The Method Study Of Neural Network Pattern Recognition Applied To Reservoir Parameters Predicting

Petroleum cannot be reformed by experiment, and man cannot made it repeatedly underground. Man havesome unclear and indeterminacy cognition about underground oil reservoir, it make waste a lot of money everyyear. So it is very important that man realize the structure and parameter distribution of oil reservoir clearly. Itcan heighten production efficiency and save a lot of money .In this paper , some scientific research item of oil field are used as the background. And it is used asobject that the modality of oil reservoir is reappeared clearly and the accuracy rating of exploration isimproved. We study the pattern recognition theory and the methods for accurate fast prediction the parametersof oil reservoir. The main contents are as follows:1. After analyse and study systematically artificial neural network theory, the structure and computeformula of BP is studied. Considering the nonlinear relation between reservoir parameter and seismiccharacteristic parameters, the neural network is a advantageous tool to solve above problem, the coursedescription of reservoir parameter predicting using neural network is provided.2. Based on studying radical basic function neural network and genetic algorithm, a optimize method ofradical basic function neural network based on self-adapting genetic algorithm is proposed, and this method isapplied successful to the reservoir parameter predicting and obtain better effect. In this method, the centerparameter and width parameter are considered chromosome, the network structure optimize and parameterlearning are divided into training and evolution two parts. At first, learning the center parameter and widthparameter using gradient descend algorithm, then learning the linear weight value and offset constant usingleast square algorithm. Secondly, optimize the hidden node numbers using self-adapting genetic algorithm.Finally, we can obtain the RBF neural network which satisfy the error demand by alternate executing abovetwo courses.3. The wavelet transform theory and edge detection principle is in-deep studied, the seismic sections usedfor seismic interpretation are actually regarded as some 2D images, so the reflection interface of seismicsection can be considered as edges on the seismic section images. The wavelet transform Modulus-Max edgedetection algorithm which applied to seismic section processing is proposed first, its purpose is to eliminatethe random interference and enhanced the quality of seismic section and the signal-to-noise rate and resolutionof seismic signal, furthermore, predicting the parameter distribution of oil reservoir accurately and credibility.This algorithm is used to process actual data and obtain better effect.4. Based on analyse and studying the structure and property and approach ability of wavelet neuralnetwork , a method of wavelet neural network which is used to predict reservoir parameter is proposed ,meanwhile, the optimize network structure and learning algorithm of network are presented. Artificial neuralnetwork has the capability of self-learning and self-adapting and approaching nonlinear, the wavelet transformhas good partial property both in time domain and frequency domain, so the wavelet neural network whichcombining the advantage of ANN and wavelet has stronger approaching nonlinear capability and allowanceerror capability. The actual data predicting shows that this method provides higher predict accuracy and fasterconvergence speed.5. The fuzzy system theory is studied, considering to the advantage and disadvantage of the neuralnetwork and fuzzy system, the fusion problem of fuzzy system and neural network is in-deep studied,combining the salient feature and difficulty of volcanic reservoir, a fuzzy neural network system which appliedrecognition and predicting the volcanic reservoir is presented, meanwhile, the system structure and parameterlearning algorithm and predicting steps are given. In this thesis, stronger self-learning ability of neural networkand good express ability of fuzzy system are combined to recognise and predict the volcanic reservoir. Actualapplication result appear that this method recognise and predict the volcanic reservoir accurately and fast.6. Based on studying the statistical learning theory and machine learning and support vector machineprincipal, considering the ANN has some problems in application such as converge to local minimum, theoverfitting and the structure of ANN is always decided by experience because it doesn't have a good guidingtheory, especially, when the number of the training sample is not enough, the predicting accuracy will beinfluenced, a method of recognition and predicting the volcanic reservoir based on regression support vectormachine(SVM) is proposed first. SVM is a newly developed technique which based on statistical learningtheory, it adopts Structure Risk Minimization principle which avoids the disadvantages of ANN. In this thesis,the recognition and predicting of regression algorithm based on SVM is studied, the fast learning algorithm isstudied and the concrete realization is presented. Actual application result shows that this method remedy thescarcity of few sample data, overcome the disadvantages of traditional neural network, therefore, theconverging speed and the predicting accuracy of system are enhanced.7. Programming some programmes using C++ such as the radical basic function neural network based onself-adapting genetic algorithm, the wavelet transform Modulus-Max edge detection algorithm which appliedto seismic section processing, the wavelet neural network algorithm which is used to predict reservoirparameter, the fuzzy neural network system which applied recognition and predicting the volcanic reservoir,the recognition and predicting the volcanic reservoir based on regression support vector machine algorithm.