Carbon dioxide fracturing-cyclic steam recovery method, apparatus, device, medium, and program
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-11-27
- Publication Date
- 2026-05-29
Smart Images
Figure CN122106519A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of oil and gas development technology, and in particular to a carbon dioxide fracturing-huff and puff extraction method, apparatus, equipment, medium and procedure. Background Technology
[0002] In oil and gas extraction, carbon dioxide fracturing-huff and puff is a commonly used method, offering advantages such as reducing crude oil viscosity, replenishing formation energy, and removing near-wellbore contamination. However, due to the presence of various factors affecting single-well productivity to varying degrees, it is difficult to determine suitable construction data, resulting in low single-well productivity. Summary of the Invention
[0003] This disclosure provides a carbon dioxide fracturing-huff and puff method, apparatus, equipment, medium, and procedure to improve single-well productivity.
[0004] In a first aspect, this disclosure provides a carbon dioxide fracturing-huff-puff extraction method, comprising:
[0005] Obtain sample data from the sample well during the extraction process;
[0006] Based on the sample data, determine the main controlling factors affecting the sample well's production capacity;
[0007] Based on the aforementioned key control factors and reference data related to the target well, a genetic iteration mechanism is established with the goal of maximizing production capacity.
[0008] Based on the genetic iteration mechanism, the target data of the target well during the production process is obtained through iteration. The target data includes integrated construction data of the fracturing stage and the injection and release production stage.
[0009] In some embodiments, prior to acquiring sample data from the sample well during the extraction process, the method further includes:
[0010] Establish a sample well database containing the sample data;
[0011] The acquisition of sample data from the sample well during the extraction process includes:
[0012] The sample data is obtained from the sample well database.
[0013] In some embodiments, determining the key factors affecting the sample productivity of the sample well based on the sample data includes:
[0014] Based on the sample data, the first factor with the first correlation weight in the first percentage among the influencing factors is selected. The first correlation weight is the correlation weight between each of the influencing factors and the sample production capacity.
[0015] The second factor with the second association weight in the first factor in each group is selected by grouping and filtering. The second association weight is the association weight between each first factor in each group and the sample production capacity.
[0016] The controlling factors are selected by group screening, and the third correlation weight of each of the second factors is in the top third percentage. The third correlation weight is the correlation weight between each of the second factors and the sample production capacity.
[0017] In some embodiments, the step of filtering second factors within each group whose second association weight ranks in the top second percentage of each of the first factors includes:
[0018] Based on the fourth correlation weight between each pair of factors in each of the first factors, each of the first factors is grouped to obtain multiple groups of influencing factors.
[0019] Within each group of influencing factors, each of the second factors is selected based on the sample data corresponding to the first factor; and / or,
[0020] The method of filtering the controlling factors by grouping and selecting those whose third correlation weight ranks in the top third percentage among the second factors includes:
[0021] Each of the second factors is combined into one group of influencing factors;
[0022] Based on the sample data corresponding to the second factor, each of the main controlling factors is selected.
[0023] In some embodiments, each of the first association weights, each of the second association weights, each of the third association weights, and each of the fourth association weights are determined based on an improved multi-level grey relational analysis method.
[0024] In some embodiments, establishing a genetic iteration mechanism with the goal of maximizing production capacity based on the controlling factors and reference data related to the target well includes:
[0025] Based on the aforementioned key control factors, a capacity forecasting model is established;
[0026] The aforementioned capacity prediction model is incorporated into a genetic optimization system;
[0027] Based on the reference data, establish an initial population;
[0028] Based on the genetic optimization system and the initial population, the genetic iteration mechanism is established with the goal of maximizing productivity.
[0029] Secondly, this disclosure provides a carbon dioxide fracturing-huff-puff extraction apparatus, comprising:
[0030] The acquisition module is configured to acquire sample data from the sample well during the extraction process.
[0031] The determination module is configured to determine the key factors affecting the sample productivity of the sample well based on the sample data.
[0032] A module is configured to establish a genetic iteration mechanism with the goal of maximizing production capacity, based on the main control factors and reference data related to the target well.
[0033] An iterative module is configured to iterate based on the genetic iterative mechanism to obtain target data of the target well during the production process, the target data including integrated construction data of the fracturing stage and the injection production stage.
[0034] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.
[0035] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.
[0036] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described in the above aspects.
[0037] The carbon dioxide fracturing-puffing production method, apparatus, equipment, media and program disclosed herein obtain target data through a genetic iterative mechanism established with the goal of maximizing production capacity. The integrated construction data of the fracturing stage and the puffing production stage can maximize the production capacity of a single well, thereby improving the production capacity of a single well.
[0038] This disclosure establishes a genetic iteration mechanism by combining the main controlling factors affecting single-well production with reference data related to the target well. The target data obtained by the genetic iteration mechanism through iteration can correspond to the target well, thereby further improving the single-well productivity.
