CN113673183A - Reservoir development parameter determination method and device combining particle migration influence - Google Patents
Reservoir development parameter determination method and device combining particle migration influence Download PDFInfo
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Abstract
The specification relates to the technical field of oil and gas field development, and particularly discloses a method and a device for determining reservoir development parameters in combination with particle migration influence, wherein the method comprises the following steps: acquiring particle attribute data of a target reservoir; inputting the particle attribute data of the target reservoir into a target model to obtain particle migration data in the target reservoir, wherein the target model is used for simulating the migration of particles in a complex porous medium; determining development parameters for the target reservoir based on the particle migration data. According to the scheme, the target model can be used for carrying out particle migration simulation based on particle attribute data to obtain particle migration data of the target reservoir, and then development parameters of the target reservoir can be determined based on the particle migration data to improve the reservoir development efficiency.
Description
Technical Field
The specification relates to the technical field of oil and gas field development, in particular to a method and a device for determining reservoir development parameters by combining particle migration influence.
Background
In nature and engineering practice, the phenomenon of migration of particles within porous media under various forces is widespread, for example: migration of soil particles in soil, migration of various blood cells in blood, migration of coal dust in a coal seam fracture system, migration of a hydraulic fracturing propping agent and the like.
During coal bed gas development, coal fines migrate in the fissures or pores, possibly resulting in reduced permeability. This is because the coal dust is transported and condensed together, which easily causes stagnation, resulting in a decrease in permeability, which may affect the efficiency of coal bed methane development.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the specification provides a method and a device for determining reservoir development parameters by combining particle migration influence, so that the reservoir development efficiency is improved.
The embodiment of the specification provides a reservoir development parameter determination method combining particle migration influence, which comprises the following steps: acquiring particle attribute data of a target reservoir; inputting the particle attribute data of the target reservoir into a target model to obtain particle migration data in the target reservoir, wherein the target model is used for simulating the migration of particles in a complex porous medium; determining development parameters for the target reservoir based on the particle migration data.
Embodiments of the present disclosure also provide a device for determining reservoir development parameters in combination with particle migration effects, including: the acquisition module is used for acquiring particle attribute data of a target reservoir; the input module is used for inputting the particle attribute data of the target reservoir into a target model to obtain particle migration data in the target reservoir, wherein the target model is used for simulating the migration of particles in a complex porous medium; a determination module to determine development parameters for the target reservoir based on the particle migration data.
Embodiments of the present description also provide a computer device comprising a processor and a memory for storing processor-executable instructions, which when executed by the processor implement the steps of the method for determining reservoir development parameters in conjunction with particle migration effects as described in any of the above embodiments.
Embodiments of the present description also provide a computer-readable storage medium having stored thereon computer instructions that, when executed, implement the steps of the method for determining reservoir development parameters in conjunction with the effects of particle migration described in any of the embodiments above.
In the embodiment of the description, a method for determining reservoir development parameters in combination with particle migration influence is provided, particle migration data of a target reservoir are obtained by obtaining particle attribute data of the target reservoir and performing particle migration simulation based on the particle attribute data by using a target model, and then development parameters of the target reservoir can be determined based on the particle migration data, so that reservoir development efficiency is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification, are incorporated in and constitute a part of this specification, and are not intended to limit the specification. In the drawings:
FIG. 1 is a diagram illustrating a prior art grid search method;
FIG. 2 shows a schematic of a particle migration boundary;
FIG. 3 is a diagram illustrating criteria for whether the outermost grid is searched in an embodiment of the present specification;
FIG. 4 is a diagram illustrating search and irradiation of layers of a grid in an embodiment of the present description;
FIG. 5 is a diagram illustrating the results of a boundary grid search of grains with different diameters by using the boundary grid search method in the embodiment of the present disclosure;
FIG. 6 is a diagram illustrating a layer-by-layer boundary grid search performed in three dimensions in an embodiment of the present disclosure;
FIG. 7 is a graph illustrating grain boundary grid search times compared to conventional methods in accordance with an embodiment of the present disclosure;
FIG. 8 is a diagram showing a position relationship between particles and a grid in an embodiment of the present specification;
FIG. 9 shows a block diagram of a contact search algorithm embodied in an embodiment of the present specification;
FIG. 10 is a schematic view showing a positional relationship between a particle and a boundary in the case of an irregular boundary;
FIG. 11 is a schematic diagram showing the processing of a regional solid boundary in the two-dimensional case in the embodiment of the present specification;
FIG. 12 illustrates a flow chart of a method for reservoir development parameter determination in conjunction with particle migration effects in one embodiment of the present description;
FIG. 13 shows a flow diagram of a reservoir development parameter determination apparatus incorporating particle migration effects in one embodiment of the present description;
FIG. 14 shows a schematic diagram of a computer device in an embodiment of the present description.
Detailed Description
The principles and spirit of the present description will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given solely to enable those skilled in the art to better understand and to implement the present description, and are not intended to limit the scope of the present description in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As will be appreciated by one skilled in the art, embodiments of the present description may be embodied as a system, an apparatus, a method, or a computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
In the process of developing reservoirs such as coal bed gas and the like, migration of particles such as coal dust in cracks or pores may cause reduction of permeability and further reduction of development efficiency, and the migration characteristic of the particles in a complex porous medium needs to be researched. The present specification performs a particle migration simulation based on a Lattice Boltzmann-Discrete Element Method (LBM-DEM) Method, and coupling between fluid and solid uses an immersion Moving Boundary condition (Immersed Moving Boundary Method-IMB) based on a Bounce Boundary (BB), so that this Method is hereinafter abbreviated as: LBM-IMB-DEM method.
