CN117876629A - Shallow sea phase delta reservoir training image generation method, system, medium and equipment - Google Patents
Shallow sea phase delta reservoir training image generation method, system, medium and equipment Download PDFInfo
- Publication number
- CN117876629A CN117876629A CN202410065297.3A CN202410065297A CN117876629A CN 117876629 A CN117876629 A CN 117876629A CN 202410065297 A CN202410065297 A CN 202410065297A CN 117876629 A CN117876629 A CN 117876629A
- Authority
- CN
- China
- Prior art keywords
- phase
- deposition
- dimensional
- delta
- sediment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012549 training Methods 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 56
- 230000008021 deposition Effects 0.000 claims abstract description 150
- 239000004576 sand Substances 0.000 claims abstract description 53
- 239000011229 interlayer Substances 0.000 claims abstract description 39
- 238000009825 accumulation Methods 0.000 claims abstract description 26
- 239000004927 clay Substances 0.000 claims abstract description 15
- 239000013049 sediment Substances 0.000 claims description 74
- 238000004590 computer program Methods 0.000 claims description 23
- 239000010410 layer Substances 0.000 claims description 23
- 238000003860 storage Methods 0.000 claims description 15
- 238000010276 construction Methods 0.000 claims description 13
- 238000004891 communication Methods 0.000 claims description 9
- 230000000877 morphologic effect Effects 0.000 claims description 6
- 239000000654 additive Substances 0.000 claims description 4
- 230000000996 additive effect Effects 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 4
- 208000035126 Facies Diseases 0.000 abstract description 7
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000000151 deposition Methods 0.000 description 104
- 230000006870 function Effects 0.000 description 12
- 238000010586 diagram Methods 0.000 description 11
- 238000012545 processing Methods 0.000 description 6
- 238000004088 simulation Methods 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a shallow sea phase delta reservoir training image generation method, a system, a medium and equipment, wherein the method comprises the following steps: constructing an initial three-dimensional grid model of a single-period shallow sea phase delta reservoir training image; sequentially constructing a delta plain deposition phase, a front sand accumulation body deposition phase and a clay interlayer deposition phase in the initial three-dimensional grid model to obtain a single-stage three-dimensional deposition model; superposing two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model; constructing a river channel sedimentary facies in a delta plain sedimentary facies of the multi-stage three-dimensional sedimentary model to obtain a shallow sea facies delta reservoir training image; the method can generate the three-dimensional training image, is beneficial to better representing the spatial structure and variability of the underground reservoir, and improves the prediction and modeling capability of reservoir characteristics.
Description
Technical Field
The invention relates to the technical field of geological detection and drawing, in particular to a method, a system, a medium and equipment for generating a training image of a shallow sea phase delta reservoir.
Background
Multipoint geostatistics is a method of building geologic models that has better pattern reproduction capabilities than traditional two-point geostatistics. The multi-point geostatistics uses "training images" to express correlations between points, i.e., spatial structuring of geologic variables, instead of the traditional two-point geostatistical variation model. The training image is a digitized image capable of representing the actual reservoir structure, geometry and distribution pattern, is a conceptual model, and can reflect priori geological concepts and other geological features in the reservoir. Up to now, the establishment of training images in the multipoint geostatistical method has no mature and unified method, but the quality of the training images directly determines the accuracy and the reliability of reservoir simulation.
The acquisition of training images depends largely on the speculation of geological personnel, and the uncertainty is large. And the space structure of the three-dimensional river channel is hard to be represented by the common two-dimensional training image, and the acquisition difficulty of the three-dimensional training image is higher, so that the application of the multi-point geostatistical method in reservoir geological modeling is limited.
Thus, training images are one of the indispensable conditions for modeling using multi-point geostatistics, and developing training image generation schemes helps to better characterize the spatial structure and variability of subsurface reservoirs, thereby improving the ability to predict and model reservoir characteristics, which is of great significance to the oil and gas exploration and production industry.
Disclosure of Invention
The invention provides a shallow sea phase delta reservoir training image generation method, a shallow sea phase delta reservoir training image generation system, a shallow sea phase delta reservoir training image generation medium and shallow sea phase delta reservoir training image generation equipment, which are beneficial to better representing the spatial structure and variability of a subsurface reservoir, so that the prediction and modeling capacity of reservoir characteristics is improved.
In a first aspect, a method for generating a training image of a shallow sea delta reservoir is provided, comprising the steps of:
constructing an initial three-dimensional grid model of a single-period shallow sea phase delta reservoir training image;
sequentially constructing a delta plain deposition phase, a front sand accumulation body deposition phase and a clay interlayer deposition phase in the initial three-dimensional grid model to obtain a single-stage three-dimensional deposition model;
superposing two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model;
and constructing a river sediment phase in the delta plain sediment phase of the multi-stage three-dimensional sediment model to obtain a shallow sea phase delta reservoir training image.
