Full-scale sanding optimization design method for shale oil crack

Abstract

The invention provides a shale oil fracture full-scale sand paving optimization design method. The method comprises the steps that field real parameters influencing the migration behavior of a proppant are obtained; constructing a propping agent migration numerical model, adding the obtained parameters into the numerical model, and simulating the migration of the propping agent in the complex fracture; and carrying out a multi-factor orthogonal test on the migration of the proppant to obtain target construction parameters. According to the invention, the migration and paving effect of the propping agent in the complex crack can be more accurately simulated, theoretical guidance is better provided for optimization of an on-site sand adding process, and full-scale support of the reservoir fracturing crack is realized. A new direction is provided for optimization of a current sand adding process, so that sand blocking is reduced, and the whole-area supporting effect of the crack is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil and gas field development, and particularly relates to a shale oil fracture full-scale sand placement optimization design method. BACKGROUND

[0002] With the continuous growth of global energy demand and the gradual depletion of traditional oil and gas resources, the development of unconventional oil and gas resources has gradually become an important strategic direction to ensure energy security. Deep shale reservoirs, as an important target for the development of unconventional oil and gas resources, their efficient development depends on the complex fracture network formed by hydraulic fracturing and the effective placement of proppants.

[0003] Traditional shale reservoir proppant transport numerical models are mostly based on idealized assumptions (such as smooth fracture walls, spherical particles, and not considering filtration), ignoring the influence of rock roughness, particle shape, and filtration on proppant transport behavior, and are difficult to accurately characterize the proppant transport and placement rules under real fracture conditions in deep shale, resulting in poor field application results and little improvement in proppant support effect in fractures. Secondly, the current fracturing generally uses step sanding technology, the core problem of which is that the near-well sand plugging risk is high, and the proppant distribution is uneven. At the same time, the fixed density ceramic is used for sanding in the field. Because the conventional ceramic has high density and fast settling velocity, a large amount of proppant is accumulated in the near-end of the fracture to form a sand dam, hindering the subsequent sand-carrying fluid from migrating to the deep, resulting in poor support effect in the far-well fracture and branch fracture, low proppant coverage, and severe embedding of conventional density proppant, rapid decline of conductivity, and difficulty in adapting to complex fracture networks in deep shale.

[0004] The existing shale reservoir proppant transport numerical model mostly considers smooth fracture walls and spherical particles, does not consider filtration, ignores the influence of rock roughness, particle shape, and filtration on proppant transport behavior, and at the same time, the existing step sanding technology causes high near-well sand plugging risk and uneven distribution of proppant in the far-well, and the fixed density ceramic used in the existing sanding process has fast settling velocity, resulting in poor support effect in the far-well fracture and branch fracture, low proppant coverage, and other technical problems. SUMMARY

[0005] The embodiments of the present application provide a shale oil fracture full-scale sand placement optimization design method, which proposes a new direction for the current sanding process optimization, thereby reducing sand plugging and improving the full-scale support effect of the fracture.

[0006] In a first aspect, the embodiments of the present application provide a shale oil fracture full-scale sand placement optimization design method, comprising:

[0007] Obtaining field real parameters affecting proppant transport behavior;

[0008] Constructing a high-precision numerical model of proppant transport, and adding the obtained parameters to the numerical model to simulate the proppant transport in the complex fracture;

[0009] A multi-factor orthogonal test is performed on the proppant migration to optimize the fracturing operation parameters.

[0010] In some embodiments, the parameters affecting the proppant migration behavior are obtained by:

[0011] A standard core of a target reservoir is prepared, a Brazilian split experiment is performed on the standard core of the target reservoir, a fracturing fracture is obtained, three-dimensional coordinate data of the fracture surface are obtained by a laser scanner, after denoising, a fractal dimension of the fracture surface is calculated, an average value is obtained, and the required roughness is obtained.

[0012] In some embodiments, the parameters affecting the proppant migration behavior are obtained by:

[0013] A numerical simulation and an accumulation angle experiment of the particle accumulation angle test based on the DEM method are performed, a proppant accumulation angle test device is designed, the center of the funnel should be on a vertical line; the accumulation angle of each proppant is measured three times, and the average accumulation angle is obtained; a DEM model of the particle accumulation angle test is established, the friction coefficient of the particles is adjusted continuously until the accumulation angle of the particles meets the error condition of the physical experiment, and the friction coefficient of the particles at this time is obtained.

