CN104915722A - Multi-factor geologic risk evaluation method based on parallel coordinate - Google Patents
Multi-factor geologic risk evaluation method based on parallel coordinate Download PDFInfo
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Abstract
The invention provides a multi-factor geologic risk evaluation method based on parallel coordinate and belongs to the field of geologic risk evaluation of oil-gas exploration. According to the method, on the basis of geological factor evaluation of a single exploration target and through a parallel coordinate method, geological factor evaluation of a plurality of exploration targets can be carried out. The method comprises the following steps: 1) parallel coordinate display of reservoir forming elements of the exploration target: on the basis of evaluation result of the reservoir forming elements of the single exploration target, directly displaying geological factor evaluation results of the plurality of exploration targets through the parallel coordinate method; 2) clustering analysis based on parallel coordinate display; and 3) primary factor and correlation analysis: on the basis of category division of the exploration targets, further analyzing primary factors of the same kind of exploration targets through parallel coordinate visual display, and analyzing correlation with each primary control factor through a coordinate axis interaction mode.
Description
Technical field
The invention belongs to the geologic risk evaluation field of oil-gas exploration, be specifically related to a kind of multifactor geologic risk evaluation method based on parallel coordinates.
Background technology
Oil-gas exploration is the business management activity of an excessive risk, hi-tech, high investment and high repayment, multinational petroleum corporations adopts various technology to identify always, analyze, to manage in exploration process various may risks, reduces risk in the hope of reaching and obtains maximum economic benefit.Wherein, most crucial and essential in numerous risk is exactly geologic risk.
At present, geological risk analysis for exploration object (such as basin, Hydrocarbon Accumulation System, zone, trap, hydrocarbon-bearing pool, well etc.) mainly takes special geological research or comprehensive geology evaluation, obtain each geologic parameter value of exploring object, with reference to the score value that scoring specification obtains between 0 ~ 1, score value is higher, and to represent risk lower.To multiple exploration targets by calculating the comprehensive decile after the weighting of single exploration object parameters, the form sequence based on excel is adopted to carry out preferably.Although this evaluation method is simple, be easy to operation, its Risk Results based on single numerical value is too simple, lost numerous procedural informations, can not meet the exploration decision making with excessive risk, high investment far away.
In order to obtain more effective information, the traditional statistical analysis methods such as conventional X plot, linear regression, multivariate discriminant analysis.The matrix scatter diagram etc. that the combination of conventional two-dimensional, three-dimensional scatter diagram and two one or two dimensions (unit) builds contributes to expert by directly observing the information obtaining further and hide in data, and cluster analysis, outlier identification, the judgement of linearity and non-linearity relation etc. can be carried out, also matching can form all kinds of " curve " definition further.But, when in the face of exploration object various dimensions, multi-level attributive analysis, conventional two-dimensional, three-dimensional scatter diagram visualization technique inevitably encounter the dimension tcam-exhaustion based on European orthogonal intersection space, and are difficult to the scatter diagram of more than the three-dimensional theorem in Euclid space of direct construction.
Current geological risk analysis mainly adopts geologic risk probabilistic method.The method is mainly by analyzing oil and gas reservoir forming factor, give one to each key element and hold degree value, then with probabilistic method (joint probability of the independent event namely in classical theory of probability is the product of respective probability) or mathematics method to make a concrete analysis of the geologic risk of each exploration targets, according to geology Risk Results, exploration targets is ranked preferably.The method emphasis in the reservoir forming factor evaluation of single exploration targets, and lacks aspects such as the correlativitys between the Analysis The Main Control Factor of exploration targets and factor and analyses in depth.
Summary of the invention
The object of the invention is to solve the difficult problem existed in above-mentioned prior art, a kind of multifactor geologic risk evaluation method based on parallel coordinates is provided, based on the appraisal result of exploration targets reservoir forming factor, adopt Parallel Coordinates to carry out the Type division (cluster analysis) of multiple exploration targets, Dominated Factors identification, reservoir forming factor correlation analysis by the pattern of virtual interactive interface, for evaluation unit divide, exploration targets preferably provide a kind of directly perceived, fast, analytical approach reliably.
