Shale gas well flowback system optimization method and device and electronic equipment

Abstract

The invention provides a shale gas well flowback system optimization method and device and electronic equipment, and the method comprises the steps: employing a gas reservoir numerical model, and obtaining a plurality of cumulative gas production rates in a preset time period under the condition of simulating different oil nozzle characteristics or different oil nozzle characteristic change trends through setting different gas well production pressure differences; and according to the multiple accumulated gas production rates and the corresponding relation between the oil nozzle characteristics and the gas well production pressure difference, a flow-back oil nozzle characteristic scheme or a flow-back oil nozzle characteristic change trend scheme is determined. According to the method, the flow-back oil nozzle design scheme for achieving the optimal productivity can be reasonably determined, the flow-back process is optimized, reasonable flow-back of the shale gas well after pressure is achieved, and the productivity and the development effect of the shale gas well are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and in particular to a shale gas well flowback system optimization method, device and electronic equipment. Background Art

[0002] Shale gas is a typical unconventional natural gas. The formation and enrichment of shale gas are self-generated and self-stored, and it is mainly free gas and adsorbed gas. Shale reservoirs are very dense and micro-nano pores are very developed. Shale gas reservoirs are usually called "artificial gas reservoirs". They are mainly developed by horizontal wells with large-scale hydraulic fracturing. The entire development cycle includes fracturing, well blocking, flowback and mining. Compared with conventional gas reservoirs, dense shale reservoirs greatly enhance the role of reservoir infiltration and gas placement. Usually, shale gas wells are blocked for a period of time after fracturing, which is conducive to promoting the infiltration process and increasing the complexity of the fracture network after fracturing, thereby increasing the production capacity of shale gas wells.

[0003] However, a long well-clogging time will cause the fracturing fluid to be retained in the near-wellbore area and produce "water lock", which will reduce the gas permeability near the fracturing cracks and increase the resistance to gas production, thereby reducing the production of shale gas wells. Therefore, a reasonable well-clogging time is crucial to fully exert the imbibition effect of shale reservoirs and reduce the damage of "water lock" near the wellbore. In addition, shale reservoirs have problems such as stress sensitivity and sand production in the seams. When the size of the return nozzle is too large, it may cause permeability damage and proppant reflux, while when the size of the return nozzle is too small, the fracturing fluid may not be effectively returned, affecting the production capacity of shale gas wells.

[0004] At present, the design of the post-pressure backflow sealing time and the backflow nozzle of shale gas wells mainly relies on field experience and judgment, and the design of the backflow system is blind. Summary of the invention

[0005] The disclosed embodiments provide a method, device and electronic equipment for optimizing the flowback system of a shale gas well, which can reasonably determine the design scheme of the flowback nozzle to achieve the optimal production capacity, optimize the flowback process, realize reasonable flowback after shale gas well compression, and improve the production capacity and development effect of shale gas wells.

[0006] According to one aspect of the present disclosure, a method for optimizing a shale gas well flowback system is provided, comprising:

[0007] By using the gas reservoir numerical model, setting different gas well production pressure differences and simulating different nozzle characteristics or different nozzle characteristic change trends, multiple cumulative gas production volumes in a preset time period are obtained;

[0008] According to the multiple cumulative gas production volumes and the corresponding relationship between the nozzle characteristics and the production pressure difference of the gas well, a flowback nozzle characteristic scheme or a flowback nozzle characteristic change trend scheme is determined.

[0009] In one embodiment, the method further includes: using a wellbore pipe flow model to determine a corresponding relationship between the nozzle characteristics and the gas well production pressure difference.

[0010] In one embodiment, the wellbore flow model is constructed in the following manner:

[0011] An initial wellbore pipe flow model of a shale gas well is established using steady-state multiphase flow simulation software, and wellbore attribute characteristics are input, wherein the wellbore attribute characteristics include casing length, casing size, pipeline length, pipeline size, gas-water fluid properties, and nozzle characteristics;

[0012] Based on the established initial wellbore pipe flow model, the wellbore pressure profile corresponding to different nozzle characteristics is simulated, the gas well production pressure difference is determined, and the corresponding relationship between the nozzle characteristics and the gas well production pressure difference is established.

[0013] In one embodiment, the method further comprises:

[0014] Using the gas reservoir numerical model, determine the bottom hole pressure corresponding to different well blocking times;

[0015] According to the bottom hole pressure of each well, determine the time range of the well blocking when the bottom hole pressure tends to be stable;

[0016] According to the well-clogging time range when the bottom hole pressure tends to be stable, the optimal backflow well-clogging time is obtained.

[0017] In one embodiment, the method further comprises:

[0018] Establish geological model through geological structure modeling, geological lithofacies modeling and physical property modeling;

[0019] A gas reservoir numerical model is established based on the geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data.

[0020] In one embodiment, the geological lithofacies modeling includes:

[0021] Classification of geological facies according to mud content;

[0022] The geological lithofacies modeling is completed by using a sequential Gaussian algorithm, setting preset nugget values ​​corresponding to different geological rocks, and setting a major range and a minor range according to the length of the horizontal section.

[0023] In one embodiment, the physical property modeling includes:

[0024] Coarsening of well logging curves;

[0025] The physical property modeling is completed by using geological lithofacies, well logging data, and seismic data to constrain each other and performing inter-well attribute interpolation based on a combination of sequential Gaussian and Kriging interpolation.

[0026] In one embodiment, a numerical simulation software is used to establish a gas reservoir numerical model based on geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data.

[0027] In one embodiment, a numerical simulation software is used to establish a gas reservoir numerical model based on geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data, including:

[0028] Importing geological model data outputted by the geological model into the numerical simulation software, wherein the geological model data includes data outputted from the target well in a well trajectory file format according to a data body, and the data body includes at least one of a grid coordinate and depth data body, a grid permeability data body, a porosity data body, and a gas content data body;

[0029] Adding fluid physical property parameters in the numerical simulation software, setting two sets of relative permeability curves for matrix and fracture, and adding double permeability keywords to form a single-pore double permeability model, wherein the fluid physical property parameters include at least one of fluid volume coefficient, fluid density, fluid viscosity, rock compressibility, relative permeability and capillary force curve;

[0030] Setting artificial fracturing crack data, wherein the artificial fracturing crack data includes at least one of an artificial fracturing crack position, an artificial fracturing crack attribute, an artificial fracturing crack height, and an artificial fracturing crack half length;

[0031] Initialize the gas reservoir numerical model and set the gas wells of the gas reservoir numerical model to obtain the gas reservoir numerical model; or initialize the gas reservoir numerical model and set the expansion and compaction curve and the gas wells of the gas reservoir numerical model to obtain the gas reservoir numerical model.