Generally, to guarantee the resolution of particle size, a large number of control equations need to be solved for accurately describing large particle systems, but in the early days, due to limited computer computing power, a two-fluid model (TFM) was widely used from the perspective of engineering applications, however, this simplified method cannot accurately describe particle-to-fluid and particle-to-particle interactions, and many microscopic mechanisms cannot be presented. While the description of particle systems at sub-particle scale and even particle scale remains a huge challenge with the development of computer technology, some numerical methods show good prospects for development.
Depending on the flow field solution methods, these numerical methods can be broadly divided into two categories: conventional Computational Fluid Dynamics (CFD) based methods and LBM methods. The basic principle of the fluid-particle system numerical simulation method based on CFD is as follows: the Navier-Stokes equation is solved by using a finite difference method, a finite volume method or a finite element method, and the motion of the particles is described by using DEM based on Newton's second law. Such methods generally employ a local averaging method, where a single grid can hold many particles, the properties of the grid are represented by the local average values, and the interaction between the particles and the fluid is calculated based primarily on the volume fraction of the voids in the grid. Overall, such methods are computationally efficient, but the details of fluid-solid and inter-particle interactions are still not well described. The LBM-based flow-solid phase interaction numerical simulation method is mainly realized through various boundary conditions. For a rigid boundary that is static and flat, the most widely used is the BB condition. For boundaries with complex geometries, for example: porous media, moving particles, and deformable boundaries, there are three main boundary conditions: BB-based methods, extrapolation and submerged Boundary methods (IBM). For fluid-particle systems, the motion of the particles is described by the discrete element method.
The LBM-IBM-DEM method, which was continuously improved and developed since its inception, is now widely used to describe fluid-particle systems. In implementing fluid-solid coupled computing using IBM, the grids around a grain can be classified into four categories: a fluid grid, a fluid boundary grid, a solid boundary grid, and a solid grid. Wherein the fluid mesh is a mesh that is completely uncovered by the particles, the fluid boundary mesh is a mesh that is partially covered but not covered at the center of the mesh, the solid boundary mesh is a mesh that is covered at the center of the mesh but not covered at the center of the mesh, and the solid mesh is a mesh that is completely covered. The method is used for rapidly and accurately determining the attributes of the boundary grid around the particles, and is important for calculating the solid phase volume fraction of the grid near the particles and improving the calculation efficiency.
Referring to fig. 1, a diagram of a grid search method in the prior art is shown. As shown in the left diagram of fig. 1, first, a search range is determined, all grids are traversed, the distance from the center of the grid to the center of the particle is calculated, the radius of the particle is set as r, if the distance is greater than r +0.5, the grid is considered to be a fluid grid, if the distance is between r +0.5 and r, the grid is considered to be a fluid boundary grid, if the distance is between r and r-0.5, the grid is considered to be a solid boundary grid, and if the distance is less than r-0.5, the grid is considered to be a solid grid. The grid search algorithm is simple, easy to understand and easy to implement, and therefore is widely adopted. However, this method has two major drawbacks: firstly, each time step needs to traverse all grids in a search range and calculate the distance between the center of the grid and the center of the particle, which consumes a lot of time, and particularly reduces the calculation efficiency to a great extent for the particles with larger sizes and more particles; secondly, the search results are inaccurate, as shown in the right diagram of fig. 1, the grid shown by the cross lines should be a fluid boundary grid but not recognized, and the grid shown by the cross lines should be a solid boundary grid but erroneously recognized as a solid grid. Therefore, an accurate and fast boundary grid searching method is needed.
In addition, in the discrete unit method, the contact of the particles is searched at each DEM time step to calculate the interaction force between the particles, and a more common search method includes: a Verlet neighbor directory method, a connected cell method, a connected linear list method, and the like. Because each DEM time step requires a contact search, the computational efficiency is greatly reduced for systems with a large number of particles. Referring to fig. 2, a schematic diagram of a particle migration boundary is shown. In the prior art, the direct numerical simulation of the fluid-particle system by using the LBM-IMB-DEM method is mostly considered as a straight boundary, as shown in the left diagram of FIG. 2. However, the boundaries of most practical problems are irregular boundaries, as shown in the right diagram of fig. 2, especially for porous media, the boundaries are very complex, and describing the migration of particles within the porous media is very challenging. Therefore, a treatment method is needed to achieve the transport of particles within complex porous media.
Based on the problems, the embodiment of the specification provides a direct numerical simulation method for migration of fluid-carried particles in any complex porous medium based on LBM-IMB-DEM, the method comprises an efficient and accurate particle boundary grid search algorithm, a particle contact search algorithm and a region boundary solid grid processing method, the migration of particles in a target reservoir can be accurately simulated, and then reservoir development parameters can be determined based on simulated migration data to improve reservoir development efficiency.
First, the embodiment of the present disclosure provides a method for searching a boundary grid around a particle, which is suitable for two-dimensional and three-dimensional situations, and the method only determines whether a finite point is covered by a particle, thereby achieving fast and accurate determination of a particle boundary grid and a solid grid. Especially for the condition of larger particle radius, the method can greatly improve the calculation efficiency.
The method is illustrated below by taking a two-dimensional case as an example, and comprises the following steps:
wherein x isiThe coordinate value in the ith dimension in the central coordinate data of the particle is shown, i is a coordinate system dimension serial number, i is 1 and 2 for a two-dimensional target model, and i is 1,2,3 and N for a three-dimensional target modeli maxAs an upper limit of the coordinate value of the search range in the i-th dimension, Ni minThe lower limit of the coordinate value of the search range in the ith dimension is r, and the radius of the particle is r. The processing of the upper bound by the second above formula is intended to ensure that the search range is square. Of course, in other embodiments, the search range may be rectangular.
