CN104881435A - Data mining based automatic research flow well logging evaluation expert system - Google Patents
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Abstract
The invention provides a data mining based automatic research flow well logging evaluation expert system and belongs to the technical field of geophysical well logging. The system comprises a data element model base, a classic knowledge base, an artificial intelligent knowledge base, a user application interface module, a regional research module and a processing and interpretation evaluation module. The data mining technique and the traditional well logging evaluation technique are combined to establish a classic knowledge base model and the artificial intelligent knowledge base in flow automation, real-time comprehensive flow guide and technical support of experts are provided for users to perform rapid well logging evaluation of blocks, and the automatic research flow well logging evaluation expert system is realized.
Description
Technical field
The present invention relates to Geophysical Logging field, specifically a kind of research process automation logging evaluation expert system based on data mining.
Background technology
Along with deepening constantly of offshore survey development, the log interpretation system of existing external introduction is more and more not suitable with the technological challenge that the increasing complex hydrocarbon layer such as " three is low ", high thermal, non-hydrocarbons proposes well logging interpretation evaluation.
Log data data type is various, along with the quantity of well-digging gets more and more, data separate, stored in a database, not interrelated between data, and when doing regional evaluation and research, the implication relation needing comprehensive various data and extract between various data, remove to search by manual operation the basic data carrying out research institute's needs and not only need rich experiences, and consuming time, leakiness, fallibility.
The experience accumulated for many years in conjunction with rock physics data and well logging expert is needed when doing the research of well logging rock physics, by concrete experience " plate ", becoming perceptual recognition in expert brains and region experience is that rationality in researchist's real work is cognitive, makes researchist reduce interference from human factor to greatest extent in Model Selection, parameter such as to determine at the well logging interpretation process.
Carrying out well data processing the process need reference zone historical summary explained, comprise logging data, formation testing data and Production development data, analyzed by artificial contrast, reduce explanation efficiency, and the instrument of data mining can extract valuable historical summary automatically according to historical summary, realize automatically identifying.
In regional study, some the research flow processs summed up for many years by well logging expert, do not form system, can not left-hand seat fast for new hand, and needs one according to research contents, can determine the system of research flow process automatically like this.
At present adopted in the industry data digging method does not combine with expert knowledge library, and traditional Logging Evaluation Method is not in conjunction with the method for data mining, lacks data mining and expertise to be intersected the cyberneticist's system combined.
Summary of the invention
The present invention utilizes integration data meta-model storehouse, and the content of meta-model library management comprises Professional Model, logical model, physical model; Data listings data is excavated; The input data type of default polyalgorithm node is mated with the input data type in described metadata respectively, determines the algorithm node that the match is successful; According to the node relationships table preset, all forward direction nodes of each algorithm node that the match is successful of recursive lookup and backward node, determine respectively with corresponding multiple data mining path of described each algorithm node that the match is successful, then establish research flow process through man-machine interaction optimization; Automatically corresponding research work is completed according to the flow nodes algorithm parameter template preset; As carried out robotization well logging " lithology-electrically-physical property-oiliness " relation research; Reservoir net thickness lower limit research etc.; Above-mentioned each achievement in research enters classical knowledge base subsystem in expert knowledge library, for traditional logging evaluation provides transaction module and parameter, and automatically divides Effective Reservoirs (the sub-patent of this project) under knowledge base net thickness lower limit guides; To the individual layer of AUTOMATIC ZONING, Data Mining Tools is utilized to survey in conjunction with the actual well logging gas in oil field, test pressure measurement, formation testing and storage facility located at processing plant, association analysis, cluster analysis, neural net method is adopted automatically to generate the contrast verification oil-gas layer identification conclusion that multi-method intersects, walks abreast, effective raising hydrocarbon zone interpretation evaluation precision, and join by man-machine interaction the difficult layer explanation of the raising further reliability that lays down a definition.System combines can be provided in real time for user carries out the quick logging evaluation of block, flow leading and expert's technical support all sidedly.
