CN107965318A - A kind of method of Volcanic Reservoir effective reservoir quantitative classification - Google Patents
A kind of method of Volcanic Reservoir effective reservoir quantitative classification Download PDFInfo
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- WKBPZYKAUNRMKP-UHFFFAOYSA-N 1-[2-(2,4-dichlorophenyl)pentyl]1,2,4-triazole Chemical compound C=1C=C(Cl)C=C(Cl)C=1C(CCC)CN1C=NC=N1 WKBPZYKAUNRMKP-UHFFFAOYSA-N 0.000 description 1
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- E21B47/00—Survey of boreholes or wells
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- E—FIXED CONSTRUCTIONS
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- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
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Abstract
The present invention provides a kind of method of Volcanic Reservoir effective reservoir quantitative classification.This method comprises the following steps：Obtain the reservoir quality parameter with reference to volcanic rock reservoir；Split clustering procedure using K means and cluster analysis is carried out to the reservoir quality parameter, to establish the criteria for classification of Volcanic Reservoir effective reservoir；Based on the criteria for classification of the Volcanic Reservoir effective reservoir, the linear discriminant function of all kinds of effective reservoirs is established using linear discriminant function method；Based on the linear discriminant function, the type of target volcanic rock reservoir is determined.Technical solution provided by the invention further for the deployment of such oil reservoir Efficient Development can provide technical support and be the favourable reference of perfect offer of Volcanic Reservoir effective reservoir evaluation of classification standard and technical system.
Description
Technical field
The present invention relates to a kind of method of Volcanic Reservoir effective reservoir quantitative classification, belongs to shale oil and hides development technique neck
Domain.
Background technology
In alive boundary stone exploration activity and Development history, clastic rock is often reservoir exploration and exploitation with carbonate reservoir
Main object, and relatively deep volcanic rock reservoir is buried, because its complicated characteristics of lithology and lithofacies, RESERVOIR RECOGNITION are pre- with distribution
Survey is often ignored there are larger difficulty by us.
Since early 20th century, with the development and the continuous improvement of exploration and development technology of petroleum industry, domestic and international volcano
Oil gas pool exploration and development constantly succeeds, such as the U.S., Cuba, Japan, state of Ghana, domestic huge port, the Liaohe River, Xinjiang,
The oil fields such as grand celebration, volcanic reservoirs also progressively become oil-gas exploration and development key areas, its reservoir characteristic becomes particular lithologic
The emphasis of reservoir study.
At present, domestic and foreign scholars are in Volcanic uranium deposit Lithofacies Identification, reservoir space and volcanic rock reservoir log interpretation model
Etc., great progress has been achieved, has been achieved in Volcanic uranium deposit, physical property and reservoir space etc. extensive common
Know, but in terms of Reservoir Classification, the object of forefathers' research is mostly to be directed to clastic rock oil reservoir, accidental effectively to be stored up on volcanic gas reservoir
The effective reservoir of system has been carried out Song-liao basin Xu man defensive wall volcanic gas reservoir in the research of layer classification, such as topaz imperial (2010)
Research.
In general, effective reservoir refer to possess preserve with seepage flow movable fluid (based on hydrocarbon fluid) ability, existing
There is the reservoir of commercial mining value under stage process technology and economic condition；And invalid reservoir then refers to that reservoir properties are poor,
There is no liquid-producing capacity or the reservoir less than dried layer standard.
At present, for the evaluation of classification of volcanic rock reservoir effective reservoir, ripe and effective method and skill are still lacked
Art, thus constrain the Efficient Development of such oil reservoir High-quality Reservoir.Therefore, volcanic rock reservoir effective reservoir evaluation of classification side is established
Method becomes one of this area technical problem urgently to be resolved hurrily.
The content of the invention
In order to solve the above technical problems, it is an object of the invention to provide a kind of Volcanic Reservoir effective reservoir quantitative classification
Method.This method can quickly identify volcanic rock reservoir effective reservoir, and provide technological guidance for volcanic rock reservoir Efficient Development
With reference.
