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Multiphase flow meters are used to measure the water-liquid ratio (WLR) and void fraction in a multiphase fluid stream pipeline. In the present study, a system of multiphase flow measurement has been designed by application of three thallium-doped sodium iodide scintillators and a radioactive source of Ba simulated by Monte Carlo N-particle (MCNP) transport code. In order to capture radiations passing across the pipe, two direct detectors have been installed on opposite sides of the radioactive source. Another detector has been placed perpendicular to the transmission beam emitted from the Ba source to receive radiations scattered from the fluid flow. Simulation was done by the MCNP code for different volumetric fractions of water, oil, and gas phases for two types of flow regimes, namely, homogeneous and annular; training and validation data have been provided for the artificial neural network (ANN) to develop a computation model for pattern recognition. Depending on applications of the neural system, several structures of ANNs are used in the current paper to model the flow measurement relations, while the detector outputs are considered as the input parameters of the neural networks. The first, second, and third structures benefit from two, three, and five multilayer perceptron neural networks, respectively. Increasing the number of ANNs makes the system more complicated and decreases the available data; however, it increases the accuracy of estimation of WLR and gas void fraction. According to the results, the maximum relative difference was observed in the scattering detector. It was clear that transmission detectors would demonstrate the difference between the flow regimes as well. It is necessary to note that the error calculated by the MCNP simulator is <0.5% for the direct detectors (TR1 and TR2). Due to the difference between the data of the two flow regimes and the errors of data in the simulation codes of the MCNP, it was possible to separate these flow regimes. The effect of changing WLR on the efficiency for a constant void fraction confirms a considerable variance in the results of annular and homogeneous flows occurring in the scattering detector. There is a similar trend for the void fraction; hence, one can easily distinguish changes in efficiency due to the WLR. Analysis of the simulation results revealed that in the proposed structure of the multiphase flow meter and the computation model used for simulation, the two flow regimes are simply distinguishable.

陵水17-2气田位于中国南海琼东南盆地，是中国海油自营勘探发现的第一个深水高产气田，探明储量规模超千亿立方米。在水深200～1600 m区域，采用工程调查船与自主式水下潜器调查结合的方式，进行地球物理资料采集、海底表层取样及钻探取样，并对多波束测深、后向散射强度、侧扫声呐、浅地层剖面、室内测试分析等数据进行综合分析，研究了海底表层沉积物类型、分布规律及工程地质特性。按照海底地形地貌特征，陵水17-2深水气田开发区可划分为陆架区、缓坡区和滑塌区；陆架区表层沉积物以黏土和粉质黏土为主，不同站位物理力学性质差异较大，局部夹砂层；缓坡区和滑塌区具有高含水率、低密度、高孔隙比、高液限、高可塑性、低强度等典型深水沉积特点。区域内海底泥面至泥面之下0.3 m，土质不排水抗剪强度为0～4 kPa，非常有利于海底电缆、海底管道、脐带缆的铺设。缓坡区浅层土质条件非常适合吸力式、防沉板及抓力锚施工，滑塌区浅层土质条件适合防沉板、抓力锚及打入桩基础施工。研究成果将对琼东南盆地深水油气田开发工程的设计和安装施工具有指导意义。

通过搜集汇总埕岛海域地质勘察资料,绘制表层土体的平面分布,分析该区粉质黏土、粉土和淤泥质土的土性均值、变异系数和互相关系数,结果发现,埕岛海域沉积的土体主要以粉土和粉质黏土为主;该区土体重度、含水量、孔隙比和液塑限变异系数均较小,压缩性参数变异系数相对较大;根据文中的回归公式,可由含水量求出孔隙比、重度和压缩参数。最后基于Vanmarcke土层随机场模型,采用空间递推法计算土性在竖向上的非均匀程度,竖向相关距离位于0.49~O.93m之间,且与水深无相关性。