CN110322555B - Diversion river dam type delta leading edge training image establishing method - Google Patents

Diversion river dam type delta leading edge training image establishing method Download PDF

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CN110322555B
CN110322555B CN201810269342.1A CN201810269342A CN110322555B CN 110322555 B CN110322555 B CN 110322555B CN 201810269342 A CN201810269342 A CN 201810269342A CN 110322555 B CN110322555 B CN 110322555B
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赵磊
段太忠
廉培庆
王鸣川
贺婷婷
张文彪
商晓飞
刘彦锋
李蒙
柯岭
张德民
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China Petroleum and Chemical Corp
Sinopec Exploration and Production Research Institute
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Abstract

The invention relates to a method for establishing a training image of a leading edge of a diversion river dam type delta, relates to the technical field of oil and gas exploration, and is used for solving the technical problem that the training image of a non-stable reservoir of the leading edge of the delta is difficult to establish in the prior art. On one hand, the method of the invention follows the rule of statistical characteristic parameters, namely, the range of the control generation sector and the upper limit of the river channel morphological parameters are determined by researching the distribution characteristics of the regional morphological parameters; on the other hand, the sedimentation process of the river channel is simulated in the algorithm design, and the algorithm is designed and realized by the guidance of a process modeling thought, namely, in the range of a fan body, a training image which is from a sedimentation mechanism to a condition that statistical characteristics are consistent with actual objects can be established by controlling the condition and the upper limit value of the generated river channel, so that the method has important significance for promoting the multi-point geostatistics modeling theory, and has important practical significance for the rapid and convenient application of the multi-point geostatistics and the actual reservoir modeling, the improvement of the multi-point modeling precision and the service of the oil field production.

Description

Diversion river dam type delta leading edge training image establishing method
Technical Field
The invention relates to the technical field of oil-gas exploration, in particular to a method for establishing a diversion river dam type delta leading edge training image.
Background
The traditional two-point statistical modeling method based on the variation function plays a great role in establishing a three-dimensional geological model of a continental facies sedimentary reservoir, and forcefully promotes the development of reservoir description towards the direction of fineness and quantification. However, the two-point variation function is difficult to characterize the complex reservoir morphology (such as a curved river), and a plurality of spatial point combinations are required to be relied on to finely describe the reservoir morphology and the spatial configuration relationship, so that the multi-point geostatistics are established and developed.
Multipoint geostatistics the probability of multiple points is obtained by scanning a training image (conceptualized quantitative geologic model) through a multipoint template (data template), and a possible deposition pattern at the point to be estimated is predicted. In the last 20 years, various multi-point geostatistical methods have been developed, such as Snesim, simbat, filtersmim, distance-based MPS, Smps (skeeleton-based MPS), location-based multi-point geostatistical modeling methods, and the like. However, it is still difficult to obtain training images, especially for the non-stationary reservoir on the front edge of the delta.
Disclosure of Invention
The invention provides a method for establishing a training image of a front edge of a dam-shaped delta with different acquisition parameters, which is used for solving the technical problem that the training image of a non-stable reservoir on the front edge of the delta is difficult to establish in the prior art.
The invention provides a method for establishing a training image of a diversion dam type delta front edge with different acquisition parameters, which comprises the following steps of:
s10: according to the statistical value of the morphological parameters of the research area, a geological grid model is established, and the upper limit P of the river channel sand ratio is determinedmaxThe upper limit of the ratio of the river mouth dam to the river mouth dam;
s20: obtaining a range for generating a fan body along the direction of a source of a research area according to the geological grid model;
s30: generating a river channel in the fan body range, randomly generating a estuary dam at the estuary position of the river channel, and enabling the estuary dam occupation ratio to be within the upper limit range of the estuary dam occupation ratio;
s40: obtaining a river channel sand ratio P of a generated river channel; if the river sand ratio P meets the following definition formula:
P∈Pmax；
ending and obtaining a training image; otherwise, step S30 is repeated.
In one embodiment of the present invention, step S30 includes the following sub-steps:
s31: randomly selecting the starting point position of the river channel within the fan body range;
s32: generating a river channel from the starting position to obtain a main river channel or a main river channel and a branch river channel;
s33: and randomly generating corresponding estuary dams at the estuary positions of the main river channel and the branch river channels until the estuary dam occupation ratio is within the upper limit range of the estuary dam occupation ratio. In one embodiment of the present invention, in step S32, the width of the river or the width-to-thickness ratio of the river is randomly extracted from the statistical values of the morphological parameters as an initial value, and the river is generated.