To reach above-mentioned purpose, the present invention provides a kind of method of Volcanic Reservoir effective reservoir quantitative classification, it is wrapped
Include following steps：
Obtain the reservoir quality parameter with reference to volcanic rock reservoir；
Split clustering procedure using K-means and cluster analysis is carried out to the reservoir quality parameter, to establish volcanic rock reservoir
The criteria for classification of effective reservoir；
Based on the criteria for classification of the Volcanic Reservoir effective reservoir, all kinds of effectively storages are established using linear discriminant function method
The linear discriminant function of layer；
Based on the linear discriminant function, the type of target volcanic rock reservoir is determined.
Volcanic rock reservoir is different from conventional sandstone reservoir, its reservoir quality is mainly by matrix pores and crack dual media
Influence.Technical solution provided by the invention is combined cluster is split with linear discriminant analysis method, preferably reservoir quality parameter,
Quantitative classification algorithm is established, ensures data order and stability, so as to carry out quick evaluation of classification to reservoir.
In technical solution provided by the invention, the effective reservoir, which refers to possess, to be preserved with seepage flow movable fluid (with hydrocarbon
Based on class fluid) ability, there is the reservoir of commercial mining value under technology at this stage and economic condition.
Reservoir quality is primarily referred to as the ability that reservoir preserves and is percolated fluid.In technical solution provided by the invention, institute
Stating reservoir quality parameter includes porosity, permeability, density, fluidized bed index specific productivity index.These parameters not only allow for
Electrical parameter otherness between volcanic rock reservoir difference Volcanic uranium deposit, while rock seepage flow is reflected using FZI fluidized beds index
Ability, and emphasis considers the output capacity of different type Volcanic uranium deposit, and the dynamic and static parameter of summary is as volcanic rock
The favourable parameters of reservoir effective reservoir classification.These parameters can be obtained by existing rock core information.
In the above-mentioned methods, it is preferable that the fluidized bed index FZI is counted after being deformed by Kozeny-carman equations
Obtain, specifically as shown in formula 1 to formula 3：
By formula 1 to formula 3, can be derived from：Lg RQI=lg FZI+lg Φ z.
For formula 1 into formula 3, RQI is oil reservoir qualitative index；Φ z are the ratio between pore volume and particle volume；FZI is fluidized bed
Index；K is permeability (× 10-3μm2)；Ф is porosity (decimal).
In the above-mentioned methods, it is preferable that split clustering procedure using K-means and cluster point is carried out to the reservoir quality parameter
Analysis includes：
Determine classification number k；
Sample point is categorized into k cluster, so that distance ratio of central point of cluster arrives belonging to each sample point to its
The distance of the central point of other clusters is small.
Technical solution provided by the invention splits clustering procedure to the target volcanic rock reservoir mass parameter using K-means
Cluster analysis is carried out, wherein, the core algorithm thought of K-means segmentation clustering procedures is exactly that n observation Exemplary classes are poly- to k
In class so that each central point of the observed value where it than other cluster centre points apart from smaller, apart from smaller, get over phase
Seemingly, diversity factor is smaller.On the premise of classification number k is provided, K-means can quickly handle density data collection, its center is counted
Method improves the discrete and mixing of serial number attribute and clusters, preferable for numerous and jumbled geologic data clustering effect, and should
Method influenced by singular value, Similar measure and inappropriate clustering variable it is smaller, can be into for inappropriate preliminary classification
Row adjusts iteration repeatedly, and is finally reached convergence.
In the above-mentioned methods, classification number k can be set according to the needs of goal in research, technical side provided by the invention
K is set to 3 by case, and expression can be divided into 3 classes, can represent respectively, in, it is poor.
In the above-mentioned methods, it is preferable that sample point is categorized into k cluster, so that poly- belonging to each sample point to its
The distance of central point of the distance of the central point of class than being clustered to other is small can to include procedure below：
It is assumed that sample set is { x(1), x(2)... x(m), x(i)∈R(n), according to the Euclidean distance calculation formula meter shown in formula 4
Calculate distance
K cluster centre is chosen, the affiliated classification c of each sample i is calculated according to the formula shown in formula 5(i)
For every a kind of central point μj, as shown in Equation 6, final class central point can be calculated by renewal
Recurring formula 5 and formula 6, until the quadratic sum of the Euclidean distance of the class central point of cluster belonging to all samples to its
Minimum, that is, it is minimum to reach in formula 7 function J (c, μ), and finally restrains, so as to obtain the poly- of global optimum or local optimum
Class gathering
In the above-mentioned methods, it is preferable that the criteria for classification of the volcanic rock reservoir effective reservoir such as table 1-1 and table 1-2 institutes
Show
Table 1-1
Note：Data above table 1-1 average values are minimum value to maximum；Such as the porosity of I class effective reservoirs is most
Small value is 5.6, maximum 19.3, average value 11.3.