The present invention is achieved by the following technical solutions:
Based on a multifactor geologic risk evaluation method for parallel coordinates, on the basis that single exploration targets geologic agent is evaluated, carry out the geologic risk evaluation of many exploration targetss based on parallel coordinates method;
Described method comprises:
(1): the parallel coordinates display of exploration targets reservoir forming factor: based on single exploration targets reservoir forming factor evaluation result, intuitively represented the geologic agent evaluation result of multiple exploration targets by parallel coordinates method;
(2): based on the cluster analysis of parallel coordinates display;
(3): main gene, correlation analysis
On the basis of exploration targets category division, intuitively shown the main gene analyzing similar exploration targets further by parallel coordinates;
Analyzed and each Dominated Factors correlativity by coordinate axis interactive mode.
Described step (1) is specific as follows:
(11) in parallel coordinates display, a reservoir forming factor and its subjective assessment value scope is represented with a dimension axis, according to the difference of evaluating template, evaluation of estimate and the evaluation of estimate scope of different reservoir forming factor can be shown, each reservoir forming factor arranges in equidistant mode, the meter full scale of axis is 0 ~ 1, and its value represents the height of each reservoir forming factor evaluation of estimate;
(12) in parallel coordinates display, an exploration targets or evaluation unit is represented with a broken line, represent with every bar broken line and the intersection point of axis the reservoir forming factor score value that exploration targets is corresponding, the tendency of broken line intuitively reflects the Changing Pattern of the different reservoir forming factor subjective assessment values of exploration targets.
Subjective assessment value scope in described step (11) is 0 ~ 1.
Described step (2) is specific as follows:
In parallel coordinates intuitively shows, according to each similarity that exploration targets reservoir forming factor evaluation of estimate is discrete, extent of polymerization judges exploration targets;
In conjunction with the key factor for biogas accumulation of Confidence height identification and evaluation zone;
In conjunction with reservoir forming factor principal component analysis (PCA) technology, by judging the size of each evaluation points Principal component, each evaluation points of quantitative description is to the influence value of Hydrocarbon Formation Reservoirs.
Described coordinate axis interactive mode in described step (3) is achieved in that
The DISPLAY ORDER of different dimensions axle is resequenced, make to need two the dimension axles carrying out correlation analysis to abut against together, broken line in the middle of two dimension axles two factors that meet representation mutually are positive correlation, nearly parallel expression negative correlation, it is not high that the broken line intersection between therebetween belongs to correlativity;
The not high Dominated Factors of correlativity is as separate class, and the key factor for biogas accumulation that correlativity is higher is polymerized further, carrys out further optimizing evaluation parameter system with this;
By carrying out crossplot analysis and its to selected sample, effectively can judge that reservoir forming factor is positive correlation, negative correlation according to fitting a straight line slope value.
Compared with prior art, the invention has the beneficial effects as follows:
1, for polyfactorial evaluation result, tradition adopts statistical table to express, and conceals the Changing Pattern between the factor and connects each other.Adopt the expression of figure first, be convenient to researchist and carry out multivariate statistical analysis based on geologic risk multiple-factor.
2, by showing the parallel coordinates of the multi parameter analysis result of multiple exploration targets, intuitively, fast can carry out category division to exploration targets, and identifying the Dominated Factors of different classes of exploration targets.
3, on the basis of cluster analysis, in conjunction with coordinate axis switching technology, the correlativity between any two reservoir forming factor can be judged intuitively, go forward side by side one-step optimization evaluating system according to the analysis result of correlativity.
Accompanying drawing explanation
Fig. 1 is the step block diagram of the inventive method.
Fig. 2 is the parallel coordinates display of exploration targets reservoir forming factor.
Fig. 3 is the three-dimensional properties display of exploration targets.
Fig. 4 is exploration targets cluster result figure _ I class.
