Method and device for designing particle size of fracturing filling proppant for loose sandstone stratum

Abstract

The invention relates to the technical field of oil and gas well exploitation, and provides a particle size design method and device for a fracturing filling propping agent of a loose sandstone stratum. The method comprises the following steps: acquiring geological information of a target loose sandstone stratum and particle size combinations of various proppants; according to the geological information, establishing a simulated stratum environment corresponding to the target loose sandstone stratum by using a fracture flow guide instrument; determining the forward flow conductivity and the reverse flow conductivity of each proppant particle size combination under a plurality of preset target closing pressures by utilizing the simulated formation environment; and determining the optimal particle size combination of the proppant according to the forward flow conductivity and the reverse flow conductivity. According to the embodiment of the invention, the particle size combination of the propping agent suitable for fracturing of the loose sandstone stratum can be efficiently and accurately selected, and the test and simulation cost is reduced.

Description

Technical Field

[0001] This specification relates to the technical field of oil and gas well exploitation, and particularly relates to a method and device for designing the particle size of proppants for fracturing and packing in unconsolidated sandstone formations. Background Art

[0002] Unconsolidated sandstone reservoirs are usually integral reservoirs with good hydrocarbon accumulation, large reservoir reserves, and wide distribution. During the exploitation life cycle of unconsolidated sandstone reservoirs, sand production is one of the common problems encountered. The sand production in heavy oil reservoirs in unconsolidated sandstone is even more serious. After sand production from the oil formation, on the one hand, sand grains may deposit in the well to form a sand plug, reducing the oil well production. On the other hand, it will increase the downhole operation workload of sand cleaning, wear equipment, and sand jam downhole tools. In some wells with severe sand production, it may also cause wellbore collapse, damage casing and liner, or cause sand burial of the oil layer, resulting in the shutdown of the oil well, greatly increasing the difficulty and cost of oil production.

[0003] For unconsolidated sandstone reservoirs, how to prevent formation sand production and improve the productivity of oil wells in unconsolidated sandstone reservoirs has become a very crucial issue. Conventional sand control technologies only perform single shielding, plugging, and even consolidation. While suppressing sand production, they often significantly reduce the single-well production of oil wells and cannot balance production increase and sand control. The greatest advantage of the fracturing sand control technology is precisely the balance between production and sand production. It can effectively reduce and control oil formation sand production and greatly increase the production of fractured oil wells. Specifically, fracturing and packing in unconsolidated sandstone formations aims to create fractures by high-pressure injection of fracturing fluid and use proppants to strengthen the formation to improve oil and gas exploration and exploitation. This technology injects fracturing fluid at high pressure to fracture the formation rock to form fractures and injects proppants into the fractures to prevent the fractures from closing and promote oil and gas flow. However, when performing fracturing and packing in unconsolidated sandstone formations, multiple influencing factors need to be considered, including proppant particle size selection, fracture conductivity evaluation, and optimization of proppant particle size combination. There are technical problems such as the following:

[0004] 1. The diversity of unconsolidated sandstone formations leads to the complexity of proppant selection. Currently, there is a lack of a general method to select the most suitable type and particle size range of proppants for specific geological conditions. The inappropriate selection of proppants may result in poor support effect or even blockage of fractures.

[0005] 2. Finding the best proppant particle size combination to improve formation conductivity and stability is a complex problem. The proportion, type, and injection method of proppants need to be comprehensively considered, and a large number of tests and simulations are required to find the best combination. There is a lack of an effective method to reduce the number of tests and simulations to reduce the test and simulation costs.

[0006] 3. The geological conditions of loose sandstone formations are complex and changeable, with great uncertainty, which increases the difficulty of technology application and requires more geological data and research to reduce risks. Summary of the invention

[0007] In view of the fact that the geological conditions of loose sandstone formations are complex and changeable, there is a large uncertainty in the current fracturing and filling in loose sandstone formations, and the high cost of experiments and simulations required to find the optimal proppant particle size combination, this scheme is proposed to overcome the above problems or at least partially solve the above problems.

[0008] On the one hand, the purpose of some embodiments of this specification is to provide a method for designing particle size of proppant for fracturing filling of loose sandstone formations, the method comprising:

[0009] Obtain geological information of the target loose sandstone formation and various proppant particle size combinations;

[0010] According to the geological information, using a fracture diffusor, a simulated formation environment corresponding to the target loose sandstone formation is established;

[0011] Using the simulated formation environment, determining the forward flow conductivity and reverse flow conductivity of each proppant particle size combination under a plurality of preset target closure pressures;

[0012] According to the forward flow conductivity and the reverse flow conductivity, an optimal proppant particle size combination is determined.

[0013] Furthermore, the geological information includes sandstone porosity, formation permeability, formation temperature and rock mechanical properties; the plurality of proppant particle size combinations include a plurality of different proportions of large-size proppants, medium-size proppants and small-size proppants.

[0014] Further, based on the geological information, a fracture diffusor is used to establish a simulated formation environment corresponding to the target loose sandstone formation, including:

[0015] Determine the target formation environment consistent with the sandstone porosity, formation permeability, formation temperature and rock mechanical properties in the geological information of the target unconsolidated sandstone formation;

[0016] In the target formation environment, a fracture diversion instrument is used to create fractures so that the size, shape and inclination of the fractures meet the preset requirements, thereby establishing the simulated formation environment.

[0017] Further, the forward flow conductivity and reverse flow conductivity of each proppant particle size combination under a plurality of preset target closure pressures are determined by using the simulated formation environment, including:

[0018] In the simulated formation environment, proppants and formation sand corresponding to different proppant particle size combinations are input into the fracture diversion instrument according to each target closure pressure, and fracturing filling is performed under preset instrument parameters;

[0019] Real-time monitoring of analog measurement parameters during the fracturing filling process;

[0020] Determining the forward flow conductivity of each proppant particle size combination at each target closure pressure based on the instrument parameters and the simulated measurement parameters;

[0021] The plugging gas is injected into the outlet of the fracture diversion instrument according to the preset injection time, and the reverse diversion capacity of each proppant particle size combination at each target closure pressure is calculated.

[0022] Furthermore, the instrument parameters include a closing pressure range and a closing pressure change rate; and the simulation measurement parameters include fluid viscosity, fluid displacement and a pressure difference at a port of the guide chamber.

[0023] Furthermore, according to the instrument parameters and the simulated measurement parameters, the forward flow conduction capacity of each proppant particle size combination when the current closure pressure is equal to each target closure pressure is determined using the following formula:

[0024]

[0025] Where, f represents the target closing pressure, and the current closing pressure is obtained from the closing pressure range and the closing pressure change rate, Q f represents the forward flow capacity of the proppant particle size combination when the target closure pressure is f, k f represents the permeability of the proppant particle size combination when the target closure pressure is f, μ represents the fluid viscosity, Q represents the fluid displacement, L represents the length between pressure ports, Δp represents the pressure difference in the fluid flow direction, and A represents the support cross-sectional area of ​​the diversion chamber.