Referring to fig. 3 and 4, the area enclosed by the large box in fig. 3 and 4 is the determined search area. The dotted line in fig. 3 is an area line, which includes a horizontal area line and a vertical area line, and mainly functions to divide the search area into four parts: an upper left portion, an upper right portion, a lower left portion, and a lower right portion. The area line is determined from the position of the center of the particle, rounded off from the coordinates of the center of the particle. The following search process is described by taking the upper left part and the upper right part as an example, the search starts from the uppermost grid to the middle grid (i.e., the middle grid corresponding to the rounded value of the center coordinates of the particle) and ends, and the search process is the same for the lower left part and the lower right part, except that the search starts from the lowermost grid to the middle grid.
And 2, determining whether the outermost grid needs to be searched. Referring to fig. 3, a diagram of criteria for whether the outermost grid is searched is shown. As shown in FIG. 3, the coordinates of the five-pointed star 1 are (round (x)1),N2 max-1), wherein round represents rounding. If the five-pointed star 1 is covered by a particle, then the outermost grid needs to be searched, otherwise it is not. The five-pointed star 2 coordinate is (round (x)1),N2 min+1). If the five-pointed star 2 is not covered by a particle, it represents that the underlying outermost grid does not need to be searched.
And 3, determining whether the center of a part of the grid is covered by the particles. Referring to fig. 4, a schematic diagram of the search and irradiation of each layer grid is shown. As shown in fig. 4, taking the upper left part as an example, for a certain layer of grids, starting from the unsearched grid closest to the vertical area line, whether the grid is within the particle is judged by calculating the distance from the center of the grid to the center of the particle, if the grid is covered by the particle, whether the grid on the left side of the grid is within the particle is continuously judged until a grid is not within the particle, the left search is stopped and the grid is defined as a stopped grid (such as the grids marked as 1, 9 and 16 in fig. 4). A similar search is also performed for the upper right region, with the search proceeding from left to right.
And 4, determining whether corner points of the partial grid are covered by the particles. Still taking the upper left as an example, starting from the lower right corner of the stopping grid (e.g. points 3, 12, 18 in fig. 4), determining whether a corner point is inside the grain, if the corner point is inside the grain, determining whether the corner point on the left side of the corner point is inside the grain until a corner point which is not covered by the grain stops to continue searching to the left, and defines it as a stopping corner point (e.g. points 5, 13, 18 in fig. 4), and defines its right corner point as the farthest intra-grain corner point (e.g. points 4 and 12 in fig. 4). The upper right region is also searched from left to right.
And 5, determining a solid boundary grid and a fluid boundary grid of a certain layer. Still taking the upper left as an example, based on the search results of steps 3 and 4, the grid with the center of the grid within the grain may be determined as a solid boundary grid (e.g., grids 8 and 15 in fig. 4), and the remaining grids in this layer, the grids located to the right of the stopping corner point, may be regarded as fluid boundary grids. The upper right region determination method is the same except that the fluid boundary grid is located to the left of the upper right region stopping corner point.
And 6, radiating the solid grid to the inner layer to determine the solid grid. The grid with horizontal coordinates between the inner corner points of the two farthest left and right particles in the inward-lying grid can be considered as a solid grid, as shown by the black filled grid in fig. 4.
And 7, searching the next layer until the middle layer where the vertical coordinate of the center of the particle is located is searched.
Step 8, for the lower left and lower right parts, a search is performed according to steps similar to steps 3-7 above.
Referring to fig. 5, a diagram of the result of performing a boundary grid search for grains with different diameters by using the above method is shown. As shown in the left diagram in fig. 5, the layer-by-layer search method can accurately determine the grid attributes around the particles, which lays a foundation for contact search in the discrete unit method in the embodiment of the present disclosure. Compared with the traditional boundary grid searching method in the prior art, the method for determining the grid in the searching range can greatly improve the calculation efficiency and has high identification accuracy. In addition, the method in the embodiment of the present disclosure only judges the center points and the corner points of a part of the grids near the grain boundary, and finally determines the attributes of all the boundary grids, thereby saving a large amount of calculation to a certain extent, and the larger the grain size is, the more obvious the advantages embodied by the present invention are. The right graph in fig. 5 shows the search result of a particle with a diameter of 14.42, the distance between 109 points and the center of the particle needs to be calculated in the search process to determine whether the particle covers the particle, whereas the distance between the center of all 256 grids and the center of the particle needs to be calculated in the conventional method.
Further, the layer-by-layer searching method in the embodiments described above in this specification can be generalized to a three-dimensional situation. Referring to fig. 6, a schematic diagram of a layer-by-layer searching method under three-dimensional conditions is shown. As shown in the left and middle diagrams of fig. 6, the center of the particle is first projected to the closer surface of the mesh of the target layer along the Z direction, the interface of the surface and the sphere is a circular surface, the radius and the center coordinate of the circle can be easily obtained, and then the search of the mesh of the layer can be simplified to the two-dimensional situation. Of course, unlike the two-dimensional case, the differences are as follows:
(1) when judging whether the grid center is covered by the particles, the grid center coordinate is a three-dimensional coordinate thereof;
(2) corresponding to step 5 in the two-dimensional case, in the three-dimensional case, if one grid of the current layer is determined as a solid boundary grid, for example, a grid filled with a lighter color in the middle graph of fig. 6, and then a grid at a corresponding position one layer below in the Z direction (i.e., a direction toward the center of the particle in the Z direction), for example, a lighter color grid in the right graph of fig. 6, the current layer can be directly determined as a solid boundary grid;
(3) corresponding to step 6 in the two-dimensional case, in the three-dimensional case, if the X coordinate of the center of the current mesh is located between the inner corner points of the two farthest particles, the neighbor mesh of the mesh in the diagonal direction of the ZY surface close to the center of the particle can be directly determined as the solid mesh. For example, in the middle diagram of fig. 6, the points P1 and P2 are the two farthest intra-particle corner points, respectively, and the center X coordinate of the grid filled with the lightest color is located between P1 and P2, then the black filled grid in the right diagram of fig. 6 can be directly identified as the solid grid.