Fig. 5 is exploration targets cluster result _ II class.
Fig. 6 is that coordinate axis is mutual.
Fig. 7 is the display of one-level reservoir forming factor.
Fig. 8 is two-level appraisement factor display-hydrocarbon source body.
Fig. 9 is that the two-level appraisement factor shows entirely.
Figure 10 shows based on the two-level appraisement selecting predictors of hydrocarbon source body.
Figure 11 shows in conjunction with the parallel coordinates of scatter diagram.
Figure 12 be in conjunction with cluster parallel coordinates display.
Figure 13 is the one-level reservoir forming factor display in embodiment.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in further detail:
Problems of the prior art, its essence is the information excavating for exploration object multidimensional property and mutual display directly perceived.For the problems referred to above that oil exploration and development fields exists, be positioned the various multivariate statistical analysis abilities promoting mass data further, introduce parallel coordinates visualization technology and reflect mutual relationship between multidimensional information variation tendency and each variable.
This method is that the geologic risk evaluation of many exploration targetss carried out by the software based on parallel coordinates, and what its geologic risk evaluation result was exploration targets preferably provides aid decision making on the basis that single exploration targets geologic agent is evaluated.Its innovation is to carry out geologic risk evaluation by visual display with interactive data digging: compared with traditional geology risk analysis method (such as statistical table, X plot etc.), and Parallel Coordinates preferably resolves the multidimensional property display of many exploration targetss; In conjunction with on bore the operation such as last volume, coordinate axis exchange, the geologic agent affecting Hydrocarbon Formation Reservoirs can be analysed in depth.
Based on the geologic risk evaluation of Parallel Coordinates, its evaluation procedure can be divided into following four steps: parallel coordinates display (referring to the appraisal result of the reservoir forming factor being shown multiple exploration targets by parallel coordinates), the kind of risk division of exploration targets reservoir forming factor, Reservoir model analysis, principal factor analysis (PFA), idiographic flow as shown in Figure 1, comprising:
Step 1: the parallel coordinate visualization of exploration targets reservoir forming factor
This step is based on single exploration targets reservoir forming factor evaluation result (as shown in table 1), is intuitively represented the geologic agent evaluation result (as shown in Figure 2) of multiple exploration targets by Parallel Coordinates.
Table 1
1), in Fig. 2, each dimension axis represents a reservoir forming factor (e.g., hydrocarbon source body, conductor department, trap body etc.) and its subjective assessment value scope (0-1), according to evaluating template (evaluating template is existing,
Comprise " evaluation of oil and gas bearing property simplification version, the conventional version of evaluation of oil and gas bearing property, Petroleum Accumulation System template ", the difference of these three templates is that the reservoir forming factor related to is different, wherein to simplify the reservoir forming factor that comprises of version minimum for evaluation of oil and gas bearing property, the reservoir forming factor that the reservoir forming factor of conventional version comprises is relatively more, the reservoir forming factor that Petroleum Accumulation System relates to is also many) difference, evaluation of estimate and the evaluation of estimate scope of different reservoir forming factor can be shown, each reservoir forming factor arranges in equidistant mode, the meter full scale of axis is 0 ~ 1, its value represents the height of each reservoir forming factor evaluation of estimate.
2) in Fig. 2, each broken line represents an exploration targets (or evaluation unit, exploration targets sequence number in table 1), every bar broken line and the intersection point of axis represent the reservoir forming factor score value that exploration targets is corresponding, and the tendency of broken line intuitively reflects the Changing Pattern of the different reservoir forming factor subjective assessment values of exploration targets.
