Alluvial Fan Configuration Recognition Based On Multiple Attributes Neural Network

Seismic attribute analysis is a rapid development of a reservoir prediction technology in recent years, which is the application of a variety of mathematical analysis (such as neural network) methods to extract about the reservoir properties and variety attributes of lithology from seismic data, combined with logging data of the work area to establish the target curve and the relationship of the seismic attributes of the well. Then use the maximum related properties of the seismic data to forecasts and estimates the target curve characteristics, eventually reach the reservoir prediction purposes.Probabilistic neural network is a neural network based on radial basis function kernel, it is using localized functions, the best approximation characteristics, and no local minima. The probabilistic neural network approach can be seen as a nonlinear extension of linear cluster analysis. It is a mathematical interpolation method which implemented the neural network structure. The probabilistic neural network has a high degree of fault tolerance, even if the seismic parameters of a well or a network connection is defective, all or most of the information available through Lenovo. Thus, the mapping between the property of seismic attributes and logging features is of high reliability by using the probabilistic neural network. The probabilistic neural network approach also has the dynamic adaptability, the network can automatically adapt to the new variable to adjust the weight coefficients, until convergence, when geological lithology category or seismic parameters changed. The probabilistic neural network is an effective method to describe the relationship between seismic attributes and lithology parameters for the reservoirs controlled by lithology.Reservoir architecture, also known as the reservoir architectural, structural unit, and means of different levels of the reservoir internal superposition between cell shape, size, direction, and their mutual relations. Configuration concept originally developed by the American sedimentologists Miall1985, followed by many scholars to carry out reservoir architecture studies. Configuration study highlights the level of the reservoir internal sedimentary units and structural, this research has made great progress in fluvial reservoir analysis, and a better application in the underground reservoir. However, the degree of structure of the alluvial fan reservoir is very low, although in recent years some scholars have conducted a preliminary exploration of the alluvial fan reservoir architecture, but much work remains to be done in-depth study in terms of prediction accuracy to develop the discriminate model and three-dimensional configuration of modeling methods.The paper analysis the static and dynamic data of the Eastern zone of dense well in six of the Karamay oil field, on the basis of previous research, focusing on the alluvial fan reservoir internal configuration of logging parameters distinguish the mode unit geometry characteristics and spatial combination related research, the use of multi-attribute neural network modeling method to study the applicability of sandy conglomerate reservoir internal architecture modeling, internal configuration unit of alluvial fan reservoir quantitative geometrical features and overlay mode, set up to reflect the multi-level configuration interface,3D reservoir model configuration. The main contents include the following five areas:(1) Establish of logging curve three-dimensional data fieldFinishing277wells in the Eastern of the Sixth District, well inclined, layered and well logs data organized, preferably7well logs in larger configuration, the use of sequential Gaussian simulation method7three-dimensional data field of the well logs and seismic data in SEGY format.(2) The choice of the neural networkComprehensive analysis of the basic principle of BP neural network, multilayer feedforward neural network and probabilistic neural network method, the three methods in pattern recognition accuracy of the Sixth Eastern, by contrast, chose the probabilistic neural network six in the Eastern District of pattern recognition.(3) Neural network trainingUsing multi-attribute linear regression method to preferred24kinds of post-stack seismic attributes and seven kinds of logging property, carried out several experiments to forecast parameters of the probabilistic neural network, the comprehensive comparison of the prediction error is small and the training time relatively small set of parameters.(4) Configuration division of the un-coring wellsEight sealed coring structure curves and log curve for neural network training, the establishment of the configuration curves and log-curve determination mode, this discriminant model applied to the coring Inoue, recognition does not take the structure of the core hole curve, and thus the division and correlation of a single well configuration elements.(5) Configuration modeling of Eastern District in the six Taken to the Sixth Eastern8SLIM from the closed configuration curve of the core hole is the target curve, The optimized properties, and probabilistic neural network parameters inversion of seismic body of the Sixth East, six in the Eastern configuration model.Configuration model is better characterization the constituent characteristics and the superimposition of the configuration unit of the fan root, fan and fan fringe of the six Middle East alluvial fan. Alluvial fan configurations modeling based on multi-attribute neural network provide a new method for the reservoir heterogeneity, and also a new approach for the configuration modeling.