In order to verify the superiority of the method, three-dimensional multi-particle sedimentation simulation is performed for different particle radii by using two boundary grid search methods, namely the conventional method in fig. 1 and the method in the embodiment of the present specification. In the simulation, the fluid medium is water and the density is 1000 Kg.m-3The dynamic viscosity was 1mPa · s, and the particle diameter was set as: 3. 6 and 10 (lattice unit) and a particle density of 1500 Kg. m-3The total number of particles was 41 and the DEM subcycling step within one LBM time step was set at 21.
Referring to fig. 7, a graph comparing the search time of the grain boundary grid in the method of the present embodiment and the conventional method is shown. As can be seen from fig. 7, the method can be applied to three-dimensional simulation, and particularly for the case of a large particle radius, the method has a great advantage in improving the calculation efficiency.
The following describes a particle contact search method based on the layer-by-layer search result of the boundary grid around the particle. In the LBM method, the entire flow area is divided by a regular cubic (or square) grid, and the size of the grid is typically much smaller than the particle size, which provides a precondition for the method. It is believed that within a grid, if more than one particle covers the grid, there is a possibility of contact between the particles. In the process of searching layer by layer, which particles cover each grid is recorded, so that when a certain particle is searched for contact, only the grids covered by more than one particle in the boundary fluid grid and the boundary solid grid are found, and the information of other particles covering the grids is read from the grids, and whether the current particle is in contact with other particles can be judged. The method makes full use of the results of layer-by-layer searching, does not need to separately adopt a DEM contact searching algorithm to perform particle contact searching, and greatly saves the calculated amount.
Referring to fig. 8, a diagram of the position relationship between particles and grids is shown. As shown in fig. 8, the overlapped grids (the grids marked by the cross lines in the figure) of the two grain boundaries are the target search grids, the target search grid of each grain is separately recorded during the layer-by-layer search process, and the contact can be found through the contact judgment during the DEM calculation. The contact judgment means to judge whether contact occurs or not based on the distance between two particles and the radius of two particles. The search process may be as follows: 1. traversing all the particles; 2. in the process of traversing the particles, for the ith particle, traversing the target search grid; 3. in the process of traversing the target search grid, for the jth grid, if the contact between two corresponding particles is judged, the (j +1) th grid is continuously judged, otherwise, the contact between the two particles is judged; 4. if the two particles do not contact, recording that the two particles are judged, and continuously judging the (j +1) th grid; 5. if the two particles are contacted, recording that the two particles are judged, storing the contact information into a contact linked list, and continuously judging the (j +1) th grid. Referring to FIG. 9, a block diagram of a specific contact search algorithm is shown. In FIG. 9, P is the number of particles and N is the number of target search grids for a particular particle.
The implementation of particle transport within a complex porous medium is described below. The current LBM-IMB-DEM method is mainly applied to the case of straight regular boundaries, and has not been studied in the prior art for boundaries with arbitrary shapes, especially for porous media where the geometry of the boundary is extremely complex. Referring to fig. 10, the positional relationship between the grain and the boundary in the case of an irregular boundary is shown. As shown in fig. 10, within the framework of LBM, the zone boundaries are represented by lattices, and if the particles are spherical, the interaction of spherical particles with the zone boundaries is difficult to characterize. In general, the implementation of particle migration within porous media with complex boundaries has two major difficulties: 1. how to calculate the interaction of the grain and the boundary; 2. how to achieve a response that the grain is in contact with the boundary, i.e. how to know that the grain is in contact with the boundary. The following explains the solutions of these two problems.
First, for the interaction of particles and boundaries, the basic idea of the embodiments of the present specification is: when the interaction between the particles and the boundary is calculated, the solid grid of the boundary of the region is formed into an external sphere (an external circle under the two-dimensional condition), so that the interaction between the particles and the boundary is converted into the interaction between the particles and the boundary-shaped sphere (circle) which is in contact with the particles, the action (including force and moment actions) of each shaped sphere (circle) on the particles can be obtained by applying a discrete element contact model, and finally, the actions from the boundary-shaped sphere (circle) on the particles are integrated to be the action of the boundary on the particles. The area boundary fluid grid (FB) means that at least one of the neighboring grids is an area boundary solid grid (SB), and the area boundary solid grid means a solid grid in the boundary of the hole wall and the like of the target reservoir.
For convenience of explanation, a two-dimensional case is taken as an example. Referring to fig. 11, the processing of regional solid boundaries in the two-dimensional case is shown. As shown in fig. 11, the boundary formation circles making contact with the particle P1 are SB1 and SB2, the boundary formation circle making contact with P2 is SB3, and the boundary formation circle making contact with P3 is SB 11.
Secondly, as to how to search for the contact between the particles and the boundary-shaped sphere (circle), as described above, in order to obtain the effect of the boundary such as the hole wall on the particles, it is necessary to quickly find the boundary-shaped sphere (circle) which is brought into contact with the particles. The basic idea in the embodiment of the specification is as follows: before the simulation of the particle migration, a database of the number and the positions of the solid grids at the region boundary in the neighbor grids of the fluid grids at the region boundary is established by traversing all the region grids, so that when a certain fluid grid at the region boundary is covered by a particle, whether the particle has a contact relation with the solid grid-shaped balls (circles) at the region boundary corresponding to the fluid grid at the region boundary is judged. If present, the interaction can be calculated. Taking the two-dimensional case as an example, as shown in fig. 11, the P1 grains cover the region-boundary fluid cells FB1 and FB2, while the lower cell of FB1 is the region-boundary solid cell SB1 and the lower cell of FB2 is the region-boundary solid cell SB2, and it can be judged whether or not the grains are in contact with the boundary formation circles SB1 and SB 2.
