河流-三角洲储层剩余油分布模式及预测

Aim at the variousness and complexity of the spatial distribution of Remaining Oil in the fluvial and delta facies reservoir in paper. For example, in the La-Sa-Xing oilfield of Daqing, based on the research of the control factor and formation mechanization of block, single layer, interlayer and micromechanism, synthesizing the theories and methods of geology, well logging, reservoir engineering, artificial intelligence, physical simulation test , and computer multidisciplinary; Fully utilizing the material of geology, well logging, core well, dynamic monitor of oil and water well, and experimental analysis, from macro to micro, from quality to quantity, from indoor to workplace, we predicted the potentiality and distribution according to the four levels of Block, single layer, interlayer and micromechanism, and comprehensively summarized the different distribution pattern of remaining oil in the fluvial and delta facies reservoir This paper puts forward an efficient method to predict the remaining recoverable reserves by using the water flooding characteristic curve differential method and neutral network; for the first time utilizes multilevel fuzzy comprehensive judgment method and expert neutral network technology to predict the remaining oil distribution in the single layer? comprehensively takes advantage of reservoir flowing unit, indoor physical simulation test, inspection well core analysis and well-logging watered-out layer interpretation to efficiently predict the distribution of remaining oil; makes use of core analysis of different periods and indoor water driving oil test to study the micro distribution of remaining oil and the parameters varying law of reservoir substance properties, rock properties, wetting properties. Based on above, the remaining oil distribution predicting software is developed, which contains four levels of block, single layer, interlayer and micromechanism. This achievement has been used inLa-Sa-Xing oil field of Daqing and good results have been received.

a fuzzy-based method for evaluating the trustworthiness of software processes

University of Paderborn; Fraunhofer Inst. Exp. Softw. Eng. (IESE); Chinese Academy of Science (ISCAS)

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.

基于MAX197的电量测控系统

利用单片机技术设计多路电量测控系统,实现对电量的精确测量、控制,同时输出人工智能或位式开关控制信号。系统以单片机为核心,利用模数转换器MAX197构成多路测量电路,利用放大器构成输出电路,实现多路一一对应的闭环测量控制。系统软件采用PID控制器。实践表明:可根据需要增减系统信号采样通道的数目,采用软件抗干扰措施,采样数据可靠性高。采用参数密码保护和自检系统,有效防止由于错误控制而引起的不可预知的甚至危险的后果。输入输出硬件结构简单,系统低成本高速度,具有较好的测量控制精度。