Method and device for determining well closing time after shale oil well pressure and electronic equipment

Abstract

The invention provides a method and device for determining well closing time after shale oil well pressure and electronic equipment, and the method comprises the steps: determining well bottom pressure corresponding to a plurality of different well closing time through an oil reservoir numerical model; according to the well bottom pressure, the well closing time range of the stage that the well bottom pressure tends to be stable is determined; and obtaining the optimal well closing time according to the well closing time range at the stage that the bottom hole pressure tends to be stable. According to the method, the well closing time of the shale oil well after fracturing can be optimized, the fracturing fluid is fully imbibed for oil placement, and the productivity and the development effect of the shale oil 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 method, a device and an electronic device for determining the post-pressure blocking time of a shale oil well. Background Art

[0002] With the rapid development of my country's economy, conventional oil and gas resources can no longer meet its development needs. Shale oil, as a hot spot for global unconventional oil and gas exploration and development, has become an effective replacement resource for the next step of oil exploration and development. Compared with the exploitation of conventional oil and gas, shale oil development has two significant characteristics. On the one hand, shale oil is mainly developed by means of horizontal wells and large-scale hydraulic fracturing. On the other hand, conventional oil and gas are mainly driven by fluid pressure difference, and the injection medium is used to increase the pressure difference between the formation and the bottom of the well, improve the reservoir wettability and crude oil flowability to improve the development effect. Shale oil reservoirs are dense and micro-nano voids are developed, which leads to limited pressure difference driving capacity, but enhances the spontaneous imbibition capacity of shale oil reservoirs, that is, the wetting phase spontaneously replaces the non-wetting phase, and becomes an important mechanism for shale oil exploitation.

[0003] After hydraulic fracturing of a shale oil well, the well is first sealed and the imbibition effect of the shale oil reservoir is used to allow the fracturing fluid and shale oil to be fully replaced, and then the well is opened for flowback. The sealing time is an important factor affecting the imbibition effect, and it should not be too long or too short. If the sealing time is too long, it will increase the time the fracturing fluid stays in the formation around the fracture, causing hydration and affecting the development effect of the shale oil well; if the sealing time is too short, the imbibition replacement of the fracturing fluid and shale oil will be insufficient, thus affecting the production capacity of the shale oil well. At present, the judgment of the sealing time of shale oil wells after hydraulic fracturing mainly relies on indoor core imbibition experiments and field experience, resulting in a large degree of blindness in the judgment of the sealing time of shale oil wells after hydraulic fracturing. Summary of the invention

[0004] The embodiments of the present disclosure provide a method, device and electronic equipment for determining the post-fracturing shut-in time of a shale oil well, which can optimize the post-fracturing shut-in time of a shale oil well, achieve full penetration and oil placement of the fracturing fluid, and improve the productivity and development effect of the shale oil well.

[0005] According to one aspect of the present disclosure, a method for determining the post-pressure blocking time of a shale oil well is provided, comprising:

[0006] Using the reservoir numerical model, determine the bottom hole pressure corresponding to different well blocking times;

[0007] 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;

[0008] According to the well blocking time range when the bottom hole pressure tends to be stable, the optimal well blocking time is obtained.

[0009] In one embodiment, the method further comprises:

[0010] Establish geological model through geological structure modeling, geological lithofacies modeling and physical property modeling;

[0011] A 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.

[0012] In one embodiment, the geological lithofacies modeling includes:

[0013] Classification of geological facies based on mud content;

[0014] 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.

[0015] In one embodiment, the physical property modeling includes:

[0016] Coarsening of well logging curves;

[0017] 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.

[0018] In one embodiment, numerical simulation software is used to establish a reservoir numerical model based on geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data.

[0019] In one embodiment, numerical simulation software is used to establish a reservoir numerical model based on geological model data output by the geological model, as well as fluid physical parameters and fracturing fracture data, including:

[0020] 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, and a porosity data body;

[0021] 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;

[0022] 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;

[0023] Initialize the reservoir numerical model and set the reservoir numerical model oil wells to obtain the reservoir numerical model.