Step 2: based on the cluster analysis of parallel coordinates display
Intuitively shown by parallel coordinates, according to each similarity that exploration targets reservoir forming factor evaluation of estimate (broken line) is discrete, extent of polymerization judges exploration targets, that is, on some dimension axle, serial evaluations value can whether obviously be distinguished according to different exploration targets whether identical or distinguish close to (trend of different broken line is almost consistent).Theoretical according to Hydrocarbon Formation Reservoirs, whether an exploration targets is finally containing oil gas, mainly consider the accumulating condition such as life, storage, lid, circle, fortune, guarantor or cry reservoir forming factor (explanation, these several reservoir forming factor can be subdivided into more one-tenth again and hide the factor), and to different areas, the importance of these conditions is different, some areas wherein one be main gene, namely play a decisive role, some areas are several one and work; If multiple exploration targetss that regional, are shown by parallel coordinates, lower or each score value of a certain reservoir forming factor score value of these exploration targetss compares dispersion, and so this reservoir forming factor is exactly the main gene determining that this Region of Oil-gas becomes to hide.In Fig. 2, its interval value is all between 0 ~ 1 for three reservoir forming factor (hydrocarbon source body, conductor department, trap body), but its dispersion degree is but not identical, and the dispersion degree according to reservoir forming factor can be divided into different Reservoir model exploration targets.Trap body evaluation of estimate, at about 0.5 (between 0.4-0.6), is comparatively concentrated and is not obviously distinguished different grouping, can not as the criteria for classifying; Hydrocarbon source body evaluation of estimate is uniformly distributed, and too disperses, equally should not as the criteria for classifying; Conductor department evaluation of estimate is mainly divided into two intervals, can be used as the effective Reservoir model criteria for classifying.
On the basis of cluster analysis, in conjunction with the key factor for biogas accumulation of the quick identification and evaluation zone of Confidence height.The reservoir forming factor that on dimension axle, subjective assessment score value is lower is generally the key factor affecting Hydrocarbon Formation Reservoirs, and this kind of exploration targets often has higher geologic risk; Confidence is not high or accumulating condition is medium in order to represent for the medium reservoir forming factor of score value, and this kind of exploration targets often can as deposit target; It is better that the reservoir forming factor that score value is higher brings accumulating condition, is usually preferably highest priority.On this basis, further combined with reservoir forming factor principal component analysis (PCA) technology, by judging the size of each evaluation points Principal component, can each evaluation points of quantitative description to the influence value of Hydrocarbon Formation Reservoirs.
Step 3: main gene, correlation analysis
On the basis of exploration targets category division (kind of risk, Reservoir model) (the present invention be applied to exploration targets divide result, such as there are how many exploration targetss a research area, the title of each exploration targets, and how exploration targets divides and does not belong to content of the present invention.In real work, the division of exploration targets can pass through seismic data, carrys out comprehensive division in conjunction with geological research.), intuitively show to analyze further by parallel coordinates and similarly (experienced by identical or similar geologic function, showing that score value is close on different dimensions axle) main gene of exploration targets is (theoretical according to Hydrocarbon Formation Reservoirs, whether an exploration targets is finally containing oil gas, mainly consider raw, storage, lid, circle, fortune, accumulating condition such as guarantor or be reservoir forming factor (these several reservoir forming factor can be subdivided into again more become to hide the factor), and to different areas, the importance of these conditions is different, some areas wherein one be main gene, namely play a decisive role, some areas are several one and work, if multiple exploration targetss that regional, are shown by parallel coordinates, lower or each score value of a certain reservoir forming factor score value of these exploration targetss compares dispersion, and so this reservoir forming factor is exactly the main gene determining that this Region of Oil-gas becomes to hide).On the basis that Dominated Factors (i.e. main gene) identifies, analyzed and each Dominated Factors correlativity by the mode that coordinate axis is mutual, coordinate axis interactive mode mainly comprises rearrangement different dimensions axle DISPLAY ORDER, make to need two oil accumulation factor (dimension axle) carrying out correlation analysis to abut against together, broken line in the middle of two dimension axles two factors that meet representation mutually are positive correlation, nearly parallel expression negative correlation, it is not high that the broken line intersection between therebetween belongs to correlativity.The not high Dominated Factors of correlativity is as separate class, and the key factor for biogas accumulation that correlativity is higher is polymerized further (removing a wherein dimension axle), carrys out further optimizing evaluation parameter system (removing the evaluating definition of repetition) with this.Meanwhile, by carrying out crossplot analysis and its to selected sample, effectively can judge that reservoir forming factor is positive correlation, negative correlation, without relevant, the result of its correlativity can be further used for the parameter simulation of resource evaluation according to fitting a straight line slope value.