[0032] In one embodiment, the setting of artificial fracturing crack data includes:

[0033] According to the fracturing design plan, the locations of the horizontal fracture densification of the shale gas well are determined, and the locations of the artificial fracturing fractures are set by logarithmically encrypting the corresponding grids of each cluster;

[0034] The encrypted central grid is used to describe the main fracture, and the adjacent grids are described as the transformation area. The artificial fracture aperture and artificial fracture permeability are set according to the conductivity of the encrypted grid being equal to that of the artificial fracture.

[0035] The height of the artificial fracturing crack is set by setting the number of layers that the artificial fracturing crack passes through above and below the perforated layer;

[0036] The half-length of the artificial fracturing crack is set based on the microseismic monitoring results after each section of artificial fracturing.

[0037] In one embodiment, initializing the gas reservoir numerical model includes:

[0038] The pressure field initialization and the saturation field initialization are performed on the gas reservoir numerical model.

[0039] In one embodiment, the setting of the gas well in the gas reservoir numerical model includes: correcting the coincidence correspondence between each perforation point in the gas reservoir numerical model and the geological model grid according to the requirement that the perforation position information of the gas well in the gas reservoir numerical model corresponds to the xyz sequence number of the geological model grid; and

[0040] The shale gas well parameters are set, wherein the shale gas well parameters include well type, production system data, wellbore radius, and skin coefficient.

[0041] In one embodiment, the setting of the expansion and compaction curve includes: establishing a changing relationship between pore pressure and porosity and permeability of the shale gas reservoir, and setting the expansion and compaction curve.

[0042] In one embodiment, the method further comprises calibrating the gas reservoir numerical model:

[0043] Selecting adjacent wells in the same layer to perform history matching verification of history matching parameters on the gas reservoir numerical model;

[0044] Based on the historical matching verification, the gas reservoir numerical model parameters are adjusted.

[0045] In one embodiment, the nozzle feature includes the nozzle size, and the nozzle feature change trend includes an increase in the nozzle size or a decrease in the nozzle size, or,

[0046] The nozzle characteristic change trend includes an increase in nozzle size or a decrease in nozzle size, and at least one of the following: a change amplitude of each level of nozzle size, and a working duration of each level of nozzle size.

[0047] In one embodiment, the method further comprises:

[0048] Collect geological modeling data, numerical modeling data and wellbore flow model data, wherein the geological modeling data includes wellhead coordinates, core filling elevation, well inclination, logging data, and seismic data; the numerical modeling data includes fluid state equation PVT data, phase permeability data, capillary force data, fracturing fluid injection data, and production data; the wellbore flow model data includes wellbore structure data, nozzle data, and wellhead dynamic data.

[0049] According to another aspect of the present disclosure, a shale gas well flowback system optimization device is provided, comprising:

[0050] A simulation module is used to obtain multiple cumulative gas production volumes in a preset time period by using a gas reservoir numerical model and simulating different nozzle characteristics or different nozzle characteristic change trends by setting different gas well production pressure differences;

[0051] The flowback optimization module is used to determine a flowback nozzle characteristic scheme or a flowback nozzle characteristic change trend scheme according to the multiple cumulative gas production volumes and the corresponding relationship between the nozzle characteristics and the gas well production pressure difference.

[0052] According to another aspect of the present disclosure, there is provided an electronic device, including:

[0053] at least one processor; and

[0054] at least one memory storing a computer program,

[0055] The processor calls the computer program to enable the processor to execute the shale gas well flowback system optimization method according to one aspect of the present disclosure.

[0056] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing a computer program is provided, wherein the computer program is used to enable the computer to execute the shale gas well flowback system optimization method of one aspect of the present disclosure.

[0057] The above technical features can be combined in various suitable ways or replaced by equivalent technical features as long as the purpose of the present invention can be achieved.

[0058] One or more technical solutions provided in the embodiments of the present disclosure utilize a gas reservoir numerical model to set different gas well production pressure differences to simulate different nozzle characteristics or different nozzle characteristic change trends, thereby obtaining multiple cumulative gas productions in a preset time period. Then, based on each cumulative gas production and in combination with the correspondence between the nozzle characteristics and the gas well production pressure difference, the nozzle characteristics or nozzle characteristic change trends corresponding to a better cumulative gas production are determined as a flowback nozzle characteristic scheme or a flowback nozzle characteristic change trend scheme. This can reasonably determine a flowback nozzle design scheme that achieves the optimal production capacity, realize reasonable flowback of shale gas wells after pressure relief, and improve the production capacity and development effect of shale gas wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0060] Figure 1 A schematic flow chart of a shale gas well flowback system optimization method according to an exemplary embodiment of the present disclosure is shown;

[0061] Figure 2 A schematic flow chart of another method for optimizing a shale gas well flowback system according to an exemplary embodiment of the present disclosure is shown;

[0062] Figure 3 A schematic diagram showing a geological model of a target block in a method for optimizing a shale gas well flowback system according to an exemplary embodiment of the present disclosure is shown;

[0063] Figure 4 A schematic diagram showing a matrix relative permeability curve in a shale gas well flowback system optimization method according to an exemplary embodiment of the present disclosure is shown;

[0064] Figure 5 A schematic diagram showing a fracture relative permeability curve in a shale gas well flowback system optimization method according to an exemplary embodiment of the present disclosure is shown;

[0065] Figure 6 A reference schematic diagram of artificial fracturing fracture data setting in a gas reservoir numerical model in a shale gas well flowback system optimization method according to an exemplary embodiment of the present disclosure is shown;

[0066] Figure 7 A schematic diagram of the relationship between expansion and compaction curves in a shale gas well flowback system optimization method according to an exemplary embodiment of the present disclosure is shown;

[0067] Figure 8 A structural block diagram of a shale gas well flowback system optimization device according to an exemplary embodiment of the present disclosure is shown;

[0068] Fig. 9 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0069] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0070] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0071] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0072] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0073] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0074] The following describes the solutions of the embodiments of the present disclosure with reference to the accompanying drawings.

[0075] The present disclosure provides a method for optimizing the flowback system of a shale gas well. Figure 1 As shown, Figure 1 A flow chart of a shale gas well flowback system optimization method according to an exemplary embodiment of the present disclosure is shown, and the method comprises:

[0076] S101, using a gas reservoir numerical model, by setting different gas well production pressure differences, simulating different nozzle characteristics or different nozzle characteristic change trends, and obtaining multiple cumulative gas production volumes in a preset time period;

[0077] S102, determining a flowback nozzle characteristic scheme or a flowback nozzle characteristic change trend scheme according to a plurality of accumulated gas production volumes and a correspondence between nozzle characteristics and gas well production pressure difference.