[0024] In one embodiment, the setting of artificial fracturing crack data includes:

[0025] According to the fracturing design plan, the locations of the horizontal fracture densification of the shale oil well are determined, and the locations of the artificial fracturing fractures are set by logarithmically encrypting the corresponding grids of each cluster;

[0026] 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.

[0027] 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;

[0028] The half-length of the artificial fracturing crack is set based on the microseismic monitoring results after each section of artificial fracturing.

[0029] In one embodiment, initializing the reservoir numerical model includes:

[0030] The pressure field initialization and the saturation field initialization are performed on the reservoir numerical model.

[0031] In one embodiment, the setting of the oil well in the oil reservoir numerical model includes: correcting the coincidence correspondence between each perforation point in the oil reservoir numerical model and the geological model grid according to the requirement that the perforation position information of the oil well in the oil reservoir numerical model corresponds to the xyz sequence number of the geological model grid; and

[0032] Shale oil well parameters are set, wherein the shale oil well parameters include well type, production system data, wellbore radius, and skin coefficient.

[0033] In one embodiment, the method further comprises: calibrating the reservoir numerical model.

[0034] In one embodiment, the correcting the reservoir numerical model includes:

[0035] Selecting adjacent wells in the same layer to perform history matching verification on the history matching parameters of the reservoir numerical model;

[0036] Based on the historical matching verification, the reservoir numerical model parameters are adjusted.

[0037] In one embodiment, determining the well blocking time range when the bottom hole pressure tends to be stable according to each bottom hole pressure includes:

[0038] According to the bottom hole pressure of each well, the variation law of bottom hole pressure under different well blocking time is analyzed;

[0039] Based on the bottom hole pressure variation law, the time range of the well blocking after the bottom hole pressure tends to be stable is determined. The bottom hole pressure tends to be stable when the pressure drop rate is less than 0.1 MPa / day.

[0040] In one embodiment, the method further comprises:

[0041] Collect geological modeling data and numerical modeling 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.

[0042] According to another aspect of the present disclosure, a device for determining the post-pressure blocking time of a shale oil well is provided, comprising:

[0043] A bottom hole pressure determination module is used to determine the bottom hole pressures corresponding to a plurality of different well blocking times according to a reservoir numerical model;

[0044] The stable stage determination module is used to determine the well blocking time range when the bottom hole pressure tends to be stable according to each bottom hole pressure;

[0045] The optimal well-clogging time determination module is used to obtain the optimal well-clogging time according to the well-clogging time range when the bottom hole pressure tends to be stable.

[0046] In one embodiment, the device further comprises:

[0047] A geological model building module is used to build a geological model through geological structure modeling, geological lithofacies modeling and physical property modeling;

[0048] The reservoir numerical model building module is used to build a 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.

[0049] According to another aspect of the present disclosure, there is provided an electronic device, including:

[0050] at least one processor; and

[0051] at least one memory storing a computer program,

[0052] The processor calls the computer program to enable the processor to execute a method for determining the post-pressure shut-off time of a shale oil well according to one aspect of the present disclosure.

[0053] 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 a method for determining a post-pressure shut-off time of a shale oil well according to one aspect of the present disclosure.

[0054] 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.

[0055] One or more technical solutions provided in the embodiments of the present disclosure utilize a reservoir numerical model to determine the bottom hole pressures corresponding to a plurality of different well blocking times, and then determine the well blocking time range in which the bottom hole pressure tends to be stable based on each bottom hole pressure, thereby obtaining the optimal well blocking time, optimizing the well blocking time after fracturing of the shale oil well, achieving full penetration and oil placement of the fracturing fluid, and improving the production capacity and development effect of the shale oil well. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] 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:

[0057] Figure 1 A schematic flow chart of a method for determining the post-pressure shut-in time of a shale oil well according to an exemplary embodiment of the present disclosure is shown;

[0058] Figure 2 A flow chart showing another method for determining the post-pressure shut-in time of a shale oil well according to an exemplary embodiment of the present disclosure is shown;

[0059] Figure 3 A schematic diagram of a target block geological model in a method for determining a shale oil well post-pressure shut-in time according to an exemplary embodiment of the present disclosure is shown;

[0060] Figure 4 A schematic diagram showing a matrix relative permeability curve in a method for determining a shale oil well post-pressure shut-in time according to an exemplary embodiment of the present disclosure is shown;