According to a first aspect, in a first possible implementation manner of the first aspect, the step of constructing a delta plains sedimentary phase in the initial three-dimensional grid model specifically includes the following steps:
giving the circle center, the radius, the initial boundary position and the extension direction of the deposition phase of the delta plain, and drawing the circle center, the radius, the initial boundary position and the extension direction of the deposition phase of the delta plain in the top view of the initial three-dimensional grid model according to the circle center, the radius, the initial boundary position and the extension direction of the deposition phase of the delta plain, wherein the left part of the drawn arc boundary is the deposition phase of the delta plain in the top view;
and (3) giving a front product angle between two deposition phases, and projecting each grid in the initial three-dimensional grid model to the top view by using the front product angle, wherein the projection position in the top view is the deposition phase of delta plain, and the deposition phase corresponding to the grid is regarded.
In a second possible implementation manner of the first aspect, according to the first aspect, the step of constructing a pre-packed sand sediment phase in the initial three-dimensional grid model specifically includes the following steps:
step one, acquiring the number of layers of front sand deposit phases, and defining that each layer of front sand deposit phases are distributed in sequence along the extension direction of the deposit phases;
step two, setting the boundary position of a first layer of front sand accumulation body deposition phase of the front sand accumulation body deposition phase along the extension direction of the deposition phase, wherein the boundary of the first layer of front sand accumulation body deposition phase is parallel to the boundary of a delta plain deposition phase; giving a front deposition angle between two deposition phases and the width or thickness of a first layer of front deposition sand deposition phase, and projecting each grid remained in an initial three-dimensional grid model after the delta plain deposition phase is constructed to a top view of the initial three-dimensional grid model by using the front deposition angle, wherein the projection position in the top view is regarded as the deposition phase corresponding to the grid when the projection position is the first layer of front deposition sand deposition phase;
and thirdly, constructing a plurality of layers of pre-deposition sand sediment phases which are distributed in sequence according to the second step.
In a third possible implementation manner of the first aspect, according to the first aspect, the step of constructing a clay-interlayer sedimentary phase in the initial three-dimensional grid model specifically includes the steps of:
setting an included angle of the argillaceous interlayer, the thickness of the argillaceous interlayer and the interval of the argillaceous interlayer at the boundary position of the sediment phase of the front sand accumulation body;
and generating a clay sandwich deposition phase with the clay sandwich thickness one by one from the bottom of the initial three-dimensional grid model and along the boundary position of the deposition phase of the front sand accumulation body according to a given clay sandwich included angle and a given clay sandwich interval until reaching the top of the initial three-dimensional grid model.
In a fourth possible implementation manner of the first aspect, the step of stacking two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model specifically includes the following steps:
and vertically superposing two single-phase three-dimensional deposition models based on a preset additive offset to obtain a multi-phase three-dimensional deposition model.
According to the first aspect, in a fifth possible implementation manner of the first aspect, the step of constructing a river sediment phase in a delta plains sediment phase of the multi-stage three-dimensional sediment model specifically includes the following steps:
generating a river course line on a delta plain deposition phase of the multi-stage three-dimensional deposition model according to parameters of the river course line, and carrying out smoothing treatment on the river course line to obtain a river course vector line;
given a river channel morphological parameter, traversing the distance between each grid in a delta plain deposition phase and a river channel vector line on a plane, and selecting grids with the distance smaller than the half width of the river channel;
and judging whether the selected grid is positioned in the river channel on the section or not based on the Fluvsim section modeling method, and if so, judging that the selected grid attribute is the sediment phase of the river channel.
According to the first aspect, in a sixth possible implementation manner of the first aspect, the step of constructing a river sediment phase in a delta plains sediment phase of the multi-stage three-dimensional sediment model specifically includes the following steps:
and when the grid proportion of the grid attribute of the river sediment phase reaches the preset proportion, completing construction of the river sediment phase.
In a second aspect, there is provided a shallow sea delta reservoir training image generation system comprising:
the initial grid module is used for constructing an initial three-dimensional grid model of the single-period shallow sea phase delta reservoir training image;
each sediment phase construction module is in communication connection with the initial grid module and is used for constructing a delta plain sediment phase, a front sand accumulation sediment phase and a argillaceous interlayer sediment phase in the initial three-dimensional grid model in sequence to obtain a single-period three-dimensional sediment model;
the superposition module is in communication connection with each deposition phase construction module and is used for superposing two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model; the method comprises the steps of,
and the river sediment construction module is in communication connection with the superposition module and is used for constructing a river sediment phase in the delta plain sediment phase of the multi-stage three-dimensional sediment model to obtain a shallow sea phase delta reservoir training image.
In a third aspect, a computer readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a shallow sea delta reservoir training image generation method as described above.
In a fourth aspect, there is provided an electronic device comprising a storage medium, a processor and a computer program stored in the storage medium and executable on the processor, wherein the processor, when executing the computer program, implements a shallow sea delta reservoir training image generation method as described above.
Compared with the prior art, the invention has the following advantages: to generate three-dimensional training images, to help better characterize the spatial structure and variability of subsurface reservoirs to improve the ability to predict and model reservoir characteristics, and thus has great significance to the oil and gas exploration and production industry.