[0078] The disclosed embodiment utilizes a gas reservoir numerical model to set different gas well production pressure differences to simulate different nozzle characteristics or different nozzle characteristic change trends, thereby obtaining multiple cumulative gas productions in a preset time period. Then, based on each cumulative gas production and in combination with the correspondence between the nozzle characteristics and the gas well production pressure difference, the nozzle characteristics or the nozzle characteristic change trends corresponding to a better cumulative gas production are determined as a flowback nozzle characteristic scheme or a flowback nozzle characteristic change trend scheme. This can reasonably determine a flowback nozzle design scheme that achieves the optimal production capacity, realize reasonable flowback of shale gas wells after pressure relief, and improve the production capacity and development effect of shale gas wells.

[0079] Set different unit pressure differences, such as 2.3MPa, 3.1MPa, etc.

[0080] Nozzle characteristics, such as nozzle size. The nozzle characteristic change trend includes nozzle size increase or nozzle size decrease, or the nozzle characteristic change trend includes nozzle size increase or nozzle size decrease, and at least one of the following: the change amplitude of each level of nozzle size, and the working duration of each level of nozzle size. For example, the size of the nozzle gradually increases; for another example, a two-level nozzle is configured, the nozzle characteristic change trend can also be the gradual increase in the nozzle size and the working duration of each level of the nozzle size; for another example, a multi-level nozzle is configured, the nozzle characteristic change trend can also be: the nozzle size increases, the size of each level of the nozzle changes by 2mm, and the working duration of each level of the nozzle size is different and is 1 day, 2 days, 2 days, etc.; many kinds of nozzle characteristic schemes or nozzle characteristic change trend schemes can be simulated, and by simulating the cumulative gas production corresponding to various schemes for a preset time period such as a quarter or a year under different nozzle characteristics or different nozzle characteristic change trends, the nozzle scheme corresponding to the better or optimal cumulative gas production can be known, and then the nozzle characteristic corresponding to the better cumulative gas production or the nozzle characteristic change trend is determined as the final return nozzle characteristic scheme or return nozzle characteristic change trend scheme.

[0081] In a feasible embodiment, the corresponding relationship between the choke characteristics and the gas well production pressure difference can be inferred through historical field experience combined with field data.

[0082] Preferably, some embodiments of the present disclosure utilize a wellbore pipe flow model to determine the correspondence between the nozzle characteristics and the production pressure difference of the gas well.

[0083] Exemplarily, a wellbore pipe flow model can be constructed first. Specifically, the wellbore pipe flow model can be pre-constructed in the following way: first, the initial wellbore pipe flow model of the shale gas well is established using the steady-state multiphase flow simulation software PIPESIM, and the wellbore attribute characteristics are input. The wellbore attribute characteristics include casing length, casing size, pipeline length, pipeline size, gas-water fluid properties, nozzle characteristics and other attributes. Of course, other wellbore attribute characteristics can also be included, such as wellbore material, etc.; then, based on the established initial wellbore pipe flow model, the wellbore pressure profiles corresponding to different nozzle characteristics are simulated, the gas well production pressure difference is determined, and the corresponding relationship between the nozzle characteristics and the gas well production pressure difference is established.

[0084] In some embodiments of the present disclosure, Figure 2 As shown, Figure 2 A flow chart of another shale gas well flowback system optimization method according to an exemplary embodiment of the present disclosure is shown. The method may further include:

[0085] S201, using a gas reservoir numerical model, by setting different gas well production pressure differences, simulating different nozzle characteristics or different nozzle characteristic change trends, and obtaining multiple cumulative gas productions in a preset time period;

[0086] S202, determining a flowback nozzle characteristic scheme or a flowback nozzle characteristic change trend scheme according to multiple cumulative gas production volumes and a correspondence between nozzle characteristics and gas well production pressure difference;

[0087] S203, using a gas reservoir numerical model, determining bottom hole pressures corresponding to a plurality of different well blocking times;

[0088] S204, determining a well-clogging time range when the bottom hole pressure tends to be stable according to each bottom hole pressure;

[0089] S205, obtaining an optimal backflow well blocking time according to a well blocking time range when the bottom hole pressure tends to be stable;

[0090] S206, the optimal backflow well blocking time and the backflow oil nozzle characteristic scheme or the backflow oil nozzle characteristic change trend scheme are comprehensively considered to obtain the final backflow system optimization scheme.

[0091] Among them, steps S201 and S202 are consistent with the aforementioned S101 and S102 and are not repeated here.

[0092] Steps S203-S205 have no causal relationship with steps S201-S202 and can be performed simultaneously or sequentially.

[0093] In some embodiments of the present disclosure, the bottom hole pressure corresponding to a plurality of different well blocking times can be determined by using a gas reservoir numerical model, according to the well blocking time conditions given in advance, to determine the bottom hole pressure corresponding to the well blocking time, such as giving a well blocking time of five days, six days, seven days, or even sixty days, recording the bottom hole pressure corresponding to each day, and observing the change of the bottom hole pressure every day, so as to find out from which well blocking time the bottom hole pressure tends to be stable. It can also be used to use a gas reservoir numerical model, input a period of time in advance, such as sixty days, and then let the gas reservoir numerical model display the bottom hole pressure corresponding to each time point during this period of time in the form of a curve, the horizontal axis is the well blocking time, the unit is day, and the vertical axis is the bottom hole pressure, the unit is MPa. According to each bottom hole pressure, the bottom hole pressure change law under different well blocking times can be analyzed, and then based on the bottom hole pressure change law, the well blocking time range after the bottom hole pressure tends to be stable can be determined. For example, the bottom hole pressure tends to be stable stage can be defined as a stage pressure drop rate less than 0.1MPa / d (i.e. 0.1MPa / day).

[0094] After determining the time range for well blocking after the bottom hole pressure tends to stabilize, the optimal well blocking time is obtained according to the time range for well blocking in the stabilized stage. For example, the well blocking time point in the stabilized stage is taken as the optimal well blocking time. For example, if the well blocking time in the stabilized stage is from the 15th day, the optimal well blocking time is 15 days. Or, the two days after the well blocking time point in the stabilized stage are taken as the optimal well blocking time. For example, if the well blocking time in the stabilized stage is from the 15th day, the optimal well blocking time is 17 days.

[0095] The gas reservoir numerical model is used to determine the bottom hole pressures corresponding to multiple different well blocking times, and then the well blocking time range in the stage where the bottom hole pressure tends to be stable is determined based on each bottom hole pressure, so as to obtain the optimal backflow well blocking time; at the same time, the gas reservoir numerical model is used to set different gas well production pressure differences, simulate different nozzle characteristics or different nozzle characteristic change trends, and obtain multiple cumulative gas productions in a preset time period, and then determine the backflow nozzle design scheme that achieves the optimal production capacity based on each cumulative gas production and the correspondence between the nozzle characteristics and the gas well production pressure difference; optimize the well blocking time after fracturing of shale gas wells to achieve full penetration and oil placement of fracturing fluid, and combine with the optimized backflow nozzle design scheme to further improve the production capacity and development effect of shale gas wells.