Table 1-2
In the above-mentioned methods, the effective reservoir of the volcanic rock reservoir is divided into 3 classes, is I classes effective reservoir, II classes respectively
Effective reservoir and Group III effective reservoir；Wherein, the lithology of the I classes effective reservoir is mainly by pacifying mountain matter ignimbrite, dust
One or more of compositions in lava and stomata andesite；The lithology of the II classes effective reservoir is mainly by almond basalt, peace
One or more of compositions in mountain rock, rhyolite, peace mountain matter vent breccia and dust tufa stone；The Group III effective reservoir
Lithology mainly by tufa stone, dacite, English peace matter andesite and fine grain rhyolite in one or more form.
Volcanic Facies can be typically divided between explosion facies, overflow facies, volcanic vent phase and volcanic deposit phase.Wherein, volcano
Rock overflow facies can be generally divided into, in and the parfacies of lower part three；Volcanic rock explosion facies are mainly that tephre is various forms of
Assembly, can be typically divided between sky according to volcaniclastic type and combination and falls parfacies, hot radical wave parfacies, hot chip stream
Parfacies and splash down parfacies.The lithofacies that can be seen that I class effective reservoirs from table 1-2 are mainly overflow facies-top parfacies；II classes have
The lithofacies for imitating reservoir are mainly overflow facies-lower part parfacies, alternatively, explosion facies；The lithofacies of Group III effective reservoir are predominantly broken out
Phase-hot chip stream parfacies, alternatively, overflow facies-middle part parfacies (densification).
In the criteria for classification of above-mentioned Volcanic Reservoir effective reservoir, the reservoir quality of the I classes effective reservoir, seepage flow energy
Power is fabulous, and formation testing specific productivity index is also higher.Compared with the I classes effective reservoir, the reservoir properties of the II classes effective reservoir
Take second place, FZI values are opposite to diminish, and specific productivity index is then not much different.Compared with the II classes effective reservoir, the Group III is effective
The reservoir properties of reservoir are deteriorated, but still have certain seepage flow and output capacity after pressure break, between potentiality effectively and effective reservoir
Between.What it is less than Group III criteria for classification is then invalid reservoir, and fracturing production does not go out substantially or pumping only water outlet, and liquid measure also compared with
It is low.
In general, effective reservoir just refer to possess preserve with seepage flow movable fluid (based on hydrocarbon fluid) ability,
There is the reservoir that commercial mining is worth under technology and economic condition at this stage.And invalid reservoir is then primarily referred to as reservoir thing
Poor, the reservoir without liquid-producing capacity or less than dried layer standard of property.
Technical solution provided by the invention based on cluster analysis as a result, using linear discriminant function method establish it is all kinds of effectively
The linear discriminant function of reservoir.Linear discriminant function is the criterion function for being used directly to classify to pattern, and linear discriminant divides
The purpose of analysis is that the low-dimensional feature for most having classification information is found out from high-dimensional feature space, these features can will be same category of
Sample flocks together, and makes different classes of sample separated as much as possible.The following institute of general type of N-dimensional linear discriminant function
Show：
D (X)=w1x1+w2x2+…+wnxn+w0=WTX+w0In formula, X=[x1, x2..., xn]T, W=[w1, w2...,w]TFor
Weight vector；
To M linear separability pattern class, can be usedDichotomy method, will belong to ω with linear discriminant functioniThe mould of class
Formula is not belonging to ω with remainingiThe pattern of class separates.
In the above-mentioned methods, when classification is identified using linear discriminant function, if only di(X)>0, other d (X) are equal<
0, then it is judged to ωiClass；Wherein,
In the above-mentioned methods, it is preferable that the linear discriminant function of all kinds of effective reservoirs is as shown in table 2.