Drawings
FIG. 1 is a flow chart of an embodiment of a shallow sea delta reservoir training image generation method of the present invention;
FIG. 2 is a flow chart of yet another embodiment of a shallow sea delta reservoir training image generation method of the present invention;
FIG. 3 is a plan view of a three-dimensional mesh model of the present invention;
FIG. 4 is a schematic view of the shoreline vertical morphological parameters of the present invention;
FIG. 5 is a graphical representation of the pre-integrated sand morphology parameters of the present invention;
FIG. 6 is a schematic diagram of the morphological parameters of the argillaceous interlayer of the present invention;
FIG. 7 is a schematic view of a river vector line of the present invention;
FIG. 8 is a schematic diagram of cell grid assignment on a plane of the present invention;
FIG. 9 is a schematic illustration of cell grid assignment in cross section of the present invention;
FIG. 10 is a schematic diagram of the river profile parameters according to the present invention.
Detailed Description
Reference will now be made in detail to the present embodiments of the invention, examples of which are illustrated in the accompanying drawings. While the invention will be described in conjunction with the specific embodiments, it will be understood that they are not intended to limit the invention to the described embodiments. On the contrary, it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims. It should be noted that the method steps described herein may be implemented by any functional block or arrangement of functions, and any functional block or arrangement of functions may be implemented as a physical entity or a logical entity, or a combination of both.
The present invention will be described in further detail below with reference to the drawings and detailed description for the purpose of enabling those skilled in the art to understand the invention better.
Note that: the examples to be described below are only one specific example, and not as limiting the embodiments of the present invention necessarily to the following specific steps, values, conditions, data, sequences, etc. Those skilled in the art can, upon reading the present specification, make and use the concepts of the invention to construct further embodiments not mentioned in the specification.
Referring to fig. 1 and 2, an embodiment of the present invention provides a method for generating a training image of a shallow sea delta reservoir, including the following steps:
s100, constructing an initial three-dimensional grid model of a single-period shallow sea phase delta reservoir training image;
geological models are typically represented by three-dimensional grids, which are a representation of three-dimensional space discretization, dividing the subsurface space into regular or irregular grid cells, each of which may have different properties. By using a grid, model calculations can be easily performed on each discretized cell. Subsurface reservoirs typically have complex geometries, such as faults, folds, and the like. By using a grid, these complex geologic structures can be efficiently represented and processed such that the model more closely approximates the actual geologic situation. The main reason is to provide a convenient framework for numerical simulation and model analysis.
As with the geologic model, the training image is also a geologic model. The geologic model needs to be matched with constraint data (such as logging and earthquake), namely the actual geologic model needs to express the distribution characteristics of the reservoir and also needs to be matched with logging and earthquake exploration information. The training image only represents a digitized mode, and the purpose of building the training image is to guide modeling, that is, the training image only needs to be consistent with the model to be built in statistical characteristics.
Whether training images or actual geologic models, a three-dimensional grid system needs to be defined and built in advance. The actual geologic model is generally related to the actual system, while the training image is not required, and he only needs to express the deposition pattern. The process of modeling is the process of assigning values to the grids. Judging each grid in the three-dimensional grids, judging which deposition phase each grid belongs to, assigning values, and building training images after all grid assignment is completed.
The grid definition contains the following elements: initial position, grid size, grid number
Starting position: for training image models, the model does not need an actual position correlation, as it is typically set to the origin of coordinates.
Grid size: reflecting the accuracy of the training image, a smaller value indicates a finer training image. The specific value is determined according to the modeling accuracy requirement.
Grid number: the scale of the training image is reflected, the grid size is unchanged, and the more the number is, the larger the range of the training image is depicted. The specific value is determined according to the complexity of the geological mode reflected by the training image, and more grids are possibly needed to represent the more complicated mode.
S200, sequentially constructing a delta plain deposition phase, a front sand deposition phase and a argillaceous interlayer deposition phase in the initial three-dimensional grid model to obtain a single-stage three-dimensional deposition model;
s300, superposing two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model;
s400, constructing a river channel sedimentary facies in a delta plain sedimentary facies of the multi-stage three-dimensional sedimentary model, and obtaining a shallow sea facies delta reservoir training image.
Specifically, in the embodiment, the shallow sea phase delta reservoir training image generation method designed by the invention is used for generating a three-dimensional training image, which is beneficial to better representing the spatial structure and variability of the underground reservoir so as to improve the prediction and modeling capability of reservoir characteristics, thus having important significance for oil and gas exploration and production industries.
Preferably, in another embodiment of the present application, the step of constructing delta plains sedimentary facies in the initial three-dimensional grid model includes the following steps:
giving the circle center, the radius, the initial boundary position and the extension direction of the deposition phase of the delta plain, and drawing the circle center, the radius, the initial boundary position and the extension direction of the deposition phase of the delta plain in the top view of the initial three-dimensional grid model according to the circle center, the radius, the initial boundary position and the extension direction of the deposition phase of the delta plain, wherein the left part of the drawn arc boundary is the deposition phase of the delta plain in the top view;
and (3) giving a front product angle between two deposition phases, and projecting each grid in the initial three-dimensional grid model to the top view by using the front product angle, wherein the projection position in the top view is the deposition phase of delta plain, and the deposition phase corresponding to the grid is regarded.
