CN110322555A - Distributary river dam type delta front training image method for building up - Google Patents
Distributary river dam type delta front training image method for building up Download PDFInfo
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- G06F30/00—Computer-aided design [CAD]
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Abstract
The present invention relates to a kind of distributary river dam type delta front training image method for building up, are related to oil-gas exploration technical field, the technical issues of for solving the training image existing in the prior art for being difficult to set up delta front non-stationary reservoir.On the one hand method of the invention follows statistical nature parameter regularity, i.e., by the distribution characteristics of research area's morphological parameters, determine that control generates the range of fan body and the upper limit of river morphological parameters；The deposition process of another aspect simulating riverway in algorithm for design, algorithm design and realization are instructed with process model building thinking, i.e. within the scope of fan body, the condition and upper limit value in river are generated by control, the training image being consistent from sedimentation mechanism to statistical nature with practical object can be established, this is significant to promotion Multiple-Point Geostatistics Modeling Theory, and to the quick and convenient application of multiple spot geological statistics and actual reservoir modeling, raising multiple spot modeling accuracy, serves oil field production and have important practical significance.
Description
Technical field
The present invention relates to oil-gas exploration technical field, a kind of particularly distributary river dam type delta front training image
Method for building up.
Background technique
Traditional two o'clock statistical modeling method based on variogram, in the foundation of continental reservoir three-dimensional geological model
Great function has been played, reservoir description has effectively been pushed to develop to fine, quantification direction.However, two point-variation functions are difficult
Portray reservoir complexity form (such as curved river), it is necessary to rely on multiple spatial points and combine and come fine description reservoir form and space
Configuration relation, thus Multiple-Point Geostatistics are established and develop.
Multiple-Point Geostatistics scan training image (the quantitative Geological Model of generalities by multiple spot template (data template)
Type) multiple spot probability is obtained, it predicts wait a possible depositional model at estimating.A variety of Multiple-Point Geostatistics sides are developed within nearly 20 years
Method, such as Snesim, Simpat, Filtersim, Dispat (distance-based MPS), Smps (skeleton-based
MPS), location-based Multiple-Point Geostatistics modeling method etc..But training image how is obtained, especially before acquisition delta
The training image of edge non-stationary reservoir still has very big difficulty.
Summary of the invention
The present invention provides a kind of different acquisition parameter distributary river dam type delta front training image method for building up, for solving
The technical issues of training image certainly existing in the prior art for being difficult to set up delta front non-stationary reservoir.
The present invention provides a kind of different acquisition parameter distributary river dam type delta front training image method for building up, including with
Lower step:
S10: according to the statistical value of research area's morphological parameters, geologic grid model is established, and determines the upper of channel sand accounting
Limit PmaxWith the upper limit of estuary dam accounting；
S20: the range for generating fan body is obtained along the source direction for studying area according to the geologic grid model；
S30: generating river within the scope of the fan body, generates debouch bar at random at the river mouth position in river, and make
Estuary dam accounting is within the upper range of the estuary dam accounting；
S40: the channel sand accounting P for generating river is obtained；If the channel sand meets formula defined below than accounting P:
P∈Pmax；
Then terminate and obtains training image；Otherwise, step S30 is repeated.
In an embodiment of the invention, step S30 includes following sub-step:
S31: within the scope of the fan body, the start position in river is randomly selected；
S32: generating river since the start position, obtains main stem or main stem and branch channel；
S33: generating corresponding debouch bar at the river mouth position of the main stem and the branch channel at random, until
Estuary dam accounting is within the upper range of the estuary dam accounting.In an embodiment of the invention, in step S32,
The width-thickness ratio of width or river that river is randomly selected since the statistical value of the morphological parameters generates river being initial value.
In an embodiment of the invention, in step S32, pass through the width W (y) in river, the maximum gauge t in river
(y) and the relative position a (y) of river maximum gauge come control generate river cross section geometric shape.
In an embodiment of the invention, the relative position a (y) of the river maximum gauge meets defined below
Formula:
Wherein, CvIt (y) is the curvature of channel axis；
Cv lIt (y) is the curvature of river left margin；
Cv rIt (y) is the curvature of river right margin.