[0026] Furthermore, the plugging gas is injected into the outlet of the fracture diversion instrument according to the preset injection time, and the reverse diversion capacity of each proppant particle size combination under each target closure pressure is calculated, including:

[0027] Obtain the final closure pressure after completing the fracturing filling process for each proppant particle size combination at each target closure pressure;

[0028] The final closing pressure is maintained unchanged, and according to the preset injection time, a blockage-clearing gas is injected into the water outlet of the fracture diversion instrument to clear the formation sand blocked in the diversion chamber;

[0029] Real-time monitoring and updating of simulated measurement parameters during the process of clearing the formation sand blocked in the diversion chamber;

[0030] The reverse flow conductivity of each proppant particle size combination at each target closure pressure is determined based on the instrument parameters and the updated simulation measurement parameters.

[0031] Further, according to the forward flow guiding capacity and the reverse flow guiding capacity, determining the optimal proppant particle size combination includes:

[0032] Determining a corresponding flow conductivity difference according to the forward flow conductivity and the reverse flow conductivity;

[0033] According to the forward flow conductivity and the flow conductivity difference, an objective function and constraint conditions for determining an optimal proppant particle size combination are established;

[0034] Under the constraints, an evolutionary algorithm is used to find the optimal solution of the objective function, so as to determine the best proppant particle size combination according to the optimal solution.

[0035] Further, according to the forward flow guiding capacity and the reverse flow guiding capacity, determining the corresponding flow guiding capacity difference includes:

[0036] According to the forward flow conductivity and reverse flow conductivity of each proppant particle size combination at each target closure pressure, a forward flow conductivity curve and a reverse flow conductivity curve are drawn respectively;

[0037] The flow conductivity difference is determined according to the forward flow conductivity curve and the reverse flow conductivity curve.

[0038] Furthermore, according to the forward flow conductivity and the difference between the forward and reverse flow conductivity, an objective function and constraint conditions for determining the optimal proppant particle size combination are established, including:

[0039] Taking the ratio parameter of the small-size proppant, the medium-size proppant and the large-size proppant in the proppant particle size combination as a decision variable, and determining the constraint condition according to the ratio parameter;

[0040] The objective function is constructed by taking the maximization of the forward flow guiding capacity and the minimization of the flow guiding capacity difference as the optimization objectives.

[0041] Furthermore, after determining the corresponding flow conduction capacity difference according to the forward flow conduction capacity and the reverse flow conduction capacity, the method further includes:

[0042] Determine the target influencing factors corresponding to the forward flow guiding capacity and the reverse flow guiding capacity by using a grey correlation algorithm;

[0043] The proppant particle size combination is optimized according to the target influencing factors.

[0044] Furthermore, the target influencing factors corresponding to the forward flow guiding capacity and the reverse flow guiding capacity are determined by using a grey correlation algorithm, including:

[0045] Obtaining evaluation indicators corresponding to proppant particle size combinations;

[0046] Determine the flow conductivity evaluation system and evaluation index weights of the multilayer structure according to the evaluation index;

[0047] According to the conductivity evaluation system and the evaluation index weights, an initial conductivity decision matrix is ​​established, and a reference vector is established using the forward conductivity of each proppant particle size combination;

[0048] Standardizing each evaluation index in the initial decision matrix to obtain a standardized grey relational matrix;

[0049] According to the standardized grey correlation matrix, the grey correlation coefficient is determined;

[0050] Establishing a grey correlation coefficient matrix according to the grey correlation coefficient;

[0051] Determine the correlation between each evaluation index and the reference vector using the grey correlation coefficient matrix;

[0052] The relevances are sorted from large to small, and the target influencing factors are determined according to the evaluation indicators corresponding to the top several relevances.

[0053] Furthermore, according to the evaluation index, a flow conductivity evaluation system of the multilayer structure and evaluation index weights are determined, including:

[0054] According to the evaluation index, a conductivity evaluation system including a target layer, a criterion layer, a sub-criterion layer and a proppant particle size combination layer is constructed; wherein the target layer represents the forward conductivity, the criterion layer represents the type of evaluation index, the sub-criterion layer represents the evaluation index, and the proppant particle size combination layer represents the proppant particle size combination type;

[0055] Compare the importance of the evaluation indicators in the sub-criteria layer relative to the criterion layer in pairs to obtain the importance of each evaluation indicator;

[0056] Determine the relative weights between the evaluation indicators using the importance levels;

[0057] The evaluation indicator weights are determined according to the relative weights.

[0058] Further, optimizing the proppant particle size combination according to the target influencing factors includes:

[0059] According to the target influencing factors, the forward flow conductivity of the current proppant particle size combination is evaluated to obtain a corresponding evaluation score;

[0060] According to the evaluation scores, the current proppant particle size combination is screened to optimize the proppant particle size combination.

[0061] On the other hand, some embodiments of the present specification also provide a device for designing particle size of proppant for fracturing filling of loose sandstone formations, the device comprising:

[0062] A receiving module is used to obtain geological information of the target loose sandstone formation and various proppant particle size combinations;

[0063] A simulation module, for establishing a simulated formation environment corresponding to the target loose sandstone formation using a fracture diffusor according to the geological information;

[0064] A testing module, for determining the forward flow conductivity and reverse flow conductivity of each proppant particle size combination under a plurality of preset target closure pressures by using the simulated formation environment;

[0065] The determination module is used to determine the best proppant particle size combination according to the forward flow guiding capacity and the reverse flow guiding capacity.

[0066] On the other hand, some embodiments of the present specification further provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, the instructions of the above method are executed.

[0067] On the other hand, some embodiments of the present specification further provide a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor of a computer device, the instructions of the above method are executed.

[0068] One or more technical solutions provided by some embodiments of this specification have at least the following technical effects:

[0069] The embodiments of the present specification obtain geological information of the target loose sandstone formation and a variety of proppant particle size combinations, and then use a fracture diversion instrument to create fractures based on the geological information to establish a simulated formation environment, thereby achieving efficient and accurate construction of specific geological conditions, and testing the forward and reverse conduction capabilities of each proppant particle size combination under multiple target closure pressures in the simulated formation environment, thereby efficiently and accurately selecting the best proppant particle size combination suitable for the target loose sandstone formation based on the forward and reverse conduction capabilities, thereby reducing the cost of testing and simulation.