Specifically, in this embodiment, the green delta plain is usually on water, and is a land sediment, and its shape is determined by the shoreline shape parameters. The specific rules are as follows: in fig. 3, a rectangle is a plan view (top view) of the three-dimensional mesh model, and is oriented in the north direction. The boundary points define the starting position of the delta plain phase in the training image, the deposition direction defines the extension direction of the phase band (deposition phase), and the numerical value is defined as being determined by the clockwise rotation angle from the north direction. The position of the delta plains boundary in the deposition direction is determined by the length indicated by the deposition direction arrow. The boundaries to the two sides are expressed by circular arcs, the circle center is positioned in the opposite direction of the deposition direction, and the radius determines the radian of the circular arcs. If the radius is infinitely long, the arc is a straight line. The left part of the arc boundary is all assigned as delta plain. The boundary of the subsequent front sand accumulation body is parallel to the delta plain boundary and advances along the deposition direction.
Referring to fig. 4, the vertical morphological parameter of the coastline is mainly a forward product angle α, which indicates a deposition angle between each phase band, the angle is affected by the deposition environment (when the sediment supply rate in the river is higher than the formation rate of the receivable space, a low angle appears, and when the formation rate of the receivable space is higher and the sediment supply is lower, a high angle appears).
For any grid in the three-dimensional grid, the type of the phase belt of the grid can be determined according to the projection position of the current grid on the top surface of the model, and the type of the phase belt of the projection position is the type of the phase belt of the current grid. The specific projection steps are as follows:
firstly, a projection vector is required to be configured, the deposition direction of the grid can be determined according to the plane position of the grid (an arc boundary is a plane vector from the circle center to the current grid, and the deposition directions of all grids under the straight boundary are the same), and the plane vector is determined according to the deposition direction. And vertically cutting the three-dimensional grid along the deposition to obtain a cut surface, and quantitatively calculating in the cut surface. In fig. 4, the directions of the dashed lines at the grids 1 and 2 are the deposition directions, then the plane vector is rotated vertically upwards by an angle alpha with the current grid point as the rotation center point according to the forward product angle alpha, and the vector after rotation is the projection direction. And then adopting a ray intersection algorithm to calculate the intersection point of the projection vector and the top surface (top view) of the model, and judging the phase type of the intersection point (because the phase boundary of the top surface is an arc or a straight line, the intersection point can be judged to be in or out of a circle according to the distance between the projection point and the circle center, and if the intersection point is a straight line, the judgment point is on one side of the straight line), namely the phase type of the current grid.
Preferably, in another embodiment of the present application, the step of "S200, building a pre-sand deposition phase in the initial three-dimensional grid model" specifically includes the following steps:
step one, acquiring the number of layers of front sand deposit phases, and defining that each layer of front sand deposit phases are distributed in sequence along the extension direction of the deposit phases;
step two, setting the boundary position of a first layer of front sand accumulation body deposition phase of the front sand accumulation body deposition phase along the extension direction of the deposition phase, wherein the boundary of the first layer of front sand accumulation body deposition phase is parallel to the boundary of a delta plain deposition phase; giving a front deposition angle between two deposition phases and the width or thickness of a first layer of front deposition sand deposition phase, and projecting each grid remained in an initial three-dimensional grid model after the delta plain deposition phase is constructed to a top view of the initial three-dimensional grid model by using the front deposition angle, wherein the projection position in the top view is regarded as the deposition phase corresponding to the grid when the projection position is the first layer of front deposition sand deposition phase;
and thirdly, constructing a plurality of layers of pre-deposition sand sediment phases which are distributed in sequence according to the second step.
Specifically, in this embodiment, the delta plain and the river channel phases in the shallow sea delta reservoir training image are indispensable because they are necessary conditions for forming the shallow sea delta sedimentary body, the number of the front sand bodies is generally defined by the user according to the actual requirement, the front sand bodies of different phases are generally caused by the multi-phase sedimentation process, and the petrophysical property characteristics (such as porosity, permeability, and other parameters) of the sand bodies are inconsistent. The aim of the sand accumulation before the stage-by-stage characterization is to restrict the subsequent reservoir physical property parameter modeling (porosity and permeability modeling) by different phase bands, and each phase band gives different parameters. The initial phase boundary position is the position where the sand accumulation body before the first layer appears, and is determined by the length of the deposition direction arrow, and the parameter value is input by a user.
Meanwhile, referring to fig. 5, the simulation of the front sand accumulation body is simplified and is directly pushed forward by the delta plain boundary, that is, the thickness of each place is consistent. The parameters are given in two forms, w and t (width and thickness), which have a scaling relationship between them, sinα=t/w. Depending on the source of the parameter, only one value needs to be given, e.g. the source plane of the parameter (e.g. the statistical band width on modern depositions), typically given w, e.g. the source of the parameter (e.g. the statistical band thickness on open-air cross-section), typically given t. When the parameters are given, the grid assignment mode in the phase belt is the same as the grid assignment mode of delta plain, and a projection method is adopted. Projecting the projection point to the top surface of the model, and judging according to the phase zone where the projection point is positioned.