The particle migration simulation method in the embodiment provides an efficient and accurate particle boundary grid search algorithm and a particle contact search algorithm, and provides a processing method of a region boundary solid grid, so that direct numerical simulation of fluid carrying particle migration in any complex porous medium is realized, particle attribute data of a target reservoir can be obtained during reservoir development, particle migration simulation is performed, particle migration simulation data is obtained, reservoir development parameters can be determined based on the particle migration simulation data, and reservoir development efficiency is improved.
The embodiment of the application also provides a reservoir development parameter determination method combining the particle migration influence. Fig. 12 shows a flow chart of a method for determining reservoir development parameters in conjunction with particle migration effects in one embodiment of the present description. Although the present specification provides method operational steps or apparatus configurations as illustrated in the following examples or figures, more or fewer operational steps or modular units may be included in the methods or apparatus based on conventional or non-inventive efforts. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution sequence of the steps or the module structure of the apparatus is not limited to the execution sequence or the module structure described in the embodiments and shown in the drawings. When the described method or module structure is applied in an actual device or end product, the method or module structure according to the embodiments or shown in the drawings can be executed sequentially or executed in parallel (for example, in a parallel processor or multi-thread processing environment, or even in a distributed processing environment).
Specifically, as shown in fig. 12, a method for determining reservoir development parameters in combination with particle migration effects provided by an embodiment of the present specification may include the following steps:
and step S121, acquiring particle attribute data of the target reservoir.
The method in this embodiment may be applied to a computer device. The computer device may obtain particle property data for the target reservoir. The target reservoir can be a coal reservoir or a shale reservoir. The particle attribute data may include various physical attribute data such as elemental analysis data, composition analysis data, size data, and density of the particle.
For example, migration of coal powder in cracks or pores may cause a decrease in permeability, which is not beneficial to coal bed gas development, so that the migration mechanism and rule of particles in a complex porous medium need to be researched to know the influence of various development parameters on the migration of coal powder, and further, various development parameters can be optimized to improve the development efficiency. The coal powder migration is condensed together, stagnation is easily caused, the permeability is reduced, and the coal bed gas development caused by the coal powder migration can be reduced by reducing the flow rate, adding a dispersing agent into the fracturing fluid and the like. For a target reservoir, the element analysis, the composition analysis, the size analysis and the like can be carried out on the coal seam, so that various parameters can be obtained for simulation.
And S122, inputting the particle attribute data of the target reservoir into a target model to obtain particle migration data in the target reservoir, wherein the target model is used for simulating the migration of particles in a complex porous medium.
And S123, determining development parameters of the target reservoir based on the particle migration data.
After obtaining the particle attribute data of the target reservoir, the attribute data of the target reservoir may be input into the target model to obtain particle migration data in the target reservoir. The target model can be used for simulating the migration of particles in a complex multi-space medium. When the migration simulation is carried out by using the target model, development parameters can be changed, and the development effect of the target reservoir under different development parameters is analyzed based on particle migration data under different development parameters, so that the development parameters of the target reservoir are determined. The development parameters may include various parameters such as flow rate of the fracturing fluid, composition, density and total amount of dispersant in the fracturing fluid.
The method in the embodiment obtains the particle attribute data of the target reservoir, performs particle migration simulation based on the particle attribute data by using the target model to obtain the particle migration data of the target reservoir, and then determines the development parameters of the target reservoir based on the particle migration data to improve the development efficiency.
In some embodiments of the present description, the target model may be specifically used for at least one of: determining a grid type for each of a plurality of grids surrounding each of a plurality of grains in the target reservoir; wherein the mesh type includes: a fluid grid, a fluid boundary grid, a solid boundary grid, and a solid grid; judging whether two particles in the plurality of particles are contacted or not according to the grid type of each grid in the plurality of grids around each particle; identifying a region boundary fluid grid and a region boundary solid grid of a pore wall in the target reservoir; judging whether each particle in the plurality of particles is in contact with the hole wall according to the region boundary fluid grid and the region boundary solid grid of the hole wall and the grid type around each particle; and when calculating the interaction between the hole wall and the particles in contact with the hole wall, forming the region boundary solid grid on the hole wall into an circumscribed sphere or a circumscribed circle so as to convert the interaction between the hole wall and the particles in contact with the hole wall into the interaction between the particles and the circumscribed sphere or the circumscribed circle in contact with the particles. By the mode, the mesh type of the particle boundary can be determined by using the target model, whether the particles are contacted or not is determined based on the mesh type around the particles, whether the particles are contacted with the hole wall or not is determined based on the boundary mesh type of the hole wall and the mesh type around the particles, and the solid mesh of the region boundary of the hole wall is formed into an circumscribed sphere or a circumscribed circle when the interaction is calculated, so that the interaction between the particles and the hole wall can be calculated more conveniently, early preparation can be made for the migration simulation of the particles, and the migration simulation of the particles is more accurate.
In some embodiments of the present description, determining a grid type for each of a plurality of grids around each of a plurality of grains in the target reservoir may include: acquiring radius and center coordinate data of target particles in the target reservoir; determining a target search range for the target particle based on the radius and center coordinate data of the target particle, wherein the target search range contains the target particle; dividing the target search range into a plurality of parts according to the central coordinate data; and determining the grid type of each grid in each layer of grid in the multi-layer grid from the edge of the target search range to the center layer by layer aiming at each part in the plurality of parts. By the method, the boundary grid of the particle can be determined efficiently and accurately.