[0070] The above description is only an overview of the technical solutions of some embodiments of this specification. In order to more clearly understand the technical means of some embodiments of this specification, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of some embodiments of this specification more obvious and easy to understand, the specific implementation methods of some embodiments of this specification are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate some embodiments of this specification or technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or prior art descriptions. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0072] Figure 1 A schematic diagram of an implementation system of a method for designing particle size of proppant for fracturing and filling of loose sandstone formations in some embodiments of this specification is shown;

[0073] Figure 2 A flow chart showing a method for designing particle size of proppant for fracturing filling of loose sandstone formations in some embodiments of this specification;

[0074] Figure 3 A schematic diagram of the steps for establishing a simulated formation environment in some embodiments of this specification;

[0075] Figure 4 A schematic diagram of the steps for determining the forward flow guiding capacity and the reverse flow guiding capacity in some embodiments of this specification;

[0076] Figure 5 A schematic diagram of the steps for determining the reverse flow guiding capability in some embodiments of this specification;

[0077] Figure 6 A schematic diagram of the steps for solving the optimal proppant particle size combination in some embodiments of this specification;

[0078] Figure 7 A schematic diagram of the steps for calculating the conductivity difference in some embodiments of this specification;

[0079] Figure 8 A schematic diagram of the steps of establishing an objective function and constraint conditions in some embodiments of this specification;

[0080] Fig. 9 This is a schematic diagram of the steps of using evolutionary algorithms to solve the optimal solution of the objective function in some embodiments of this specification;

[0081] Fig.10 A schematic diagram of the steps for optimizing proppant particle size combinations in some embodiments of this specification;

[0082] Fig.11 This is a schematic diagram of the steps of determining target influencing factors using the grey correlation algorithm in some embodiments of this specification;

[0083] Fig.12A schematic diagram of the steps for determining the flow conductivity evaluation system and evaluation index weights of a multilayer structure in some embodiments of this specification;

[0084] Fig.13 This is a schematic diagram of the steps for optimizing proppant particle size combinations according to target influencing factors in some embodiments of this specification;

[0085] Fig.14 This is a schematic diagram of the structure of a device for designing the particle size of proppant for fracturing and filling loose sandstone formations in some embodiments of this specification;

[0086] Fig.15 This is a schematic diagram of the computer device structure provided in some embodiments of this specification.

[0087] [Description of Reference Numerals]

[0088] 101. Terminal;

[0089] 102. Server;

[0090] 1401, receiving module;

[0091] 1402, simulation module;

[0092] 1403. Test module;

[0093] 1404. Determine module;

[0094] 1502. Computer equipment;

[0095] 1504. Processor;

[0096] 1506. Memory;

[0097] 1508, driving mechanism;

[0098] 1510, input / output interface;

[0099] 1512. Input devices;

[0100] 1514. Output device;

[0101] 1516. Presentation equipment;

[0102] 1518. Graphical user interface;

[0103] 1520, network interface;

[0104] 1522. Communication link;

[0105] 1524. Communication bus. DETAILED DESCRIPTION

[0106] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in some embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on some embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0107] It should be noted that the terms "first", "second", etc. in the specification and claims of this document and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of this document described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of relevant laws and regulations.

[0108] like Figure 1 The schematic diagram of the implementation system of a method for designing the particle size of proppant for fracturing and filling of loose sandstone formations according to an embodiment of the present invention is shown, which may include: a terminal 101 and a server 102, wherein the terminal 101 and the server 102 communicate with each other through a network, and the network may include a local area network (LAN), a wide area network (WAN), the Internet or a combination thereof, and is connected to a website, a user device (such as a computing device) and a back-end system. A staff member may send a request for designing the particle size of proppant for fracturing and filling of loose sandstone formations to the server 102 through the terminal 101, and after receiving the request for designing the particle size of proppant for fracturing and filling of loose sandstone formations, the server 102 calls the geological information of the target loose sandstone formation in the database and a plurality of proppant particle size combinations for calculation and processing, obtains the calculation result, and sends the calculation result to the terminal 101, so that the staff member processes the business according to the calculation result.

[0109] In the embodiments of this specification, the server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN, Content Delivery Network), and big data and artificial intelligence platforms.

[0110] In an optional embodiment, the terminal 101 may include but is not limited to electronic devices such as self-service terminal devices, desktop computers, tablet computers, laptop computers, smart wearable devices, etc. Optionally, the operating system running on the electronic device may include but is not limited to Android system, IOS system, Linux, Windows, etc. Of course, the terminal 101 is not limited to the above-mentioned electronic devices with a certain entity, and it can also be software running in the above-mentioned electronic devices.

[0111] In addition, it should be noted that Figure 1 What is shown is only an application environment provided by the present disclosure. In actual application, multiple terminals 101 may be included, and this specification does not limit this.

[0112] Figure 2 It is a flow chart of a method for designing particle size of proppant for fracturing filling of loose sandstone formations provided by an embodiment of the present invention. This specification provides method operation steps as described in the embodiment or flow chart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the system or device product is executed in practice, it can be executed in the order of the method shown in the embodiment or the accompanying drawings or in parallel. Specifically, Figure 2 As shown, applied to the above-mentioned server side, the method may include:

[0113] S201: Obtain geological information of the target loose sandstone formation and various proppant particle size combinations;

[0114] S202: Based on the geological information, using a fracture diffusor, establishing a simulated formation environment corresponding to the target loose sandstone formation;

[0115] S203: using the simulated formation environment, determining the forward flow conductivity and the reverse flow conductivity of each proppant particle size combination under a plurality of preset target closure pressures;

[0116] S204: Determine an optimal proppant particle size combination according to the forward flow guiding capability and the reverse flow guiding capability.

[0117] The embodiments of the present specification obtain geological information of the target loose sandstone formation and a variety of proppant particle size combinations, and then use a fracture diversion instrument to create fractures based on the geological information to establish a simulated formation environment, thereby achieving efficient and accurate construction of specific geological conditions, and testing the forward and reverse conduction capabilities of each proppant particle size combination under multiple target closure pressures in the simulated formation environment, thereby efficiently and accurately selecting the best proppant particle size combination suitable for the target loose sandstone formation based on the forward and reverse conduction capabilities, thereby reducing the cost of testing and simulation.

[0118] It can be understood that in some embodiments, geological information includes sandstone porosity, formation permeability, formation temperature and rock mechanical properties. In order to retain the characteristics of loose sandstone formations to the greatest extent, some methods and techniques need to be adopted during field investigation and collection of geological data to ensure the accuracy and representativeness of the data. When selecting sampling points, different geographical and geological conditions of loose sandstone formations should be represented as much as possible. The sampling points should cover different formation depths, temperatures, pressures and geological characteristics. At each sampling point, geological specimens of loose sandstone formations are collected, including cores (rock samples), soil samples and water samples. These specimens will provide detailed information about the characteristics of the formation. Use geological exploration tools, such as drilling machines, core drilling machines, etc., to conduct geological exploration to obtain physical data of the formation.

[0119] Sensors and measuring equipment are used to measure the temperature and pressure of the formation. These data can provide the temperature and pressure conditions of the underground environment, which is crucial for the selection and performance evaluation of proppants. Laboratory tests are performed on the collected rock samples to determine their physical and mechanical properties, such as porosity, permeability, compressive strength, etc. The collected data needs to be carefully recorded and analyzed to better understand the characteristics of the formation. Geologists and engineers can use this data to develop proppant strategies and optimal particle size ratios.

[0120] Among them, the characteristics and functions of some geological data are as follows:

[0121] Sandstone Porosity: Porosity refers to the proportion of pores (voids) in a formation, usually expressed as a percentage. It reflects the space in the formation available for fluid (such as water or oil and gas) to flow. The porosity of sandstone has an important impact on the permeability and storage capacity of fluids.

[0122] Formation Permeability: Permeability is the ability of rocks in a formation to penetrate fluids. It describes the effect of the pore connectivity and pore size of the rock on the flow of fluids. Formation permeability is usually expressed in Darcy or millidarcy (md), and a high permeability means that fluids can penetrate more easily.