[0096] In some embodiments of the present disclosure, a gas reservoir numerical model may be constructed first. Specifically, a geological model is established through geological structure modeling, geological lithofacies modeling, and physical property modeling; and a gas reservoir numerical model is established based on geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data.

[0097] The disclosed embodiment establishes a geological model by comprehensively considering geological structure, geological lithofacies, and physical properties, and then establishes a gas reservoir numerical model by combining geological model data output by the geological model with factors such as fluid physical property parameters and fracturing fracture data. This can reasonably optimize any one or combination of well blocking time and choke schemes, and is no longer blind, and has high efficiency and precision, thereby improving the production capacity and development effect of shale gas wells.

[0098] Before establishing the model, data can be collected first. Exemplarily, the collected data include geological modeling data, numerical modeling data and wellbore perfusion model data, wherein the geological modeling data include wellhead coordinates, core elevation, well inclination, logging data (such as logging curves), seismic data and other information; the numerical modeling data include fluid state equation PVT data (PVT, a state equation used to describe the relationship between fluid pressure, molar volume and temperature in equilibrium), phase permeability data, capillary force data, fracturing fluid injection data, production data and other information; the wellbore pipe flow model data include: wellbore structure, wellhead dynamic data and other information.

[0099] Specifically, for geological structure modeling, the wellhead coordinates, logging curves, seismic data and other information are input, and the main steps such as layer comparison, fault description and boundary setting are carried out in sequence to establish the target block structural model. The grid plane size, that is, the X and Y lengths of the grid unit are set to 1 / 300 to 1 / 200 of the horizontal section of the shale gas well.

[0100] For geological lithofacies modeling, the geological lithofacies are first divided according to the mud content; then the sequential Gaussian algorithm is used, and the preset nugget values ​​corresponding to different geological rocks are set, and the main range and secondary range are set according to the length of the horizontal section to complete the geological lithofacies modeling. Mud content (Vsh) is an important factor affecting the fracturing effect of shale gas reservoirs. According to the different mud contents, three geological lithofacies can be divided, namely, sandstone: Vsh≤35%, mud sandstone: 35%<Vsh≤65%, mudstone: Vsh>65%. When modeling the lithofacies, the sequential Gaussian algorithm can be used, and the preset nugget value, main range and secondary range are set. For example, the nugget value is set to 0.1, the main range is set to 70% to 90% of the horizontal section length, and the secondary range is set to 30% to 50% of the horizontal section length.

[0101] For physical property modeling, the well logging curves can be coarsened first; then geological lithofacies, logging data, and seismic data are mutually constrained, and inter-well property interpolation is performed based on a combination of sequential Gaussian and Kriging interpolation to complete physical property modeling. Coarsening well logging curves, such as vertically coarsening porosity, permeability, and gas content logging curves, matches the well logging curves with the vertical grid units, and the permeability is coarsened using the mathematical averaging method, and other parameters can be coarsened using the harmonic mean method. Using the "geological lithofacies + logging data + seismic data" multiple constraint method, the principle of combining sequential Gaussian and Kriging interpolation is used to interpolate well properties, reduce the uncertainty and error of the difference in physical properties between wells, and improve the accuracy of physical property modeling. For example, the lithofacies model is used to constrain the porosity attribute and gas content attribute, and the porosity model is used to constrain the permeability attribute.

[0102] In the process of geological model construction, in order to make the grid system reflect the heterogeneity and non-uniformity of shale reservoirs as much as possible, and make the simulation results fully reflect the control and influence of various geological factors such as lithofacies and microstructures and various development factors on the movement and distribution of gas and water in the reservoir, when the model grid is divided, for example, there are 70 simulation layers in the vertical direction; the step length in the X direction is 10m, and the step length in the Y direction is 10m on the plane, which can basically meet the requirements of studying the physical properties of shale gas reservoirs and the laws of fluid movement. Figure 3 is a schematic diagram of a geological model of a target block according to an exemplary embodiment of the present disclosure.

[0103] For the establishment of the gas reservoir numerical model, numerical simulation software can be used to establish the gas reservoir numerical model based on the geological model data output by the geological model, as well as the fluid physical property parameters and fracturing fracture data.

[0104] Specifically, (1) the geological model data output by the geological model is imported into the numerical simulation software.

[0105] The established geological model can output Petrel fine geological model data. The output geological model data includes data output of the target well in the well trajectory file format according to the data body. The data body includes one or any combination of grid coordinate and depth data body, grid permeability data body, porosity data body, and gas content data body, and even includes other data bodies. The embodiment of the present disclosure does not limit the number of data body types and the fineness of the relationship model. Preferably, in the embodiment of the present disclosure, it includes grid coordinate and depth data body, grid permeability data body, porosity data body, gas content data body, etc., and the target well is output in the well trajectory file format. The file output by the Petrel fine geological model is imported into the numerical simulation software CMG software to generate a preliminary gas reservoir numerical model in CMG.

[0106] (2) Add fluid physical property parameters in the numerical simulation software, set two sets of relative permeability curves for matrix and fracture, and add double permeability keywords to form a single-pore double-permeability model.

[0107] The fluid physical property parameters include at least one of fluid volume coefficient, fluid density, fluid viscosity, rock compression coefficient, relative permeability and capillary force curve. Specifically, after the geological model data is imported into the CMG numerical simulation software, the fluid physical property parameters such as fluid volume coefficient, fluid density, fluid viscosity, rock compression coefficient, relative permeability and capillary force curve can be added to the CMG numerical simulation software in combination with the rock and fluid physical property parameters measured by the core and fluid PVT indoor experiments. Taking into account the great differences in pore characteristics and seepage capacity between the matrix and the hydraulic fractures, in order to set up the gas reservoir numerical model more accurately and realistically, the embodiment of the present disclosure combines the relative permeability curves measured by the core wells of the block, and also sets two sets of relative permeability curves for the matrix and the fracture in the numerical simulation software for the gas reservoir numerical model, namely the matrix relative permeability curve and the fracture relative permeability curve, which are respectively applicable to the matrix and fracture reservoirs, with reference to Figure 4 and Figure 5 , Figure 4 is the matrix relative permeability curve, Figure 5 is the fracture relative permeability curve.

[0108] The DUALPERM keyword (dual permeability keyword) is added to the gas reservoir numerical model to form a single-pore dual-permeability model to describe the natural fractures of the shale reservoir. The keyword parameters such as PERMI*FRACTURE (X-direction fracture permeability), PERMJ*FRACTURE (Y-direction fracture permeability), PERMK*FRACTURE (Z-direction fracture permeability), DIFRAC (X-direction fracture density), DJFRAC (Y-direction fracture density), and DKFRAC (Z-direction fracture density) are used to describe the permeability and density of natural fractures. Then, the natural fractures of the model are set, and a gas reservoir numerical model considering natural fractures is established.