[0014] In some embodiments, the parameters affecting the proppant migration behavior are obtained by:

[0015] A plurality of proppant particles are fixed on a glass plate and placed on an inclinable wall surface, which is a target reservoir rock plate; one end of the wall surface is slowly lifted to form an inclination angle α, until the particles start to slide, and the tangent value of α is the static friction coefficient;

[0016] A movable rock plate is constructed in the upper layer, a proppant particle layer is constructed in the middle layer, and a horizontal moving rock plate is constructed in the lower layer; a force sensor is connected to the upper layer plate, and the lower layer plate moves horizontally at a constant speed; when the particles roll due to the movement of the lower layer plate, the rolling friction coefficient is calculated by measuring the force balance of the upper layer plate.

[0017] In some embodiments, the parameters affecting the proppant migration behavior are obtained by:

[0018] The filter loss coefficient obtained by multiple instantaneous pump pressure tests during the preflush stage is collected.

[0019] In some embodiments, the parameters affecting the proppant migration behavior are obtained by:

[0020] Based on a high-definition microscope, multiple types of typical particle shape images are extracted and three-dimensional drawings are made; when a proppant model is constructed, a Multisphere function is used to construct a proppant model that conforms to the true shape, and the injection mode is set to be randomly distributed in multiple shapes.

[0021] In some embodiments, the proppant transport numerical model is constructed, the acquired parameters are attached to the numerical model, and simulation of proppant transport in complex fractures includes:

[0022] The model is designed to have two secondary branch fractures, four primary branch fractures, and two horizontal fractures; fracture height data under the acquired roughness is generated by using a fracture generation software, is processed by a data processing software, is imported into a three-dimensional modeling software to construct a model from points to lines, from lines to surfaces, and from surfaces to bodies, is stitched with different branch fractures and the main fracture by a body function to form an entity, is divided into grids, and finally proppant transport is calculated based on a CFD-DEM method.

[0023] In some embodiments, the multi-factor orthogonal test is performed on the proppant transport, and the target construction parameters are acquired, including:

[0024] The set of fracturing optimization parameters includes in-fracture flow rate, sand ratio, sand adding mode, pumping mode, density, density combination, viscosity, particle size, and particle size combination, an orthogonal test scheme is established, target construction parameters are acquired, and the influence degree of each factor is obtained; for a deep shale target reservoir, a constant sand adding mode combined with different density and particle size proppant mixed pumping mode is adopted.

[0025] In some embodiments, constant sand adding and variable density proppant ratio combination regulation are adopted to optimize the fracture conductivity distribution.

[0026] In a second aspect, the application provides a sanding process optimization method, including the shale oil fracture full-scale sanding optimization design method in any of the above.

[0027] The shale oil fracture full-scale sanding optimization design method has the following beneficial effects:

[0028] The application can more accurately simulate proppant transport and placement effect in complex fractures, better provide theoretical guidance for field sanding process optimization, and realize full-scale support of reservoir fracturing fractures. The application provides a new direction for the current sanding process optimization, thereby reducing sand plugging and improving the full-scale support effect of fractures. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The flowchart of the shale oil fracture full-scale sanding optimization design method is shown;

[0030] Figure 2 Another flowchart of the shale oil fracture full-scale sanding optimization design method is shown;

[0031] Figure 3 The schematic diagram for acquiring the static friction coefficient of proppant particles is shown;

[0032] Figure 4A schematic diagram for obtaining the rolling friction coefficient of proppant particles. Detailed Implementation

[0033] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0034] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The following description provides multiple embodiments of the invention, which can be substituted or combined with each other. Therefore, this application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more other possible combinations of features A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0035] Example 1

[0036] like Figure 1 As shown, the full-scale sand-laying optimization design method for shale oil fractures in this application includes: S101, obtaining the actual field parameters affecting proppant migration behavior; S103, constructing a high-precision numerical model of proppant migration, and adding the obtained parameters to the numerical model to simulate proppant migration in complex fractures; S105, conducting multi-factor orthogonal experiments on proppant migration to obtain target construction parameters.