For solving the problems of the technologies described above, the invention provides a kind of research process automation logging evaluation expert system based on data mining, comprise In Oil Field Exploration And Development database, integration data meta-model storehouse, user's AIM, regional study module and interpretation and evaluation module, wherein:
The content of meta-model library management comprises: Professional Model, logical model, physical model;
User's AIM realizes the importing of data, user interactive, algorithm flow man-machine interactively and the display of visual effect;
Regional study module, feasible region four sexual intercourse research, reservoir parameter automation research, the research of net thickness lower limit, fluid identification research, litho-electric parameters research, lower limit sensitivity analysis, regional study is that interpretation and evaluation module provides technical support;
Interpretation and evaluation module, realize Automatic interpretation and man-machine interactively explanation, the classical knowledge base that regional study achievement is set up by Automatic interpretation and the artificial intelligence knowledge base that data digging method is set up, call engine by knowledge and automatically realize fluid identification of reservoir, and two results are contrasted, cross validation, for the evaluation of well data interpretation provides many technical supports; And the personnel that explain are revised Automatic interpretation result by man-machine interactively mode according to degree of confidence, interpretation and evaluation module realizes Real-time Feedback to regional study again simultaneously.
As the preferred embodiment of this programme, the method for building up of described classical knowledge base is as follows:
1. from basic database, extract the source datas such as assay data, well logging source book, the dynamic static data of exploration; According to the expert's research contents preset in Professional Model in data meta-model storehouse and logical model and flow process, set up algorithm node matching table, algorithm node and study flow process relation table, study flow process plate and relation table thereof;
2. the correlation rule between logging suite and core data set up by application data digging tool, correlation rule is mated with the polyalgorithm node preset, determine the algorithm node that the match is successful, formation algorithm node matching table, the critical field comprised in table has: node ID, node index, hunting zone, algorithm node, arthmetic statement;
3. according to the node relationships table preset, recursive lookup to all forward direction nodes of each algorithm node that the match is successful and backward node, determine respectively with corresponding many research flow process of described each algorithm node that the match is successful.Formation algorithm node and research flow process relation table, the critical field comprised in table has: node ID, node index, hunting zone, flow nodes, flow algorithm describe;
4. usage data mining algorithm instrument, creates according to the node parameter preset and studies multiple robotization knowledge research flow processs corresponding to flow process with described multiple data mining.Set up the correlation rule of logging suite and rock core assay (A) and algorithm node matching module (B), algorithm flow (R), algorithm template (L): A->B->R->L, A->B, A->R, B->R, B->L, R->L;
5. man-machine interactively confirms the correlation rule set up and the algorithm flow set up by correlation rule and revises, and the rule set up and flow process are put in storage, application data digging tool sets up algorithm node and the correlation rule between research flow process and flow process plate, formation algorithm node, research flow process and research flow process plate relation table, the critical field wherein comprised in table has: node ID, node index, hunting zone, template type, template describe;
6. automatically can produce according to template the chart and model etc. that research needs by process (5), can select to enter in classical knowledge base and preserve, call for logging data processing Study on Interpretation.
As the preferred embodiment of this programme, the method for building up of described artificial intelligence knowledge base is as follows:
1. from basic database, extract test, formation testing, pressure measurement sampling and Production development data etc. confirmed that stratum is fuel-displaced, water outlet information data;
2. pair data extracted carry out Data Environments, ensure quality and the integrality of data;
3. utilize the statistical study of data mining, association analysis, neural net method, cluster analysis to the data analysis extracted, according to the classification of knowledge, the individual layer automatic modeling to AUTOMATIC ZONING such as region-by-region, layering position, point lithology, and Confidence Analysis is carried out to modeling result;
4. by knowledge acquisition card, knowledge acquisition is carried out to the knowledge model set up, form artificial intelligence knowledge base.
As the preferred embodiment of this programme, described artificial intelligence knowledge base and classical knowledge base, call engine by upper layer application Automatically invoked by knowledge, and knowledge base is applied to fluid identification, logging data processing is explained, Effective Reservoirs divides.
As the preferred embodiment of this programme, described knowledge calls engine, possesses artificial input Keywords matching and calls and the large function of flow process Automatically invoked two.
As the preferred embodiment of this programme, described logging suite excavates module according to the data of logging suite at database purchase, the several data such as cluster analysis and model tree method for digging is adopted to set up the correlation rule of logging suite, realize user to input keyword and inquire about, and provide association for follow-up automatic flow generates.
As the preferred embodiment of this programme, described rock core laboratory analysis of data excavates module according to core experiment analysis data, in conjunction with logging suite, sets up core data correlation rule by adopting data digging method.