[0109] The fluid physical properties and PVT attribute parameters reflect the flow capacity of the model fluid, including: water density under ground conditions, formation water volume coefficient, gas volume coefficient, formation water viscosity, gas viscosity under formation conditions, gas compressibility coefficient, formation water compressibility coefficient, rock compressibility coefficient, ground pressure, ground temperature, original gas reservoir temperature, saturation pressure and other parameters. Other data can use the default data of CMG numerical simulation software.

[0110] (3) Setting artificial fracturing crack data in the numerical simulation software, the artificial fracturing crack data including any combination of artificial fracturing crack position, artificial fracturing crack attribute, artificial fracturing crack height and artificial fracturing crack half length.

[0111] For the setting of artificial fracturing crack positions: According to the fracturing design plan, the locations of the horizontal section of the shale gas well fractures are determined, and the "one cluster one encryption" method is adopted to logarithmically encrypt the corresponding grids of each cluster to fully describe the development of artificial fracturing cracks and improve the convergence of the model. Each artificial fracturing crack is divided into two areas, from the inside to the outside: the main fracturing crack and the transformation area.

[0112] For the setting of artificial fracturing crack properties: artificial fracturing cracks are set using the logarithmic grid encryption method, the encrypted central grid is used to describe the main fracturing seam, and the adjacent grids are described as the transformation area. The artificial fracturing crack opening and artificial fracturing crack permeability are set based on the conductivity of the encrypted grid being equal to that of the artificial fracturing crack.

[0113] Setting of artificial fracturing crack height: The artificial fracturing crack height is set by setting the number of layers that the artificial fracturing crack passes through above and below the perforation layer.

[0114] Setting of half-length of artificial fracturing cracks: Based on the microseismic monitoring results after each section of artificial fracturing, the half-length of artificial fracturing cracks is set.

[0115] The artificial fracturing crack data of the model is set by setting parameters such as the artificial fracturing crack position, artificial fracturing crack opening, artificial fracturing crack permeability, artificial fracturing crack height and artificial fracturing crack half-length. Figure 6 6 is a reference schematic diagram for setting artificial fracturing fracture data, wherein 61 represents the wellbore, 62 represents the matrix, 63 represents the main fracturing fracture, and 64 represents the transformation area.

[0116] (4) Initialize the gas reservoir numerical model.

[0117] The balance method is used to initialize the gas reservoir numerical model. The medium depth of the gas reservoir, reference pressure, and gas-water interface depth are input into the CMG gas reservoir numerical model, and the pressure field and saturation field initialization are performed on the gas reservoir numerical model.

[0118] Among them, the initialization of pressure field is specifically: taking the formation pressure of a certain depth in the field test as the benchmark, the formation pressure of each grid of the gas reservoir numerical model is calculated through the hydrostatic relationship and fluid density. The initialization of saturation field is specifically: using the oil-water and gas-water interface depths in the field test and the capillary force curve measured experimentally to calculate the gas-water saturation corresponding to different depths. In the calculation of gas-water saturation distribution, the water saturation below the gas-water interface is set to the maximum water saturation of the movable zone in the gas-water relative permeability curve, and the gas saturation above the gas-water interface is set to the maximum gas saturation of the movable zone in the gas-water relative permeability curve. The gas and water saturations in the transition zone are calculated by the provided capillary force curve.

[0119] (5) Set the expansion and compaction curve.

[0120] Since shale gas reservoirs have well-developed natural fractures and are stress sensitive, an expansion and compaction curve is set in the gas reservoir numerical model to correlate the pore pressure with the changes in porosity and permeability of the shale gas reservoir without directly calculating the relationship between stress and strain. Figure 7 , Figure 7 Schematic diagram of the relationship between expansion and compaction curves.

[0121] (6) Set up gas wells in the gas reservoir numerical model.

[0122] On the one hand, according to the requirement that the perforation position information of the gas well in the gas reservoir numerical model corresponds to the xyz sequence number of the geological model grid, the coincidence correspondence between each perforation point in the gas reservoir numerical model and the geological model grid is corrected. On the other hand, shale gas well parameters are set, including well type, production system data (such as daily gas production, daily production, cumulative gas production, etc.), wellbore radius, skin coefficient, etc.

[0123] Through the above (1) to (6), the gas reservoir numerical model is established.

[0124] In some embodiments of the present disclosure, the established gas reservoir numerical model may be further calibrated.

[0125] Specifically, adjacent wells in the same layer are selected to perform historical matching verification on the gas reservoir numerical model. The historical matching parameters include: reserves, daily production, cumulative production, water content, bottom hole pressure, etc., and the matching is carried out in the order of first fitting reserves and then fitting production performance.

[0126] In the process of historical matching, since the gas reservoir numerical model has many changing parameters and a large adjustable degree of freedom, parameter sensitivity analysis can be performed to determine the adjustable parameters that have a significant impact on the fitting results. By modifying and calculating the adjustable parameters, the adjustable range of the geological parameters of the gas reservoir numerical model is determined, so that the modification of the gas reservoir numerical model parameters is within a reasonable and acceptable range.

[0127] For example, the reserve error is less than 5%, the single well production dynamic fitting rate is greater than 90%, the late fitting accuracy of shale gas wells is greater than the early and mid-term fitting accuracy, and the parameters of the model are modified in the whole area or part of the area through historical matching to obtain the final gas reservoir numerical model. Based on the historical matching verification, the parameters of the gas reservoir numerical model are modified in the whole area or part of the area to obtain the final gas reservoir numerical model.

[0128] The adjustment of the parameters of the gas reservoir numerical model may, for example, include the adjustment of porosity, the adjustment of permeability, the adjustment of relative permeability curve, the adjustment of rock and fluid compressibility coefficient, etc.

[0129] Porosity is the main factor affecting reserves. During the fitting process, according to the difference in reserves between different sub-layers and the whole, reasonable and small adjustments can be made to the porosity in individual areas. The porosity of artificial fracturing fractures can be used to correct the model reserves and also to fit the pressure of the fracturing fluid injection stage, as shown in the following calculation formula (1). By changing the porosity of artificial fracturing fractures φ nh , the injection volume of the model can be adjusted to fit the pressure in the fracturing stage.

[0130] Q 实际注入 =V·S w ·(φ nh -φ int )+Q 模型注入 (1)

[0131] Among them, Q 实际注入 The injection volume of fracturing fluid during the actual on-site fracturing process, in m 3 ;

[0132] Q 模型注入 The fracturing fluid injection volume set for the gas reservoir numerical model, in m 3 ;

[0133] V is the volume of artificial fracture, in m 3 ;

[0134] S w is the water saturation, as a decimal;

[0135] φ int is the original porosity, as a decimal;

[0136] φ nh It is the porosity of artificial fracturing fractures after correction, as a decimal.