Table 2
| The type of effective reservoir | Linear discriminant function |
| I | Y1=0.145 × FZI+229.9 × DEN+0.5 × Jos-273.02 |
| II | Y2=-1.423 × FZI+238.9 × DEN+0.15 × Jos-282.53 |
| III | Y3=-1.184 × FZI+241.6 × DEN+0.03 × Jos-300.66 |
In the above-mentioned methods, it is preferable that based on the linear discriminant function, determining the type of target volcanic rock reservoir includes
Procedure below：
The reservoir quality parameter of target volcanic rock reservoir is obtained, and is substituted into the linear discriminant function；
As the functional value ＞ 0 of the linear function of acquisition, determine that the type of target volcanic rock reservoir belongs to the linear function institute
The type of representative；Otherwise, it determines it is not belonging to the type representated by linear function；
When acquisition linear function value≤0, determine to belong to invalid reservoir.
In a detailed embodiment, based on the linear discriminant function, determine that the type of target volcanic rock reservoir can
With including procedure below：
By the reservoir quality parameter of target volcanic rock reservoir, linear discriminant function Y is substituted into1In, such as functional value of acquisition>0,
Determine to belong to Y1Representative type；Otherwise, it determines it is not belonging to Y1Representative type, substitutes into next linear discriminant function at this time
Y2In, such as functional value of acquisition>0, determine to belong to Y2Representative type；Otherwise, it determines it is not belonging to Y2Representative type, this
When substitute into next linear discriminant function Y3In, such as functional value of acquisition>0, determine to belong to Y3Representative type；Otherwise, it determines not
Belong to Y2Representative type, but belong to invalid reservoir.
In the above-mentioned methods, it is preferable that the reservoir quality parameter of the target volcanic rock reservoir and the linear discriminant letter
Variable in number is corresponding.It is highly preferred that the reservoir quality parameter of the target volcanic rock reservoir includes fluidized bed index, density
And specific productivity index.
Beneficial effects of the present invention：
Technical solution provided by the invention has important theory and practical significance, can be further efficient for such oil reservoir
Development deployment provides technical support, and can be the perfect offer of volcanic rock reservoir effective reservoir evaluation of classification standard and technical system
Favourable reference.
Brief description of the drawings
Fig. 1 is the typical clustering and discriminant classification chart that embodiment of the present invention provides；
Fig. 2 is the classification chart of certain oil field individual well volcanic reservoirs；
Fig. 3 is the classification plan of certain oil field volcanic reservoirs；
Fig. 4 is 1 year cumulative production prediction plan of certain oil field volcanic rock reservoir；
Fig. 5 is certain oil field volcanic rock reservoir individual well Production development curve map.
Embodiment
In order to which technical characteristic, purpose and the beneficial effect of the present invention is more clearly understood, now to the skill of the present invention
Art scheme carry out it is described further below, but it is not intended that to the present invention can practical range restriction.
Present embodiments provide for a kind of method of Volcanic Reservoir effective reservoir quantitative classification.This method is with certain volcanic rock
The effective reservoir of the 20 mouthfuls of individual wells in oil field is research object, and 250 samples therein are studied.Detailed process is as follows：
Step S101：Obtain the reservoir quality parameter with reference to volcanic rock reservoir
It can include porosity, permeability, density, fluidized bed index and ratio with reference to the reservoir quality parameter of volcanic rock reservoir
Productivity index；These parameters can be obtained by existing rock core information.
It is preferred that collecting volcanic rock reservoir mass parameter sample, parameter sample set is formed, wherein, fluidized bed index FZI can
With by being calculated after the deformation of Kozeny-carman equations, calculating process is as shown in formula 1 to formula 3：
Formula 1 is into formula 3：RQI is oil reservoir qualitative index；Φ z are the ratio between pore volume and particle volume；FZI is fluidized bed
Index；K is permeability (× 10-3μm2)；Ф is porosity (decimal).
Step S102：Split clustering procedure using K-means and cluster analysis is carried out to the reservoir quality parameter, to establish fire
The criteria for classification of mountain rock reservoir effective reservoir
First, priori classification number k is provided, k=3 in the present embodiment.