Preferably, in a further embodiment of the present application, the step of constructing a argillaceous interlayer deposition phase in the initial three-dimensional grid model, specifically includes the following steps:
setting an included angle of the argillaceous interlayer, the thickness of the argillaceous interlayer and the interval of the argillaceous interlayer at the boundary position of the sediment phase of the front sand accumulation body;
and generating a clay sandwich deposition phase with the clay sandwich thickness one by one from the bottom of the initial three-dimensional grid model and along the boundary position of the deposition phase of the front sand accumulation body according to a given clay sandwich included angle and a given clay sandwich interval until reaching the top of the initial three-dimensional grid model.
Specifically, in the present embodiment, in the front sand volume, there are thin mudstone interlayers that represent the boundaries between deposition events, typically covered or cemented with mud. These phenomena are manifested in outages of ancient deposits. The muddy interlayer in the front sand accumulation body is controlled by two parameters, namely the included angle of the interlayer and the development scale of the interlayer. As shown in fig. 6, the included angle α defines the inclination of the argillaceous interlayer, the scale being determined by the argillaceous interlayer spacing h, the interlayer being gradually generated from the bottom in accordance with the argillaceous interlayer spacing, until the top of the model. The smaller the interlayer spacing, the denser the interlayer. Whether an interlayer is created in the pre-integration is an optional parameter, as the muddy deposit may not be preserved.
The parameters for constructing the deposition phase of the argillaceous interlayer are three, namely the included angle of the argillaceous interlayer, the thickness of the argillaceous interlayer and the interval/number of the argillaceous interlayers (namely the interval between two layers, the given number can obtain the interval, and the given interval can calculate the number).
Similar to the previous delta plain and previous integrated judgment mode, a vertical section is determined according to the deposition direction of the current grid, calculation is performed on the vertical section, and whether the grid is a mudstone interlayer is judged. The specific calculation mode is shown in the figure, from the vertical section, the intersection point of the current front integrated body and the bottom surface of the model is taken as an initial anchor point, an anchor point is added every h until the top of the model, and all anchor point coordinates (termination coordinates of four dotted lines) of the current front integrated sand body can be obtained. For any grid in the front volume, judging whether the ray (dotted line in the figure) from the grid coordinate to the anchor point is parallel to the interlayer direction (according to the included angle alpha), if the parallel state indicates that the current grid is the interlayer, otherwise, the current grid is not the interlayer. The above determination is performed on all grids in the front volume, and the sandwich can be modeled in the front volume.
Preferably, in another embodiment of the present application, the step of "S300, stacking two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model" specifically includes the following steps:
and vertically superposing two single-phase three-dimensional deposition models based on a preset additive offset to obtain a multi-phase three-dimensional deposition model.
In particular, in this embodiment, when the training image needs to depict multiple shallow sea delta, it can be implemented by using two sets of overlapping grids with the same scale, and other attributes of the grids except for the vertical position should be kept consistent, such as the size of the grids and the number of grids. In order to simplify the model, the simulation parameters of the two sets of grid models should be identical (including the number of pre-sand bodies, thickness and deposition direction) except for the delta plains. The vertical stacking feature of the pre-delta sand bodies is characterized by varying the offset of delta plains (preset additive product offset d in the fifth plot from the left to the bottom of fig. 2). The two groups of grids are simulated separately, and grid combination is carried out after simulation is completed.
Preferably, in another embodiment of the present application, the step of constructing a river sediment phase in the delta plains sediment phase of the multi-stage three-dimensional sediment model in S400 specifically includes the following steps:
s410, generating a river course line on a delta plain deposition phase of the multi-stage three-dimensional deposition model according to parameters of the river course line, and carrying out smoothing treatment on the river course line to obtain a river course vector line;
s420, setting a river channel morphological parameter, traversing the distance between each grid in the delta plain deposition phase and the river channel vector line on a plane, and selecting a grid with the distance smaller than the half width of the river channel;
s430, judging whether the selected grid is positioned in the river channel on the section or not based on the Fluvsim section modeling method, and if so, judging that the selected grid attribute is the sediment phase of the river channel.
Specifically, in this embodiment, a point set is used to represent a vector line, i.e., a river center line. The point set is a set of a series of space point coordinates, the vector line quantitatively represents the center line of the river channel, and after the vector line is determined, the space position and the basic form of one river channel can be determined. The river course central line generation includes five parameters, namely a starting point, an azimuth angle, a control point interval, a maximum offset distance and a total length. The meaning of the parameters is also described with reference to fig. 7.
The starting point is the first point of the river course centerline, i.e. the vector line, and the open circle in the figure represents the location of the initial point (the starting point is defined outside the three-dimensional mesh, and the river course generally extends from land to sea entrance). The river origin is set outside the left side of the three-dimensional grid and is only simulated inside the delta plain phase zone.
The azimuth angle indicates the main direction of river course extension, takes the initial point as the origin of coordinates, the direction of the positive east (E) is 0 degree, and the angle corresponding to the main direction is the azimuth angle when the river course rotates anticlockwise.
The distance between control points represents the distance between the control points in the main direction of the river channel, namely the distance between scales in the main direction in a river channel vector diagram, and the river channel can generate one control point at regular intervals along the main direction.