[0137] Regarding the adjustment of the permeability K, since there are certain differences between the logging interpretation data and the core analysis results and the actual situation, and the permeabilities of the wells in the gas reservoir also vary greatly, the permeabilities of the various layers in the gas reservoir vary greatly in the plane and vertical directions. During the fitting process, the permeability value can be adjusted in the range of 0.01 to 100 times.

[0138] Regarding the adjustment of the relative permeability curve, since the measured matrix relative permeability curve only reflects the situation in a limited area, the matrix relative permeability curve can be treated as an uncertain parameter. After giving a suitable initial value, a larger adjustment can be made according to the actual dynamic situation of the gas reservoir.

[0139] Regarding the adjustment of rock and fluid compressibility coefficient C, the variation range of these coefficients is generally small and can be treated as determined parameters. However, due to the influence of saturation pressure in the rock, pressure, temperature and dissolved gas in the gas reservoir, some necessary adjustments can be made to the rock and fluid compressibility coefficients during the fitting process. In addition, appropriate and minor adjustments can be made to the PVT properties of oil, gas and water during the fitting process.

[0140] After modifying the parameters of the gas reservoir numerical model in the whole area or in part of the area in the above manner, the final gas reservoir numerical model is obtained.

[0141] The corresponding relationship between the nozzle characteristics and the gas well production pressure difference can be determined by using the wellbore pipe flow model. The construction of the wellbore pipe flow model can refer to the above embodiment and will not be repeated here.

[0142] After obtaining the final gas reservoir numerical model and wellbore pipe flow model, the simulation scheme is confirmed. In a preferred embodiment of the present disclosure, the nozzle design scheme and the well blocking time scheme are simulated at the same time. For the simulation of the nozzle scheme, countless schemes with different nozzle characteristic change trends are simulated from three aspects: nozzle size, the change amplitude of each nozzle size, and the working duration of each nozzle size. For example, a simulation scheme: the nozzles are from small to large, and the liquid is discharged continuously. The working duration of each nozzle size is not less than 10 days. The change amplitude of each nozzle size is 2 mm. The cumulative gas production of the shale gas well under this scheme can be clearly seen. By simulating countless different nozzle characteristic change trends, the nozzle characteristic change trend scheme corresponding to the optimal cumulative gas production can be found, and then used as the return nozzle characteristic scheme or the return nozzle characteristic change trend scheme. For the simulation of the well blocking time plan, a period of time, such as sixty days, can be input, and then the gas reservoir numerical model can display the bottom hole pressure corresponding to each time point during this period as the well blocking time increases in the form of a curve, analyze the change pattern of bottom hole pressure under different well blocking times, and then determine the well blocking time range after the bottom hole pressure tends to the stable stage, and finally get the optimal return well blocking time plan.

[0143] In some embodiments of the present disclosure, the nozzle size can be between 2mm and 10mm, the size change range of each level of nozzle can be between 0.5-2mm, and the working duration of each level of nozzle can be between 1-15 days for automatic simulation.

[0144] Since the CMG software cannot directly set nozzle characteristics such as nozzle size, the embodiment of the present disclosure first establishes a corresponding relationship between the nozzle characteristics and the gas well production pressure difference, and sets different gas well production pressure differences in the gas reservoir numerical model to simulate the cumulative gas production under different nozzle characteristic schemes, thereby optimizing the nozzle scheme.

[0145] The disclosed embodiment utilizes a gas reservoir numerical model to obtain multiple cumulative gas productions in a preset time period by setting different gas well production pressure differences to simulate different nozzle characteristics or different nozzle characteristic change trends, and then, based on each cumulative gas production and in combination with the correspondence between the nozzle characteristics and the gas well production pressure difference, the nozzle characteristics or nozzle characteristic change trends corresponding to the better cumulative gas production are determined as a return nozzle characteristic scheme or a return nozzle characteristic change trend scheme, thereby avoiding the blindness of selecting nozzle size based on experience; combined with the optimization of the return well blocking time, the return effect can be further improved; in the process of establishing the wellbore pipe flow model, the attribute characteristics of various aspects of the wellbore, such as the wellbore structure characteristics and the production parameters, are comprehensively considered, so that when establishing the correspondence between the nozzle characteristics and the gas well production pressure difference, the accuracy is improved; in the process of constructing the gas reservoir numerical model, the shale formation characteristics, the artificial fracturing fractures and the natural fracture network, the fracturing fluid injection and the shale reservoir imbibition capacity and other factors are comprehensively considered, and geological modeling, gas reservoir numerical simulation and node analysis methods are utilized. Through the above, the comprehensive geological model, wellbore flow model and gas reservoir numerical model have overcome the blindness of the existing flowback design scheme, achieved reasonable flowback after shale gas well compression, and improved the shale gas well production capacity and development effect.

[0146] The disclosed embodiment also provides a shale gas well flowback system optimization device, such as Figure 8 As shown, Figure 8 The schematic diagram of the structure of a shale gas well flowback system optimization device is shown, and the device comprises:

[0147] The simulation module 801 is used to obtain multiple cumulative gas production volumes in a preset time period by using a gas reservoir numerical model and simulating different nozzle characteristics or different nozzle characteristic change trends by setting different gas well production pressure differences;

[0148] The flowback optimization module 802 is used to determine a flowback nozzle characteristic scheme or a flowback nozzle characteristic change trend scheme according to multiple cumulative gas production volumes and the corresponding relationship between the nozzle characteristics and the gas well production pressure difference.

[0149] In some embodiments of the present disclosure, the device further includes a wellbore pipe flow model construction module for constructing a wellbore pipe flow model and determining a corresponding relationship between the nozzle characteristics and the gas well production pressure difference.

[0150] In some embodiments of the present disclosure, the wellbore pipe flow model construction module is specifically used to establish an initial wellbore pipe flow model of a shale gas well using steady-state multiphase flow simulation software, and input wellbore attribute characteristics, the wellbore attribute characteristics including casing length, casing size, pipeline length, pipeline size, gas-water fluid properties, and nozzle characteristics; and, based on the established initial wellbore pipe flow model, simulate the wellbore pressure profile corresponding to different nozzle characteristics, determine the gas well production pressure difference, and establish a corresponding relationship between the nozzle characteristics and the gas well production pressure difference.

[0151] In some embodiments of the present disclosure, the simulation module 801 is also used to determine the bottom hole pressures corresponding to multiple different wellbore blocking times using a gas reservoir numerical model; and to determine the wellbore blocking time range in which the bottom hole pressure tends to be stable based on each bottom hole pressure; the backflow optimization module 802 is also used to obtain the optimal backflow wellbore blocking time based on the wellbore blocking time range in which the bottom hole pressure tends to be stable.

[0152] In some embodiments of the present disclosure, the device further comprises:

[0153] A geological model building module is used to build a geological model through geological structure modeling, geological lithofacies modeling and physical property modeling;

[0154] The gas reservoir numerical model building module is used to build a gas reservoir numerical model based on the geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data.