In some embodiments of the present description, determining the target search range of the target particle based on the radius and the central coordinate data of the target particle may include: determining the target search range according to the following formula:
wherein x isiI is a coordinate value on the ith dimension in the central coordinate data, i is a coordinate system dimension serial number in the target model, i is 1 and 2 for a two-dimensional target model, and i is 1,2,3 and N for a three-dimensional target modeli maxIs the coordinate value upper limit, N, of the target search range in the ith dimensioni minAnd f, setting the coordinate value lower limit of the target search range in the ith dimension, wherein r is the radius of the target particle.
In some embodiments of the present description, determining the target search range of the target particle based on the radius and the central coordinate data of the target particle may include: determining an external square of the target particle based on the radius and the central coordinate data of the target particle, determining a target search range for a rectangle which surrounds the external square and has four sides with values as integers, and determining a rectangle which surrounds the rounded four straight lines as the target search range. For example, if the coordinates of the center of the particle are (3.2,4.5) and the radius of the particle is 3, the equation for the four sides of the circumscribed square of the particle is x equal to 0.2, x equal to 6.2, y equal to 1.5, and y equal to 7.5. The target search range may be determined as a square surrounded by four straight lines, x being 0, x being 7, y being 1, and y being 8.
Two methods for determining the target search range are given in the above embodiments, and those skilled in the art will understand that other methods may be adopted to determine the target search range.
In some embodiments of the present disclosure, the target model is a two-dimensional target model, and accordingly, dividing the target search range into a plurality of parts according to the central coordinate data may include: rounding off each coordinate value of the central coordinate data to obtain an approximate central point coordinate; and dividing the target searching range into four parts by using a horizontal area line and a vertical area line, wherein the horizontal area line and the vertical area line respectively penetrate through the approximate center point coordinate along two coordinate axis directions of a two-dimensional coordinate system in the target model. In the above manner, the target search range may be divided into four parts, i.e., an upper left part, a lower left part, an upper right part, and a lower right part.
In some embodiments of the present disclosure, the target model is a two-dimensional target model, and accordingly, dividing the target search range into a plurality of parts according to the central coordinate data may include: rounding each coordinate value of the central coordinate data to obtain an approximate central point coordinate; and dividing the target searching range into four parts by using a horizontal area line and a vertical area line, wherein the horizontal area line and the vertical area line respectively penetrate through the approximate center point coordinate along two coordinate axis directions of a two-dimensional coordinate system in the target model. In the above manner, the target search range may be divided into four parts, i.e., an upper left part, a lower left part, an upper right part, and a lower right part.
In some embodiments of the present description, determining, for each of the plurality of portions, a mesh type of each of the meshes in each of the multi-layer meshes layer by layer from an edge to a center of the target search range includes: determining whether the outermost grid of the target search area needs to be searched, wherein the outermost grid comprises four outermost grids of the target search area in two coordinate axis directions of the two-dimensional coordinate system; determining a mesh type of each mesh in an outermost mesh of the target search area as a fluid mesh, in a case where it is determined that a search for the outermost mesh is not required.
In some embodiments of the present specification, determining, for each of the plurality of portions, a mesh type of each of the meshes in each of the multi-layer meshes layer by layer from an edge to a center of the target search range, further includes: for grids of which the grid types are not determined in the upper left part of the four parts, for a layer of grids farthest from the horizontal area line, searching to the left from the grid closest to the vertical area line, determining whether the center of each grid is in the target particle, stopping the left search after obtaining a left-side stopped grid, starting the left search from the left-side stopped grid, determining whether a lower right corner point of each grid is in the target particle, and stopping the left search after obtaining a left-side stopped corner point, wherein the left-side stopped grid refers to the grid of which the first searched center is not in the target particle during the left search, and the left-side stopped corner point refers to the lower right corner point of which the first searched lower right corner point is not in the target particle during the left search; for grids of which the grid types are not determined in the upper right part of the four parts, for a layer of grids farthest from the horizontal area line, searching from the grid closest to the vertical area line to the right, determining whether the center of each grid is covered by the target particle, obtaining a right stopping grid, stopping searching to the right, starting searching from the right stopping grid, determining whether the lower left corner point of each grid is covered by the target particle, and obtaining a right stopping corner point, stopping searching to the right, wherein the right stopping grid refers to a grid of which the center is not in the target particle and is first searched during searching to the right, and the right stopping corner point refers to the lower left corner point is not in the target particle and is first searched during searching to the right; determining grids on the left side of the left side stopping corner point and grids on the right side stopping corner point as fluid grids, determining grids with centers in the target particles as solid boundary grids, determining grids between the corner points in the farthest left side particles and the corner points in the farthest right side particles as fluid boundary grids, determining grids between the corner points in the farthest left side particles and the corner points in the farthest right side particles on an inward layer as solid grids, wherein the corner points in the farthest left side particles are lower right corner points adjacent to the stopping corner points on the right side, and the corner points in the farthest right side particles are lower left corner points adjacent to the stopping corner points on the left side. In the above manner, the grid types of the outermost layers of the upper left part and the upper right part can be determined, and the grid types of the upper left part and the upper right part can be determined by searching inwards layer by layer.
In the above embodiments, the upper left part and the upper right part are taken as examples for explanation, and it is understood that similar search can be performed for the lower right part and the lower left part, and the difference is only that the direction of layer-by-layer search is from bottom to top.
In some embodiments of the present disclosure, determining whether contact occurs between two particles of the plurality of particles according to a mesh type of each mesh of a plurality of meshes around each particle includes: determining whether the fluid boundary mesh or the solid boundary mesh of the first particle coincides with the fluid boundary mesh or the solid boundary mesh of the second particle; calculating a distance between the first particle and the second particle in a case where it is determined that coincidence occurs; determining whether the first particle and the second particle are in contact according to a distance between the first particle and the second particle and radii of the first particle and the second particle. The type and the corresponding coordinates of the boundary grids of each particle are recorded during the searching, so that when judging whether contact occurs, the particles with overlapped boundary grids can be determined as the particles which are likely to contact, and then the contact judgment is carried out according to the distance between the first particle and the second particle which are likely to contact and the radius of the first particle and the second particle, and the particle contact judgment method is efficient and accurate.