[0037] This application can more accurately simulate the proppant migration and placement effect in complex fractures, provide better theoretical guidance for the optimization of field sand addition processes, and achieve full-scale support for reservoir fracturing fractures.

[0038] Example 2

[0039] like Figure 2 As shown, the full-scale sand-laying optimization design method for shale oil fractures in this application includes:

[0040] Step 1: Prepare four standard core samples of the target reservoir, each 2.5 cm in diameter and 5 cm in length. Conduct Brazilian fracturing experiments on the standard core samples of the target reservoir to obtain fracturing fractures. Combine this with a laser scanner to obtain the three-dimensional coordinate data of eight fracture surfaces. After noise reduction, calculate the fractal dimension of the fracture surfaces using a fractal dimension calculation program, and obtain the average value to get the required roughness.

[0041] Step 2: Conduct numerical simulation of particle packing angle test based on DEM method (which can be completed by EDEM commercial software) and packing angle experiment, design proppant packing angle test device, wherein the taper of the funnel is 60°, the flow outlet diameter is 5 mm, the center of the funnel should be on a vertical line, the flow outlet bottom is 80 mm away from the disc surface, and the disc diameter is 80 mm. Measure the packing angle of each proppant three times to obtain the average packing angle. At the same time, establish a DEM model of particle packing angle test (which can be completed by commercial software EDEM), simulate by adjusting the friction coefficient of the particles until the packing angle of the particles meets the error condition with the physical experiment, for example, the error is not more than 5%, and obtain the friction coefficient of the particles at this time.

[0042] Step 3: As shown in Figure 3 , fix a plurality of proppant particles on a small glass plate (simulate a particle group) on an inclinable wall (target reservoir rock plate). One end of the wall can be slowly lifted to form an inclination angle a, until the particles start to slide, and the tangent value of a is the static friction coefficient.

[0043] μ = tan a

[0044] As shown in Figure 4 , build an upper movable rock plate, a middle proppant particle layer, and a lower horizontal moving rock plate. The force sensor is connected to the upper plate, and the lower plate moves horizontally at a constant speed. When the particles roll due to the movement of the lower plate, the rolling friction coefficient is calculated by measuring the force balance of the upper plate.

[0045]

[0046] Step 4: Collect the filtration coefficient obtained from the 2-time instantaneous pump pressure test in the preflush stage.

[0047] Step 5: Extract at least 5 types of typical particle morphology images based on high-definition microscope, and perform three-dimensional depiction. When building a proppant model in the DEM method (which can be realized by commercial software EDEM), use the Multisphere function to build a proppant model that conforms to the real shape, and set its injection mode to five random shapes.

[0048] Step 6: Design the model with two secondary branch fractures, four primary branch fractures, and two horizontal fractures. Use fracture generation software (such as Synfrac) to generate fracture height data under the roughness obtained in Step 1 (assuming a wall roughness of 2.7 here), process it with data processing software (such as EXECL), and import it into 3D modeling software (such as SolidWorks) to construct the model from points to lines, from lines to surfaces, and from surfaces to bodies. Stitch different branch fractures with the main fracture through the equipment body function to form a solid, divide the grid, and finally calculate the proppant migration based on the CFD-DEM method (which can be realized with commercial software FLUENT-EDEM).

[0049] Step 7: With the goal of obtaining the optimal proppant area and conductivity, based on the set of fracturing optimization parameters and value range, analyze the multi-factor orthogonal test scheme for proppant migration, obtain the optimal construction parameters, and the different influence degrees of each factor. Meanwhile, for deep shale target reservoirs, it is recommended to use constant sanding combined with different density and particle size proppant mixed pumping to improve the full-scale proppant support effect of the fracture.

[0050] The set of fracturing optimization parameters includes in-fracture flow rate, sand ratio, sanding method, pumping method, density, density combination, viscosity, particle size, and particle size combination. An orthogonal test scheme is established to obtain the optimal construction parameters and the different influence degrees of each factor.