In one embodiment of the present invention, in step S32, the geometric shape of the cross-section of the river channel is controlled by the width w (y) of the river channel, the maximum thickness t (y) of the river channel, and the relative position a (y) of the maximum thickness of the river channel.
In one embodiment of the present invention, the relative position a (y) of the maximum thickness of the river satisfies the following defined formula:
Figure GDA0003082965900000021
wherein, Cv(y) is the curvature of the channel centerline;
Cv l(y) curvature of the left border of the channel;
Cv r(y) is the curvature of the right border of the channel.
In one embodiment of the present invention, when the relative position a (y) of the maximum thickness t (y) of the river is greater than 0.5, the depth d (w, y) of the river satisfies the following defined formula:
Figure GDA0003082965900000022
wherein w ∈ [0, W (y) ];
b (y) satisfies the following defined formula:
Figure GDA0003082965900000031
in one embodiment of the present invention, when the relative position a (y) of the maximum thickness t (y) of the river is less than or equal to 0.5, the depth d (w, y) of the river satisfies the following defined formula:
Figure GDA0003082965900000032
wherein w ∈ [0, W (y) ];
b (y) satisfies the following defined formula:
Figure GDA0003082965900000033
in one embodiment of the present invention, in step S32, the branch river is generated where the curvature of the main river is the largest.
In one embodiment of the present invention, the main river channel upper node TiTo node Ti+1Local curvature C ofv(yTi) Satisfies the following defined formula:
Figure GDA0003082965900000034
wherein, thetaTiAs a node TiAngle of tangent to, thetaTiSatisfies the following defined formula:
Figure GDA0003082965900000035
yTias a node TiCoordinates on the y-axis;
xTias a node TiCoordinates on the x-axis;
i is an integer of 1 or more.
In an embodiment of the present invention, in step S10, the statistical values of the morphological parameters include a fan parameter statistical value, a river channel parameter statistical value, and a dam parameter statistical value;
wherein the fan body parameter statistics comprise: the width, the extension distance, the opening angle and the thickness of the fan body;
the river channel parameter statistics include: width, thickness, extension distance and amplitude of the river;
the dam body parameter statistic comprises: the length, width, thickness and declination angle of the dam body.
Compared with the prior art, the invention has the advantages that:
(1) on one hand, the range of the control generation fan body and the upper limit of the river channel morphological parameter are determined by following the statistical characteristic parameter rule, namely, by researching the distribution characteristic of the regional morphological parameter; on the other hand, the sedimentation process of the river channel is simulated in the algorithm design, and the algorithm is designed and realized by the guidance of a process modeling thought, namely, in the range of a fan body, a training image which is from a sedimentation mechanism to a condition that statistical characteristics are consistent with actual objects can be established by controlling the condition and the upper limit value of the generated river channel, so that the method has important significance for promoting the multi-point geostatistics modeling theory, and has important practical significance for the rapid and convenient application of the multi-point geostatistics and the actual reservoir modeling, the improvement of the multi-point modeling precision and the service of the oil field production.
(2) The training image is established on the basis of the statistical value of the morphological parameters of the research area, and an address network model consistent with the research area is established, so that the obtained training image is more suitable for the actual work area, and the guarantee is provided for the next multipoint modeling.
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The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings.
FIG. 1 is a flow chart of building a training image in an embodiment of the present invention;
FIG. 2 is a statistical value of a morphological parameter in an embodiment of the invention;
FIG. 3 is a flat section of an automatically generated training image in an embodiment of the present invention;
FIG. 4 is one of the cross-sections of an automatically generated training image;
FIG. 5 is another cross-section of an automatically generated training image;
FIG. 6 is one of the longitudinal sections of an automatically generated training image;
fig. 7 is another longitudinal section of an automatically generated training image.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1, the invention provides a method for establishing a training image of a leading edge of a dam-type delta by shunting with different acquisition parameters, which comprises the following steps:
the first step is as follows: counting the morphological parameters of the research area, establishing a geological grid model according to the statistical value of the morphological parameters of the research area, and determining the upper limit P of the river channel sand ratiomaxAnd the upper limit of the ratio of the estuary dam. Wherein, the upper limit P of the river channel sand proportionmaxMay be a range of values and similarly, the upper limit of the estuary dam may be a range of values. In the invention, the upper limit P is the ratio of river channel sandmaxTaking the upper limit of the river mouth dam occupation ratio as a screening condition, and if the obtained river channel sand occupation ratio and the river mouth dam occupation ratio both fall into the corresponding upper limit range, receiving the result; if the obtained river channel sand proportion and the river mouth dam proportion both exceed the upper limit range, the simulation result is abandoned. Specifically, the statistical values of the morphological parameters include a fan body parameter statistical value, a river channel parameter statistical value and a dam body parameter statistical value.
