Research On Dynamic Model Of Reservoir With The Method Of Neural Network

Reservoir parameters have been changed by the exploitation fluid. The reservoir dynamic model, which can reflect the reservoir parameter's variation based on spatial and temporal, is set up. This has a significant meaning to lift the recovery ratio of oilfield and extend the life of oilfield.Mathematics and reservoir geology have been combined closely by applying synthetically multi-subject theories, methods and technology and making full use of computer. A new method to build the 4D reservoir model is proposed. Main achievements of the study are summarized as following: (1) Traditional BP derivation algorithm is put forward through the study of algorithm principle and basic structure of neural network and the BP neural network which commonly used in reservoir modeling. (2) New improvement measure (optimization weight and threshold of BP neural network based on genetic algorithm) is offered to overcome difficulties of BP algorithm applied in reservoir modeling. Then, the network can effectively avoid local optimization problem of network training and save network training time. The model built in this paper can meet the requirement of high accuracy. (3) Optimization BP algorithm based on genetic algorithm is offered by making full use of computer, especially programming language. And reservoir modeling system of GA-BP neural network is built by the optimization BP algorithm. (4) A new method to built 4D reservoir model is proposed in this paper. Firstly, build a prediction model of reservoir parameters to have its 4Ddata volume. Secondly, build 3D geological models of each development phases by means of modeling software to get the 4D reservoir model. (5) A 4D reservoir model of macro-parameter is built based on change rule of delta reservoir parameters were researched in Layer 83 of Es 2 in the second block of Shengtuo Oilfield exploited by water-flooding for a long time, and have a flavor result by verification.