heating time and vertical distance in the experimental data set are converted into actual heating time and actual vertical distance, respectively, and the test temperature is used as the formation temperature; The converted data set is used to establish a second correspondence between the actual heating time, the actual vertical distance and the formation temperature; According to the second corresponding relationship, a first corresponding relationship among the vertical heating layer distance, the formation temperature and the actual heating time is obtained.

10. The method according to claim 9, characterized in that The step of converting the heating time and vertical distance in the experimental data set into actual heating time and actual vertical distance respectively comprises: The heating time in the experimental data set is converted into the actual heating time according to formula (1): ta=te / R (1) In formula (1), ta is the actual heating time, te is the experimental heating time, and R is the scaling ratio of the designed heating well pattern unit to the experimental heating well pattern unit; The vertical distance in the experimental data set is converted into the actual vertical distance according to formula (2): La=Le / R (2) In formula (2), La is the actual vertical distance obtained by conversion, and Le is the vertical distance between the first selected heat source and the first temperature sensor in the rock sample.

11. The method according to claim 10, characterized in that The method of using the converted data set to establish a second corresponding relationship between the actual heating time, the actual vertical distance and the formation temperature includes: For each actual heating time, according to the corresponding multiple pairs of data including the actual vertical distance and the formation temperature, the first fitting relationship between the actual vertical distance and the formation temperature is obtained by fitting: La*T b (3) In formula (3), T is the formation temperature, a and b are coefficients determined by fitting; According to each actual heating time and its corresponding coefficients a and b, the relationship between the actual heating time ta and the coefficient a and the relationship between the actual heating time ta and the coefficient b are fitted respectively: a=a1*e a2*ta (4) b=b1*ta+b2 (5) In formulas (4) and (5), a1, a2, b1, and b2 are coefficients determined by fitting; According to the coefficients a1, a2, b1 and b2 determined by fitting, the second corresponding relationship between the actual heating time, the actual vertical distance and the formation temperature is established: At=(a1*e a2*ta )*T (b1*ta+b2) (6)。 12. The method according to claim 11, characterized in that The first corresponding relationship between the vertical heating layer distance, the formation temperature and the actual heating time includes: The first corresponding relationship between the vertical heating interval, formation temperature and actual heating time is obtained as follows: Lv=(a1*e a2*ta )*T (b1*ta+b2) (7) In formula (7), Lv is the vertical heating layer distance.

13. The method according to claim 8, characterized in that The multiple first temperature sensors are arranged at equal intervals between the center of the heat source group and the first selected heat source, the first selected heat source is on the same vertical line as the center, and the number of the first temperature sensors is at least 6.

14. The method according to claim 8, characterized in that A second temperature sensor is deployed between the two heat sources in the horizontal direction. The second temperature sensor is on the same horizontal line as the center of the heat source group and the second selected heat source. The horizontal distance between the second temperature sensor and the second selected heat source is equal to the vertical distance between one of the first temperature sensors and the first selected heat source. Correspondingly, Determining the ratio of the horizontal heating well spacing to the vertical heating layer spacing includes: For each set of data, the ratio of the test temperature corresponding to the horizontal distance to the test temperature corresponding to the vertical distance is determined, and the average value of the multiple ratios obtained is used as the ratio of the horizontal heating well distance to the vertical heating layer distance.

15. A method for arranging electric heating wells for shale mining based on thermal field simulation, characterized in that: include: In the process of deploying the electric heating well network for in-situ mining of shale, the heating well network units are deployed according to the optimized electric heating well network units determined by any one of the methods described in claims 1 to 14.

16. A shale heating mining well pattern unit optimization device based on thermal field simulation, characterized in that: include: The experimental heating well network unit design module is used to design the heating well network unit and determine the scaling ratio in combination with the set experimental heat source distance, and obtain the experimental heating well network unit by scaling down in proportion; An experimental data set acquisition module is used to acquire an experimental data set obtained by performing a thermal simulation experiment using a shale sample, wherein the sample is deployed with a heat source group deployed according to the experimental heating well pattern unit, and temperature sensors are deployed in the horizontal and vertical directions respectively; A corresponding relationship establishing module, used for establishing a first corresponding relationship between the vertical heating layer distance, the formation temperature and the actual heating time based on the experimental data set; A module for determining the ratio of horizontal heating well spacing to vertical heating layer spacing, for determining the ratio of horizontal heating well spacing to vertical heating layer spacing based on the experimental data set and according to the different temperatures at the same distance from the heat source in the horizontal and vertical directions; A vertical heating layer spacing optimization module is used to determine the optimal vertical heating layer spacing of the shale electric heating well pattern according to the minimum kerogen conversion temperature and the optimal heating time of the shale using the first corresponding relationship; The electric heating well network unit optimization module is used to determine the optimal horizontal heating well spacing according to the optimal vertical heating layer spacing and the ratio, and obtain the optimal electric heating well network unit on the basis of retaining the designed heating well network unit structure.

17. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by the processor, the shale heating production well pattern unit optimization method based on thermal field simulation described in any one of claims 1 to 14 is implemented.

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