[0123] Formation temperature: Formation temperature refers to the temperature at depth below the ground. Understanding formation temperature is important for proppant selection and performance evaluation because temperature can affect proppant stability and performance.

[0124] Rock mechanical properties: Rock mechanical properties include rock compressive strength, elastic modulus, shear strength, etc. These properties reflect the mechanical properties of rock. Understanding the mechanical properties of rock can help determine the selection and use of proppants.

[0125] Furthermore, in some embodiments, the plurality of proppant particle size combinations include large-size proppants, medium-size proppants and small-size proppants in different proportions. Specifically, the large-size proppant has a particle size range of 60-80 mesh, the medium-size proppant has a particle size range of 30-50 mesh, and the small-size proppant has a particle size range of 10-20 mesh. Each proppant particle size combination must include three different types of proppants, namely, large-size proppants, medium-size proppants and small-size proppants. In some typical embodiments, the large-size proppant is located at the seam mouth, and its main function is to improve the seam mouth conductivity. The medium-size proppant is located in the middle of the crack and plays a main supporting role. The small-size proppant is located at the end of the crack to support microcracks and reduce filtration. At the same time, the combination ratio of proppants with different particle sizes directly affects the reflux volume of proppants and the distribution state of proppants in the fractures when the fracturing fluid is returned, as well as the role of formation sand prevention during later production and steam stimulation production. Subsequently, the functional role of proppants with different particle sizes in the fracturing filling process is determined by adjusting the ratio, so as to select the optimal ratio.

[0126] Further, in some embodiments, the forward conductivity refers to the fracture conductivity when the closing pressure of the fracture conductivity meter is the target closing pressure when the proppant is used for fracturing filling, and the reverse conductivity refers to the fracture conductivity when the closing pressure of the fracture conductivity meter is maintained unchanged at the target closing pressure when the blocking gas is injected into the water outlet of the fracture conductivity meter according to the preset injection time after the proppant is used for fracturing filling, that is, after the test is completed. The greater the forward conductivity, and the smaller the forward conductivity and reverse conductivity, the less likely the corresponding proppant particle size combination is to cause blockage, and the better the fracturing effect.

[0127] Refer to the attached Figure 3 In some embodiments, based on the geological information, using a fracture diffusor to establish a simulated formation environment corresponding to the target loose sandstone formation may include:

[0128] S301: Determine a target formation environment that is consistent with the sandstone porosity, formation permeability, formation temperature, and rock mechanical properties in the geological information of the target loose sandstone formation;

[0129] S302: In the target formation environment, fractures are created using a fracture diversion instrument so that the size, shape and inclination of the fractures meet preset requirements, thereby establishing the simulated formation environment.

[0130] It can be understood that, in some embodiments, field surveys are conducted and geological information in the actual environment of loose sandstone formations is collected to determine the target formation environment for simulation experiments. The fracture diversion instrument can simulate downhole pressure, create fractures, and evaluate the diversion capacity of fracture proppants under experimental conditions, thereby comparing the performance of various proppants. The fractures created by the fracture diversion instrument meet preset requirements, which are established based on the fracture characteristics in the actual environment of loose sandstone formations, and the preset requirements allow for a certain gap in characteristics between the fractures created by the fracture diversion instrument and the fractures existing in the actual environment of loose sandstone formations.

[0131] Specifically, in some embodiments, the fracture diversion instrument needs to be calibrated and set up simulation conditions, including temperature, pressure, and fluid parameters. These conditions should reflect the actual conditions of the loose sandstone formation. Then, a simulated loose sandstone environment is created in the fracture diversion instrument, which involves placing appropriate rock samples or other simulation materials to simulate the pore structure and characteristics of the formation. Then, a simulated fracture is created in the simulated loose sandstone environment by introducing artificial fractures, fracture models, or other suitable methods in the simulated environment. According to the research objectives, different proppant particle size combinations to be tested are selected to ensure that each proppant particle size combination has been preset and has a corresponding particle size ratio. The selected proppant particle size combination is injected into the simulated fracture, and pressure is applied in the fracture diversion instrument to simulate the actual formation fracturing process.

[0132] See attached Figure 4 In some embodiments, using the simulated formation environment, determining the forward flow conductivity and reverse flow conductivity of each proppant particle size combination under a plurality of preset target closure pressures may include:

[0133] S401: In the simulated formation environment, according to each target closure pressure, proppants and formation sand corresponding to different proppant particle size combinations are input into the fracture diversion instrument, and fracturing filling is performed under preset instrument parameters;

[0134] S402: real-time monitoring of simulated measurement parameters during the fracturing filling process;

[0135] S403: determining the forward flow conductivity of each proppant particle size combination at each target closure pressure according to the instrument parameters and the simulation measurement parameters;

[0136] S404: injecting unblocking gas into the outlet of the fracture diversion instrument according to the preset injection time, and calculating the reverse diversion capacity of each proppant particle size combination under each target closure pressure.

[0137] It can be understood that, in some embodiments, the instrument parameters may include simulation duration, simulation temperature value, sand thickness value, closing pressure range and closing pressure change rate, and the simulation measurement parameters may include construction time, fluid viscosity, fluid displacement and diversion chamber port pressure difference. Specifically, simulation duration means that all tests last the same time to ensure that the results are comparable, simulation temperature value means keeping the simulation temperature at the same level to maintain consistent temperature conditions, sand thickness value means that the setting of sand thickness should also be consistent to control the test conditions, closing pressure range means setting the closing pressure range to ensure that the pressure changes within this range during the test, and closing pressure change rate means controlling the rate of change of closing pressure to maintain consistency. The fracture diversion instrument can simulate the actual situation in the formation, including temperature. The characteristics of the formation can be well reflected by the simulation time, simulated temperature, sand thickness, closing pressure range and closing pressure change rate. The fluid viscosity, fluid displacement and pressure difference of the diversion chamber port in the fracturing filling process change with the construction time. The current closing pressure that changes with time can be determined according to the closing pressure range and closing pressure change rate. When the current closing pressure in the fracture diversion instrument reaches the target closing pressure, the forward flow conductivity of each proppant particle size combination under each target closing pressure can be measured. After the forward flow conductivity is measured, a plugging gas, such as hot steam, is injected into the water outlet of the fracture diversion instrument according to the preset injection time, and the reverse flow conductivity under the target closing pressure is calculated by the same diversion capacity formula as that for calculating the forward flow conductivity.

[0138] Further, in some embodiments, according to the instrument parameters and the simulation measurement parameters, the following formulas can be used to respectively determine the forward flow conductivity of each proppant particle size combination when the current closure pressure is equal to each target closure pressure:

[0139]

[0140] Where, f represents the target closing pressure, and the current closing pressure is obtained from the closing pressure range and the closing pressure change rate, Q f represents the forward flow capacity of the proppant particle size combination when the target closure pressure is f, k f represents the permeability of the proppant particle size combination when the target closure pressure is f, W represents the sand thickness value of the current proppant particle size combination, W is a dynamically changing intermediate quantity, μ represents the fluid viscosity, Q represents the fluid displacement, L represents the length between pressure ports, Δp represents the pressure difference in the fluid flow direction, and A represents the support cross-sectional area of ​​the diversion chamber. Among them, the permeability k fis a preset known value, determined through field geological surveys, etc., and the length L between pressure ports is a preset known value, which is related to the fracture conductivity meter. By changing one or more of the instrument parameters and the simulation measurement parameters and repeating the experiment, the forward conductivity and reverse conductivity of each proppant particle size combination under each target closure pressure can be quickly and accurately obtained. It should be noted that although parameters such as simulation time and simulation temperature value are not intuitively reflected in the formula for calculating the forward conductivity, they may indirectly affect the variables in the formula for calculating the forward conductivity. Therefore, when setting the instrument parameters and simulation measurement parameters, all parameters need to be fully set to ensure the accuracy of subsequent experimental data.