During enforcement, concrete operations are as follows:
1, the parallel coordinates display of exploration targets reservoir forming factor evaluation result
In Fig. 3, three at equal intervals vertical pivot represent hydrocarbon source body, conductor department, trap body three reservoir forming factor of exploration targets respectively;
1) every bar vertical pivot adopts linear graduation to represent the height of reservoir forming factor score value, and interval range is 0-1;
2) vertical pivot indicator gauge shows the difference of reservoir forming factor Confidence;
3) every bar broken line represents an exploration targets or evaluation unit, when mouse moves in corresponding exploration targets, demonstrates the exploration targets title that this broken line is corresponding.
2, exploration targets cluster analysis
Fig. 4 and Fig. 5 is by display directly perceived, and further exploration targets is divided into two large classes, wherein I class is the exploration targets that geologic risk is less, and such target is the emphasis of lower step exploration; II class is the medium exploration targets of geologic risk, can as potential target.
3, coordinate axis is mutual
As shown in Figure 6, changed the ordering of corresponding reservoir forming factor by UP or DOWN, better to analyze its Changing Pattern.
4. reservoir forming factor is shown by different level
As shown in Figure 7 and Figure 8, be shown as by different level hide factors evaluation result by roll up or drill down, the level of detail of the geologic risk evaluation research of different layering research is different.
5. reservoir forming factor screening
On the basis of Fig. 7, right button of clicking the mouse, popup menu is selected " lower brill " (drill down), can show all two-level appraisement factors, i.e. Fig. 9;
On the basis of Fig. 9, to click the mouse right button, popup menu selects " selecting predictors " (factor option), in pop-up window list, (see Figure 10) clicks " selection " (i.e. Yes) or " closedown " (No), can show the evaluation points of one-level reservoir forming factor subordinate.
6. the parallel coordinates in conjunction with other analytical technology shows
As is illustrated by figs. 11 and 12.
An embodiment of the inventive method is as follows:
For Bohai gulf basin evaluation unit, further illustrate method and the flow process of carrying out geologic risk evaluation based on Parallel Coordinates.
1, evaluation unit divides
According to this district's tectonic cycle period and oil and gas evolution feature, adopt the probability of migration slot segmentation, study area is divided into 16 evaluation units, as shown in table 2.
Table 2
2, reservoir forming factor evaluation
Have selected hydrocarbon source body, conductor department, large reservoir forming factor totally 22 evaluatings of trap body three according to Hydrocarbon Formation Reservoirs theory, as shown in table 3.And 22 parameters of 16 evaluation units are given a mark,
Table 3
3, parallel coordinates intuitively shows
Based on the geologic risk evaluation software developed voluntarily, 3 reservoir forming factor of above-mentioned 16 evaluation units and 22 two-level appraisement factors are shown.According to display result, evaluation unit is divided into three classes, and I class is the evaluation unit that geologic risk is less, and II is the medium evaluation unit of geologic risk, and III is the evaluation unit that geologic risk is larger.As shown in figure 13.
4, Dominated Factors identification
For I class evaluation unit, the conductor department evaluation of estimate of 16 evaluation units is interval at 0.6-0.8, and the evaluation of estimate of trap body is interval at 0.35-0.6, and the evaluation of estimate of hydrocarbon source body is at 0.35-0.8.According to score value distribution range, the geologic risk size of different evaluation unit depends primarily on the evaluation result of hydrocarbon source body.Therefore, further determined that hydrocarbon source body quality is the key of its Hydrocarbon Formation Reservoirs.
5, correlation analysis
For the key factor for biogas accumulation identified, checked the two-level appraisement factor values of hydrocarbon source body subordinate by drill down operator further, and each its correlativity of two-level appraisement factor is analyzed.Known by analyzing, raw hydrocarbon intensity becomes positive correlation with organic carbon content.