[0061] Figure 5 A schematic diagram showing a fracture relative permeability curve in a method for determining a shale oil well post-pressure shut-in time according to an exemplary embodiment of the present disclosure is shown;

[0062] Figure 6 A reference schematic diagram of artificial fracturing fracture data setting in a reservoir numerical model in a method for determining the post-pressure shut-in time of a shale oil well according to an exemplary embodiment of the present disclosure is shown;

[0063] Figure 7 A curve diagram showing changes in bottom hole pressure after simulation for different well shut-in times in a method for determining well shut-in time after pressure reduction of a shale oil well according to an exemplary embodiment of the present disclosure is shown;

[0064] Figure 8 A structural block diagram of a device for determining the post-pressure blocking time of a shale oil well according to an exemplary embodiment of the present disclosure is shown;

[0065] 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

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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".

[0070] 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.

[0071] The following describes the solutions of the embodiments of the present disclosure with reference to the accompanying drawings.

[0072] The present disclosure provides a method for determining the post-pressure blocking time of a shale oil well. Figure 1 As shown, Figure 1A flow chart of a method for determining the post-pressure blocking time of a shale oil well is shown in an exemplary embodiment of the present disclosure. The method comprises:

[0073] S101, using a reservoir numerical model, determining bottom hole pressures corresponding to a plurality of different well blocking times;

[0074] S102, determining a well-clogging time range when the bottom hole pressure tends to be stable according to each bottom hole pressure;

[0075] S103, obtaining an optimal well blocking time according to a well blocking time range when the bottom hole pressure tends to be stable.

[0076] The disclosed embodiment utilizes a reservoir numerical model to determine the bottom hole pressures corresponding to a plurality of different well shut-in times, and then determines the well shut-in time range in which the bottom hole pressure tends to be stable according to each bottom hole pressure, thereby obtaining the optimal well shut-in time, optimizing the well shut-in time after fracturing of the shale oil well, achieving full penetration and oil placement of the fracturing fluid, and improving the production capacity and development effect of the shale oil well.

[0077] 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 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 reservoir numerical model, input a period of time in advance, such as sixty days, and then let the 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).

[0078] 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.

[0079] In some embodiments of the present disclosure, a reservoir numerical model may be constructed first. Figure 2 , Figure 2 A flow chart showing another method for determining the post-pressure shut-in time of a shale oil well according to an exemplary embodiment of the present disclosure is shown. The method may further include:

[0080] S201, establishing a geological model through geological structure modeling, geological lithofacies modeling and physical property modeling;

[0081] S202, establishing a reservoir numerical model based on geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data;

[0082] S203, using a reservoir numerical model, determining bottom hole pressures corresponding to a plurality of different well blocking times;

[0083] S204, determining a well-clogging time range when the bottom hole pressure tends to be stable according to each bottom hole pressure;

[0084] S205, obtaining an optimal well blocking time according to a well blocking time range when the bottom hole pressure tends to be stable.

[0085] Among them, S203 to S205 are similar to the aforementioned S101 to S103 and will not be repeated here.

[0086] The geological model is established by comprehensively considering the geological structure, geological lithofacies and physical properties. The geological model data output by the geological model is then combined with factors such as fluid physical properties and fracturing fracture data to establish a numerical model of the reservoir, thereby optimizing the well blocking time, achieving full penetration and oil placement of the fracturing fluid, and improving the production capacity and development effect of shale oil wells.

[0087] Before building a model, it is generally necessary to collect data first. For example, the collected data include geological modeling data and numerical modeling data, wherein the geological modeling data include wellhead coordinates, core elevation, well inclination, logging data (such as logging curves), seismic data and other information, and 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.

[0088] By sorting and analyzing the existing geological and well data and arranging the modeling data, you can choose to use Petrel software to start building a geological model. Start building a geological model through geological structure modeling, geological lithofacies modeling, and physical attribute modeling.

[0089] 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 oil well.

[0090] 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 oil 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.

[0091] 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 other 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 the porosity model is used to constrain the permeability attribute.

[0092] 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 oil and water in the reservoir, when the model grid is divided, for example, there are 50 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 oil 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.