[0141] See attached Figure 5 In some embodiments, injecting the plugging gas into the outlet of the fracture flow guide instrument according to the preset injection time, and calculating the reverse flow guiding capacity of each proppant particle size combination at each target closure pressure may include:

[0142] S501: Obtaining the final closure pressure after completing the fracturing filling process for each proppant particle size combination at each target closure pressure;

[0143] S502: maintaining the final closing pressure unchanged, and injecting a blockage-clearing gas into the water outlet of the fracture diversion instrument according to a preset injection time to clear the formation sand blocked in the diversion chamber;

[0144] S503: real-time monitoring and updating of simulation measurement parameters in the process of clearing formation sand blocked in the diversion chamber;

[0145] S504: Determine the reverse flow conductivity of each proppant particle size combination at each target closure pressure according to the instrument parameters and the updated simulation measurement parameters.

[0146] It can be understood that, in some embodiments, during the forward flow guide test, the flow conductivity of the proppant is actually measured under a preset simulation condition. In order to conduct the reverse flow guide test, the same closing pressure needs to be maintained so that the subsequent test results can be compared under the same conditions. Specifically, the blockage of the flow guide chamber is cleared by injecting a blocking gas into the water outlet of the fracture flow guide instrument to ensure that the test conditions are changed from the forward flow guide test to the reverse flow guide test, and the reverse flow guide capacity is calculated using the same formula as that for calculating the forward flow guide capacity. In some embodiments, the specific operation may be: after the forward flow guide test is completed, the liquid input channel is closed to ensure that the liquid no longer flows into the flow guide chamber; a blocking gas is injected into the water outlet of the fracture flow guide instrument, and hot steam or nitrogen can usually be selected. The gas will pass through the flow guide chamber to help clear the liquid and proppant residue therein to restore the normal state of the flow guide channel; the duration of the blocking gas injection is set to ensure that the residue in the flow guide chamber is fully cleared; once the blocking gas injection time is over, it is confirmed that the flow guide chamber has been fully cleaned and there is no residue.

[0147] Refer to the attached Figure 6 In some embodiments, determining the optimal proppant particle size combination according to the forward flow conductivity and the reverse flow conductivity may include:

[0148] S601: determining a corresponding flow conductivity difference according to the forward flow conductivity and the reverse flow conductivity;

[0149] S602: establishing an objective function and constraint conditions for determining an optimal proppant particle size combination according to the forward flow conductivity and the flow conductivity difference;

[0150] S603: Under the constraints, an evolutionary algorithm is used to find an optimal solution to the objective function, so as to determine an optimal proppant particle size combination according to the optimal solution.

[0151] It can be understood that, in some embodiments, the conductivity difference is the difference between the forward conductivity and the reverse conductivity. The purpose of establishing the objective function for determining the optimal proppant particle size combination is to extract the optimal proppant particle size combination so that the conductivity difference is minimized and the forward conductivity is maximized. Furthermore, when establishing the objective function, different weight parameters can be assigned to the conductivity difference and the forward conductivity. The constraint condition refers to the sum of the proportions of small-size proppants, medium-size proppants and large-size proppants in the proppant particle size combination as a fixed value, which is usually 1. The evolutionary algorithm is used to expand the solution space of the optimal solution of the objective function and select the solution with the optimal objective function value (i.e., the optimal fitness) in the solution space to obtain the optimal solution, thereby quickly and accurately obtaining the optimal proppant particle size combination.

[0152] Refer to the attached Figure 7In some embodiments, determining the corresponding flow conductivity difference according to the forward flow conductivity and the reverse flow conductivity may include:

[0153] S701: according to the forward flow conductivity and reverse flow conductivity of each proppant particle size combination at each target closure pressure, a forward flow conductivity curve and a reverse flow conductivity curve are respectively drawn;

[0154] S702: Determine the flow conductivity difference according to the forward flow conductivity curve and the reverse flow conductivity curve.

[0155] It can be understood that in some embodiments, in addition to the forward flow conduction capacity, reverse flow conduction capacity and flow conduction capacity difference obtained by simulation tests, the forward flow conduction capacity curve, the reverse flow conduction capacity curve and the flow conduction capacity difference curve can also be obtained based on regression fitting, so as to obtain more corresponding flow conduction capacity difference data for subsequent optimal solution of the objective function.

[0156] See attached Figure 8 In some embodiments, according to the forward flow conductivity and the difference between the forward and reverse flow conductivity, establishing an objective function and constraint conditions for determining the optimal proppant particle size combination may include:

[0157] S801: taking the ratio parameter of the small-size proppant, the medium-size proppant and the large-size proppant in the proppant particle size combination as a decision variable, and determining the constraint condition according to the ratio parameter;

[0158] S802: Taking maximizing the forward flow guiding capacity and minimizing the flow guiding capacity difference as optimization objectives, constructing the objective function.

[0159] It can be understood that, in some embodiments, it is assumed that the proportion parameters of the small-size proppant, the medium-size proppant and the large-size proppant in the proppant particle size combination are a, b, and c, respectively, and the constraint condition is a+b+c=Q, where Q is a fixed value, usually 1. The proportion of proppant of different sizes in the current proppant particle size combination can be determined according to the numerical values ​​of a, b, and c. In some embodiments, the objective function can be constructed using the following formula:

[0160]

[0161] Among them, f(a,b,c) is the objective function, f(a,b,c), ω 1 is the first weight, ω 2 is the second weight, Q f (a, b, c) are the forward flow conduction capabilities, δ Q (a, b, c) is the difference in conductivity, and the maximum value f of f(a, b, c) is obtained by solving max(a,b,c), thus obtaining the maximum value f max The optimal solution corresponding to (a,b,c).

[0162] Refer to the attached Fig. 9 In some embodiments, under the constraints, solving the optimal solution of the objective function using an evolutionary algorithm to determine the optimal proppant particle size combination according to the optimal solution may include:

[0163] S901: Randomly initialize the population;

[0164] S902: Determine the fitness of individuals in the population according to the objective function;

[0165] S903: According to the fitness, select a number of individuals from the current population as first-generation individuals;

[0166] S904: Generate corresponding second-generation individuals according to the first-generation individuals using a crossover strategy;

[0167] S905: the second generation individuals undergo mutation operation;

[0168] S906: Using the mutation operation result, expand the current population to increase the diversity of the current population;

[0169] S907: Determine the fitness of individuals in the expanded population according to the objective function;

[0170] S908: using the greedy rule, according to the fitness of the individuals in the expanded population, selecting the third generation individuals from the first generation individuals and the second generation individuals;

[0171] S909: Update the population according to the set of third-generation individuals, and record the number of updated iterations of the population;

[0172] S910: Repeat the above steps of updating and iterating the population until the number of update iterations reaches a preset threshold and / or the optimal fitness of individuals in the population reaches a preset condition;

[0173] S911: Determine the optimal solution according to the individual information corresponding to the optimal individual fitness in the population to obtain the optimal proppant particle size combination.