In an embodiment of the invention, when the relative position a (y) of the river maximum gauge t (y) is greater than 0.5
When, the depth d (w, y) in river meets formula defined below:
Wherein, [0, W (y)] w ∈；
B (y) meets formula defined below:
In an embodiment of the invention, when the relative position a (y) of the river maximum gauge t (y) is less than or waits
When 0.5, the depth d (w, y) in river meets formula defined below:
Wherein, [0, W (y)] w ∈；
B (y) meets formula defined below:
In an embodiment of the invention, in step S32, described point is generated at the maximum curvature of the main stem
Zhi Hedao.
In an embodiment of the invention, node T on the main stemiTo node Ti+1The local curvature C at placev
(yTi) meet formula defined below:
Wherein, θTiFor node TiThe tangent line at place and the angle of x-axis, θTiMeet formula defined below:
yTiFor node TiCoordinate on the y axis；
xTiFor node TiCoordinate in x-axis；
I is the integer more than or equal to 1.
In an embodiment of the invention, in step S10, the statistical value of the morphological parameters includes fan body parameter system
Evaluation, river parameter statistics and dam body parameter statistics；
Wherein, the fan body parameter statistics include: width, extended distance, opening angle and the thickness of fan body；
The river parameter statistics include: width, thickness, extended distance and the amplitude in river；
The dam body parameter statistics include: length, width, thickness and the tilt angled down of dam body.
Compared with the prior art, the advantages of the present invention are as follows:
(1) statistical nature parameter regularity is on the one hand followed, i.e., by the distribution characteristics of research area's morphological parameters, determines control
Generate the range of fan body and the upper limit of river morphological parameters；The deposition process of another aspect simulating riverway in algorithm for design, with
Process model building thinking instruct algorithm design and realize, i.e., within the scope of fan body, by control generate river condition and on
Limit value can establish the training image being consistent from sedimentation mechanism to statistical nature with practical object, this is to promotion multiple spot geology system
It is significant that meter learns Modeling Theory, and to the quick and convenient application of multiple spot geological statistics and actual reservoir modeling, raising multiple spot
Modeling accuracy serves oil field production and has important practical significance.
(2) foundation of training image, be based on the statistical value for studying area's morphological parameters, and establish with study area it is consistent
Address network model, therefore training image obtained is more bonded practical work area, provides guarantor for the multiple spot modeling of next step
Barrier.
Detailed description of the invention
The invention will be described in more detail below based on embodiments and refering to the accompanying drawings.
Fig. 1 is the flow chart for establishing training image in the embodiment of the present invention；
Fig. 2 is the statistical value of morphological parameters in the embodiment of the present invention；
Fig. 3 is the plane section that training image is automatically generated in the embodiment of the present invention；
Fig. 4 is the one of cross section for automatically generating training image；
Fig. 5 is another cross section for automatically generating training image；
Fig. 6 is the one of longitudinal section for automatically generating training image；
Fig. 7 is another longitudinal section for automatically generating training image.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings.
As shown in Figure 1, the present invention provides a kind of different acquisition parameter distributary river dam type delta front training image foundation
Method, method includes the following steps:
Step 1: the morphological parameters in statistical research area establish geologic grid mould according to the statistical value of research area's morphological parameters
Type, and determine the upper limit P of channel sand accountingmaxWith the upper limit of estuary dam accounting.Wherein, the upper limit P of channel sand accountingmaxIt can be
One numberical range, similarly, the upper limit of estuary dam accounting can be a numberical range.It in the present invention, is accounted for channel sand
The upper limit P of ratiomaxThe upper limit with estuary dam accounting is screening conditions, if the channel sand accounting and estuary dam accounting that obtain are each fallen within
In corresponding upper range, then the secondary result is received；If the channel sand accounting and estuary dam accounting that obtain exceed the upper limit
Range, then the secondary analog result is rejected.Specifically, the statistical value of morphological parameters includes fan body parameter statistics, river parameter
Statistical value and dam body parameter statistics.
Wherein, fan body parameter statistics include: width (maximum width and minimum widith), the extended distance, opening of fan body
Angle and thickness (maximum gauge and minimum thickness).
