会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 1. 发明申请
    • METHOD AND APPARATUS FOR CENTRIFUGAL BLENDING SYSTEM
    • 离心混合系统的方法与装置
    • WO2014042707A1
    • 2014-03-20
    • PCT/US2013/039436
    • 2013-05-03
    • HALLIBURTON ENERGY SERVICES, INC.
    • STEGEMOELLER, CalvinHEITMAN, ChadSTEPHENSON, StanleyHORINEK, Herbert
    • B01F7/16B01F15/02B01F3/12B01F5/12
    • B01F15/0227B01F3/1228B01F5/22B01F2003/125E21B21/062E21B43/00
    • Blending particulate and liquid to make slurry for use in oilfield operations is addressed. The blender has an upwardly facing particulate expeller with a flat base, raised hub, and generally radially extending, circumferentially spaced vanes extending upwardly from the base. The vanes extend from leading edges spaced about a vane inner diameter to tips spaced about a vane outer diameter. Adjacent expeller vanes define expeller passageways therebetween. The particulate expeller does not serve as a meaningful liquid impeller and the blender does not act significantly as a pump. The expeller has a several preferred diameter, clearance, height and length dimensions and ratios. Wide, deep expeller inlets and shallow, narrow outlets enhance particulate entry and minimize expeller torque. Vane extensions impart velocity to the particulate upon contact and minimize sensitivity to particulate entry velocity. Maximized circumferential overlap of adjacent vanes reduces liquid back-flow.
    • 混合颗粒和液体以制备用于油田作业的浆料。 搅拌器具有面向上的微粒排出器,其具有平坦的基部,凸起的毂,以及从基部向上延伸的大致径向延伸的周向隔开的叶片。 叶片从围绕叶片内径间隔开的前缘延伸到围绕叶片外径间隔开的尖端。 相邻的叶片叶片限定了其间的推出器通道。 颗粒排出器不用作有意义的液体叶轮,并且搅拌器不会像泵那样显着地起作用。 排出器具有几个优选的直径,间隙,高度和长度尺寸和比率。 宽,深的推进器入口和浅,狭窄的出口增强了颗粒进入并最大限度地减少了推力器扭矩。 叶片延伸部在接触时赋予颗粒速度,并使对颗粒进入速度的敏感性最小化。 相邻叶片的最大化圆周重叠减少了液体回流。
    • 5. 发明申请
    • NEURAL-NETWORK BASED SURROGATE MODEL CONSTRUCTION METHODS AND APPLICATIONS THEREOF
    • 基于神经网络的现场建模方法及其应用
    • WO2008112921A1
    • 2008-09-18
    • PCT/US2008/056894
    • 2008-03-13
    • HALLIBURTON ENERGY SERVICES, INC.CHEN, DingdingZHONG, AllanHAMID, SyedSTEPHENSON, Stanley
    • CHEN, DingdingZHONG, AllanHAMID, SyedSTEPHENSON, Stanley
    • G06F17/00
    • G06N3/0454B33Y80/00
    • Various neural-network based surrogate model construction methods are disclosed herein, along with various applications of such models. Designed for use when only a sparse amount of data is available (a "sparse data condition"), some embodiments of the disclosed systems and methods: create a pool of neural networks trained on a first portion of a sparse data set; generate for each of various multi-objective functions a set of neural network ensembles that minimize the multi-objective function; select a local ensemble from each set of ensembles based on data not included in said first portion of said sparse data set; and combine a subset of the local ensembles to form a global ensemble. This approach enables usage of larger candidate pools, multi-stage validation, and a comprehensive performance measure that provides more robust predictions in the voids of parameter space.
    • 本文公开了各种基于神经网络的替代模型构建方法,以及这些模型的各种应用。 设计为仅在少量数据可用时使用(“稀疏数据条件”),所公开的系统和方法的一些实施例:创建在稀疏数据集的第一部分上训练的神经网络池; 为各种多目标函数中的每一个生成一组最小化多目标函数的神经网络集合; 基于不包括在所述稀疏数据集的所述第一部分中的数据,从每组集合中选择本地集合; 并组合一个本地组合的子集以形成全局集合。 这种方法使得可以使用更大的候选池,多级验证以及综合性能测量,从而在参数空间的空白中提供更强大的预测。