[0155] Before establishing a model, it is generally necessary to collect data first. For example, the shale gas well flowback system optimization device can also include a data collection module for collecting geological modeling data, numerical modeling data and wellbore perfusion model data, wherein the geological modeling data include wellhead coordinates, core elevation, well inclination, logging data (such as logging curves), seismic data and other information, the numerical modeling data include fluid state equation PVT data (PVT, a state equation used to describe the relationship between fluid pressure, molar volume and temperature in equilibrium), phase permeability data, capillary force data, fracturing fluid injection data, production data and other information, the wellbore pipe flow model data include wellbore structure, wellhead dynamic data and other information.

[0156] In some embodiments of the present disclosure, the geological model construction module is specifically used to: divide the geological lithofacies according to the mud content; use the sequential Gaussian algorithm and set the preset nugget values ​​corresponding to different geological rocks, and set the main range and secondary range according to the length of the horizontal section to complete the geological lithofacies modeling.

[0157] In some embodiments of the present disclosure, when performing physical property modeling, the geological model construction module is specifically used to: coarsen the logging curve; use geological lithofacies, logging data, and seismic data to constrain each other, and perform inter-well attribute interpolation based on a combination of sequential Gaussian and Kriging interpolation to complete physical property modeling.

[0158] In some embodiments of the present disclosure, a gas reservoir numerical model building module is used to build a gas reservoir numerical model using numerical simulation software based on geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data.

[0159] In some embodiments of the present disclosure, the gas reservoir numerical model building module is specifically used to:

[0160] Importing geological model data outputted by the geological model into the numerical simulation software, wherein the geological model data includes data outputted from the target well in a well trajectory file format according to the data body, and the data body includes at least one of a grid coordinate and depth data body, a grid permeability data body, a porosity data body, and a gas content data body;

[0161] Add fluid physical property parameters in the numerical simulation software, set two sets of relative permeability curves for matrix and fracture, and add double permeability keywords to form a single-pore double permeability model. The fluid physical property parameters include at least one of fluid volume coefficient, fluid density, fluid viscosity, rock compression coefficient, relative permeability and capillary force curve;

[0162] Setting artificial fracturing crack data, the artificial fracturing crack data including at least one of an artificial fracturing crack position, an artificial fracturing crack attribute, an artificial fracturing crack height, and an artificial fracturing crack half length;

[0163] Initialize the gas reservoir numerical model and set the gas reservoir numerical model gas wells to obtain the gas reservoir numerical model; or initialize the gas reservoir numerical model, and set the expansion and compaction curve and the gas reservoir numerical model gas wells to obtain the gas reservoir numerical model.

[0164] In some embodiments of the present disclosure, when setting artificial fracturing crack data, the gas reservoir numerical model construction module: determines the location of the horizontal section of the shale gas well fracture encryption according to the fracturing design plan, and sets the artificial fracturing crack location by logarithmically encrypting each cluster of corresponding grids; uses the encrypted central grid to describe the main fracturing seam, and the adjacent grids are described as the transformation zone, and sets the artificial fracturing crack opening and artificial fracturing crack permeability according to the conductivity of the encrypted grid being equal to the conductivity of the artificial fracturing crack; sets the artificial fracturing crack height by setting the number of layers that the artificial fracturing crack passes through above and below the perforation layer; and sets the artificial fracturing crack half-length according to the microseismic monitoring results after each section of artificial fracturing.

[0165] In some embodiments of the present disclosure, when initializing the gas reservoir numerical model, the gas reservoir numerical model construction module mainly performs pressure field initialization and saturation field initialization on the gas reservoir numerical model.

[0166] In some embodiments of the present disclosure, when setting up a gas well in the gas reservoir numerical model, the gas reservoir numerical model construction module: according to the requirement of one-to-one correspondence between the perforation position information of the gas well in the gas reservoir numerical model and the xyz serial number of the geological model grid, corrects the coincidence correspondence between each perforation point in the gas reservoir numerical model and the geological model grid; and sets shale gas well parameters, which include well type, production system data, wellbore radius, and skin coefficient.

[0167] In some embodiments of the present disclosure, when setting the expansion and compaction curve, the gas reservoir numerical model construction module: establishes the relationship between the pore pressure and the porosity and permeability of the shale gas reservoir, and sets the expansion and compaction curve.

[0168] In some embodiments of the present disclosure, the gas reservoir numerical model construction module is also used to calibrate the established gas reservoir numerical model to obtain a final gas reservoir numerical model. Specifically, adjacent wells in the same layer are selected to perform historical matching verification of historical matching parameters on the gas reservoir numerical model; based on the historical matching verification results, the gas reservoir numerical model parameters are adjusted.

[0169] In some embodiments of the present disclosure, the nozzle characteristic includes the nozzle size, the nozzle characteristic change trend includes an increase in the nozzle size or a decrease in the nozzle size, or the return nozzle characteristic change trend includes an increase in the nozzle size or a decrease in the nozzle size, and at least one of the following: the change amplitude of each stage nozzle size, and the working duration of each stage nozzle size.

[0170] The relevant contents of the shale gas well flowback system optimization device provided in the embodiment of the present disclosure correspond to the shale gas well flowback system optimization method of the aforementioned exemplary embodiment. For any unfinished matters, please refer to the relevant description of the aforementioned shale gas well flowback system optimization method, which will not be repeated here.

[0171] The exemplary embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to cause the electronic device to perform the method according to the embodiment of the present disclosure when executed by the at least one processor.

[0172] The exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method according to the embodiments of the present disclosure.

[0173] refer to Fig. 9, a block diagram of an electronic device 900 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0174] like Fig. 9 As shown, the electronic device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0175] A plurality of components in the electronic device 900 are connected to the I / O interface 905, including: an input unit 906, an output unit 907, a storage unit 908, and a communication unit 909. The input unit 906 may be any type of device capable of inputting information to the electronic device 900, and the input unit 906 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 907 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 904 may include, but is not limited to, a disk, an optical disk. The communication unit 909 allows the electronic device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0176] The computing unit 901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 901 performs the various methods and processes described above. For example, in some embodiments, the foregoing method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 900 via the ROM 902 and / or the communication unit 909. In some embodiments, the computing unit 901 may be configured to perform the method of the above-described embodiment of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0177] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0178] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0179] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0180] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0181] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0182] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0183] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. A method for optimizing the flowback system of a shale gas well, characterized in that: include: By using the gas reservoir numerical model, setting different gas well production pressure differences and simulating different nozzle characteristics or different nozzle characteristic change trends, multiple cumulative gas production volumes in a preset time period are obtained; According to the multiple cumulative gas production volumes and the corresponding relationship between the nozzle characteristics and the production pressure difference of the gas well, a flowback nozzle characteristic scheme or a flowback nozzle characteristic change trend scheme is determined.