[0093] With respect to step S202, numerical simulation software may be used to establish a reservoir numerical model based on geological model data output by the geological model, as well as fluid physical property parameters and fracturing fracture data.

[0094] Specifically, (1) the geological model data output by the geological model is imported into the numerical simulation software.

[0095] The geological model established in S201 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, and porosity 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, 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 reservoir numerical model in CMG.

[0096] (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.

[0097] 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 matrix and fracturing fractures, in order to set up the reservoir numerical model more accurately and realistically, the embodiment of the present disclosure simulates the relative permeability curve measured by the coring wells in the block, and two sets of relative permeability curves for matrix and fracture are also set in the numerical simulation software for the 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.

[0098] The DUALPERM keyword (dual permeability keyword) is added to the reservoir numerical model to form a single-pore dual-permeability model to describe the natural fractures of shale reservoirs, and 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, and then the natural fractures of the model are set to establish a reservoir numerical model taking natural fractures into consideration.

[0099] The fluid physical properties and PVT attribute parameters reflect the flow ability of the model fluid, including: water density under ground conditions, oil density under ground conditions, formation water volume coefficient, crude oil volume coefficient, formation water viscosity, oil viscosity under formation conditions, crude oil compressibility coefficient, formation water compressibility coefficient, rock compressibility coefficient, ground pressure, ground temperature, original reservoir temperature, saturation pressure, crude oil dissolved oil-gas ratio and other parameters. Other data can use the default data of CMG numerical simulation software.

[0100] (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.

[0101] For the setting of artificial fracturing crack positions: According to the fracturing design plan, the locations of the horizontal section of the shale oil 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] (4) Initialize the reservoir numerical model.

[0107] The equilibrium method is used to initialize the reservoir numerical model. The reservoir medium depth, reference pressure, and oil-water interface depth are input into the CMG reservoir numerical model, and the pressure field and saturation field initialization are performed on the reservoir numerical model.

[0108] Among them, the pressure field is initialized, specifically: the formation pressure of a certain depth tested on site is used as a benchmark, and the formation pressure of each grid of the reservoir numerical model is calculated through the hydrostatic relationship and fluid density. The saturation field is initialized, specifically: the oil-water saturation corresponding to different depths is calculated based on the depth of the oil-water and oil-gas interfaces tested on site and the capillary force curve measured experimentally. In the calculation of oil-water saturation distribution, the water saturation below the oil-water interface is set to the maximum water saturation of the movable zone in the oil-water relative permeability curve, and the oil saturation above the oil-water interface is set to the maximum oil saturation of the movable zone in the oil-water relative permeability curve. The oil and water saturations in the transition zone are calculated by the provided capillary force curve.

[0109] (5) Set up the oil wells in the numerical reservoir model.

[0110] On the one hand, according to the requirement that the perforation position information of the oil well in the reservoir numerical model corresponds to the xyz sequence number of the geological model grid, the coincidence correspondence between each perforation point in the reservoir numerical model and the geological model grid is corrected. On the other hand, shale oil well parameters are set, including well type, production system data (such as daily liquid production, daily production, cumulative liquid production, etc.), wellbore radius, skin coefficient, etc.

[0111] Through the above (1) to (5), the reservoir numerical model is established.

[0112] In some embodiments of the present disclosure, the established reservoir numerical model may be further calibrated.

[0113] Specifically, adjacent wells in the same layer are selected to perform historical fitting verification on the reservoir numerical model. The historical fitting parameters include reserves, daily production, cumulative production, bottom hole pressure, etc., and the procedure is carried out in the order of fitting reserves first and then fitting production performance.

[0114] In the process of historical matching, since the 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 reservoir numerical model is determined, so that the modification of the reservoir numerical model parameters is within a reasonable and acceptable range.

[0115] Based on the historical matching verification, the parameters of the reservoir numerical model are modified in the whole area or part of the area to obtain the final reservoir numerical model.

[0116] The adjustment of the parameters of the 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, etc.

[0117] 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.

