The research of artificial neural networks(NN), genetic algorithms(GA), and simulated annealing algorithms(SAA) are the focus of modern information technology. It also opens a new route for the develop of nonlinear system identification. In this paper, we have made a thorough research in methods of nonlinear system identification and prediction of oil field systems. A set of NN identification and prediction methods can be obtained for oil field systems based on hybrid algorithms. We have finished the research as follows.First, the oil field well test is taken an application example. On the bases of partial differential equations on well test interpretation,the model of well test interpretation basis function neural network(WTBFNN) has been given by mathematical induction, which is the theoretical bases of identification and prediction for well test systems. The well test interpretation basis function is a typical complex multimodal function. Its (stratigrsphic) parameters are the foundation of well test interpretation, so we request their unique estimate values. In order to solve the difficult problem, two new pattern hybrid GA have been put forward by means of technique synthesis for system identification ,GA and cluster algorithms and so on. The two algorithms are species genetic evolution algorithm(SGEA) and heuristic GA. It is shows from simulation experiment that SGEA has advantage over the GA with sharing on global convergence aspect. The global convergence of the two new GA have been proved by using Markovian chain theory. As a result, the new methods of WTBFNN identification and prediction can be got based on the above new hybrid GA. Where WTBFNN is taken as the model frame, the model structure parameters are determined by using Ftest method, weight values of WTBFNN are identified by using leastsquares(LS) method, and stratigraphic parameters in WTBFNN are estimated by using one of the above two hybrid algorithms. The new scheme can successful be applied to well test interpretation of low penetrability oil field, and excellent results be got. The average relative errors of fitting and predicting are within 1% for many set of oil well data. The global optimum estimate values of stratigraphic parameters can be obtained, which provides scientific basis for well test interpretation. And the new scheme can obviously reduce closing well time than normal well test schemes. Therefore it could bring great economic and social benefit. Second, in order to solve the problem of the prediction of deep volcanic rocks reservoir, the two NN identifiers can be given. radial basis function neural network(RBFNN) is taken as the first identifier model. The structure, parameters and weight values of RBFNN can totally be identified by using a new hybrid algorithm proposed in this paper. The hybrid algorithm is consists of orthogonal LS method, gradient method with inertia terms and optimal fuzzy C means cluster method posed in this paper. The multilayer feedforward NN is taken as second identifier model. The network model can be trained by using a new hybrid SAA given in this paper. The hybrid algorithm is consists of Powell algorithm and adaptive SAA posed in this paper. The high prediction precision is obtained, when the two new schemes are applied to the prediction of volcanic rocks reservoir. And they have advantage over the tradition NN prediction schemes on convergent speed. Last, a novel fuzzy NN identifier with Laplace membership function is posed by us in this paper. The universal approximation of the network can be proved by using differential median theorem and Weierstrass theorem. In the scheme, the structure of the network is determined by using the optimal K means cluster method posed in this paper, the premise parameters and the weight values are identified by the hybrid algorithms given in this paper. The very good results can be gained, when the above new scheme is applied to prediction of accumulative oil product. The prediction average relative errors are within 1%.Specially, the accumulative oil product longterm prediction of basis wells, first and twice thicken wells for three oil recovery plants can be done, and there prediction results can provide the scientific basis for production decision whether or not to drill new thicken wells.