[0039] This disclosure uses sample data to determine the main controlling factors, avoiding interference from non-controlling factors on single-well productivity, thereby improving the reliability of oil and gas extraction and reducing the complexity of determining construction data. Attached Figure Description
[0040] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:
[0041] Figure 1 This is a schematic flowchart of a carbon dioxide fracturing-huff and puff extraction method provided in an embodiment of this disclosure.
[0042] Figure 2 This is a schematic diagram of a hierarchical structure of influencing factors provided in an embodiment of this disclosure.
[0043] Figure 3a This is a schematic diagram illustrating the relationship between porosity and sample productivity, provided as an embodiment of the present disclosure.
[0044] Figure 3b This is a schematic diagram illustrating the relationship between Young's modulus and sample production capacity, provided as an embodiment of this disclosure.
[0045] Figure 3c This is a schematic diagram illustrating the relationship between the amount of fracturing fluid per meter and the sample production capacity, provided as an embodiment of this disclosure.
[0046] Figure 3d This is a schematic diagram illustrating the relationship between carbon dioxide injection volume and sample production capacity, provided as an embodiment of this disclosure.
[0047] Figure 3e This is a schematic diagram illustrating the relationship between the amount of fracturing fluid per meter and the average fracture length, provided as an embodiment of this disclosure.
[0048] Figure 3f This is a schematic diagram illustrating the relationship between principal horizontal stress difference and crack modification volume, provided as an embodiment of the present disclosure.
[0049] Figure 4a This is a schematic diagram illustrating the first correlation weight between geological property influencing factors and sample productivity, provided in an embodiment of this disclosure.
[0050] Figure 4b This is a schematic diagram illustrating the first correlation weight between fluid-related influencing factors and sample production capacity, provided in an embodiment of this disclosure.
[0051] Figure 4c This is a schematic diagram illustrating the first correlation weight between rock mechanics-related influencing factors and sample productivity, provided in an embodiment of this disclosure.
[0052] Figure 4d This is a schematic diagram illustrating the first correlation weight between fracturing fracture influencing factors and sample production capacity, as provided in an embodiment of this disclosure.
[0053] Figure 4e This is a schematic diagram illustrating the first correlation weight between fracturing construction influencing factors and sample production capacity, provided in an embodiment of this disclosure.
[0054] Figure 4fThis is a schematic diagram illustrating the first correlation weight between post-pressure well drainage influencing factors and sample production capacity, provided in an embodiment of this disclosure.
[0055] Figure 4g This is a schematic diagram illustrating the first correlation weight between carbon dioxide throughput and extraction influencing factors and sample production capacity, provided in an embodiment of this disclosure.
[0056] Figure 5 This is a schematic diagram illustrating a third correlation weight between a second factor and sample production capacity, provided as an embodiment of the present disclosure.
[0057] Figure 6a This is a schematic diagram of an initial population provided in an embodiment of this disclosure.
[0058] Figure 6b This is a schematic diagram of a population that has been iterated 100 times, as provided in an embodiment of this disclosure.
[0059] Figure 6c This is a schematic diagram of a population that has been iterated 150 times, as provided in an embodiment of this disclosure.
[0060] Figure 6d This is a schematic diagram of a population that has been iterated 200 times, as provided in an embodiment of this disclosure.
[0061] Figure 7 This is a block diagram of a carbon dioxide fracturing-through-puff extraction apparatus provided in an embodiment of the present disclosure.
[0062] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation
[0063] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.
[0064] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0065] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0066] In oil and gas extraction, CO2 fracturing-huff-puff is a commonly used method due to its advantages such as reducing crude oil viscosity, replenishing formation energy, and removing near-wellbore contamination. The CO2 fracturing-huff-puff process includes CO2 fracturing operations, post-fracturing shut-in, flowback, depletion-stage development, CO2 injection-shut-in, and well opening for production. These stages are interconnected, and developing an integrated optimization plan requires careful consideration of each stage to maximize extraction efficiency. Because many factors influence the CO2 fracturing-huff-puff process, and the correlation between these factors varies across stages, the relationships between them are complex. Existing numerical simulation and physical simulation methods involve a large workload and struggle to accurately simulate the actual extraction process and clearly identify the relationships between various factors and production capacity, as well as between different factors themselves, resulting in low single-well production capacity.
[0067] Example 1
[0068] Figure 1 This is a schematic flowchart of a carbon dioxide fracturing-huff-puff extraction method provided as an embodiment of this disclosure. Figure 1 As shown, a carbon dioxide fracturing-huff-puff extraction method includes:
[0069] Step S100: Obtain sample data from the sample well during the mining process.