Then, by sample classification to be clustered into k cluster, so that the central point of cluster belonging to each sample to its
The distance of central point of the distance than being clustered to other is small, and process is as follows：
It is assumed that sample set to be clustered is { x(1), x(2)... x(m), x(i)∈R(n), according to the Euclidean distance meter shown in formula 4
Calculate formula and calculate distance
The affiliated classification c of each sample i is calculated according to the formula shown in formula 5(i)
For every a kind of central point μj, as shown in Equation 6, final class central point can be calculated by renewal
Recurring formula 5 and formula 6, until the quadratic sum of the Euclidean distance at the center of cluster belonging to all samples to its is most
It is small, that is, reach function J (c, μ) value minimum in formula 7, and finally restrain, so as to obtain the clustering cluster of global optimum or local optimum
Collection；Wherein, the calculation formula of the J (c, μ) is as shown in Equation 7
Table 1-1
Note：Data above table 1-1 average values are minimum value to maximum；Such as the porosity of I class effective reservoirs is most
Small value is 5.6, maximum 19.3, average value 11.3.
Table 1-2
Using K-means Fast Segmentation clustering method statistical analysis parameter samples, volcanic rock reservoir effective reservoir point is established
Class evaluation criterion, as shown in table 1-1 and table 1-2.
Step S103：Based on the criteria for classification of the Volcanic Reservoir effective reservoir, established using linear discriminant function method
The linear discriminant function of all kinds of effective reservoirs
Established with fluidized bed index FZI, density DEN, specific productivity index Jos as variable using linear discriminant function method
The linear discriminant function of all kinds of effective reservoirs, the results are shown in Table 2
Table 2
| The type of effective reservoir | Linear discriminant function |
| I | Y1=0.145 × FZI+229.9 × DEN+0.5 × Jos-273.02 |
| II | Y2=-1.423 × FZI+238.9 × DEN+0.15 × Jos-282.53 |
| III | Y3=-1.184 × FZI+241.6 × DEN+0.03 × Jos-300.66 |
Step S104：Based on the linear discriminant function, the type of target volcanic rock reservoir is determined.
It is corresponding with the variable in linear discriminant function, the reservoir quality parameter of target volcanic rock reservoir is obtained, these ginsengs
Number includes density, fluidized bed index and specific productivity index；
The mass parameter of target volcanic rock reservoir is substituted into the linear discriminant function shown in table 2；
As the functional value ＞ 0 of the linear function of acquisition, determine to belong to the type representated by the linear function；Otherwise, do not belong to
In the type representated by the linear function；
When acquisition linear function functional value≤0, determine to belong to invalid reservoir.
Differentiate result as shown in Fig. 1 and table 3.
Table 3
| Type | Cluster sample |
| I | 6 |
| II | 201 |
| III | 43 |
Table 4
Application analysis
In the 20 mouthfuls of individual well effective reservoir quantitative assessments in certain volcanic rock oil field classification, the volcanic rock that will provide according to the present invention
It is that the method for oil reservoir effective reservoir quantitative classification obtains as a result, the classification results obtained with individual well formation testing pilot production carry out contrast tests
Card, the results are shown in Table 4.In 250 samples of discriminant analysis, Volcanic Reservoir effective reservoir quantitative classification provided by the invention
The right judging rate of method reach 92%, it is seen that the result reliability of technical solution provided by the invention is high, can be with to non-core hole
Phase reservoir quality parameter (with fluidized bed index FZI, density DEN, specific productivity index Jos) is asked for according to well log interpretation, is substituted into each
The linear discriminant function of class effective reservoir carries out the Classification and Identification of effective reservoir, can rapidly and accurately to individual well volcanic rock reservoir into
The quantitative division (attached drawing 2, attached drawing 3) of row.
The present invention is according to different well point volcanic rock interval effective reservoir quantitative classifications and its thickness statistical result, comprehensive production
Dynamic data, takes production forecast time term, final calculating and plotting draws research area's effective reservoir production forecast distribution map, excellent
Choosing determines development deployment dominant area (attached drawing 4).As can be seen that research area the north J214-J215 well area lists from prognostic chart
One term of well tire out production it is relatively low, the current stage be not suitable for guide exploitation, J208, JL2008, J213, J204 well area individual well one
It is higher that term tires out production prediction, can the of a relatively high J208 wells fault block of prioritized deployment well control degree and J204 well areas remaining region conducts
REGION OF WATER INJECTION OILFIELD is disposed in lower step rolling test.