Wherein, the fan body parameter statistic comprises: the width (maximum width and minimum width), extension distance, opening angle and thickness (maximum thickness and minimum thickness) of the fan body.
The river channel parameter statistic value comprises the following steps: width (maximum width, minimum width and average width), thickness (maximum thickness, minimum thickness and average thickness), extension distance (maximum extension distance, minimum extension distance and average extension distance) and amplitude (maximum amplitude, minimum amplitude and average amplitude) of the river channel, wherein the above parameters are all in m.
The dam body parameter statistic comprises the following steps: the length (maximum length, minimum length and average length), width (maximum width, minimum width and average width), thickness (maximum thickness, minimum thickness and average thickness) and declination angle (maximum declination angle, minimum declination angle and average declination angle) of the dam body. According to the statistical value, the upper limit P of the river channel sand ratio can be determinedmaxAnd river mouth damThe upper limit of the ratio. The second step is that: and obtaining the range of generating the fan body along the object source direction of the research area according to the geological grid model.
The third step: generating a river channel in the fan body range, and randomly generating a estuary dam at the estuary position of the river channel until the estuary dam occupation ratio is within the upper limit range of the estuary dam occupation ratio; at this time, the probability of generating the estuary dam is 0 (i.e. no new estuary dam can be generated), and the shape of all the estuary dams is controlled by the length, width and thickness of the dam body in the above statistics.
Specifically, the steps of generating the river channel are as follows:
firstly, randomly selecting the starting point position of the river channel within the range of the fan body.
And secondly, generating a river channel from the starting position to obtain a main river channel or a main river channel and branch river channels. When the river course is bent to reach a limit, the river course can produce branch river courses, and main river course and branch river course extend forward respectively until the river course extends to the end.
When the river channel extends, the width of the river channel or the width-thickness ratio of the river channel is randomly extracted from the statistical values of the morphological parameters as an initial value to start generating the river channel. That is, when the river is generated, the width of the river or the width-to-thickness ratio of the river is input as a known condition from the above statistical values.
When the channel is generated, the geometric form of the cross section of the channel is controlled by the width w (y) of the channel, the maximum thickness t (y) of the channel, and the relative position a (y) of the maximum thickness of the channel.
In one embodiment of the present invention, the relative position a (y) of the maximum thickness t (y) of the river satisfies the following definition:
Figure GDA0003082965900000061
wherein, Cv(y) is the curvature of the channel centerline;
Figure GDA0003082965900000062
is the left side of the river channelThe curvature of the boundary;
Figure GDA0003082965900000063
the curvature of the right border of the channel.
When the relative position a (y) of the maximum thickness t (y) of the river channel is greater than 0.5, the depth d (w, y) of the river channel satisfies the following definition:
Figure GDA0003082965900000064
wherein w ∈ [0, W (y) ];
b (y) satisfies the following defined formula:
Figure GDA0003082965900000065
when the relative position a (y) of the maximum thickness t (y) of the river channel is less than or equal to 0.5, the depth d (w, y) of the river channel satisfies the following defined formula:
Figure GDA0003082965900000066
wherein w ∈ [0, W (y) ];
b (y) satisfies the following defined formula:
Figure GDA0003082965900000067
because the relative position a (y) corresponds to the maximum thickness t (y) of the river channel, and the river channel is subjected to a process from shallow to deep to shallow in the generation process, the maximum depth (or the maximum thickness) and the position of the river channel in the depth change process of the river channel can be obtained through the calculation method, and the sedimentation process of the river channel can be carved.
In addition, when the river channel is generated, the generated main river channel can generate a branch river channel in the extending process if the main river channel is bent to a certain degree, and relevant parameters and a bifurcation angle of the branch river channel can be obtained after the branch river channel is generated.
In particular, a branching river is created where the curvature of the main river is greatest.
Main river course upper node TiTo node Ti+1Local curvature C ofv(yTi) Satisfies the following defined formula:
Figure GDA0003082965900000071
wherein, thetaTiAs a node TiAngle of tangent to, thetaTiSatisfies the following defined formula:
Figure GDA0003082965900000072
yTias a node TiCoordinates on the y-axis;
xTias a node TiCoordinates on the x-axis;
i is an integer of 1 or more.