[0070] It should be noted that, due to the existence of sample data during the extraction process of the sample wells, and the correlation characteristics between individual wells, the sample data obtained during the extraction process are used to obtain historical data on influencing factors and sample production capacity, in order to obtain optimal construction data during the extraction process of the target well.
[0071] Figure 2 This is a schematic diagram illustrating a hierarchical structure of influencing factors provided in an embodiment of this disclosure. For example... Figure 2 As shown, the sample data includes data on multiple categories of influencing factors related to production capacity. For example, the sample data includes geological property data, fluid data, rock mechanics data, fracturing operation data, fracturing fracture data, post-fracturing well flowback data, and carbon dioxide huff and puff production data. Each category of influencing factors includes multiple factors. For example, geological property factors include pressure coefficient, formation pressure, formation temperature, reservoir depth, effective reservoir thickness, natural fracture density, clay content, saturation, permeability, and porosity. G1 to G nG Representing different geological physical properties, nG represents the number of geological physical properties. L1 to L nL Representing different fluid factors, nL represents the number of fluid factors. R1 to R nR Representing different rock mechanics factors, nR represents the number of rock mechanics factors. T1 to T nT This represents different fracturing operation factors, where nT represents the number of fracturing operation factors. f1 to f nf This represents different fracturing fracture factors, where nf represents the number of fracturing fracture factors. B1 to B nB This represents different post-pressure well flowback factors, where nB represents the number of post-pressure well flowback factors. S1 to S nS This represents different carbon dioxide throughput and extraction factors, where nS represents the number of carbon dioxide throughput and extraction factors.
[0072] According to the carbon dioxide fracturing-huff-puff production method provided in this disclosure, before obtaining sample data from the sample well during the production process in step S100, the carbon dioxide fracturing-huff-puff production method further includes:
[0073] Establish a sample well database containing sample data.
[0074] Step S100, obtaining sample data from the sample well during the extraction process, includes:
[0075] Sample data is obtained from the sample well database.
[0076] The carbon dioxide fracturing-huff and puff extraction method disclosed herein establishes a sample well database containing sample data before acquiring sample data from sample wells during the extraction process, thus giving the sample data a certain degree of structure. Sample data is then obtained from the sample well database to obtain sample production capacity data and corresponding influencing factor data. By establishing the sample well database to obtain sample data, it is easier to systematically analyze the relationship between various influencing factors and sample production capacity, as well as the relationships between various influencing factors, to maximize single-well production capacity, thereby improving the reliability of oil and gas extraction.
[0077] It should be noted that before establishing the sample well database containing sample data in the above steps, it is necessary to filter and integrate the relevant data of the sample wells to obtain the sample data.
[0078] Step S200: Based on the sample data, determine the main controlling factors affecting the sample well's production capacity.
[0079] It should be noted that, since there are multiple influencing factors and their impact on sample production capacity varies, the main controlling factors affecting the sample production capacity of the sample wells are determined based on the sample data.
[0080] According to the carbon dioxide fracturing-huff and puff production method provided in this disclosure, step S200, which involves determining the main controlling factors affecting the sample well's production capacity based on sample data, includes:
[0081] Based on the sample data, the first factor with the first association weight in the first percentage among all influencing factors is selected. The first association weight is the association weight between each influencing factor and the sample production capacity.
[0082] The second factors with the second association weight in each group are selected by grouping and screening. The second association weight is the association weight between each first factor in each group and the sample production capacity.
[0083] The control factors with the third association weight in the top three percent of each second factor are selected by group screening. The third association weight is the association weight between each second factor and the sample production capacity.
[0084] The carbon dioxide fracturing-huff and puff method disclosed herein, based on sample data, screens the first factors whose first correlation weight ranks in the top first percentage among all influencing factors for preliminary screening. Since some influencing factors interfere with each other, a group screening method is used to screen the second factors within each group whose second correlation weight ranks in the top second percentage among the first factors, thus performing a secondary screening to reduce interference. Because some influencing factors after group screening are not primary controlling factors, a combined group screening method is used to screen the primary controlling factors whose third correlation weight ranks in the top third percentage among the second factors, thus finally screening all influencing factors. By screening influencing factors through a grouping followed by a combined grouping method, the primary controlling factors with a significant impact on production capacity can be identified while overcoming interference, thereby improving the reliability of the determination of primary controlling factors.
[0085] It should be noted that the step above, in which the first factor with the highest correlation weight among the influencing factors is selected based on the sample data, can be achieved using an improved multi-level grey relational analysis method.
[0086] 1. Normalize the sample capacity and various influencing factors:
[0087]
[0088] Among them, Q j This represents the sample productivity value of the j-th sample, in m units. 3 / d, m represents the number of sample wells. Let represent the normalized production capacity value of the j-th sample, which is dimensionless.
[0089]
[0090] Where, x ij This represents the value of the i-th factor in the j-th sample. This represents the normalized value of the i-th factor in the j-th sample.