So far, deployment 1 mouthful of water horizontal well of finishing drilling in the J208 well areas of prioritized deployment, oil-producing 33.5 at initial stage day of going into operation
Ton day, produces 230 days, adds up 7700 tons of oil-producing.1 mouthful of JL2011 well areas deployment finishing drilling straight well, oil-producing 13.5 at initial stage day of going into operation
Ton day, produces 584 days, adds up 6311 tons of oil-producing, average 11 ton day of individual well day oil-producing.Horizontal well daily output is that straight well is made in Japan
(attached drawing 5) 1.9-5.8 again.As it can be seen that on the basis of volcanic rock reservoir effective reservoir quantitative judge with classification understanding, can be effectively
Instruct the efficient well site deployment in scene.
Shown by application analysis, classification results of the present invention and live actual effect matching degree are high, are volcanic rock
The deployment of reservoir Efficient Development provides strong technical support, has very strong practicality.
It is comprehensive volcanic rock reservoir lithology lithofacies and its electrical response parameter, microcosmic in conclusion method provided by the invention
Feature, fracture development parameter and Production development parameter, are known using clustering method and typical linear techniques of discriminant analysis scheduling theory
Know and examined with oil reservoir expert, being association of activity and inertia realizes that reservoir quality parameter is preferred, and the volcanic rock reservoir effective reservoir of foundation quantifies
Distinguished number, has preliminarily formed effective reservoir quickly identification and classification, to instruct the Efficient Development of volcanic rock reservoir.
Claims (10)
1. a kind of method of Volcanic Reservoir effective reservoir quantitative classification, it comprises the following steps：
Obtain the reservoir quality parameter with reference to volcanic rock reservoir；
Split clustering procedure using K-means and cluster analysis is carried out to the reservoir quality parameter, it is effective to establish Volcanic Reservoir
The criteria for classification of reservoir；
Based on the criteria for classification of the Volcanic Reservoir effective reservoir, all kinds of effective reservoirs are established using linear discriminant function method
Linear discriminant function；
Based on the linear discriminant function, the type of target volcanic rock reservoir is determined.
2. according to the method described in claim 1, wherein, the reservoir quality parameter with reference to volcanic rock reservoir includes hole
Degree, permeability, density, fluidized bed index and specific productivity index.
3. according to the method described in claim 2, wherein, the calculation formula of the fluidized bed index is as shown in formula 1 to formula 3
In formula 1- formulas 3, RQI is oil reservoir qualitative index；Φ z are the ratio between pore volume and particle volume；FZI is fluidized bed index；K
For permeability；Ф is porosity.
4. according to the method described in claim 1, wherein, using K-means split clustering procedure to the reservoir quality parameter into
Row cluster analysis includes：
Determine classification number k；
Sample point is categorized into k cluster, so that distance ratio of central point of cluster arrives other belonging to each sample point to its
The distance of the central point of cluster is small.
5. according to the method described in claim 4, wherein, the classification number k=3.
6. according to the method described in claim 1, wherein, the criteria for classification such as table 1-1 of the Volcanic Reservoir effective reservoir and
Shown in table 1-2：
Table 1-1
Table 1-2
7. according to the method described in claim 6, wherein, the linear discriminant function of all kinds of effective reservoirs is respectively：
Y1=0.145 × FZI+229.9 × DEN+0.5 × Jos-273.02；
Y2=-1.423 × FZI+238.9 × DEN+0.15 × Jos-282.53；
Y3=-1.184 × FZI+241.6 × DEN+0.03 × Jos-300.66；Wherein,
Y1Represent the linear discriminant function of I class effective reservoirs, Y2Represent the linear discriminant function of II class effective reservoirs, Y3Represent III
The linear discriminant function of class effective reservoir；In Y1、Y2And Y3In, FZI represents fluidized bed index, and DEN represents density, and Jos represents ratio
Productivity index.
8. the method according to claim 1 or 7, wherein, based on the linear discriminant function, determine target volcanic rock reservoir
Type include procedure below：
The reservoir quality parameter of target volcanic rock reservoir is obtained, and is substituted into the linear discriminant function；
As the functional value ＞ 0 of the linear function of acquisition, determine to belong to the type representated by the linear function；Otherwise, it is not belonging to this
Type representated by linear function；
When acquisition linear function functional value≤0, determine to belong to invalid reservoir.
9. according to the method described in claim 8, wherein, the reservoir quality parameter of the target volcanic rock reservoir with it is described linear
Variable in discriminant function is corresponding.
10. according to the method described in claim 9, wherein, the reservoir quality parameter of the target volcanic rock reservoir includes flowing
Layer index, density and specific productivity index.
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