River parameter statistics include: the width (maximum width, minimum widith and mean breadth) in river, thickness (maximum
Thickness, minimum thickness and average thickness), extended distance (maximum extension distance, minimum extended distance and average extended distance) with
And amplitude (peak swing, minimum amplitude and mean amplitude of tide), wherein the unit of above-mentioned parameter is m.
Dam body parameter statistics include: the length (maximum length, minimum length and average length) of dam body, width (maximum
Width, minimum widith and mean breadth), (maximum has a down dip for thickness (maximum gauge, minimum thickness and average thickness) and tilt angled down
Angle, minimum tilt angled down and average tilt angled down).According to above-mentioned statistical value, it may be determined that the upper limit P of channel sand accountingmaxThe river and
The upper limit of mouth dam accounting.Step 2:, along the source direction for studying area, obtaining according to the geologic grid model and generating fan body
Range.
Step 3: generating river within the scope of fan body, debouch bar, Zhi Daohe are generated at random at the river mouth position in river
Mouth dam accounting is within the upper range of estuary dam accounting；The probability for generating estuary dam at this time is 0 (i.e. it is not possible that generate again new
Estuary dam), the form of all estuary dams is controlled by length, the width and thickness of dam body in above-mentioned statistical value.
Specifically, the step of generating river is as follows:
Firstly, randomly selecting the start position in river within the scope of fan body.
Secondly, generating river since start position, main stem or main stem and branch channel are obtained.When river, bending reaches
When to certain limit, river can produce branch channel, and main stem respectively forwardly extends with branch channel, until river extends knot
Beam.
Wherein, river extend when, randomly selected from the statistical value of morphological parameters river width or river it is generous
Than starting to generate river for initial value.That is, when generating river, with the width in river in above-mentioned statistical value or the width in river
Thickness rate is that known conditions is inputted.
It is maximum thick by the width W (y) in river, the maximum gauge t (y) in river and river also, when generating river
The relative position a (y) of degree come control generate river cross section geometric shape.
In one embodiment of the invention, the relative position a (y) of river maximum gauge t (y) meets formula defined below:
Wherein, CvIt (y) is the curvature of channel axis；
Cv lIt (y) is the curvature of river left margin；
Cv rIt (y) is the curvature of river right margin.
When the relative position a (y) of river maximum gauge t (y) is greater than 0.5, the depth d (w, y) in river meets following fixed
Adopted formula:
Wherein, [0, W (y)] w ∈；
B (y) meets formula defined below:
When the relative position a (y) of river maximum gauge t (y) is less than or equal to 0.5, the depth d (w, y) in river meets
Formula defined below:
Wherein, [0, W (y)] w ∈；
B (y) meets formula defined below:
Since the maximum gauge t (y) in the corresponding river relative position a (y) can be undergone since river is during generation
It arrives shallow process again from shallow to deep, therefore by above-mentioned calculation, can get river of the river in depth change procedure
Position depth capacity (or maximum gauge) and occurred can depict the deposition process in river.
Furthermore when generating river, the main stem of generation such as bends to a certain degree during extension, then can produce
Branch channel can obtain the relevant parameter and bifurcation angle of the branch channel after generating branch channel.
Specifically, branch channel is generated at the maximum curvature of main stem.
Node T on main stemiTo node Ti+1The local curvature C at placev(yTi) meet formula defined below:
Wherein, θTiFor node TiThe tangent line at place and the angle of x-axis, θTiMeet formula defined below:
yTiFor node TiCoordinate on the y axis；
xTiFor node TiCoordinate in x-axis；
I is the integer more than or equal to 1.
As a result, according to local curvature C on main stemv(yTi), the width W (y) in river, river maximum gauge t (y) and
Relationship between the relative position a (y) of river maximum gauge can control the geometric shape for generating the cross section in river.
Step 4: obtaining the channel sand accounting P for generating river when new estuary dam can not be generated；If channel sand ratio accounts for
Meet formula defined below than P:
P∈Pmax；
Then terminate and obtains training image；Otherwise, step third step is repeated.
