孔南地区孔一段、孔二段储层构型及改善开发效果对策研究

Integrating geology, core, well-logging, experimental data, and production data, with the guide of sequence stratigraphy, sedimentology, reservoir exploitation geology and other disciplines’ theories, combinating the sequence stratigraphy and Maill’s reservoir architectures concepts and theories, the research and analysis methods of non-marine fan-delta reservoir architectures are systemly set out. And the correspondence of reservoir structures, sedimentology and reservoir geology is established. An integral and systematical research approach and theory and conception of reservoir architecture is developed, which enriched the reservoir research theory. Considering the requirement to the reservoir research in different development phase, the six classification systems of reservoir architectures are brought up. According to different reservoir’s connection and location of Ek different levels of reservoir architecture, 3 types, 20 kind architectures styles are summarized. The research about undisturbed reservoir characterization is launched, through analyzing reservoir characterization to pour water to the different reservoirs of Kongnan region, the changing regular pattern of reservoir quality during pouring water process is summarized. Combined with the actual zone data, inner-well reservoir geometry relationship of injection-production model is designed, and the models of development process are dynamic simulated. In view of seven laboratory samples of 3 types, six order architecture unit of braided stream, fan-delta and nearshore subsea apron in Kongnan region, the remaining oil distribution model is determined. Using the geo-statistics methods dissect the key regions, the tectono-stratigraphical model and the reservoir parameters model are established. The distribution of the characteristics of the underground reservoir is quantitatively described. Based on the reservoir research, carrying out the development of different characteristics of reservoir, the development pattern and countermeasures are determined. The relationships between reservoir structure levels and reservoir development stages are summed up, the relationships between architecture unit of different levels and exploration develop stages are determined.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

Cancer classification based on direction-basis-function neural networks

Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Priority-Ordered algorithm are applied to neural networks. And the leukemia gene expression dataset is used as an example to testify the classifier. The result of our method is compared to that of SVM. It shows that our method makes a better performance than SVM.

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

FROM DIMER TO POLYMER .2. EXTENDED TO SYSTEMS POSSESSING 2-FOLD SCREW AXIS OR GLIDE PLANE

The approach for constructing the qualitative band structure of a polymer from corresponding dimer has been extended to the system possessing two-fold screw axis or. glide plane. The classification of energy levels of the dimer in the present case depends on pseudo-symmetry/antisymmetry instead of psendo-in-phase/out-of-phase property of the orbitals. Several typical conductive polymers are then discussed follow this approach. Among them are cis-polyacetylene, polyparaphenylene with a twist ang...

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.