From there, according to the local curvature C on the main channelv(yTi) The relationship between the width w (y) of the channel, the maximum thickness t (y) of the channel, and the relative position a (y) of the maximum thickness of the channel controls the geometry of the cross-section of the channel being generated.
The fourth step: when a new estuary dam cannot be generated, acquiring a river channel sand ratio P of a generated river channel; if the river sand ratio P meets the following definition formula:
P∈Pmax；
ending and obtaining a training image; otherwise, repeating the third step.
That is, the river sand ratio P is the upper limit (P) of the statistical valuemax) The method is characterized in that a circulation condition is adopted within the range, when the river channel sand ratio P of the generated river channel falls within the upper limit range of the statistical value, a training image can be obtained, and circulation is finished; when the river channel sand ratio P of the generated river channel does not fall within the upper limit range of the statistical value,and generating a new river channel, namely continuing to circulate the third step until the river channel sand ratio P meets the circulation condition.
Therefore, the algorithm of the invention simulates the sedimentation process of the river channel on the basis of the statistical characteristic parameter rule and guides the design and realization of the algorithm by the process modeling thought, thereby establishing a training image from the sedimentation mechanism to the condition that the statistical characteristics are consistent with the actual object, and enabling the multi-point geological statistics to be quickly and conveniently applied to the oil field production. Compared with the existing method, the method has the characteristics of simplicity in operation, cost saving, strong graphic display intuition, high effectiveness and the like, and can be used for more effectively forming the training image and carrying out multi-point geostatistical modeling.
The method of the present invention will be described in detail below, taking a certain research area as an example.
Firstly, counting parameters such as the quantity change, the bifurcation angle, the width change of the river channel, the length-width ratio of a river mouth dam and the like of a plurality of delta diversion river channels in the propelling process according to google earth to form a quantitative knowledge base.
As shown in fig. 2, statistical values of morphological parameters of the study area are input, and the grid specification of the training image is determined to be columns and rows and layers (columns and rows and vertical grids) (columns and rows are 400, rows and 400, layers are 20), and the number of original grid points is 3200000.
Determining the upper limit P of the sand ratio of the river channelmaxComprises the following steps: [ 14.25%, 15.75% ]](ii) a The upper limit of the river mouth dam is as follows: [ 4.5%, 5.5% ]]. As long as in the process of generating river channels and estuary dams, the obtained estuary dams account for (4.5 percent and 5.5 percent)]And the river sand accounts for 14.25 percent and 15.75 percent]Within the range of (c), the results obtained are acceptable.
And secondly, determining that the direction of the source is 180 degrees and the deposition range of the front edge of the delta (namely the range of the generated fan body) by using the existing knowledge base and the measurement data of the work area.
And thirdly, randomly selecting the starting position of the river channel to start generating the river channel in the range. And extracting the width of the river channel in the knowledge base as a known condition, and inputting the width to enable the river channel to start extending to the farthest point of the river channel.
Wherein the branch channels are randomly generated at the place where the channel curve is the largest.
Calculating according to the above definition formula to obtain:
maximum curvature C in main channelv(yTmax) Is 1.5;
the width W (y) of the river is 17 m;
the maximum thickness t (y) of the river is 9 m;
the relative position a (y) of the maximum thickness of the channel varies with the curvature of the channel.
And randomly generating an estuary dam at the position of the estuary of the river channel, and enabling the occupancy of the estuary dam to fall within the upper limit range of the occupancy of the estuary dam (4.5 percent and 5.5 percent), wherein the probability of generating the estuary dam is 0, and the obtained sand occupancy P of the river channel is 6.3 percent.
It does not fall into the upper limit P of the ratio of river channel sandmaxWithin range, and therefore go to the next channel node, i.e. repeat the third step, and again generate a main channel and let it generate.
After repeating the third step for 9 times, the obtained river sand ratio P is 15.4 percent and falls into the upper limit P of the river sand ratiomaxWithin the range, the training image can be obtained after the circulation is finished.
The generated training images are as shown in fig. 3-7, are consistent with the deposition rule, can better reflect the deposition characteristics of the delta front edge, and can be used as training images for multipoint simulation.