[0174] Specifically, in some embodiments, the random mapping method of the randomly initialized population can be chaotic mapping, etc., the individual information of the population includes the ratio parameters of small-size proppants, medium-size proppants and large-size proppants, the fitness of the individual is the objective function value, and according to the fitness, roulette wheel selection or other selection methods can be used to select several individuals from the current population as the first generation of individuals. This document does not limit this. The crossover strategy can be a single-point crossover strategy or a uniform crossover strategy, etc. After the optimal proppant particle size ratio is determined by a genetic algorithm or other optimization method, the optimal proppant combination is prepared and a fracturing filling simulation test is performed to verify the performance of the ratio. When repeating the steps of updating the iterative population as above, the steps described in S904-S909 can be repeated to continuously update the iterative population and its individuals.

[0175] Specifically, in some embodiments, verifying the performance of the ratio generally includes the following steps:

[0176] Prepare the best proppant size combination: Based on the determined optimal particle size ratio, prepare the proppant size combination. This involves mixing the appropriate amount of small-size, medium-size, and large-size proppants in the optimal ratio.

[0177] Simulated test environment setting: Create a simulated formation environment in the fracture diversion instrument, including setting simulated fractures, simulated temperature, sand thickness, closing pressure range, etc., to ensure that the test environment is close to the actual application conditions.

[0178] Fracturing filling simulation test: The prepared optimal proppant particle size combination is injected into the simulated fracture and a fracturing filling simulation test is performed. During the test, important parameters such as closure pressure, temperature, fluid displacement, fluid viscosity, etc. are recorded.

[0179] Data collection: Based on the test results, collect data on the forward flow conductivity and the difference between forward and reverse flow conductivity of the proppant particle size combination under different conditions.

[0180] Results Analysis: Analyze the test data and compare with previous simulation and calculation results to verify the performance of the optimal proppant size combination. Ensure that the forward flow capacity is maximized and the difference between forward and reverse flow capacity is minimized.

[0181] In addition, the present invention uses a fracture conductivity meter to perform simulation of fracturing and filling of loose sandstone formations. The instrument is used for laboratory research and simulation of underground rock fracture conductivity. The following are general features and possible specifications of the fracture conductivity meter:

[0182] Simulating fracture environment: Fracture diffusivity instrument can create and simulate the environment of underground rock fractures, including the size, shape and inclination of the fractures;

[0183] Conductivity test: This instrument is mainly used to test the conductivity of liquid (usually water) in the fracture. It can simulate the fluid flow process in the fracture and measure the conductivity parameters;

[0184] Closure pressure control: Closure pressure can usually be controlled to simulate diversion under different underground pressure conditions;

[0185] Temperature control: In some cases, the flowmeter can also control the temperature of the test environment to examine the effect of temperature on flow conductivity;

[0186] Real-time data acquisition: The instrument usually has real-time data acquisition and recording functions to record important parameters during the test process, such as pressure, temperature, flow rate, etc.

[0187] Controllable flow rates and fluid properties: Users can often adjust the flow rate and properties of the fluid used in a test to simulate different subsurface conditions.

[0188] See attached Fig.10 In some embodiments, after determining the corresponding flow conductivity difference according to the forward flow conductivity and the reverse flow conductivity, the following may be further included:

[0189] S1001: Determine target influencing factors corresponding to the forward flow guidance capability and the reverse flow guidance capability by using a grey correlation algorithm;

[0190] S1002: Optimize proppant particle size combination according to the target influencing factors.

[0191] It can be understood that, in some embodiments, after obtaining a plurality of preset proppant particle size combinations, they can be further optimized, and the target influencing factors corresponding to the forward flow conduction capacity and the reverse flow conduction capacity can be evaluated using the grey correlation algorithm, so as to subsequently optimize the proppant particle size combination according to the target influencing factors to improve the accuracy of subsequent tests and reduce the occurrence of inefficient tests. At the same time, the target influencing factors can also be used to screen the most suitable proppant particle size combination from the best proppant particle size combination.

[0192] See attached Fig.11 In some embodiments, using a grey correlation algorithm to determine the target influencing factors corresponding to the forward flow guiding capability and the reverse flow guiding capability may include:

[0193] S1101: Obtaining evaluation indicators corresponding to proppant particle size combinations;

[0194] S1102: Determine a flow conductivity evaluation system and evaluation index weights of the multilayer structure according to the evaluation index;

[0195] S1103: establishing an initial decision matrix for conductivity according to the conductivity evaluation system and evaluation index weights, and establishing a reference vector using the forward conductivity of each proppant particle size combination;

[0196] S1104: performing standardization processing on each evaluation index in the initial decision matrix to obtain a standardized grey relational matrix;

[0197] S1105: determining a grey correlation coefficient according to the standardized grey correlation matrix;

[0198] S1106: Establishing a grey correlation coefficient matrix according to the grey correlation coefficient;

[0199] S1107: Determine the correlation between each evaluation index and the reference vector using the grey correlation coefficient matrix;

[0200] S1108: Sort the relevances from large to small, and determine the target influencing factor according to the evaluation indicators corresponding to the top ranked relevances.

[0201] It can be understood that, in some embodiments, m proppant combinations and n influencing factors (i.e., evaluation indicators) are set, and the scoring indicators can be used to construct an m×n initial decision matrix of conductivity. At the same time, the forward conductivity of each proppant particle size combination is used to establish a reference vector. Then, in order to ensure the reliability of the results, the unit restriction of the data of the initial decision matrix of conductivity is removed by standardization processing to improve the accuracy and efficiency of the analysis. The grey correlation coefficient is extracted and a grey correlation coefficient matrix is ​​established to analyze the correlation between the evaluation index and the forward conductivity. The correlation is numerically reflected as a correlation degree. The higher the correlation degree, the greater the influence of the corresponding evaluation index on the forward conductivity, thereby realizing accurate determination of the target influencing factor according to the correlation degree.

[0202] Furthermore, in some embodiments, the following formula may be used to standardize the evaluation indicators in the initial decision matrix:

[0203]

[0204] Where, X i (k) represents the standardized i-th evaluation index sequence, x i (k) represents the scoring index of the i-th evaluation index sequence at time k, represents the expectation of the i-th evaluation indicator sequence, s i (k) represents the standard deviation of the i-th evaluation indicator sequence.

[0205] Furthermore, in some embodiments, the grey relational coefficient may be determined using the following formula:

[0206]

[0207] In the formula, ζ i (k) represents the grey correlation coefficient between the i-th evaluation index and the reference sequence at time k, Δ i (k) represents the absolute value of the difference between the reference sequence at time k and the i-th evaluation index, and ρ represents the resolution coefficient.