2. The method according to claim 1, characterized in that The method further comprises: using a wellbore pipe flow model to determine a corresponding relationship between the nozzle characteristics and the production pressure difference of the gas well.

3. The method according to claim 2, characterized in that The wellbore pipe flow model is constructed in the following way: An initial wellbore pipe flow model of a shale gas well is established using steady-state multiphase flow simulation software, and wellbore attribute characteristics are input, wherein the wellbore attribute characteristics include casing length, casing size, pipeline length, pipeline size, gas-water fluid properties, and nozzle characteristics; Based on the established initial wellbore pipe flow model, the wellbore pressure profile corresponding to different nozzle characteristics is simulated, the gas well production pressure difference is determined, and the corresponding relationship between the nozzle characteristics and the gas well production pressure difference is established.

4. The method according to claim 1, characterized in that: The method further comprises: Using the gas reservoir numerical model, determine the bottom hole pressure corresponding to different well blocking times; According to the bottom hole pressure of each well, determine the time range of the well blocking when the bottom hole pressure tends to be stable; According to the well-clogging time range when the bottom hole pressure tends to be stable, the optimal backflow well-clogging time is obtained.

5. The method according to claim 1, characterized in that The method further comprises: Establish geological model through geological structure modeling, geological lithofacies modeling and physical property modeling; A gas reservoir numerical model is established based on the geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data.

6. The method according to claim 5, characterized in that The geological lithofacies modeling includes: Classification of geological facies based on mud content; The geological lithofacies modeling is completed by using a sequential Gaussian algorithm, setting preset nugget values ​​corresponding to different geological rocks, and setting a major range and a minor range according to the length of the horizontal section.

7. The method according to claim 5, characterized in that The physical property modeling includes: Coarsening of well logging curves; The physical property modeling is completed by using geological lithofacies, well logging data, and seismic data to constrain each other and performing inter-well attribute interpolation based on a combination of sequential Gaussian and Kriging interpolation.

8. The method according to claim 5, characterized in that Using numerical simulation software, a gas reservoir numerical model is established based on the geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data.

9. The method according to claim 8, characterized in that Using numerical simulation software, a gas reservoir numerical model is established based on the geological model data output by the geological model, as well as fluid physical parameters and fracturing fracture data, including: Importing geological model data outputted by the geological model into the numerical simulation software, wherein the geological model data includes data outputted from the target well in a well trajectory file format according to a data body, and the data body includes at least one of a grid coordinate and depth data body, a grid permeability data body, a porosity data body, and a gas content data body; Adding fluid physical property parameters in the numerical simulation software, setting two sets of relative permeability curves for matrix and fracture, and adding double permeability keywords to form a single-pore double permeability model, wherein the fluid physical property parameters include at least one of fluid volume coefficient, fluid density, fluid viscosity, rock compressibility, relative permeability and capillary force curve; Setting artificial fracturing crack data, wherein the artificial fracturing crack data includes at least one of an artificial fracturing crack position, an artificial fracturing crack attribute, an artificial fracturing crack height, and an artificial fracturing crack half length; Initialize the gas reservoir numerical model and set the gas wells of the gas reservoir numerical model to obtain the gas reservoir numerical model; or initialize the gas reservoir numerical model and set the expansion and compaction curve and the gas wells of the gas reservoir numerical model to obtain the gas reservoir numerical model.

10. The method according to claim 9, characterized in that The setting of artificial fracturing crack data includes: According to the fracturing design plan, the locations of the horizontal fracture densification of the shale gas well are determined, and the locations of the artificial fracturing fractures are set by logarithmically encrypting the corresponding grids of each cluster; The encrypted central grid is used to describe the main fracture, and the adjacent grids are described as the transformation area. The artificial fracture aperture and artificial fracture permeability are set according to the conductivity of the encrypted grid being equal to that of the artificial fracture. The height of the artificial fracturing crack is set by setting the number of layers that the artificial fracturing crack passes through above and below the perforated layer; The half-length of the artificial fracturing crack is set based on the microseismic monitoring results after each section of artificial fracturing.

11. The method according to claim 9, characterized in that The initializing the gas reservoir numerical model comprises: The pressure field initialization and the saturation field initialization are performed on the gas reservoir numerical model.

12. The method according to claim 9, characterized in that The gas well of the gas reservoir numerical model is set, including: correcting the coincidence correspondence between each perforation point in the gas reservoir numerical model and the geological model grid according to the requirement that the perforation position information of the gas well in the gas reservoir numerical model corresponds to the xyz sequence number of the geological model grid; and The shale gas well parameters are set, wherein the shale gas well parameters include well type, production system data, wellbore radius, and skin coefficient.

13. The method according to claim 9, characterized in that The setting of the expansion and compaction curve includes: establishing a changing relationship between pore pressure and porosity and permeability of the shale gas reservoir, and setting the expansion and compaction curve.

14. The method according to claim 5, characterized in that The method further comprises calibrating the gas reservoir numerical model: Selecting adjacent wells in the same layer to perform history matching verification of history matching parameters on the gas reservoir numerical model; Based on the historical matching verification, the gas reservoir numerical model parameters are adjusted.

15. The method according to claim 1, characterized in that The nozzle feature includes the nozzle size, and the nozzle feature change trend includes the nozzle size increasing or the nozzle size decreasing, or, The nozzle characteristic change trend includes an increase in nozzle size or a decrease in nozzle size, and at least one of the following: a change amplitude of each level of nozzle size, and a working duration of each level of nozzle size.

16. The method according to claim 4, characterized in that The method further comprises: Collect geological modeling data, numerical modeling data and wellbore flow model data, wherein the geological modeling data includes wellhead coordinates, core filling elevation, well inclination, logging data, and seismic data; the numerical modeling data includes fluid state equation PVT data, phase permeability data, capillary force data, fracturing fluid injection data, and production data; the wellbore flow model data includes wellbore structure data, nozzle data, and wellhead dynamic data.

17. A shale gas well flowback system optimization device, characterized in that: include: A simulation module is used to obtain multiple cumulative gas production volumes in a preset time period by using a gas reservoir numerical model and simulating different nozzle characteristics or different nozzle characteristic change trends by setting different gas well production pressure differences; The flowback optimization module is used to determine a flowback nozzle characteristic scheme or a flowback nozzle characteristic change trend scheme according to the multiple cumulative gas production volumes and the corresponding relationship between the nozzle characteristics and the gas well production pressure difference.

18. An electronic device, characterized in that: include: at least one processor; as well as at least one memory storing a computer program, The processor calls the computer program to enable the processor to execute the method according to any one of claims 1 to 16.

19. A non-transitory computer-readable storage medium storing a computer program, characterized in that: The computer program is used to make the computer execute the method according to any one of claims 1-16.

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