As the preferred embodiment of this programme, described algorithm node matching module by the input of default polyalgorithm node, export data type respectively with the input in described metadata, export data type and mate, determine the algorithm node that the match is successful.
As the preferred embodiment of this programme, described field exploration and development database, store content and contain Geophysical-chemical, drilling well, well logging, well logging, oil and gas well testing, analytical test, stratigraphy study, tectonic cycle period, Evaluation of source rocks research, evaluating reservoir, cap rock is studied, evaluation of trap, oil-gas migration is studied, reservoir-forming study, prospect pit well location designs, reserves management, hydrocarbon reservoir evaluation, oil reservoir development pre feasibility study, oil reservoir development feasibility study, ODP design and implementation, exploitation well designs, Reservoir Development performance analysis and adjustment, recover the oil and inject production management, borehole operation, dynamic monitoring, adopt the library management of integration data meta-model, realize integrated management and the share service of data.
The one or more technical schemes provided in the application, at least have following beneficial effect:
Data mining technology is combined with traditional logging evaluation technology, the classical knowledge base model of Establishing process robotization and artificial intelligence knowledge base, there is provided in real time for user carries out the quick logging evaluation of block, flow leading and expert's technical support all sidedly, the Study of the Realization process automation logging evaluation expert system.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the system construction drawing of the embodiment of the present application.
Embodiment
In order to better understand technique scheme, below in conjunction with Figure of description and concrete embodiment, technique scheme is described in detail.
Embodiment 1
As shown in Figure 1, a kind of research process automation logging evaluation expert system based on data mining, comprises In Oil Field Exploration And Development database, classical knowledge base, artificial intelligence knowledge base, user's AIM, regional study module and interpretation and evaluation module, wherein:
User's AIM realizes the importing of data, user interactive, algorithm flow man-machine interactively and the display of visual effect.
Regional study module, four sexual intercourse researchs in feasible region, reservoir parameter automation research, the research of net thickness lower limit, fluid identification research, litho-electric parameters research, lower limit sensitivity analysis etc., regional study is that interpretation and evaluation module provides technical support.
Interpretation and evaluation module, realize Automatic interpretation and man-machine interactively explanation, the classical knowledge base that regional study achievement is set up by Automatic interpretation and the artificial intelligence knowledge base that data digging method is set up, call engine by knowledge and automatically realize fluid identification of reservoir, and two results are contrasted, cross validation, for the evaluation of well data interpretation provides many technical supports; And explain that personnel are revised Automatic interpretation result by man-machine interactively mode according to degree of confidence.Interpretation and evaluation module realizes Real-time Feedback to regional study again simultaneously.
Evaluating expert system technology route is:
Based on a research process automation logging evaluation expert system for data mining, comprise technology and artificial intelligence automatic technology two technology paths that man-machine interactively data mining is combined with expertise knowledge, wherein:
The technology path that man-machine interactively data mining is combined with expertise knowledge, this route sets up logging suite by data digging method, the correlation rule of rock core laboratory analysis of data etc. and expertise algorithm node, sets up the correlation rule between expertise algorithm node and flow template, parameterized template, drawing template by data mining algorithm simultaneously.Knowledge study on classics flow process is set up by above step, as research flow processs such as four sexual intercourse, reservoir parameter automation research, the research of net thickness lower limit, fluid identification research, litho-electric parameters research, lower limit sensitivity analysiss, realize the foundation of classical knowledge base model.
Artificial intelligence automatic technology, adopts data mining algorithm automatic learning knowledge from test data, formation testing data and Production development data, and the knowledge model learnt is carried out collection storage, forms artificial intelligence knowledge base.
The classical knowledge base set up according to two technology paths and artificial intelligence database, engine calling is called by unified knowledge, both cross-application, cross validation, expertise is realized to combine with the artificial intelligence technology of robotization, and apply it to during logging data processing and explanation, fluid identification of reservoir, computing permeability etc. apply, thus set up research process automation logging evaluation expert system.
Evaluate expert system data source:
Data are from the large-scale exploration-development integration database of oily company, contain physical prospecting, drilling well, well logging, well logging, oil and gas well testing, analytical test, stratigraphy study, tectonic cycle period, Evaluation of source rocks research, evaluating reservoir, cap rock is studied, evaluation of trap, oil-gas migration is studied, reservoir-forming study, prospect pit well location designs, reserves management, hydrocarbon reservoir evaluation, oil reservoir development pre feasibility study, oil reservoir development feasibility study, ODP design and implementation, exploitation well designs, Reservoir Development performance analysis and adjustment, recover the oil and inject production management, borehole operation, dynamic monitoring etc., exist in unified hub database.