[0091] 2. Determine the first grey relational coefficient between each influencing factor and the sample production capacity:
[0092]
[0093] 3. Determine the first grey relational degree based on the first grey relational coefficient:
[0094]
[0095] 4. Based on the first grey relational degree, establish the first contrast matrix:
[0096]
[0097] Among them, g1 to g n This represents the weighted quantitative scale value of the impact of each influencing factor on the sample's production capacity, where n represents the number of influencing factors.
[0098] 5. Based on the first comparison matrix, construct s first random comparison matrices G1 to G2. s The positions of the elements in the first random comparison matrix are different from those in the first comparison matrix.
[0099] 6. Determine the consistency index of the first random comparison matrix:
[0100]
[0101] in, G represents the k-th first random comparison matrix. k Consistency index, λ max,k G is the k-th first random comparison matrix. k The largest eigenvalue.
[0102] 7. Determine the average random consistency index of the first random comparison matrix:
[0103]
[0104] 8. Determine the consistency ratio based on the consistency index and the average random consistency index:
[0105]
[0106] 9. If the consistency ratio is less than the first preset value, the consistency test is passed. The first association weight is determined based on the first random comparison matrix.
[0107] 10. Select the first factor whose first correlation weight ranks in the top percentage among all influencing factors.
[0108] It should be noted that the first percentage can range from 60% to 95%. The first percentage can be 70%, 75%, or 80%. The first preset value can range from 0.005 to 0.1. The first preset value can be 0.01, 0.03, 0.05, etc.
[0109] According to the carbon dioxide fracturing-huff and puff extraction method provided in this disclosure, the step of screening the second factors with the second correlation weight ranking in the top second percentage of each first factor within each group by grouping and screening includes:
[0110] Based on the fourth correlation weight between each pair of factors in each first factor, the first factors are grouped to obtain multiple groups of influencing factors.
[0111] Within each group of influencing factors, each second factor is selected based on the sample data corresponding to the first factor.
[0112] The carbon dioxide fracturing-huff and puff method disclosed herein utilizes a fourth correlation weight to reflect the degree of influence between pairs of factors. Based on the fourth correlation weights between each pair of first factors, multiple influencing factor groups are obtained by grouping each first factor. Within each influencing factor group, second factors with high impact on sample production capacity are selected based on the sample data corresponding to the first factor. By grouping the first factors according to the fourth correlation weights to select second factors, the interference between pairs of factors on sample production capacity can be reduced, thus improving the reliability of the determination of the controlling factors.
[0113] It should be noted that the above steps, which group each first factor according to the fourth correlation weight between each pair of factors to obtain multiple influencing factor groups, can be achieved using an improved multi-level grey relational analysis method. For example, the number of influencing factor groups can be two, divided into influencing factor groups for the fracturing stage and the throughput production stage.
[0114] 1. After normalizing the sample capacity and each influencing factor, determine the second grey relational coefficient between each pair of influencing factors:
[0115]
[0116] 2. Determine the second grey relational degree based on the second grey relational coefficient:
[0117]
[0118] 3. Based on the second grey relational degree, establish the second contrast matrix:
[0119]
[0120] 4. Based on the second comparison matrix, construct multiple second random comparison matrices.
[0121] 5. If the consistency ratio corresponding to the second comparison matrix is less than the first preset value, the consistency test is passed.
[0122] 6. Determine the second association weight based on the second random comparison matrix.
[0123] 7. Assign the first factor whose second association weight is less than the second preset value to the same influencing factor group.
[0124] It should be noted that the second percentage can range from 60% to 95%. The second percentage can be 70%, 75%, or 80%. The second preset value can range from 0.35 to 0.65. The second preset value can be 0.4, 0.5, 0.6, etc.
[0125] It should be noted that in the above steps, within each group of influencing factors, each second factor is screened based on the sample data corresponding to the first factor. This can be achieved using the same improved multi-level grey relational analysis method as in the above steps, which screens the first factor with the first association weight in the first percentage of each influencing factor based on the sample data. This will not be elaborated here.
[0126] According to the carbon dioxide fracturing-huff and puff extraction method provided in this disclosure, the main controlling factors in the above steps, which are selected by grouping and screening in a manner that ranks among the top three percent in terms of the third correlation weight of each second factor, include:
[0127] Each of the second factors is combined into one group of influencing factors.
[0128] Based on the sample data corresponding to the second factor, each controlling factor is selected.
[0129] The carbon dioxide fracturing-huff and puff extraction method disclosed herein requires a comprehensive screening of the second factors, as each second factor falls into a different influencing factor group. The second factors are then grouped together into a single influencing factor group. Based on the sample data corresponding to each second factor, key controlling factors are screened to comprehensively screen the second factors. By screening the second factors as a whole after grouping, key controlling factors can be identified in an integrated manner, thereby improving the reliability of key controlling factor determination.