That is, the upper limit (P of the channel sand accounting P in statistical valuemax) within the scope of be cycling condition, when generate river
When the channel sand accounting P in road is fallen within the upper range of statistical value, training image can be obtained, i.e., circulation terminates；Work as generation
When the channel sand accounting P in river is not fallen within the upper range of statistical value, then new river is generated, that is, continues cycling through third
Step, until channel sand accounting P meets above-mentioned cycling condition and can just terminate.
Therefore, algorithm of the invention is the deposition process in river to be imitated, with process based on statistical nature parameter regularity
The design and realization for instructing algorithm of modeling approach, thus what foundation was consistent from sedimentation mechanism to statistical nature with practical object
Training image is applied to multiple spot geological statistics quickly and easily in the production of oil field.Compared with the existing methods, there is operation
Simply, save the cost, the features such as graphical display intuitive is strong, validity is high can more effectively form training image, carry out more
Point geostatistics modeling.
Below by taking certain studies area as an example, method of the invention is specifically described.
The first step changes according to quantity of the multiple delta distributary channels of google geostatistical in progradation,
The parameters such as the length-width ratio of bifurcation angle, the change width in river and estuary dam form Quantitative Knowledge library.
As shown in Fig. 2, the statistical value of input research area's morphological parameters, determines that the grid specification of training image is columns
(columns) * rows (line number) * layers (vertical grid) (columns=400, rows=400, layers=20), original net
Lattice point number is 3200000.
Determine the upper limit P of channel sand accountingmaxAre as follows: [14.25%, 15.75%]；The upper limit of estuary dam accounting are as follows:
[4.5%, 5.5%].As long as the estuary dam accounting of acquisition is at [4.5%, 5.5%] during generating river and estuary dam
In range, and channel sand accounting in the range of [14.25%, 15.75%], then the result obtained can receive.
Second step determines that source direction is 180 °, draws a circle to approve delta using the measurement data of existing knowledge base and work area
Up-front range of deposition (generating the range of fan body).
Third step, within this range, the start position for randomly selecting river start to generate river.It extracts in above-mentioned knowledge base
The width in river is inputted as known conditions, and river is made to start to extend, until river farthest point.
Wherein, maximum place being bent in river, branch channel is randomly generated.
It is calculated, is obtained according to above-mentioned definition:
Maximum curvature C on main stemv(yTmax) it is 1.5；
The width W (y) in river is 17m；
The maximum gauge t (y) in river is 9m；
The relative position a (y) of river maximum gauge changes with the variation of river curvature.
Debouch bar is generated at random at the river mouth position in river, and estuary dam accounting is made to fall into the upper limit of estuary dam accounting
Within range [4.5%, 5.5%], the probability for generating estuary dam at this time is 0, the channel sand accounting P=6.3% of acquisition.
Its upper limit P for not falling within channel sand accountingmaxWithin the scope of, therefore next river node is turned to, i.e. repeatedly third
Step generates a main stem again, and makes its generation.
After third step 9 times are repeated altogether, the channel sand accounting P=15.4% of acquisition falls into the upper limit of channel sand accounting
PmaxWithin the scope of, circulation terminates at this time, can be obtained training image.
The training image of generation is as shown in fig. 3 to 7, consistent with sedimentary facies rule, can preferably reflect delta front
Deposition characteristics, can be used as multiple-point simulation training image use.
In conclusion the present invention is by the scale parameter and distribution characteristics of statistical research area fan body, river, dam body, knot
The thinking of the simulation algorithm based on target and deposition process is closed, realizes automatically generating for delta front training image.This hair
On the one hand bright method follows its statistical nature parameter regularity, its deposition process is on the other hand imitated in algorithm for design, with
Instructing algorithm design and realizing for process model building thinking, can establish and be consistent with practical object from sedimentation mechanism to statistical nature
Training image, this is significant and quick and convenient to multiple spot geological statistics to promotion Multiple-Point Geostatistics Modeling Theory
Using being modeled with actual reservoir, improving multiple spot modeling accuracy, serves oil field production and have important practical significance.