The present invention is a kind of many key elements based on parallel coordinates geologic risk evaluation method, on the basis that single exploration targets geologic agent is evaluated, carry out the Type division (cluster analysis) of multiple exploration targets, Dominated Factors identification, reservoir forming factor correlation analysis by the pattern of virtual interactive interface, can fast, directly perceived, effectively judge exploration targets geologic risk and influence factor thereof.
Be mainly used in the geologic risk evaluation for exploration targets at present, the method reflects mutual relationship between multidimensional information variation tendency and each variable by visualization technique, be that geological personnel carries out cluster analysis, Dominated Factors identification, each reservoir forming factor correlation analysis provide and intuitively, fast, reliably help by interactive operation, its geologic risk evaluation result can be directly used in target preferably, resource potential evaluation.
The method is deepened and perfect traditional geologic risk evaluation method further, by intuitively, mutual mode is that expert carries out cluster analysis, Dominated Factors identification, correlation analysis provide a kind of more convenient and science technological means, all can apply this technology to carry out correlative study work in the research field such as oil-gas exploration, decision analysis and related scientific research unit, have broad application prospects.
Technique scheme is one embodiment of the present invention, for those skilled in the art, on the basis that the invention discloses application process and principle, be easy to make various types of improvement or distortion, and the method be not limited only to described by the above-mentioned embodiment of the present invention, therefore previously described mode is just preferred, and does not have restrictive meaning.
Claims (5)
1. based on a multifactor geologic risk evaluation method for parallel coordinates, it is characterized in that: described method, on the basis that single exploration targets geologic agent is evaluated, carries out the geologic risk evaluation of many exploration targetss based on parallel coordinates method;
Described method comprises:
(1): the parallel coordinates display of exploration targets reservoir forming factor: based on single exploration targets reservoir forming factor evaluation result, intuitively represented the geologic agent evaluation result of multiple exploration targets by parallel coordinates method;
(2): based on the cluster analysis of parallel coordinates display;
(3): main gene, correlation analysis:
On the basis of exploration targets category division, intuitively shown the main gene analyzing similar exploration targets further by parallel coordinates;
Analyzed and each Dominated Factors correlativity by coordinate axis interactive mode.
2. the multifactor geologic risk evaluation method based on parallel coordinates according to claim 1, is characterized in that: described step (1) is specific as follows:
(11) in parallel coordinates display, a reservoir forming factor and its subjective assessment value scope is represented with a dimension axis, according to the difference of evaluating template, evaluation of estimate and the evaluation of estimate scope of different reservoir forming factor can be shown, each reservoir forming factor arranges in equidistant mode, the meter full scale of axis is 0 ~ 1, and its value represents the height of each reservoir forming factor evaluation of estimate;
(12) in parallel coordinates display, an exploration targets or evaluation unit is represented with a broken line, represent with every bar broken line and the intersection point of axis the reservoir forming factor score value that exploration targets is corresponding, the tendency of broken line intuitively reflects the Changing Pattern of the different reservoir forming factor subjective assessment values of exploration targets.
3. the multifactor geologic risk evaluation method based on parallel coordinates according to claim 2, is characterized in that: the subjective assessment value scope in described step (11) is 0 ~ 1.
4. the multifactor geologic risk evaluation method based on parallel coordinates according to claim 3, is characterized in that: described step (2) is specific as follows:
In parallel coordinates intuitively shows, according to each similarity that exploration targets reservoir forming factor evaluation of estimate is discrete, extent of polymerization judges exploration targets;
In conjunction with the key factor for biogas accumulation of Confidence height identification and evaluation zone;
In conjunction with reservoir forming factor principal component analysis (PCA) technology, by judging the size of each evaluation points Principal component, each evaluation points of quantitative description is to the influence value of Hydrocarbon Formation Reservoirs.