[0130] It should be noted that the above steps of screening each controlling factor based on the sample data corresponding to the second factor can be achieved using the same improved multi-level grey relational analysis method as the above steps of screening the first factor with the first correlation weight in the first percentage of each influencing factor based on the sample data. This will not be elaborated here.
[0131] It should be noted that the value of the third percentage can range from 60% to 95%. The third percentage can be 70%, 75%, or 80%.
[0132] According to the carbon dioxide fracturing-huffing extraction method provided in this disclosure, each of the first, second, third, and fourth correlation weights is determined based on the improved multi-level grey relational analysis method.
[0133] The carbon dioxide fracturing-huff and puff method disclosed herein uses an improved multi-level grey relational analysis method to determine the first, second, third, and fourth correlation weights. This method can ensure the reliability of the correlation weights through consistency checks, thereby improving the reliability of the determination of the main control factors.
[0134] Step S300: Based on the main control factors and reference data related to the target well, establish a genetic iteration mechanism with the goal of maximizing production capacity.
[0135] It should be noted that since both the controlling factors and the reference data related to the target well are relevant to the construction of the target well, and the goal during construction is to maximize production capacity, it is necessary to combine the controlling factors and reference data to obtain the optimal construction data. Based on the controlling factors and the reference data related to the target well, a genetic iteration mechanism is established with the goal of maximizing production capacity. This mechanism can calculate the construction data that maximizes production capacity. The reference data includes data on multiple sets of influencing factors defined based on the field construction and production experience of the target well. For example, the reference data includes multiple sets of defined fracturing construction data, post-fracturing well flowback data, and carbon dioxide huff and puff production data.
[0136] According to the carbon dioxide fracturing-huff-puff production method provided in this disclosure, step S300 establishes a genetic iteration mechanism with the goal of maximizing production capacity based on the main controlling factors and reference data related to the target well, including:
[0137] A capacity forecasting model is established based on the main controlling factors.
[0138] Introduce the capacity prediction model into the genetic optimization system.
[0139] Based on the reference data, an initial population was established.
[0140] Based on the genetic optimization system and the initial population, a genetic iteration mechanism is established with the goal of maximizing productivity.
[0141] The carbon dioxide fracturing-huff-puff extraction method disclosed herein, since the main controlling factor is the factor that maximizes the impact on production capacity, establishes a production capacity prediction model based on the main controlling factor to predict production capacity. The production capacity prediction model is then introduced into a genetic optimization system to iteratively optimize the construction data. An initial population is established based on reference data as the foundation of the genetic optimization system. Based on the genetic optimization system and the initial population, a genetic iteration mechanism is established with the goal of maximizing production capacity. This mechanism obtains the construction data that maximizes production capacity. By establishing a genetic iteration mechanism based on the production capacity prediction model and the genetic optimization system, construction data that maximizes production capacity can be iteratively generated, thereby improving single-well production capacity.
[0142] It should be noted that the step of establishing a production capacity prediction model based on the main controlling factors mentioned above can be interpreted as establishing a production capacity prediction model for integrated carbon dioxide fracturing-huff and puff production using an improved nonlinear polynomial regression method based on the main controlling factors. The nonlinear polynomial is expressed as:
[0143]
[0144] The difference between actual capacity and sample capacity is expressed as:
[0145]
[0146] Since a smaller capacity difference results in higher fitting accuracy, setting the capacity difference to 0 yields the following formula:
[0147]
[0148] Then, the coefficients are solved to obtain the production capacity prediction model.
[0149] It should be noted that, prior to establishing a genetic iteration mechanism based on the genetic optimization system and initial population with the goal of maximizing production capacity in the above steps, the carbon dioxide fracturing-huff-puff extraction method also includes:
[0150] The relevant parameters in the genetic optimization system are defined based on the main controlling factors and the geological, physical, fluid, and rock mechanical data from the target well construction site.
[0151] The above steps, which establish a genetic iteration mechanism with the goal of maximizing production capacity based on the genetic optimization system and the initial population, can be used to establish a genetic iteration mechanism with the goal of maximizing production capacity based on the genetic optimization system and the initial population after parameter definition.
[0152] Step S400: Iterate based on the genetic iteration mechanism to obtain target data of the target well during the production process. The target data includes integrated construction data of the fracturing stage and the injection and release production stage.
[0153] It should be noted that the data in the genetic iteration mechanism is not optimal at the initial stage; optimal data is required. The genetic iteration mechanism is used to iterate and obtain target data for the target well during the production process, which is then used for construction. It should be noted that the target data includes integrated construction data for both the fracturing and flowback phases. For example, the target data includes optimized fracturing construction data, post-fracturing flowback data, and carbon dioxide flowback data.