[0208] Furthermore, in some embodiments, the following formula may be used to determine the correlation between each evaluation indicator and the reference vector:

[0209]

[0210] In the formula, r i represents the correlation of the i-th evaluation index, ζ ik Represents the grey correlation coefficient matrix, and n represents the number of evaluation indicators.

[0211] See attached Fig.12 In some embodiments, determining the flow conductivity evaluation system and evaluation index weights of the multilayer structure according to the evaluation index may include:

[0212] S1201: constructing a conductivity evaluation system including a target layer, a criterion layer, a sub-criterion layer and a proppant particle size combination layer according to the evaluation index; wherein the target layer represents the forward conductivity, the criterion layer represents the type of the evaluation index, the sub-criterion layer represents the evaluation index, and the proppant particle size combination layer represents the proppant particle size combination type;

[0213] S1202: Compare the importance of the evaluation indicators in the sub-criterion layer relative to the criterion layer in pairs to obtain the importance of each evaluation indicator;

[0214] S1203: Determine the relative weights between the evaluation indicators using the importance levels;

[0215] S1204: Determine the evaluation indicator weight according to the relative weight.

[0216] It can be understood that, in some embodiments, the target layer includes multiple criterion layers, and each criterion layer includes multiple different evaluation indicators. For example, the criterion layer can be a proppant coating polymer, and the evaluation indicators of its sub-criterion layer can include epoxy resin, furan resin, polyester, urea aldehyde, etc. After completing the construction of the conductivity evaluation system using the hierarchical analysis method, the importance of the evaluation indicators in the sub-criterion layer relative to the criterion layer can be compared pairwise using the nine-level scaling method to obtain the importance of each evaluation indicator, and then use the importance to determine the weight of each evaluation indicator.

[0217] See attached Fig.13 In some embodiments, optimizing the proppant particle size combination according to the target influencing factors may include:

[0218] S1301: Evaluate the forward flow conductivity of the current proppant particle size combination according to the target influencing factors to obtain a corresponding evaluation score;

[0219] S1302: According to the evaluation score, the current proppant particle size combination is screened to optimize the proppant particle size combination.

[0220] It can be understood that, in some embodiments, the target influencing factor refers to an influencing factor that is directly related to the conductivity to the expected degree. By using the target influencing factor, the performance corresponding to each proppant particle size combination can be evaluated to obtain a comprehensive evaluation score, and then the proppant particle size combination with an evaluation score greater than a preset threshold is screened, thereby optimizing the proppant particle size combination and avoiding simulation tests on proppant particle size combinations with poor performance, which causes waste of manpower and material resources. At the same time, the target influencing factor can also be used to screen the most suitable proppant particle size combination from the best proppant particle size combination to improve the design accuracy of the proppant particle size combination.

[0221] It should be noted that, although the operations of the method of the present invention are described in a specific order in the above embodiments and the accompanying drawings, this does not require or imply that the operations must be performed in the specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0222] Corresponding to the above-mentioned loose sandstone formation fracturing filling proppant particle size design method, some embodiments of this specification also provide a loose sandstone formation fracturing filling proppant particle size design device, refer to Fig.14 As shown, in some embodiments, the apparatus may include:

[0223] Receiving module 1401, used to obtain geological information of target loose sandstone formation and multiple proppant particle size combinations;

[0224] A simulation module 1402 is used to establish a simulated formation environment corresponding to the target loose sandstone formation using a fracture diffusor according to the geological information;

[0225] A testing module 1403 is used to determine the forward flow conductivity and reverse flow conductivity of each proppant particle size combination under a plurality of preset target closure pressures by using the simulated formation environment;

[0226] The determination module 1404 is used to determine the best proppant particle size combination according to the forward flow guiding capacity and the reverse flow guiding capacity.

[0227] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0228] It should be noted that in the embodiments of this specification, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user and fully authorized by all parties.

[0229] The embodiments of this specification also provide a computer device. Fig.15 As shown, in some embodiments of the present specification, the computer device 1502 may include one or more processors 1504, such as one or more central processing units (CPUs) or graphics processing units (GPUs), and each processing unit may implement one or more hardware threads. The computer device 1502 may also include any memory 1506, which is used to store any kind of information such as code, settings, data, etc. In a specific embodiment, the computer program on the memory 1506 and can be run on the processor 1504, when the computer program is run by the processor 1504, the instructions of the method described in any of the above embodiments may be executed. Non-limitingly, for example, the memory 1506 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 1502. In one embodiment, when the processor 1504 executes the associated instructions stored in any memory or combination of memories, the computer device 1502 can perform any operation of the associated instructions. The computer device 1502 also includes one or more drive mechanisms 1508 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0230] The computer device 1502 may also include an input / output interface 1510 (I / O) for receiving various inputs (via input devices 1512) and for providing various outputs (via output devices 1514). A specific output mechanism may include a presentation device 1516 and an associated graphical user interface 1518 (GUI). In other embodiments, the input / output interface 1510 (I / O), input device 1512, and output device 1514 may not be included, and the computer device 1502 may be used as a computer device in a network. The computer device 1502 may also include one or more network interfaces 1520 for exchanging data with other devices via one or more communication links 1522. One or more communication buses 1524 couple the components described above together.

[0231] The communication link 1522 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 1522 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.

[0232] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), computer-readable storage media, and computer program products of some embodiments of the present specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processor to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processor generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0233] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processor to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0234] These computer program instructions can also be loaded onto a computer or other programmable data processor so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0235] In a typical configuration, a computer device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0236] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0237] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computer device. As defined in this specification, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0238] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may take the form of complete hardware embodiments, complete software embodiments or embodiments combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0239] The present specification embodiments may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present specification embodiments may also be practiced in distributed computing environments where tasks are performed by remote processors connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0240] It should also be understood that in the embodiments of this specification, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0241] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0242] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of this specification. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0243] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for designing particle size of proppant for fracturing filling in loose sandstone formations. It is characterized in that The method comprises: Obtain geological information of the target loose sandstone formation and various proppant particle size combinations; According to the geological information, using a fracture diffusor, a simulated formation environment corresponding to the target loose sandstone formation is established; Using the simulated formation environment, determining the forward flow conductivity and reverse flow conductivity of each proppant particle size combination under a plurality of preset target closure pressures; According to the forward flow conductivity and the reverse flow conductivity, an optimal proppant particle size combination is determined.

2. The method according to claim 1, It is characterized in that The geological information includes sandstone porosity, formation permeability, formation temperature and rock mechanical properties; the plurality of proppant particle size combinations include a plurality of different proportions of large particle size proppant, medium particle size proppant and small particle size proppant.

3. The method according to claim 1, It is characterized in that According to the geological information, a fracture diffusor is used to establish a simulated formation environment corresponding to the target loose sandstone formation, including: Determine the target formation environment consistent with the sandstone porosity, formation permeability, formation temperature and rock mechanical properties in the geological information of the target unconsolidated sandstone formation; In the target formation environment, a fracture diversion instrument is used to create fractures so that the size, shape and inclination of the fractures meet the preset requirements, thereby establishing the simulated formation environment.

