The Theories And Methods About Vertical Prediction Of Oil Reservoir Based On Fusion Soft Computing And Hard Computing

This paper is supported by both National Natural Science Foundations of China (NSFC)funded project"A Study on Integration of Soft Computing Theories and Methods in Oilexploration Management (NO.70573101)"and the Specialized Research Fund for the DoctoralProgram of Higher Education of China"A Study on Fusion of Soft Computing and HardComputing Theories and Methods of Oil Reservoir Forecast (NO.20070491011)".In 1996,in China oil production and consumption was 15729×10~4t and 17307×10~4trespectively,and external dependence rate was 9.12%.In 2004,oil production was 17499×10~4t,oil consumption was 31873×10~4t,and external dependence rate was 45.10%.In 2004 oilconsumption ranked the second place and oil import volume ranked the third place in the world;In 2007,oil production was 18665.7×10~4t,oil consumption was 34593.7×10~4t,and externaldependence rate was 46.04%.These data shows that although Chinese oil production increasingsharply,the annual rate of growth of oil production is relatively backward because of the rapiddevelopment of national economy,and the problems existing between supply and demand of oilare more serious.As the area of oil exploration continuously enlarge,the research objects of theforecast of reservoir are more and more complicated,the contradictions between the forecasttechnique existing and the explanation demand constantly increasing are prominent.How toeffectively utilize new technology and idea to forecast lithology and oiliness of all kinds of oilreservoirs has important practical significance for directing oil exploration,and causes extensiveconcern.This research takes a certain block in Jianghan oil field as the case of oil reservoir forecast,based on the dynamic process of transforming well logging data into information,thentransforming information into acknowledge,and fusing soft computing with hard computing,putforward the patterns of fusion of soft computing and hard computing,and design algorithm underthe fusion patterns to forecast oil reservoir:(1)On basis of analyzing the fundamental theory and principle of soft computing and hardcomputing,put forward the patterns of fusion of soft computing and hard computing (isolatedpattern,parallel pattern,cascaded pattern and nested pattern),and analyze the feature of allpatterns. (2)Developing a three dimensional categorizing framework.One dimension is searchstrategy including complete search,sequential search and random search.Another dimension isevaluation criteria including filter model,the wrapper model and the hybrid model.The thirddimension is task including clustering with unlabeled data (unsupervised feature selection)andclassification with labeled data (supervised feature selection)and put forward algorithm selectionplatform,and we can select matched algorithm to optimize attributes from the induced featureselection methods according to algorithm selection platform.(3)Analyzing data with hard computing methods in the recognition of reservoir lithology,then combine the matching algorithm which was selected from categorizing framework based onalgorithm selection platform with classifiers of various soft computing methods and hardcomputing methods,and get the optimal attribute set of recognition lithology in this block is POR.Training the data with ANN and extract the corresponding recognition functions (hard computingfunctions).Then use recognition function as objective function for rule extraction with GA.①If POR is at middle level,this layer is sandstone;②If POR is at low level,this layer issandstone;③If POR is at high level,this layer is mudstone.(4)Combining the matching algorithm which was selected from categorizing frameworkbased on algorithm selection platform with classifiers of various soft computing methods andhard computing methods,and get the optimal attribute set of oiliness in this block is AC and So,then propose a method of data driven gray relational analysis for recognizing oil-bearingcharacteristics in reservoir and reduce samples,at last training the data with ANN and extract thecorresponding recognition functions (hard computing functions).Use recognition function asobjective function for rule extraction with GA.①If both of AC and So are at low level,this isdry layer;②If AC is at middle level and So is at low level,this is dry layer;③If AC is athigh level and So is at low level,this is water layer;④If both of AC and So are at middle level,this layer is inferior oil layer;⑤If AC is at low level and So is at middle level,this layer is oillayer;⑥If AC is high level and So is at middle level,this layer is oil layer;⑦if So is at highlevel,this layer is oil layer.(5)Based on the optimal attribute set of recognition lithology is POR and the optimalattribute set of oiliness is AC and So in this block,the problem to be solved is how to establishand predict the models of POR,AC and So without additional expenditure (well drilling,chemical examination etc.)if there is no or not enough key attributes in the data set of welllogging.Firstly,establish the regressive equations for the prediction of POR,AC and So withregression model of hard computing methods.Then,obtain satisfactory neural network model ofBP to predict and recognize POR,AC and So by nested genetic algorithm of soft computingmethods and BP neural network (The genetic algorithm is used to optimize the input attributescombination of BP neural network and determine the number of neurons in the hidden layer).Lastly,compare the models which are obtained from hard computing methods and softcomputing methods and conclude that GA-BP model is better than multiple regression model.(6)This research block are divided into four classes:①If the lithology of this ayer issandstone and the oiliness is oil layer,the reservoir belongs to classⅠ;②If the lithology of this layer is mudstone and the oiliness is oil layer,the reservoir belongs to classⅡ;③If thelithology of this layer is sandstone and the oiliness is inferior oil layer,the reservoir belongs toclassⅢ;④If the lithology of this layer is mudstone and the oiliness is inferior oil layer,thereservoir belongs to classⅣ.The prediction of oil reservoir provides important decision basis to reduce oil explorationrisks,evaluate oil reserves correctly,clear scientific development plan and increase the oilrecovery rate.