In some embodiments of the present description, determining whether contact between each particle of the plurality of particles and the pore wall occurs based on the region-boundary fluid mesh and the region-boundary solid mesh of the pore wall and the type of mesh around each particle comprises: determining a target region boundary fluid mesh of the pore wall, wherein at least one neighbor mesh of the target region boundary fluid mesh is a region boundary solid mesh; judging whether the fluid boundary grid or the solid boundary grid of each particle in the plurality of particles is overlapped with the target area boundary fluid grid of the hole wall; and under the condition that the coincidence is judged to occur, determining whether the particles are in contact with the regional boundary solid grids in the neighbor grids of the corresponding target boundary fluid grids.
Specifically, when judging whether the particles contact with boundaries such as hole walls or not, a database of regional boundary fluid grids of the hole walls is established, and the number and positions of regional boundary solid grids in the neighboring grids of the regional boundary fluid grids are recorded in the database. When the particle covers the regional fluid boundary grid, it can be determined whether the particle is in contact with all regional solid boundary grids corresponding to the regional fluid boundary grid. In the presence of contact, the interaction can be calculated. During calculation of interaction, the region boundary solid grids can be formed into circumscribed balls or circumscribed circles for calculation, so that interaction between the particles and the boundary of the hole wall is converted into the effect of the particles and the circumscribed circles or circumscribed balls formed by the boundary solid grids in contact with the particles, and the effect of the circumscribed circles or circumscribed circles is integrated. Through the method, whether the particles are in contact with the boundary of the hole wall or not can be judged, and the interaction between the particles and the boundary of the hole wall can be calculated.
Based on the same inventive concept, the embodiment of the present specification further provides a device for determining reservoir development parameters in combination with the influence of particle migration, as described in the following embodiments. Because the principle of solving the problems of the device for determining the reservoir development parameters in combination with the particle migration influence is similar to the method for determining the reservoir development parameters in combination with the particle migration influence, the implementation of the device for determining the reservoir development parameters in combination with the particle migration influence can be referred to the implementation of the method for determining the reservoir development parameters in combination with the particle migration influence, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Fig. 13 is a block diagram of a structure of a reservoir development parameter determining apparatus incorporating particle migration influence according to an embodiment of the present disclosure, as shown in fig. 13, including: an acquisition module 131, an input module 132, and a determination module 133, the structure of which is described below.
The obtaining module 131 is configured to obtain particle property data of a target reservoir.
The input module 132 is configured to input the particle property data of the target reservoir into a target model, so as to obtain particle migration data in the target reservoir, where the target model is used to simulate migration of particles in a complex porous medium.
The determining module 133 is configured to determine development parameters of the target reservoir based on the particle migration data.
The embodiment of the present specification further provides a computer device, which may specifically refer to a schematic structural diagram of a computer device shown in fig. 14 and based on the method for determining reservoir development parameters in combination with particle migration influence provided in the embodiment of the present specification, where the computer device may specifically include an input device 141, a processor 142, and a memory 143. Wherein the memory 143 is configured to store processor-executable instructions. The processor 142, when executing the instructions, performs the steps of the method for determining reservoir development parameters in combination with the effect of particle migration as described in any of the embodiments above.
In this embodiment, the input device may be one of the main apparatuses for information exchange between a user and a computer system. The input device may include a keyboard, a mouse, a camera, a scanner, a light pen, a handwriting input board, a voice input device, etc.; the input device is used to input raw data and a program for processing the data into the computer. The input device can also acquire and receive data transmitted by other modules, units and devices. The processor may be implemented in any suitable way. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The memory may in particular be a memory device used in modern information technology for storing information. The memory may include multiple levels, and in a digital system, the memory may be any memory as long as it can store binary data; in an integrated circuit, a circuit without a physical form and with a storage function is also called a memory, such as a RAM, a FIFO and the like; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card and the like.
In this embodiment, the functions and effects of the specific implementation of the computer device can be explained in comparison with other embodiments, and are not described herein again.
There is also provided in an embodiment of the present specification a computer storage medium for a method for determining reservoir development parameters based on a combined particle migration effect, the computer storage medium storing computer program instructions which, when executed, implement the steps of the method for determining reservoir development parameters combined particle migration effect described in any of the above embodiments.
In this embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.
In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer storage medium can be explained by comparing with other embodiments, and are not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the present specification described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed over a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different from that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present description are not limited to any specific combination of hardware and software.
It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided will be apparent to those of skill in the art upon reading the above description. The scope of the description should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above description is only a preferred embodiment of the present disclosure, and is not intended to limit the present disclosure, and it will be apparent to those skilled in the art that various modifications and variations can be made in the embodiment of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present specification shall be included in the protection scope of the present specification.
Claims (10)
1. A method for determining reservoir development parameters in combination with particle migration effects, comprising:
acquiring particle attribute data of a target reservoir;
inputting the particle attribute data of the target reservoir into a target model to obtain particle migration data in the target reservoir, wherein the target model is used for simulating the migration of particles in a complex porous medium;
determining development parameters for the target reservoir based on the particle migration data.