4. The method according to claim 1, It is characterized in that Using the simulated formation environment, the forward flow conductivity and reverse flow conductivity of each proppant particle size combination under a plurality of preset target closure pressures are determined, including: In the simulated formation environment, proppants and formation sand corresponding to different proppant particle size combinations are input into the fracture diversion instrument according to each target closure pressure, and fracturing filling is performed under preset instrument parameters; Real-time monitoring of analog measurement parameters during the fracturing filling process; Determining the forward flow conductivity of each proppant particle size combination at each target closure pressure based on the instrument parameters and the simulated measurement parameters; The plugging gas is injected into the outlet of the fracture diversion instrument according to the preset injection time, and the reverse diversion capacity of each proppant particle size combination at each target closure pressure is calculated.

5. The method according to claim 4, It is characterized in that The instrument parameters include the closing pressure range and the closing pressure change rate; the simulation measurement parameters include fluid viscosity, fluid displacement and the pressure difference of the guide chamber port.

6. The method according to claim 5, It is characterized in that According to the instrument parameters and the simulated measurement parameters, the forward flow conduction capacity of each proppant particle size combination is determined when the current closure pressure is equal to each target closure pressure using the following formula: Where, f represents the target closing pressure, and the current closing pressure is obtained from the closing pressure range and the closing pressure change rate, Q f represents the forward flow capacity of the proppant particle size combination when the target closure pressure is f, k f represents the permeability of the proppant particle size combination when the target closure pressure is f, μ represents the fluid viscosity, Q represents the fluid displacement, L represents the length between pressure ports, Δp represents the pressure difference in the fluid flow direction, and A represents the support cross-sectional area of ​​the diversion chamber.

7. The method according to claim 4, It is characterized in that Inject plugging gas into the outlet of the fracture diversion instrument according to the preset injection time, and calculate the reverse diversion capacity of each proppant particle size combination at each target closure pressure, including: Obtain the final closure pressure after completing the fracturing filling process for each proppant particle size combination at each target closure pressure; The final closing pressure is maintained unchanged, and according to the preset injection time, a blockage-clearing gas is injected into the water outlet of the fracture diversion instrument to clear the formation sand blocked in the diversion chamber; Real-time monitoring and updating of simulated measurement parameters during the process of clearing the formation sand blocked in the diversion chamber; The reverse flow conductivity of each proppant particle size combination at each target closure pressure is determined based on the instrument parameters and the updated simulation measurement parameters.

8. The method according to claim 1, It is characterized in that According to the forward flow conductivity and the reverse flow conductivity, the optimal proppant particle size combination is determined, including: Determining a corresponding flow conductivity difference according to the forward flow conductivity and the reverse flow conductivity; According to the forward flow conductivity and the flow conductivity difference, an objective function and constraint conditions for determining an optimal proppant particle size combination are established; Under the constraints, an evolutionary algorithm is used to find the optimal solution of the objective function, so as to determine the best proppant particle size combination according to the optimal solution.

9. The method according to claim 8, It is characterized in that Determining a corresponding flow conductivity difference according to the forward flow conductivity and the reverse flow conductivity includes: According to the forward flow conductivity and reverse flow conductivity of each proppant particle size combination under each target closure pressure, a forward flow conductivity curve and a reverse flow conductivity curve are drawn respectively; The flow conductivity difference is determined according to the forward flow conductivity curve and the reverse flow conductivity curve.

10. The method according to claim 8, It is characterized in that According to the forward flow conductivity and the difference between the forward and reverse flow conductivity, an objective function and constraint conditions for determining the optimal proppant particle size combination are established, including: Taking the ratio parameter of the small-size proppant, the medium-size proppant and the large-size proppant in the proppant particle size combination as a decision variable, and determining the constraint condition according to the ratio parameter; The objective function is constructed by taking the maximization of the forward flow guiding capacity and the minimization of the flow guiding capacity difference as the optimization objectives.

11. The method according to claim 8, It is characterized in that After determining the corresponding flow conductivity difference according to the forward flow conductivity and the reverse flow conductivity, the method further includes: Determine the target influencing factors corresponding to the forward flow guiding capacity and the reverse flow guiding capacity by using a grey correlation algorithm; The proppant particle size combination is optimized according to the target influencing factors.

12. The method according to claim 11, It is characterized in that The target influencing factors corresponding to the forward flow guiding capacity and the reverse flow guiding capacity are determined by using a grey correlation algorithm, including: Obtaining evaluation indicators corresponding to proppant particle size combinations; Determine the flow conductivity evaluation system and evaluation index weights of the multilayer structure according to the evaluation index; According to the conductivity evaluation system and the evaluation index weights, an initial conductivity decision matrix is ​​established, and a reference vector is established using the forward conductivity of each proppant particle size combination; Standardizing each evaluation index in the initial decision matrix to obtain a standardized grey relational matrix; According to the standardized grey correlation matrix, the grey correlation coefficient is determined; Establishing a grey correlation coefficient matrix according to the grey correlation coefficient; Using the grey correlation coefficient matrix, determine the correlation degree between each evaluation index and the reference vector; Sort the correlation degrees from large to small, and determine the target influencing factors according to the evaluation indexes corresponding to several relatively high-ranked correlation degrees.

13. The method according to claim 12, wherein, According to the evaluation indexes, determine the diversion capacity evaluation system of the multi-layer structure and the evaluation index weights, including: According to the evaluation indexes, construct a diversion capacity evaluation system including a target layer, a criterion layer, a sub-criterion layer and a proppant particle size combination layer; wherein, the target layer represents the forward diversion capacity, the criterion layer represents the type of evaluation indexes, the sub-criterion layer represents the evaluation indexes, and the proppant particle size combination layer represents the proppant particle size combination type; Compare the importance of the evaluation indexes in the sub-criterion layer pairwise with respect to the criterion layer to obtain the importance degree of each evaluation index; Use the importance degree to determine the relative weights between the evaluation indexes; Determine the evaluation index weights according to the relative weights.

14. The method according to claim 11, wherein, Optimize the proppant particle size combination according to the target influencing factors, including: According to the target influencing factors, evaluate the forward diversion capacity of the current proppant particle size combination to obtain the corresponding evaluation score; According to the evaluation score, screen the current proppant particle size combination to optimize the proppant particle size combination.

15. A device for designing the proppant particle size for fracturing and packing in a loose sandstone formation, wherein, The device includes: A receiving module, configured to obtain the geological information of the target loose sandstone formation and a variety of proppant particle size combinations; A simulation module, configured to establish a simulated formation environment corresponding to the target loose sandstone formation by using a fracture conductivity instrument according to the geological information; A testing module, configured to use the simulated formation environment to determine the forward diversion capacity and the reverse diversion capacity of each proppant particle size combination under a plurality of preset target closure pressures; A determination module, configured to determine the optimal proppant particle size combination according to the forward diversion capacity and the reverse diversion capacity.

16. A computer device, including a memory, a processor, and a computer program stored on the memory, wherein, When the computer program is run by the processor, it executes the instructions of the method according to any one of claims 1-14.

17. A computer storage medium, on which a computer program is stored, wherein, When the computer program is run by the processor of a computer device, it executes the instructions of the method according to any one of claims 1-14.

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