The maximum offset distance represents the maximum distance of the control point to the main direction for limiting the extent to which the control point must be on both sides of the main direction axis.
Along the main direction of the river channel, a river channel control point is generated at intervals of the control points, the distance of the control points deviating from the main direction (meanwhile, the maximum offset distance also determines the curvature of the river channel and is a curvature parameter input by a user), and sampling is carried out between the negative maximum offset distance and the positive maximum offset distance, and the sampling is generally generated through a random function.
The total length represents the furthest range of river channel extension, and when the distance between the generated river channel control point and the starting point is greater than the total length of the river channel, the generation of the next river channel control point is stopped, and the generation of the initial control node of the river channel center line is completed.
And smoothing and encrypting the vector line by taking the initial control point of the river channel center line as a reference to obtain a river channel center line node set, wherein the node set is the river channel vector line finally required by us.
And traversing each grid in the delta plain, and assigning a river channel phase when the grid is in the river channel according to the position relation between the grid and the river channel, otherwise, assigning the river channel phase as the delta plain phase. As shown in fig. 8, the solid line represents the center line of the river, the distance between the broken line and the solid line represents the maximum width of the river on the plane, the width is input by the user through parameters, and when the center point of the grid is within the broken line, the grid is considered to be inside the river. The specific judgment is carried out by the distance between the points and the lines, the grid can be regarded as the points, the river channel can be regarded as the line with a certain width, the distance between the grid and the center line of the river channel can be calculated according to the distance formula from the points to the line segments, if the distance is larger than the half width of the river channel, the grid is outside the river channel, if the distance is smaller than the half width of the river channel, the grid is further judged on the section.
Because the training image is a three-dimensional grid model, after the unit grid is judged on the plane, the unit grid is further judged according to the position of the unit grid on the section. As shown in fig. 9, the river channel has a convex shape with a top and a bottom in the cross section, and the specific shape is determined by the geological parameters of the river channel, including the width (top width) of the river channel and the thickness (maximum thickness of the river channel). And if the plane and the section of each unit grid are positioned in the river channel, assigning the unit grid as the river channel phase.
The modeling method for the Fluvsim, the modeling method (Fluvsim) based on the target of the C.V. Deutsch design can better represent the cause relation of different reservoir structure units, and is an important river phase reservoir modeling method. The method quantitatively describes the configuration of a river phase reservoir based on the research of river phase deposition. The Fluvsim algorithm program was developed by Deutsch in 2002 and the construction of a model of a river phase type reservoir using a goal-based concept is discussed in detail herein. Unlike the traditional goal-based approach, the Fluvsim approach has several features: 1, a clear reversible coordinate hierarchy system; 2, controlling the geometric form of the target body by reflecting the input parameters of geological significance; 3, accurately controlling the phase proportion in the vertical direction; 4, truly asymmetric river geometry; and 5, truly non-fluctuation river channel top surface. The ideal river channel mode marked by the Fluvsim method comprises three sediment phase types of river channel filling, natural dike and breach fan besides background phase, and the mode diagram fully reflects the superposition and combination modes of each sediment in the vertical direction and the plane. The geometric form of the target body of various phase types can be controlled through a parameter file, and parameters are generally determined through geological research and mainly comprise related data such as field outcrop, earthquake, logging, sediment simulation experiments, geological knowledge base and the like.
The calculation method for judging whether the grid is in the river channel based on the Fluvsim profile modeling method is as follows, and specifically, see fig. 10:
wherein w (y) is the width of the river channel, c v (y) is a local curvature,Is the maximum curvature.
When a (y) <=0.5, the depth of the river base below the river top is calculated as:
wherein b (y) = -ln (2)/ln (a (y)), w e [0,w (y) ] w e [0,w (y) ],
when a (y) > 0.5, the depth of the river base below the river top is calculated as:
wherein c (y) = -ln (2)/ln (1-a (y)).
Preferably, in another embodiment of the present application, the step of constructing a river sediment phase in the delta plains sediment phase of the multi-stage three-dimensional sediment model in S400 specifically includes the following steps:
and when the grid proportion of the grid attribute of the river sediment phase reaches the preset proportion, completing construction of the river sediment phase.
Specifically, in this embodiment, the number of channels in the delta plain is controlled by the channel proportion parameters, and each time a complete channel is generated, the proportion of the channel grid in the delta plain needs to be counted, and when the proportion reaches the channel proportion input by the user, the generation of the channel is stopped.
The embodiment of the invention provides a shallow sea phase delta reservoir training image generation system, which comprises:
the initial grid module is used for constructing an initial three-dimensional grid model of the single-period shallow sea phase delta reservoir training image;
each sediment phase construction module is in communication connection with the initial grid module and is used for constructing a delta plain sediment phase, a front sand accumulation sediment phase and a argillaceous interlayer sediment phase in the initial three-dimensional grid model in sequence to obtain a single-period three-dimensional sediment model;
the superposition module is in communication connection with each deposition phase construction module and is used for superposing two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model; the method comprises the steps of,
and the river sediment construction module is in communication connection with the superposition module and is used for constructing a river sediment phase in the delta plain sediment phase of the multi-stage three-dimensional sediment model to obtain a shallow sea phase delta reservoir training image.