2. The method of claim 1, wherein the object model is specific to at least one of:
determining a grid type for each of a plurality of grids surrounding each of a plurality of grains in the target reservoir; wherein the mesh type includes: a fluid grid, a fluid boundary grid, a solid boundary grid, and a solid grid;
judging whether two particles in the plurality of particles are contacted or not according to the grid type of each grid in the plurality of grids around each particle;
identifying a region boundary fluid grid and a region boundary solid grid of a pore wall in the target reservoir; judging whether each particle in the plurality of particles is in contact with the hole wall according to the region boundary fluid grid and the region boundary solid grid of the hole wall and the grid type around each particle;
and when calculating the interaction between the hole wall and the particles in contact with the hole wall, forming the region boundary solid grid on the hole wall into an circumscribed sphere or a circumscribed circle so as to convert the interaction between the hole wall and the particles in contact with the hole wall into the interaction between the particles and the circumscribed sphere or the circumscribed circle in contact with the particles.
3. The method of claim 2, wherein determining the grid type for each of a plurality of grids surrounding each of a plurality of particles in the target reservoir comprises:
acquiring radius and center coordinate data of target particles in the target reservoir;
determining a target search range for the target particle based on the radius and center coordinate data of the target particle, wherein the target search range contains the target particle;
dividing the target search range into a plurality of parts according to the central coordinate data;
and determining the grid type of each grid in each layer of grid in the multi-layer grid from the edge of the target search range to the center layer by layer aiming at each part in the plurality of parts.
4. The method of claim 3, wherein the object model is a two-dimensional object model, and wherein correspondingly dividing the object search range into a plurality of portions according to the center coordinate data comprises:
rounding off each coordinate value of the central coordinate data to obtain an approximate central point coordinate;
and dividing the target searching range into four parts by using a horizontal area line and a vertical area line, wherein the horizontal area line and the vertical area line respectively penetrate through the approximate center point coordinate along two coordinate axis directions of a two-dimensional coordinate system in the target model.
5. The method of claim 4, wherein determining, for each of the plurality of portions, a mesh type for each of the layers of the multi-layer mesh layer-by-layer from an edge to a center of the target search range comprises:
determining whether the outermost grid of the target search area needs to be searched, wherein the outermost grid comprises four outermost grids of the target search area in two coordinate axis directions of the two-dimensional coordinate system;
determining a mesh type of each mesh in an outermost mesh of the target search area as a fluid mesh, in a case where it is determined that a search for the outermost mesh is not required.
6. The method of claim 5, wherein determining, for each of the plurality of portions, a mesh type for each of the layers of the multi-layer mesh layer-by-layer from an edge to a center of the target search range, further comprises:
for grids of which the grid types are not determined in the upper left part of the four parts, for a layer of grids farthest from the horizontal area line, searching to the left from the grid closest to the vertical area line, determining whether the center of each grid is in the target particle, stopping the left search after obtaining a left-side stopped grid, starting the left search from the left-side stopped grid, determining whether a lower right corner point of each grid is in the target particle, and stopping the left search after obtaining a left-side stopped corner point, wherein the left-side stopped grid refers to the grid of which the first searched center is not in the target particle during the left search, and the left-side stopped corner point refers to the lower right corner point of which the first searched lower right corner point is not in the target particle during the left search;
for grids of which the grid types are not determined in the upper right part of the four parts, for a layer of grids farthest from the horizontal area line, searching from the grid closest to the vertical area line to the right, determining whether the center of each grid is covered by the target particle, obtaining a right stopping grid, stopping searching to the right, starting searching from the right stopping grid, determining whether the lower left corner point of each grid is covered by the target particle, and obtaining a right stopping corner point, stopping searching to the right, wherein the right stopping grid refers to a grid of which the center is not in the target particle and is first searched during searching to the right, and the right stopping corner point refers to the lower left corner point is not in the target particle and is first searched during searching to the right;
determining grids on the left side of the left side stopping corner point and grids on the right side stopping corner point as fluid grids, determining grids with centers in the target particles as solid boundary grids, determining grids between the corner points in the farthest left side particles and the corner points in the farthest right side particles as fluid boundary grids, determining grids between the corner points in the farthest left side particles and the corner points in the farthest right side particles on an inward layer as solid grids, wherein the corner points in the farthest left side particles are lower right corner points adjacent to the stopping corner points on the right side, and the corner points in the farthest right side particles are lower left corner points adjacent to the stopping corner points on the left side.
7. The method of claim 2, wherein determining whether contact has occurred between two particles of the plurality of particles based on a mesh type of each of a plurality of meshes around the respective particle comprises:
determining whether the fluid boundary mesh or the solid boundary mesh of the first particle coincides with the fluid boundary mesh or the solid boundary mesh of the second particle;
calculating a distance between the first particle and the second particle in a case where it is determined that coincidence occurs;
determining whether the first particle and the second particle are in contact according to a distance between the first particle and the second particle and radii of the first particle and the second particle.
8. The method of claim 2, wherein determining whether contact between each particle of the plurality of particles and the pore wall occurs based on the zonal boundary fluid mesh and the zonal boundary solid mesh of the pore wall and the type of mesh surrounding each particle comprises:
determining a target region boundary fluid mesh of the pore wall, wherein at least one neighbor mesh of the target region boundary fluid mesh is a region boundary solid mesh;
judging whether the fluid boundary grid or the solid boundary grid of each particle in the plurality of particles is overlapped with the target area boundary fluid grid of the hole wall;
and under the condition that the coincidence is judged to occur, determining whether the particles are in contact with the regional boundary solid grids in the neighbor grids of the corresponding target boundary fluid grids.
9. A reservoir development parameter determination apparatus incorporating particle migration effects, comprising:
the acquisition module is used for acquiring particle attribute data of a target reservoir;
the input module is used for inputting the particle attribute data of the target reservoir into a target model to obtain particle migration data in the target reservoir, wherein the target model is used for simulating the migration of particles in a complex porous medium;
a determination module to determine development parameters for the target reservoir based on the particle migration data.
10. A computer device comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 1 to 8.
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