In summary, the invention realizes the automatic generation of the training image of the front edge of the delta by counting the scale parameters and the distribution characteristics of the sector, the river channel and the dam in the research area and combining the simulation algorithm based on the target and the idea of the deposition process. The method of the invention follows the statistical characteristic parameter rule on one hand, simulates the deposition process in the algorithm design on the other hand, and can establish a training image which is consistent with the actual object from the deposition mechanism to the statistical characteristic by the guidance algorithm design and realization of the process modeling thought, which has important significance for promoting the multipoint geostatistics modeling theory, and has important practical significance for the quick and convenient application of the multipoint geostatistics and the actual reservoir modeling, the improvement of the multipoint modeling precision and the service of the oil field production.
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any way as long as there is no structural conflict. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims (8)

1. A method for establishing a training image of a front edge of a diversion river dam type delta is characterized by comprising the following steps of:
s10: according to the statistical value of the morphological parameters of the research area, a geological grid model is established, and the upper limit P of the river channel sand ratio is determinedmaxThe upper limit of the ratio of the river mouth dam to the river mouth dam;
s20: obtaining a range for generating a fan body along the direction of a source of a research area according to the geological grid model;
s30: generating a river channel in the fan body range, randomly generating a estuary dam at the estuary position of the river channel, and enabling the estuary dam occupation ratio to be within the upper limit range of the estuary dam occupation ratio;
s40: obtaining a river channel sand ratio P of a generated river channel; if the river sand ratio P meets the following definition formula:
P∈Pmax；
ending and obtaining a training image; otherwise, repeating step S30;
step S30 includes the following substeps:
s31: randomly selecting the starting point position of the river channel within the fan body range;
s32: generating a river channel from the starting position to obtain a main river channel or a main river channel and a branch river channel;
s33: randomly generating corresponding estuary dams at the estuary positions of the main river channel and the branch river channels until the estuary dam occupation ratio is within the upper limit range of the estuary dam occupation ratio;
in step S10, the statistical values of the morphological parameters include a fan parameter statistical value, a river channel parameter statistical value, and a dam parameter statistical value;
wherein the fan body parameter statistics comprise: the width, the extension distance, the opening angle and the thickness of the fan body;
the river channel parameter statistics include: width, thickness, extension distance and amplitude of the river;
the dam body parameter statistic comprises: the length, width, thickness and declination angle of the dam body.
2. The split-flow dam-type delta front edge training image creation method according to claim 1, wherein in step S32, a river width or a river width-to-thickness ratio is randomly extracted from the statistical values of the morphological parameters as an initial value to start to create a river.
3. The diversion river dam type delta front edge training image establishing method according to claim 1 or 2, wherein in step S32, the geometric shape of the cross section of the river channel is controlled by the width w (y) of the river channel, the maximum thickness t (y) of the river channel, and the relative position a (y) of the maximum thickness of the river channel.
4. The split-flow dam-type Delta front edge training image establishment method according to claim 3, wherein the relative position of the maximum river thickness a (y) satisfies the following definitional formula:
Figure FDA0003082965890000021
wherein, Cv(y) is the curvature of the channel centerline;
Cv l(y) curvature of the left border of the channel;
Cv r(y) is the curvature of the right border of the channel.
5. The split-flow dam-type Delta front edge training image establishment method as claimed in claim 4, wherein when the relative position a (y) of the maximum river thickness t (y) is greater than 0.5, the depth d (w, y) of the river satisfies the following definition:
Figure FDA0003082965890000022
wherein w ∈ [0, W (y) ];
b (y) satisfies the following defined formula:
Figure FDA0003082965890000023
6. the split-flow dam-type Delta front edge training image establishment method as claimed in claim 4, wherein when the relative position a (y) of the maximum river thickness t (y) is less than or equal to 0.5, the depth d (w, y) of the river satisfies the following definition:
Figure FDA0003082965890000024
wherein w ∈ [0, W (y) ];
b (y) satisfies the following defined formula:
Figure FDA0003082965890000025
7. the diversion river dam type delta leading edge training image establishing method according to claim 1 or 2, wherein in step S32, the branch river is generated where the curvature of the main river is maximum.
8. The split-flow dam-type Delta front edge training image creation method of claim 7, wherein the principalRiver course upper node TiTo node Ti+1Local curvature C ofv(yTi) Satisfies the following defined formula:
Figure FDA0003082965890000031
wherein, thetaTiAs a node TiAngle of tangent to, thetaTiSatisfies the following defined formula:
Figure FDA0003082965890000032
yTias a node TiCoordinates on the y-axis;
xTias a node TiCoordinates on the x-axis;
i is an integer of 1 or more.
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