[0154] It should be noted that the step S400, which iterates based on the genetic iteration mechanism to obtain the target data of the target well during the mining process, can be obtained by iterating multiple times based on the genetic iteration mechanism until convergence.
[0155] It should be noted that after obtaining the target data of the target well during the production process through iteration based on the genetic iteration mechanism in step S400, the impact of single-well CO2 fracturing-huff-puff production on the overall geological properties, fluid factors, and rock mechanics factors of the block can be analyzed. Data from the next well is then imported for genetic iteration optimization, intelligently formulating integrated CO2 fracturing-huff-puff production construction data for the entire target reservoir block level by level and well by well. Here, the target well is a single well within the target reservoir block.
[0156] Example 2
[0157] Based on the above embodiments, this embodiment provides an application example.
[0158] For the X shale oilfield block, the carbon dioxide fracturing-huff and puff production method provided in this disclosure is applied to optimize the integrated construction data of the fracturing stage and the huff and puff production stage of well W1.
[0159] The X shale oil block has reservoir permeability of 0.0005 mD to 0.03 mD, porosity of 8% to 15%, oil saturation of 42% to 69%, maximum horizontal principal stress of 66.8 to 72.5 MPa, minimum horizontal principal stress of 55.9 to 62.3 MPa, Young's modulus of 27.2 to 41.9 GPa, Poisson's ratio of 0.19 to 0.23, crude oil viscosity of 2.1 to 17.7 mPa·s, and crude oil density of 0.75 to 0.95 g / cm³. 3 Data on geological factors, fluid factors, rock mechanics factors, fracturing operation factors, and CO2 injection production factors were collected and analyzed from 32 wells in the X shale oil block. After filtering and integrating the geological property data, fluid data, rock mechanics data, fracturing operation data, fracturing fracture data, post-fracturing flowback data, depletion-type development data, and CO2 injection production data, a sample well database was established. Single-factor analysis was used to statistically analyze the correlation between each influencing factor and the sample well productivity, as well as among the influencing factors themselves. Figures 3a to 3f This diagram illustrates the correlation between some influencing factors and sample production capacity, as well as among some influencing factors, as provided in embodiments of this disclosure. Figures 3a to 3f As shown, it is preliminarily believed that there is a significant correlation between porosity, fracturing fluid volume per meter, carbon dioxide injection volume and daily oil production, and a significant correlation between fracturing fluid volume per meter and average half-fracture length, horizontal principal stress difference and fracture modification volume.
[0160] All influencing factors were categorized into geological physical properties, fluid dynamics, rock mechanics, fracturing operation, fracturing fractures, post-fracturing well runoff, and carbon dioxide injection / release factors, and a multi-factor hierarchical structure model was established. An improved multi-level grey relational analysis method was applied to calculate the first correlation weight between different influencing factors and sample production capacity. Figures 4a to 4g This diagram illustrates a first correlation weight (grey correlation weight) between influencing factors and sample production capacity, provided in an embodiment of this disclosure. Figures 4a to 4gAs shown, the top 75% of high-proportion influencing factors were selected as the primary factors. These included: geological physical properties (porosity, permeability, oil saturation, natural fracture density, effective reservoir thickness, formation pressure, and pressure coefficient); fluid factors (crude oil viscosity, formation water salinity, crude oil volumetric expansion coefficient, and dissolved gas-oil ratio); rock mechanics factors (minimum horizontal principal stress, horizontal principal stress difference, brittleness index, and reservoir stress difference); fracturing operation factors (fracturing fluid volume per meter, carbon dioxide ratio, operation displacement, and sand ratio); fracturing fracture factors (fracturing fracture parameters) (number of fracturing stages, total number of fracturing clusters, average fracture half-length, fracture height, and fracture stimulation volume); post-fracturing well flowback factors (well shut-in time, flowback time, and flowback nozzle diameter); and carbon dioxide huff and puff production factors (huff and puff cycle, carbon dioxide huff and puff well shut-in time, single-cycle recovery time, and total carbon dioxide huff and puff injection volume). An improved multi-level grey relational analysis method was used to calculate the fourth correlation weights among the different influencing factors. The fourth correlation weights among the different influencing factors are shown in Table 1.
[0161] Table 1:
[0162]
[0163] According to the fourth correlation weight, the fourth correlation weights between rock mechanics factors, fracturing construction factors, and fracturing fracture factors are all higher than 0.4, indicating strong interference among influencing factors. Therefore, they should be avoided in grouping. Geological properties, fluid factors, rock mechanics factors, fracturing construction factors, and post-fracturing well flowback factors are grouped into one group (Group I). Geological properties, fluid factors, fracturing fracture factors, post-fracturing well flowback factors, and carbon dioxide injection and production factors are grouped into another group (Group II). The improved multi-level grey relational analysis method is used to calculate the second correlation weights between each influencing factor in different groups and the sample production capacity. The second correlation weights of Group I are shown in Table 2.