Evaluate expert system data management:
The management of data source adopts integration data meta-model storehouse, comprise Professional Model, logical model, physical model, projection model, the functions such as implementation model management, Data Migration, data security, data access, data general-purpose inquiry, application support, set up the data, services based on data service platform.
Evaluate expert system data source to arrange and screening:
Evaluation system realizes in conjunction with data digging method and expertise knowledge, therefore needs to arrange data source and screen, and such as when setting up fluid identification of reservoir data mining model, needs to select the data of formation testing checking as training sample; When setting up the correlation rule of logging suite and algorithm node, need the integrality and the reliability that ensure data.
Evaluate expert system, the step of the foundation of classical knowledge base model wherein comprises:
From database, screen the data source such as logging suite, rock core laboratory analysis of data and screen;
Algorithm node table is set up according to expertise knowledge;
Research flow process concordance list is set up according to expertise knowledge;
Parameterized template, drawing template and algorithm template is set up, the templates such as " four property " relation of such as logging well, the research of well logging reserves research associated reservoir parameter automatization, the research of net thickness lower limit, fluid identification research, litho-electric parameters research, the sensitivity analysis of net thickness lower limit according to expertise knowledge;
Set up the relation table of logging suite and algorithm node;
Set up the relation table of rock core laboratory analysis of data and algorithm node;
Set up algorithm node and the relation table studying flow process;
Set up algorithm node, research flow process and template between relation table;
The correlation rule of logging suite and algorithm node set up by application data digging tool;
The correlation rule of rock core laboratory analysis of data and algorithm node set up by application data digging tool;
Application data digging tool sets up algorithm node and the correlation rule studying flow process;
The correlation rule between algorithm node and template set up by application data digging tool;
The correlation rule between research flow process and template set up by application data digging tool;
Application data digging tool is set up and is studied flow process to algorithm node, research flow process to the integral automation of template base from logging suite, rock core laboratory analysis of data, curve data;
Gather the correlation rule set up, and all kinds of Well Data Processing model formations, logging data processing that foregoing schemes produces explain that key parameter etc. is stored in database, set up classical knowledge base model, reference utilization when being provided with rear region research and new exploration areas.
Evaluate expert system, the step of the foundation of artificial intelligence knowledge base wherein comprises:
To AUTOMATIC SORTING IN LAYERS BY WELL-LOGGING CURVES, and individual-layer data is screened and arranges;
Automatically divide on the basis of explanation at logging trace Effective Reservoirs, research block existing test oil test data, data such as pressure measurement sampling data and field produces multidate information etc. are excavated, by statistical study, association analysis, neural net method, clustering method etc., according to the classification of knowledge, the individual layer automatic modeling to AUTOMATIC ZONING such as region-by-region, layering position, point lithology, point thickness; And Confidence Analysis is carried out to modeling result, after man-machine interaction, carry out collection store, set up artificial intelligence knowledge base.
Artificial intelligence knowledge and classical knowledge are set up knowledge base by knowledge acquisition.
Evaluate expert system, wherein man-machine interactively illustrates:
For the research flow process that robotization is set up, if it is not too consistent with researchist's demand, then by man-machine interactively mode, supervised learning, until meet the demands, well data interpretation is evaluated, contrasts classical knowledge base model and artificial intelligence knowledge base two kinds of explanation results, by inconsistent local man-machine interactively correction.
For Effective Reservoirs evaluation application flow process and the effect of logging well:
During Well Data Processing, call engine by knowledge, transaction module in the classical knowledge base of Automatically invoked, the model needed as saturation computation and wherein key parameter, as a b m n litho-electric parameters; After a bite well processes, need division and the fluid properties judgement of carrying out Effective Reservoirs, during to this flow process, system starts layering value module automatically, and quote net thickness lower limit in classical knowledge base and automatically divide Effective Reservoirs, and form quantification well logging interpretation achievement chart;
Explain according to key word evaluating reservoir and carry out searching for forward and backward in knowledge base, recurrence finds algorithm node and the algorithm flow of coupling.
According to the algorithm node calculate shale index, factor of porosity, water saturation, permeability etc. of coupling.
