Underground Oil Reservoir Emulation And Prediction

Petroleum cannot be reformed by experiment,and man cannot made it repeatedly underground. Man have some unclear cognition about underground oil reservoir,it make waste a lot of money every year. So it is very important that man realize the structure and parameter of oil reservoir clearly. It can heighten production efficiency and save a lot of money .In this thesis,some scientific research item of Daqing and Liaohe oil field are used as the background. And it is used as object that the modality of oil reservoir is reappeared clearly. We make three-dimensional visual emulation to the underground oil reservoir . Also we study the methods for accurate fast prediction the parameters of oil reservoir. The main contents are as follows:1. One kind of intelligent amplifier is studied. It have 16 grades numeric gain,and compensate zero-drift automatically,and prediction input signal then adjust gain automatically. One kind of adaptive data acquisition system is constituted using computer and intelligent amplifier. The distinguishability of data acquisition system is elevated. The circuits of the are designed. This system can provide good quality data for the follow work.2.The method of marginal checking using wavelet neural network is used to process the seismic data for increasing distinguishability to Recognition the thin interbedded oil reservoir .Small structure of oil reservoir and thin interbedded are find . Oil reservoir can be reappeared on computer clearly.3. Three-dimensional emulation model is established using seismic data. The methods of displaying slice about the three-dimensional emulation model are studied. They are follow three methods.the method to make multilayer horizontal slice;the method to make multidirection and multiangle slice the method to make single oil layer display in three space . By above three methods the oil engineer can apprehend the underground oil reservoir structure very clearly. In this thesis,an oil reservoir is selected as example and some typical images are offered.4. Krijing estimation and Bayes-Krijing estimation technique are studied.Contraposed disadvantage of Krijing estimation on prediction oil reservoir,Bayes-Krijing estimation technique are studied to predict oil reservoir parameter Because Krijing estimation depend on well data to predict oil reservoir,while the wells is few,it is limited .Here Bayes-Krijing estimation technique is studied to predict oil reservoir. The seismic data and the well data are combined in Bayes-Kerijing estimation to predict oil reservoir parameter. By this way,we can get better estimation result of oil reservoir parameter.5. In allusion to the disadvantage of genetic algorithm and BP neural network,the genetic algorithm BP neural network to predict oil reservoir parameters is studied . In this thesis the genetic operator and control parameter are ameliorated. Good nonlinear approach ability of neural network and searching optimization solution ability of genetic algorithm are combined to predict oil reservoir parameters . Actual application result appear that this algorithm predictoil reservoir is accurate and fast.6. Based on studying simulated annealing algorithm and Powell algorithm,in allusion to simulated annealing algorithm convergence slow,the two fast simulated annealing combination optimization algorithm are offered. One is optimal reservation simulated annealing and Powell combination algorithm (F-PSA). The other one is optimal reservation simulated annealing and genetic algorithm optimal combination algorithm(F-GSA).And these two optimal combination algorithm are combined with BP neural network to predict oil reservoir parameters. In actual application the data of different area have different characteristic,when the hidden layers and input nerve centers are less,we use F-PSA combining with BP to predict oil reservoir parameters;Otherwise we use F-GSA combining with BP to predict oil reservoir parameters .In this thesis some emulation results and actual application results are offered . Those result...