Although by reference to preferred embodiment, invention has been described, the case where not departing from the scope of the present invention
Under, various improvement can be carried out to it and can replace component therein with equivalent.Especially, as long as there is no structures to rush
Prominent, items technical characteristic mentioned in the various embodiments can be combined in any way.The invention is not limited to texts
Disclosed in specific embodiment, but include all technical solutions falling within the scope of the claims.
Claims (10)
1. a kind of distributary river dam type delta front training image method for building up, which comprises the following steps:
S10: according to the statistical value of research area's morphological parameters, geologic grid model is established, and determines the upper limit P of channel sand accountingmax
With the upper limit of estuary dam accounting；
S20: the range for generating fan body is obtained along the source direction for studying area according to the geologic grid model；
S30: generating river within the scope of the fan body, generates debouch bar at random at the river mouth position in river, and make river mouth
Dam accounting is within the upper range of the estuary dam accounting；
S40: the channel sand accounting P for generating river is obtained；If the channel sand meets formula defined below than accounting P:
P∈Pmax；
Then terminate and obtains training image；Otherwise, step S30 is repeated.
2. distributary river dam type according to claim 1 delta front training image method for building up, which is characterized in that step
S30 includes following sub-step:
S31: within the scope of the fan body, the start position in river is randomly selected；
S32: generating river since the start position, obtains main stem or main stem and branch channel；
S33: generating corresponding debouch bar at the river mouth position of the main stem and the branch channel at random, until river mouth
Dam accounting is within the upper range of the estuary dam accounting.
3. distributary river dam type according to claim 2 delta front training image method for building up, which is characterized in that step
In S32, the width-thickness ratio of width or river that river is randomly selected since the statistical value of the morphological parameters is given birth to being initial value
At river.
4. distributary river dam type according to claim 2 or 3 delta front training image method for building up, which is characterized in that
In step S32, pass through the relative position a (y) of the width W (y) in river, the maximum gauge t (y) in river and river maximum gauge
To control the geometric shape in the cross section for generating river.
5. distributary river dam type according to claim 4 delta front training image method for building up, which is characterized in that described
The relative position a (y) of river maximum gauge meets formula defined below:
Wherein, CvIt (y) is the curvature of channel axis；
Cv lIt (y) is the curvature of river left margin；
Cv rIt (y) is the curvature of river right margin.
6. distributary river dam type according to claim 5 delta front training image method for building up, which is characterized in that work as institute
When stating the relative position a (y) of river maximum gauge t (y) greater than 0.5, the depth d (w, y) in river meets formula defined below:
Wherein, [0, W (y)] w ∈；
B (y) meets formula defined below:
7. distributary river dam type according to claim 5 delta front training image method for building up, which is characterized in that work as institute
When stating the relative position a (y) of river maximum gauge t (y) less than or equal to 0.5, the depth d (w, y) in river meets defined below
Formula:
Wherein, [0, W (y)] w ∈；
B (y) meets formula defined below:
8. distributary river dam type according to claim 2 or 3 delta front training image method for building up, which is characterized in that
In step S32, the branch channel is generated at the maximum curvature of the main stem.
9. distributary river dam type according to claim 8 delta front training image method for building up, which is characterized in that described
Node T on main stemiTo node Ti+1The local curvature C at placev(yTi) meet formula defined below:
Wherein, θTiFor node TiThe tangent line at place and the angle of x-axis, θTiMeet formula defined below:
yTiFor node TiCoordinate on the y axis；
xTiFor node TiCoordinate in x-axis；
I is the integer more than or equal to 1.
10. distributary river dam type according to claim 1 delta front training image method for building up, which is characterized in that step
In rapid S10, the statistical value of the morphological parameters includes fan body parameter statistics, river parameter statistics and dam body parametric statistics
Value；
Wherein, the fan body parameter statistics include: width, extended distance, opening angle and the thickness of fan body；
The river parameter statistics include: width, thickness, extended distance and the amplitude in river；
The dam body parameter statistics include: length, width, thickness and the tilt angled down of dam body.
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| CN108805982A (en) * | 2018-05-24 | 2018-11-13 | 长江大学 | A kind of distributary channel-river mouth dam type delta front training image method for building up |
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