[0118] Q 实际注入 =V·S w ·(φ nh -φ int )+Q 模型注入 (1)

[0119] Among them, Q 实际注入 The injection volume of fracturing fluid during the actual on-site fracturing process, in m 3 ;

[0120] Q 模型注入 The injection volume of fracturing fluid set for the reservoir numerical model, in m 3 ;

[0121] V is the volume of artificial fracture, in m 3 ;

[0122] S w is the water saturation, as a decimal;

[0123] φ int is the original porosity, as a decimal;

[0124] φ nh It is the porosity of artificial fracturing fractures after correction, as a decimal.

[0125] 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 reservoir also vary greatly, the permeabilities of the various layers in the 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.

[0126] 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 reservoir.

[0127] 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 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.

[0128] After modifying the parameters of the reservoir numerical model in the whole area or part of the area in the above way, the final reservoir numerical model is obtained, and then the bottom hole pressure variation law under different well blocking time is analyzed according to each bottom hole pressure using the final reservoir numerical model; based on the bottom hole pressure variation law, the well blocking time range after the bottom hole pressure tends to be stable is determined, and the well blocking is ended 1 to 2 days after the bottom hole pressure tends to be stable, thereby obtaining the optimal well blocking time after pressure reduction of shale oil wells. For example, refer to Figure 7 , Figure 7 The figure shows the change of bottom hole pressure after simulation for different well blocking time. The analysis shows that with the increase of well blocking time, the bottom hole pressure decreases and tends to be flat in the third stage to reach a stable stage. Therefore, the best well blocking time for this shale oil well is 15 days.

[0129] The disclosed embodiment utilizes a reservoir numerical model to determine the optimal well-clogging time, thereby avoiding the blindness of well-clogging time judgment. In the process of constructing the reservoir numerical model, factors such as shale formation characteristics, artificial fracturing fractures and natural fracture networks, fracturing fluid injection, and shale reservoir imbibition capacity are comprehensively considered. By utilizing geological modeling and reservoir numerical simulation methods, the well-clogging time after shale oil well fracturing is optimized, and sufficient fracturing fluid infiltration and oil placement are achieved, thereby improving the production capacity and development effect of shale oil wells.

[0130] The disclosed embodiment also provides a device for determining the post-pressure blocking time of a shale oil well. Figure 8 As shown, Figure 8 The schematic diagram of the structure of the device for determining the time of blocking the well after the shale oil well is shown, and the device comprises:

[0131] The bottom hole pressure determination module 801 is used to determine the bottom hole pressures corresponding to a plurality of different well blocking times according to the reservoir numerical model;

[0132] The stable stage determination module 802 is used to determine the well blocking time range when the bottom hole pressure tends to be stable according to each bottom hole pressure;

[0133] The optimal well-clogging time determination module 803 is used to obtain the optimal well-clogging time according to the well-clogging time range when the bottom hole pressure tends to be stable.

[0134] In some embodiments of the present disclosure, the device for determining the post-pressure blocking time of a shale oil well further includes:

[0135] A geological model building module is used to build a geological model through geological structure modeling, geological lithofacies modeling and physical property modeling;

[0136] The reservoir numerical model building module is used to build a 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.

[0137] Before establishing a model, it is generally necessary to collect data first. For example, the device for determining the time of shale oil well shut-in after wellbore pressure can also include a data collection module for collecting geological modeling data and numerical modeling data, wherein the geological modeling data include wellhead coordinates, core elevation, well inclination, logging data (such as logging curves), seismic data and other information, and 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.

[0138] 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.

[0139] 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.

[0140] In some embodiments of the present disclosure, the reservoir numerical model building module is used to build a 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.

[0141] In some embodiments of the present disclosure, the reservoir numerical model building module is specifically used to:

[0142] 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, and a porosity data body;

[0143] 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;

[0144] 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;

[0145] Initialize the reservoir numerical model and set the reservoir numerical model oil wells to obtain the reservoir numerical model.

[0146] In some embodiments of the present disclosure, when setting artificial fracturing crack data, the reservoir numerical model construction module: determines the location of the encrypted fractures in the horizontal section of the shale oil well according to the fracturing design plan, and sets the location of the artificial fracturing cracks 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.

[0147] In some embodiments of the present disclosure, when initializing the reservoir numerical model, the reservoir numerical model construction module mainly performs pressure field initialization and saturation field initialization on the reservoir numerical model.