The invention helps to better characterize the spatial structure and variability of the subsurface reservoir to improve the ability to predict and model reservoir characteristics, and thus has great significance to the oil and gas exploration and production industry.
Specifically, the present embodiment corresponds to the foregoing method embodiments one by one, and the functions of each module are described in detail in the corresponding method embodiments, so that a detailed description is not given.
Based on the same inventive concept, the embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements all or part of the method steps of the above method.
The present invention may be implemented by implementing all or part of the above-described method flow, or by instructing the relevant hardware by a computer program, which may be stored in a computer readable storage medium, and which when executed by a processor, may implement the steps of the above-described method embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
Based on the same inventive concept, the embodiments of the present application further provide an electronic device, including a memory and a processor, where the memory stores a computer program running on the processor, and when the processor executes the computer program, the processor implements all or part of the method steps in the above method.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being a control center of the computer device, and the various interfaces and lines connecting the various parts of the overall computer device.
The memory may be used to store computer programs and/or modules, and the processor implements various functions of the computer device by running or executing the computer programs and/or modules stored in the memory, and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (e.g., a sound playing function, an image playing function, etc.); the storage data area may store data (e.g., audio data, video data, etc.) created according to the use of the handset. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, server, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), servers and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The method for generating the shallow sea delta reservoir training image is characterized by comprising the following steps of:
constructing an initial three-dimensional grid model of a single-period shallow sea phase delta reservoir training image;
sequentially constructing a delta plain deposition phase, a front sand accumulation body deposition phase and a clay interlayer deposition phase in the initial three-dimensional grid model to obtain a single-stage three-dimensional deposition model;
superposing two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model;
and constructing a river sediment phase in the delta plain sediment phase of the multi-stage three-dimensional sediment model to obtain a shallow sea phase delta reservoir training image.
2. The shallow sea phase delta reservoir training image generation method according to claim 1, wherein the step of constructing delta plains sedimentary phases in the initial three-dimensional mesh model comprises the following steps:
giving the circle center, the radius, the initial boundary position and the extension direction of the deposition phase of the delta plain, and drawing the circle center, the radius, the initial boundary position and the extension direction of the deposition phase of the delta plain in the top view of the initial three-dimensional grid model according to the circle center, the radius, the initial boundary position and the extension direction of the deposition phase of the delta plain, wherein the left part of the drawn arc boundary is the deposition phase of the delta plain in the top view;
and (3) giving a front product angle between two deposition phases, and projecting each grid in the initial three-dimensional grid model to the top view by using the front product angle, wherein the projection position in the top view is the deposition phase of delta plain, and the deposition phase corresponding to the grid is regarded.
3. The shallow sea phase delta reservoir training image generation method according to claim 1, wherein the step of constructing a pre-sand sediment phase in the initial three-dimensional grid model comprises the following steps:
step one, acquiring the number of layers of front sand deposit phases, and defining that each layer of front sand deposit phases are distributed in sequence along the extension direction of the deposit phases;
step two, setting the boundary position of a first layer of front sand accumulation body deposition phase of the front sand accumulation body deposition phase along the extension direction of the deposition phase, wherein the boundary of the first layer of front sand accumulation body deposition phase is parallel to the boundary of a delta plain deposition phase; giving a front deposition angle between two deposition phases and the width or thickness of a first layer of front deposition sand deposition phase, and projecting each grid remained in an initial three-dimensional grid model after the delta plain deposition phase is constructed to a top view of the initial three-dimensional grid model by using the front deposition angle, wherein the projection position in the top view is regarded as the deposition phase corresponding to the grid when the projection position is the first layer of front deposition sand deposition phase;
and thirdly, constructing a plurality of layers of pre-deposition sand sediment phases which are distributed in sequence according to the second step.
4. The shallow sea phase delta reservoir training image generation method according to claim 1, wherein the step of constructing a argillaceous interlayer sedimentary phase in the initial three-dimensional grid model comprises the following steps:
setting an included angle of the argillaceous interlayer, the thickness of the argillaceous interlayer and the interval of the argillaceous interlayer at the boundary position of the sediment phase of the front sand accumulation body;
and generating a clay sandwich deposition phase with the clay sandwich thickness one by one from the bottom of the initial three-dimensional grid model and along the boundary position of the deposition phase of the front sand accumulation body according to a given clay sandwich included angle and a given clay sandwich interval until reaching the top of the initial three-dimensional grid model.
5. The shallow sea phase delta reservoir training image generation method according to claim 1, wherein the step of superposing two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model comprises the following steps:
and vertically superposing two single-phase three-dimensional deposition models based on a preset additive offset to obtain a multi-phase three-dimensional deposition model.