[0164] Table 2
[0165]
[0166] The second association weights for group II are shown in Table 3:
[0167] Table 3
[0168]
[0169] The top 80% of the primary factors in Groups I and II were selected as the secondary factors. The secondary factors included: porosity, permeability, oil saturation, natural fracture density, effective reservoir thickness, formation pressure, crude oil viscosity, crude oil volumetric expansion coefficient, horizontal principal stress difference, brittleness index, reservoir-interstitial stress difference, fracturing fluid volume per meter, carbon dioxide ratio, operational displacement, number of fracturing stages, total number of fracturing clusters, average fracture half-length, fracture height, fracture stimulation volume, well shut-in time, flowback time, flowback nozzle diameter, huff and puff cycle, carbon dioxide huff and puff well shut-in time, single-cycle recovery time, and total carbon dioxide huff and puff injection volume.
[0170] An improved multi-level grey relational analysis method was applied to calculate the third association weights between different second factors and sample production capacity. Figure 5 This diagram illustrates a third correlation weight (weight) between a second factor and sample production capacity, as provided in an embodiment of this disclosure. Figure 5 As shown, the top 90% of the second-ranked factors were selected as the controlling factors. These controlling factors included porosity, permeability, oil saturation, natural fracture density, effective reservoir thickness, formation pressure, crude oil viscosity, horizontal principal stress difference, brittleness index, fracturing fluid volume per meter, carbon dioxide ratio, operational flow rate, number of fracturing stages, total number of fracturing clusters, average fracture half-length, fracture height, fracture stimulation volume, well shut-in time, flowback nozzle diameter, huff and puff cycle, carbon dioxide huff and puff well shut-in time, single-cycle recovery time, and total carbon dioxide huff and puff injection volume. An improved nonlinear polynomial regression method was applied to establish a production capacity prediction model, incorporating a genetic optimization system. Relevant parameters were defined based on the controlling factor selection results and field geological property data, fluid data, and rock mechanics data. Multiple sets of carbon dioxide fracturing operation data, post-fracturing well shut-in flowback data, and carbon dioxide huff and puff production data were defined based on field construction and development experience to establish an initial population. A genetic iteration mechanism was established with the goal of maximizing production capacity. After 200 iterations, the population converged to obtain the target data. Figures 6a to 6d This is a schematic diagram of an iterative process provided in an embodiment of this disclosure. The target data is shown in Table 4:
[0171] Table 4
[0172] Controlling factors Target data <![CDATA[Single - meter fracturing fluid volume (m 3 / m)]]> 15.7 Carbon dioxide percentage (%) 33.2 <![CDATA[Construction displacement (m 3 / min)]]> 9.5 Well closure time (days) 10 Return nozzle diameter (mm) 10 Throughput cycle (cycle) 8 Carbon dioxide inhalation and discharge well closure time (days) 12 Single-cycle mining time (days) 105 <![CDATA[Total carbon dioxide huff and puff injection volume (10 4 m 3 )]]> 3082
[0173] Example 3
[0174] Figure 7 This is a block diagram of a carbon dioxide fracturing-huff-puff extraction apparatus provided as an embodiment of this disclosure. Figure 7 As shown, a carbon dioxide fracturing-huff-puff extraction device includes an acquisition module 100, a determination module 200, an establishment module 300, and an iteration module 400, wherein...
[0175] The acquisition module 100 is configured to acquire sample data from the sample well during the mining process.
[0176] The determination module 200 is configured to determine the key control factors affecting the sample well productivity based on sample data.
[0177] Module 300 is configured to establish a genetic iteration mechanism with the goal of maximizing production capacity, based on the main control factors and reference data related to the target well.
[0178] The iteration module 400 is configured to perform iterations based on a genetic iteration mechanism to obtain target data of the target well during the production process. The target data includes integrated construction data of the fracturing stage and the injection production stage.
[0179] The carbon dioxide fracturing-huffing extraction apparatus provided in this disclosure also includes:
[0180] The module is configured to create a sample well database containing sample data.
[0181] According to the carbon dioxide fracturing-huff-puff extraction apparatus provided in this disclosure, the acquisition module 100 is configured to:
[0182] Sample data is obtained from the sample well database.
[0183] Based on the carbon dioxide fracturing-huff and puff extraction apparatus provided in this disclosure, module 200 is configured for:
[0184] Based on the sample data, the first factor with the first association weight in the first percentage among all influencing factors is selected. The first association weight is the association weight between each influencing factor and the sample production capacity.
[0185] The second factors with the second association weight in each group are selected by grouping and screening. The second association weight is the association weight between each first factor in each group and the sample production capacity.
[0186] The control factors with the third association weight in the top three percent of each second factor are selected by group screening. The third association weight is the association weight between each second factor and the sample production capacity.
[0187] Based on the carbon dioxide fracturing-huff and puff extraction apparatus provided in this disclosure, module 200 is configured for:
[0188] Based on the fourth correlation weight between each pair of factors in each first factor, the first factors are grouped to obtain multiple groups of influencing factors.
[0189] Within each group of influencing factors, each second factor is selected based on the sample data corresponding to the first factor.
[0190] Based on the carbon dioxide fracturing-huff and puff extraction apparatus provided in this disclosure, module 200 is configured for:
[0191] Each of the second factors is combined into one group of influencing factors.
[0192] Based on the sample data corresponding to the second factor, each controlling factor is selected.
[0193] According to the carbon dioxide fracturing-huff and puff extraction apparatus provided in this disclosure, module 300 is configured to:
[0194] A capacity forecasting model is established based on the main controlling factors.
[0195] Introduce the capacity prediction model into the genetic optimization system.
[0196] Based on the reference data, an initial population was established.
[0197] Based on the genetic optimization system and the initial population, a genetic iteration mechanism is established with the goal of maximizing productivity.
[0198] Example 4
[0199] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.
[0200] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiments.
[0201] In some embodiments of this example, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above embodiments.
[0202] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.
[0203] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, Blu-ray discs, etc.).
[0204] Computer-readable storage media may also store at least one computer-executable program, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.
[0205] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).
[0206] The processor can communicate with external devices via the I / O bus through wired or wireless networks.
[0207] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.
[0208] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0209] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0210] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.
Claims
1. A carbon dioxide fracturing-huff-puff extraction method, characterized in that, include: Obtain sample data from the sample well during the extraction process; Based on the sample data, determine the main controlling factors affecting the sample well's production capacity; Based on the aforementioned key control factors and reference data related to the target well, a genetic iteration mechanism is established with the goal of maximizing production capacity. Based on the genetic iteration mechanism, the target data of the target well during the production process is obtained through iteration. The target data includes integrated construction data of the fracturing stage and the injection and release production stage.
2. The method according to claim 1, characterized in that, Prior to obtaining sample data from the sample well during the extraction process, the method further includes: Establish a sample well database containing the sample data; The acquisition of sample data from the sample well during the extraction process includes: The sample data is obtained from the sample well database.
3. The method according to claim 1, characterized in that, The step of determining the main controlling factors affecting the sample productivity of the sample well based on the sample data includes: Based on the sample data, the first factor with the first correlation weight in the first percentage among the influencing factors is selected. The first correlation weight is the correlation weight between each of the influencing factors and the sample production capacity. The second factor with the second association weight in the first factor in each group is selected by grouping and filtering. The second association weight is the association weight between each first factor in each group and the sample production capacity. The controlling factors are selected by group screening, and the third correlation weight of each of the second factors is in the top third percentage. The third correlation weight is the correlation weight between each of the second factors and the sample production capacity.
4. The method according to claim 3, characterized in that, The method of filtering by grouping includes selecting the second factor whose second association weight ranks in the top second percentage among the first factors in each group, including: Based on the fourth correlation weight between each pair of factors in each of the first factors, each of the first factors is grouped to obtain multiple groups of influencing factors. Within each group of influencing factors, each of the second factors is selected based on the sample data corresponding to the first factor; and / or, The method of filtering the controlling factors by grouping and selecting those whose third correlation weight ranks in the top third percentage among the second factors includes: Each of the second factors is combined into one group of influencing factors; Based on the sample data corresponding to the second factor, each of the main controlling factors is selected.
5. The method according to claim 4, characterized in that, Each of the first association weights, each of the second association weights, each of the third association weights, and each of the fourth association weights are determined based on the improved multi-level grey relational analysis method.
6. The method according to any one of claims 1 to 5, characterized in that, The establishment of a genetic iteration mechanism based on the main controlling factors and reference data related to the target well, with the goal of maximizing production capacity, includes: Based on the aforementioned key control factors, a capacity forecasting model is established; The aforementioned capacity prediction model is incorporated into a genetic optimization system; Based on the reference data, establish an initial population; Based on the genetic optimization system and the initial population, the genetic iteration mechanism is established with the goal of maximizing productivity.
7. A carbon dioxide fracturing-huff and puff extraction device, characterized in that, include: The acquisition module is configured to acquire sample data from the sample well during the extraction process. The determination module is configured to determine the key factors affecting the sample productivity of the sample well based on the sample data. A module is configured to establish a genetic iteration mechanism with the goal of maximizing production capacity, based on the main control factors and reference data related to the target well. An iterative module is configured to iterate based on the genetic iterative mechanism to obtain target data of the target well during the production process, the target data including integrated construction data of the fracturing stage and the injection production stage.
8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.