[0148] In some embodiments of the present disclosure, when setting up the oil wells in the oil reservoir numerical model, the reservoir numerical model construction module: according to the requirement of one-to-one correspondence between the perforation position information of the oil wells in the oil reservoir numerical model and the xyz serial number of the geological model grid, corrects the coincidence correspondence between each perforation point in the oil reservoir numerical model and the geological model grid; and sets shale oil well parameters, which include well type, production system data, wellbore radius, and skin coefficient.

[0149] In some embodiments of the present disclosure, the reservoir numerical model building module is also used to calibrate the established reservoir numerical model to obtain a final reservoir numerical model. Specifically, adjacent wells in the same layer are selected to perform history matching verification of history matching parameters on the reservoir numerical model; based on the history matching verification results, the reservoir numerical model parameters are adjusted.

[0150] The relevant contents of the device for determining the time of a shale oil well being blocked after pressure reduction provided in the embodiment of the present disclosure correspond to the method for determining the time of a shale oil well being blocked after pressure reduction of the aforementioned exemplary embodiment. For any matters not covered, please refer to the relevant description of the method for determining the time of a shale oil well being blocked after pressure reduction, which will not be repeated here.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] like Fig. 9As 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.

[0155] 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.

[0156] 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 aforementioned 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 method XXX in any other appropriate manner (e.g., by means of firmware).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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 determining the post-pressure blocking time of a shale oil well, characterized in that: include: Using the 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 blocking time range when the bottom hole pressure tends to be stable, the optimal well blocking time is obtained.

2. 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 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.

3. The method according to claim 2, 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.

4. The method according to claim 2, 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.

5. The method according to claim 2, characterized in that: Using numerical simulation software, a 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 Using numerical simulation software, a 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, and a porosity 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 reservoir numerical model and set the reservoir numerical model oil wells to obtain the reservoir numerical model.

7. The method according to claim 6, 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 oil 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.

8. The method according to claim 6, characterized in that The initializing the reservoir numerical model comprises: The pressure field initialization and the saturation field initialization are performed on the reservoir numerical model.

9. The method according to claim 6, characterized in that The setting of the oil well in the oil reservoir numerical model includes: correcting the coincidence correspondence between each perforation point in the oil reservoir numerical model and the geological model grid according to the requirement that the perforation position information of the oil well in the oil reservoir numerical model corresponds to the xyz sequence number of the geological model grid; and Shale oil well parameters are set, wherein the shale oil well parameters include well type, production system data, wellbore radius, and skin coefficient.

10. The method according to claim 2, characterized in that The method further comprises: correcting the reservoir numerical model.

11. The method according to claim 10, characterized in that The correcting of the reservoir numerical model comprises: Selecting adjacent wells in the same layer to perform history matching verification on the history matching parameters of the reservoir numerical model; Based on the historical matching verification, the reservoir numerical model parameters are adjusted.

12. The method according to claim 1, characterized in that Determining the well-clogging time range when the bottom hole pressure tends to be stable according to each bottom hole pressure includes: According to the bottom hole pressure of each well, the variation law of bottom hole pressure under different well blocking time is analyzed; Based on the bottom hole pressure variation law, the time range of the well blocking after the bottom hole pressure tends to be stable is determined. The bottom hole pressure tends to be stable when the pressure drop rate is less than 0.1 MPa / day.

13. The method according to claim 2, characterized in that The method further comprises: Collect geological modeling data and numerical modeling 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.

14. A device for determining the time of a shale oil well being blocked after pressure reduction, characterized in that: include: A bottom hole pressure determination module is used to determine the bottom hole pressures corresponding to a plurality of different well blocking times according to a reservoir numerical model; The stable stage determination module is used to determine the well blocking time range when the bottom hole pressure tends to be stable according to each bottom hole pressure; The optimal well-clogging time determination module is used to obtain the optimal well-clogging time according to the well-clogging time range when the bottom hole pressure tends to be stable.

15. The device according to claim 14, characterized in that The device also includes: A geological model building module is used to build a geological model through geological structure modeling, geological lithofacies modeling and physical property modeling; The reservoir numerical model building module is used to build a 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.

16. 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 13.

17. 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 1-13.

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