According to keyword recursive search Reservoir Parameter Research models in classical knowledge base model such as region, layer groups, obtain net thickness lower limit research parameter.
According to net thickness lower limit parameter, layering value submodule divides Effective Reservoirs automatically.
Obtaining evaluating reservoir according to the algorithm flow robotization of coupling and explain the visual display evaluation result of template, is more than that the quantitative logging data processing of a set of classics explains flow process.
Expert is according to the evaluation result of above-mentioned flow process in well logging, carries out man-machine interactively correction, simultaneously can knowledge model in playback interpretation and evaluation flow process, if the flow process automatically generated does not meet the demands, is then formulated by man-machine interactively and explains flow process.
Simultaneously, when carrying out the division of quantitative Effective Reservoirs to oil, gas, water layer, well logging scholar usually runs into some reservoirs near net thickness lower limit standard, the explanation of these layers can not simply go to judge fluid properties according to lower limit standard, need a large amount of theoretical foundation and region practice especially, by removing make concrete analyses of concrete problems to the assurance degree of all kinds of data of local area, utilize knowledge base and Data Mining Tools like this, just can play a role, native system just provides using artificial intelligence and knowledge base carries out the function of qualitative well logging interpretation evaluation;
Administer data in classification is carried out to well logs section, point thickness.
According to well-log information affiliated area, obtain all models of the affiliated area in artificial intelligence knowledge base.
Interval, thickness etc. contained according to well-log information in obtained model obtain corresponding model.
According to the model obtained, input corresponding well-log information, automatic decision fluid type of reservoir through, and the degree of confidence of calculating results.
Quantification interpretation and evaluation result under classical knowledge base being guided and data mining artificial intelligence knowledge base qualitative interpretation evaluation result carry out Comprehensive Correlation.
According to both result cross validations, for the evaluation result that conclusion is inconsistent, implement man-machine interactively evaluation, complete Effective Reservoirs evaluation.
The above, it is only preferred embodiment of the present invention, not any pro forma restriction is done to the present invention, although the present invention with preferred embodiment demonstration as above, but and be not used to limit the present invention, any those skilled in the art, do not departing within the scope of technical solution of the present invention, the technology contents of above-mentioned announcement can be utilized to make a little change or be modified to the Equivalent embodiments of equivalent variations, in every case be the content not departing from technical solution of the present invention, according to any simple modification that technical spirit of the present invention is done above embodiment, equivalent variations and modification, all still belong in the scope of technical solution of the present invention.
Claims (10)
1. the research process automation logging evaluation expert system based on data mining, it is characterized in that: comprise data meta-model storehouse, classical knowledge base, artificial intelligence knowledge base, user's AIM, regional study module and process interpretation and evaluation module, wherein:
The content of meta-model library management comprises: Professional Model, logical model, physical model;
User's AIM realizes data importing, user interactive, algorithm flow man-machine interactively and the display of visual effect;
Regional study module, feasible region four sexual intercourse research, reservoir parameter automation research, the research of net thickness lower limit, fluid identification research, litho-electric parameters research, lower limit sensitivity analysis, regional study can form classical knowledge base, for process interpretation and evaluation module provides key parameter, model supports;
Process interpretation and evaluation module, realize automatic business processing to explain and man-machine interactively explanation, automatic business processing is explained and is utilized classical knowledge base and artificial intelligence knowledge base, call engine by knowledge and automatically realize logging data processing explanation and fluid identification, and result is contrasted, cross validation, explains for the process of well data and provides technical support; And the personnel that explain are revised Automatic interpretation result by man-machine interactively mode according to degree of confidence; Interpretation and evaluation module realizes Real-time Feedback to regional study knowledge base again simultaneously.