[0051] In the proppant migration simulation experiment under different in-fracture flow rates, the in-fracture flow rate value range is 0.05 m / s to 0.25 m / s. This range is preferred because the fracturing fluid displacement will affect the settling speed of the proppant, changing its migration behavior. Too large displacement will cause the proppant to advance towards the far well, resulting in poor near-well proppant support and fracture closure; too small displacement will cause the proppant to form a sand dam near the well too early, causing sand plugging.

[0052] In the proppant migration simulation experiment under different sand ratios, the sand ratio value range is 5% to 25%. This range is preferred because too small sand ratio will result in insufficient fracture proppant support, while too large sand ratio will cause sand plugging. Moreover, after the sand ratio exceeds 25%, the sand dam height in the fracture does not increase significantly, and the full-scale proppant support effect of the fracture is maximized within this range.

[0053] In the proppant migration simulation experiment under different sanding methods, the sanding method value range is constant sanding, stepwise sanding, and slug sanding. This range is preferred because stepwise sanding is commonly used in the field, but it is prone to sand plugging and uneven distribution of proppant at the far well, while constant sanding can effectively avoid these problems.

[0054] In the proppant migration simulation experiment under different pumping methods, the pumping method value range is pulse pumping and continuous pumping.

[0055] In the proppant migration simulation experiment under different densities, the density is selected as 2000 kg / m 3 , 2500 kg / m 3 , and the density combination is to adopt the injection mode of low density first and high density later, and the ratio of the two can be set as 3:1, 2:1, 1:1, 1:2, and 1:3. In the orthogonal experiment, the average density can be used instead of the combined density. This range is preferred because currently fixed density proppants are usually used in the field, but due to their high density, they settle early and have insufficient propping effect far from the well, while the unique migration performance of low density proppants makes them more easily enter the far well branch fractures, forming better support.

[0056] In the proppant migration simulation experiment under different viscosities, the viscosity of the fracturing fluid is selected in the range of 2 mPa·s to 40 mPa·s. This range is preferred because the viscosity of the fracturing fluid will affect the settling speed of the proppant, changing its migration behavior. Too high viscosity will cause the proppant to advance towards the far well, resulting in poor near-well propping effect and fracture closure. Too low viscosity will cause the proppant to form a sand dam near the well too early, causing sand plugging.

[0057] In the proppant migration simulation experiment under different particle sizes, the particle size is selected as 40 / 70 mesh and 70 / 140 mesh. The particle size combination is to adopt the injection mode of small particle size first and large particle size later, and the ratio of the two can be set as 70 / 140 mesh:40 / 70 mesh = 2:1, 70 / 140 mesh:40 / 70 mesh = 1:1, and 70 / 140 mesh:40 / 70 mesh = 1:2. In the orthogonal experiment, the average particle size can be used instead of the combined particle size. This range is preferred because currently fixed particle size proppants are usually used in the field, which cannot achieve directional propping. However, the injection mode of small particle size first and large particle size later can better balance the propping effect improvement near and far from the well, achieving full-scale propping of the fracture.

[0058] The present application obtains the fracture wall roughness through Brazilian splitting and laser scanner; obtains the real shape of the proppant through high-definition microscope; obtains the friction coefficient of the proppant through particle packing angle experiment and numerical simulation; and determines the filtration coefficient of the fracturing fluid through high temperature and high pressure filtration instrument. Based on the CFD-DEM coupling method, the fracture wall surface with a certain roughness is generated by means of the fracture generation software, and the fracture wall surface is stitched by using the three-dimensional modeling software to generate a complex fracture system numerical model with multiple branch fractures and natural fractures that meet the requirements, and various parameters obtained in the early stage are added to the numerical simulation, so that the numerical simulation is more consistent with the real environment of the reservoir, more accurately simulates the proppant migration and placement effect in the complex fracture, better provides theoretical guidance for the optimization of the field sanding process, and realizes the full-scale propping of the reservoir fracturing fracture. The constant sanding combined with dynamic density proppant combination pumping technology is proposed, which provides a new direction for the current sanding process optimization, thereby reducing sand plugging and improving the full-scale propping effect of the fracture.

[0059] The application also provides a sanding process optimization method, comprising any one of the shale oil fracture full-scale sanding optimization design methods.