5. the multifactor geologic risk evaluation method based on parallel coordinates according to claim 4, is characterized in that: the described coordinate axis interactive mode in described step (3) is achieved in that
The DISPLAY ORDER of different dimensions axle is resequenced, make to need two the dimension axles carrying out correlation analysis to abut against together, broken line in the middle of two dimension axles two factors that meet representation mutually are positive correlation, nearly parallel expression negative correlation, it is not high that the broken line intersection between therebetween belongs to correlativity;
The not high Dominated Factors of correlativity is as separate class, and the key factor for biogas accumulation that correlativity is higher is polymerized further, carrys out further optimizing evaluation parameter system with this;
By carrying out crossplot analysis and its to selected sample, effectively can judge that reservoir forming factor is positive correlation, negative correlation according to fitting a straight line slope value.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106709507A (en) * | 2016-11-29 | 2017-05-24 | 北京林业大学 | Force-directed segmented bone parallel coordinates plot clustering data bundling method |
| CN107305649A (en) * | 2016-04-19 | 2017-10-31 | 中国石油化工股份有限公司 | Target For Drilling key geology risk factors bore post analysis method before boring |
| CN108537396A (en) * | 2017-03-01 | 2018-09-14 | 北京大学 | A kind of grain security vulnerability analysis method and system |
| CN112765256A (en) * | 2020-12-16 | 2021-05-07 | 山东师范大学 | Parallel coordinate axis data visualization method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7574454B2 (en) * | 2003-06-25 | 2009-08-11 | Drvision Technologies Llc | Dynamic learning and knowledge representation for data mining |
| CN101578512A (en) * | 2007-01-15 | 2009-11-11 | 雪佛龙美国公司 | Method and system for assessing exploration prospect risk and uncertainty |
| CN102855662A (en) * | 2012-07-25 | 2013-01-02 | 中国科学院对地观测与数字地球科学中心 | Ocean environment visualization method |
-
2014
- 2014-03-14 CN CN201410095044.7A patent/CN104915722A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7574454B2 (en) * | 2003-06-25 | 2009-08-11 | Drvision Technologies Llc | Dynamic learning and knowledge representation for data mining |
| CN101578512A (en) * | 2007-01-15 | 2009-11-11 | 雪佛龙美国公司 | Method and system for assessing exploration prospect risk and uncertainty |
| CN102855662A (en) * | 2012-07-25 | 2013-01-02 | 中国科学院对地观测与数字地球科学中心 | Ocean environment visualization method |
Non-Patent Citations (4)
| Title |
|---|
| 李建忠 等: "基于几何坐标法的多维数据可视化技术在地质数据处理中的应用", 《地学前缘(中国地质大学(北京)；北京大学)》 * |
| 盛秀杰 等: "一种新的面向多元统计分析的信息可视化技术", 《石油地球物理勘探》 * |
| 翟旭君，李春平: "平行坐标及其在聚类分析中的应用", 《计算机应用研究》 * |
| 路燕梅: "基于平行坐标的可视化多维数据挖掘的研究", 《现代计算机(专业版)》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107305649A (en) * | 2016-04-19 | 2017-10-31 | 中国石油化工股份有限公司 | Target For Drilling key geology risk factors bore post analysis method before boring |
| CN107305649B (en) * | 2016-04-19 | 2021-01-01 | 中国石油化工股份有限公司 | Pre-drilling and post-drilling analysis method for key geological risk factors of drilling target |
| CN106709507A (en) * | 2016-11-29 | 2017-05-24 | 北京林业大学 | Force-directed segmented bone parallel coordinates plot clustering data bundling method |
| CN106709507B (en) * | 2016-11-29 | 2019-11-08 | 北京林业大学 | A kind of parallel coordinate system view cluster data binding method of power guiding segmentation bone |
| CN108537396A (en) * | 2017-03-01 | 2018-09-14 | 北京大学 | A kind of grain security vulnerability analysis method and system |
| CN112765256A (en) * | 2020-12-16 | 2021-05-07 | 山东师范大学 | Parallel coordinate axis data visualization method and system |
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