2. a kind of research process automation logging evaluation expert system based on data mining according to claim 1, is characterized in that: the method for building up of described classical knowledge base is as follows:
(1) from basic database, extract assay data, well logging source book, exploration sound state data source data; According to the expert's research contents preset in Professional Model in data meta-model storehouse and logical model and flow process, set up algorithm node matching table, algorithm node and study flow process relation table, study flow process plate and relation table thereof;
(2) correlation rule between logging suite and core data set up by application data digging tool, correlation rule is mated with the polyalgorithm node preset, determine the algorithm node that the match is successful, formation algorithm node matching table, the critical field comprised in table has: node ID, node index, hunting zone, algorithm node, arthmetic statement;
(3) according to the node relationships table preset, recursive lookup is to all forward direction nodes of each algorithm node that the match is successful and backward node, determine respectively with corresponding many research flow process of described each algorithm node that the match is successful, formation algorithm node and research flow process relation table, the critical field comprised in table has: node ID, node index, hunting zone, flow nodes, flow algorithm describe;
(4) usage data mining algorithm instrument, create according to the node parameter preset and study multiple robotization knowledge research flow processs corresponding to flow process with described multiple data mining, set up logging suite and rock core assay (A) and algorithm node matching module (B), algorithm flow (R), the correlation rule of algorithm template (L): A->B->R->L, A->B, A->R, B->R, B->L, R->L,
(5) man-machine interactively confirms the correlation rule set up and the algorithm flow set up by correlation rule and revises, and the rule set up and flow process are put in storage, application data digging tool sets up algorithm node and the correlation rule between research flow process and flow process plate, formation algorithm node, research flow process and research flow process plate relation table, the critical field wherein comprised in table has: node ID, node index, hunting zone, template type, template describe;
(6) automatically can be produced chart and the model of research needs by process (5) according to template, can select to enter in classical knowledge base and preserve, call for logging data processing Study on Interpretation.
3. a kind of research process automation logging evaluation expert system based on data mining according to claim 1, is characterized in that: the method for building up of described artificial intelligence knowledge base is as follows:
(1) from basic database, extract test, formation testing, pressure measurement sampling and Production development data and confirmed that stratum is fuel-displaced, water outlet information data;
(2) Data Environments is carried out to the data extracted, ensure quality and the integrality of data;
(3) utilize the statistical study of data mining, association analysis, neural net method, cluster analysis to the data analysis extracted, according to the classification of knowledge, the individual layer automatic modeling to AUTOMATIC ZONING such as region-by-region, layering position, point lithology, and Confidence Analysis is carried out to modeling result;
(4) by knowledge acquisition card, knowledge acquisition is carried out to the knowledge model set up, form artificial intelligence knowledge base.
4. a kind of research process automation logging evaluation expert system based on data mining according to claim 1, it is characterized in that: described artificial intelligence knowledge base and classical knowledge base, call engine by upper layer application Automatically invoked by knowledge, knowledge base is applied to fluid identification, logging data processing explanation, Effective Reservoirs division.
5. a kind of research process automation logging evaluation expert system based on data mining according to claim 4, is characterized in that: described knowledge calls engine, possesses artificial input Keywords matching and calls and the large function of flow process Automatically invoked two.
6. a kind of research process automation logging evaluation expert system based on data mining according to claim 2, it is characterized in that: described logging suite excavates module according to the data of logging suite at database purchase, the several data such as cluster analysis and model tree method for digging is adopted to set up the correlation rule of logging suite, realize user to input keyword and inquire about, and provide association for follow-up automatic flow generates.
7. a kind of research process automation logging evaluation expert system based on data mining according to claim 2, it is characterized in that: described rock core laboratory analysis of data excavates module according to core experiment analysis data, in conjunction with logging suite, set up core data correlation rule by adopting data digging method.
8. a kind of research process automation logging evaluation expert system based on data mining according to claim 2, it is characterized in that: described algorithm node matching module by the input of default polyalgorithm node, export data type respectively with the input in described metadata, export data type and mate, determine the algorithm node that the match is successful.
9. a kind of research process automation logging evaluation expert system based on data mining according to claim 1, it is characterized in that: the generation of described data mining research flow process, according to the node relationships table preset, recursive lookup, to all front nodal points of each algorithm node that the match is successful and posterior nodal point, is determined and the multiple data mining path corresponding to described each algorithm node that the match is successful respectively; And determine described multiple data digging flow according to the result and default Rules of Assessment, find optimal data to excavate flow process.
10. a kind of research process automation logging evaluation expert system based on data mining according to claim 1, is characterized in that: by data mining mathematical tool, realizes artificial intelligence and expertise knowledge organically combines; Artificial intelligence is combined with expertise knowledge and sets up automation research flow process, set up classical knowledge base; The mechanism of the automatic learning of application data excavation is simultaneously excavated well logging information storehouse and learns, and gather model of mind, set up artificial intelligence knowledge base, both walk abreast and carry out, and influence each other again, cross-application, cross validation.
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