[0060] The above introduction is only the preferred embodiment of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for full-scale sand placement optimization design for shale oil fractures, characterized in that, The method comprises the following steps: Obtaining real field parameters affecting the migration behavior of proppants; Building a numerical model of proppant migration, adding the obtained parameters to the numerical model, and simulating the migration of proppants in complex fractures; Carrying out multi-factor orthogonal test on the migration of proppants to obtain target fracturing operation parameters.

2. The method of claim 1, wherein, Obtaining real field parameters affecting the migration behavior of proppants includes: Preparing standard cores of multiple target reservoirs, carrying out Brazilian splitting experiments on the standard cores of the target reservoirs, obtaining fracturing fractures, combining with the three-dimensional coordinate data of the fracture surface obtained by a laser scanner, after denoising, calculating the fractal dimension of the fracture surface, and obtaining the average value, that is, the required roughness.

3. The method of claim 1 or 2, wherein, Obtaining real field parameters affecting the migration behavior of proppants includes: Carrying out numerical simulation and accumulation angle experiment based on DEM method, designing a proppant accumulation angle test device, the center of the funnel should be on a vertical line; measuring the accumulation angle of each type of proppant three times to obtain the average accumulation angle; at the same time, a DEM model of the proppant accumulation angle test is established, the friction coefficient of the particles is adjusted continuously until the accumulation angle of the particles meets the error condition of the physical experiment, and the friction coefficient of the particles at this time is obtained.

4. The method of claim 1 or 2, wherein, Obtaining real field parameters affecting the migration behavior of proppants includes: Fixing multiple proppant particles on a glass plate and placing them on an inclinable wall, which is a target reservoir rock plate; slowly lifting one end of the wall to form an inclination angle α, until the particles start to slide, and the tangent value of α is the static friction coefficient; Building an upper layer of movable rock plate, a middle layer of proppant particle layer, and a lower layer of horizontally moving rock plate, connecting the upper layer plate with a force sensor, and moving the lower layer plate at a constant speed; when the particles roll due to the movement of the lower layer plate, the rolling friction coefficient is calculated by measuring the force balance of the upper layer plate.

5. The method of claim 1 or 2, wherein, Obtaining real field parameters affecting the migration behavior of proppants includes: Collecting the filtration coefficient obtained from multiple instantaneous pump stop pressure tests during the preflush stage.

6. The method of claim 1 or 2, wherein, Obtaining real field parameters affecting the migration behavior of proppants includes: Extracting multiple types of typical particle shape images based on a high-definition microscope, and performing three-dimensional depiction; when building a proppant model, the Multisphere function is used to build a proppant model that conforms to the real shape, and the injection mode is set to random distribution of multiple shapes.

7. The method of claim 1 or 2, wherein, Building a numerical model of proppant migration, adding the obtained parameters to the numerical model, and simulating the migration of proppants in complex fractures includes: Designing a model with two secondary branch fractures, four primary branch fractures, and two horizontal fractures; using a fracture generation software to generate fracture height data under the obtained roughness, processing the data with a data processing software, importing it into a three-dimensional modeling software to build a model from points to lines, from lines to surfaces, and from surfaces to bodies, and stitching different branch fractures with the main fracture through the equipped body function to form an entity, and dividing the grid, and finally calculating the migration of proppants based on the CFD-DEM method.

8. The method of claim 1 or 2, wherein, Carrying out multi-factor orthogonal test on the migration of proppants to obtain target fracturing operation parameters includes: The fracturing optimization parameter set comprises in-slit flow rate, sand ratio, sand adding mode, pumping mode, density, density combination, viscosity, particle size, and particle size combination, an orthogonal test scheme is established, target construction parameters are obtained, and the influence degree of each factor is obtained. For deep shale target reservoirs, a constant sand adding combined with different density and particle size proppant mixed pumping mode is adopted.

9. The method of claim 8, wherein, Constant sand adding and variable density proppant ratio combination regulation are adopted to optimize the fracture conductivity distribution.

10. A method of optimizing a sand addition process, characterized in that, The shale oil fracture full-scale sanding optimization design method of any one of claims 1-9 is included.

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