6. The shallow sea phase delta reservoir training image generation method according to claim 1, wherein the step of constructing a river sediment phase in a delta plains sediment phase of the multi-stage three-dimensional sediment model specifically comprises the following steps:
generating a river course line on a delta plain deposition phase of the multi-stage three-dimensional deposition model according to parameters of the river course line, and carrying out smoothing treatment on the river course line to obtain a river course vector line;
given a river channel morphological parameter, traversing the distance between each grid in a delta plain deposition phase and a river channel vector line on a plane, and selecting grids with the distance smaller than the half width of the river channel;
and judging whether the selected grid is positioned in the river channel on the section or not based on the Fluvsim section modeling method, and if so, judging that the selected grid attribute is the sediment phase of the river channel.
7. The shallow sea phase delta reservoir training image generation method according to claim 1, wherein after the step of constructing a river sediment phase in a delta plains sediment phase of the multi-stage three-dimensional sediment model, the method specifically comprises the following steps:
and when the grid proportion of the grid attribute of the river sediment phase reaches the preset proportion, completing construction of the river sediment phase.
8. A shallow sea delta reservoir training image generation system, comprising:
the initial grid module is used for constructing an initial three-dimensional grid model of the single-period shallow sea phase delta reservoir training image;
each sediment phase construction module is in communication connection with the initial grid module and is used for constructing a delta plain sediment phase, a front sand accumulation sediment phase and a argillaceous interlayer sediment phase in the initial three-dimensional grid model in sequence to obtain a single-period three-dimensional sediment model;
the superposition module is in communication connection with each deposition phase construction module and is used for superposing two single-phase three-dimensional deposition models to obtain a multi-phase three-dimensional deposition model; the method comprises the steps of,
and the river sediment construction module is in communication connection with the superposition module and is used for constructing a river sediment phase in the delta plain sediment phase of the multi-stage three-dimensional sediment model to obtain a shallow sea phase delta reservoir training image.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the shallow sea delta reservoir training image generation method of any of claims 1 to 7.
10. An electronic device comprising a storage medium, a processor and a computer program stored in the storage medium and executable on the processor, characterized in that the processor implements the shallow sea delta reservoir training image generation method according to any one of claims 1 to 7 when the computer program is run.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410065297.3A CN117876629A (en) | 2024-01-16 | 2024-01-16 | Shallow sea phase delta reservoir training image generation method, system, medium and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410065297.3A CN117876629A (en) | 2024-01-16 | 2024-01-16 | Shallow sea phase delta reservoir training image generation method, system, medium and equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117876629A true CN117876629A (en) | 2024-04-12 |
Family
ID=90596446
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410065297.3A Pending CN117876629A (en) | 2024-01-16 | 2024-01-16 | Shallow sea phase delta reservoir training image generation method, system, medium and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117876629A (en) |
-
2024
- 2024-01-16 CN CN202410065297.3A patent/CN117876629A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11209560B2 (en) | Assignment of systems tracts | |
| EP3293552B1 (en) | System and method for editing geological models by switching between volume-based models and surface-based structural models augmented with stratigraphic fiber bundles | |
| AU2011339017B2 (en) | Constructing geologic models from geologic concepts | |
| CN104850682B (en) | Location-based Multiple-Point Geostatistics modeling method | |
| AU2020397825B2 (en) | Generation of subsurface representations using layer-space | |
| AU2020398568B2 (en) | Generation of subsurface representations using layer-space | |
| EA022006B1 (en) | Method of geophysical survey of prospective oil-bearing area | |
| CN103077558A (en) | Method for establishing large-sized karst cave reservoir body distribution model of fractured-vuggy carbonate rock reservoir | |
| EP1556820A1 (en) | Method and program storage device for generating grids representing the architecture of fluvial reservoirs | |
| CN102867332A (en) | Multi-level subdivided mesh surface fitting method based on complicated boundary constraint | |
| Koneshloo et al. | A workflow for static reservoir modeling guided by seismic data in a fluvial system | |
| Iltaf et al. | Facies and petrophysical modeling of Triassic Chang 6 tight sandstone reservoir, Heshui oil field, Ordos basin, China | |
| CN117876629A (en) | Shallow sea phase delta reservoir training image generation method, system, medium and equipment | |
| CN108876916B (en) | Braided river training image generation method and equipment | |
| CN108763731B (en) | Overlapped type duo body delta training image establishing method | |
| Branets et al. | Capturing geologic complexity in a simulation grid | |
| EP3371627A1 (en) | Three-dimensional, stratigraphically-consistent seismic attributes | |
| Beucher et al. | Reservoir characterization | |
| CN112346117B (en) | Reservoir characteristic prediction method and device based on seismic attribute fusion | |
| Podnebesnykh et al. | New approach to the evaluation of the structure of initial reserves in Ozhginskoe gas-oil field | |
| WO2023139401A1 (en) | Process for positioning or predicting flow in at least a fluid production well and/or at least a fluid injection well in a field, related electronic system and computer program product | |
| RU2515081C1 (en) | Automatic allocation of surfaces for building of geologic and hydrodynamic model of oil and gas deposit against seismic data | |
| CN115146463A (en) | Hierarchical modeling method and system based on configuration unit | |
| Syversveen et al. | A stochastic object model conditioned to high-quality seismic data | |
| Casar-González et al. | A 3D geostatistical model of Upper Jurassic Kimmeridgian facies distribution in Cantarell oil field, Mexico |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |