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    • 2. 发明授权
    • Injection manifold and method
    • 注射歧管和方法
    • US4953618A
    • 1990-09-04
    • US296265
    • 1989-01-12
    • Syed HamidJackie K. LucasRussell R. Lockman
    • Syed HamidJackie K. LucasRussell R. Lockman
    • E21B33/068E21B43/12E21B49/00
    • E21B33/068E21B43/12E21B49/008
    • An injection manifold for use in injecting fluid into a well and method of use. Several embodiments of the apparatus are shown. In each embodiment, a bypass valve is used to control the discharge pressure of a pump, and a throttling valve is used to control the fluid injection flow rate to the well in response to a flow rate measured by a flow meter. In a first embodiment, the throttling is carried out manually. In a second embodiment, the throttling is carried out automatically by a controller which compares a flow rate signal from the meter with a predetermined flow rate set point and sends a corresponding output signal to an actuator of the throttling valve. A third embodiment is a combination manifold which may also be used for flowback tests from the well. In this third embodiment, the electronic controller may also be used to control the flow rate during the flowback test in response to a flow rate signal from the flow meter. A method of injecting fluid into a well using the apparatus is also disclosed.
    • 用于将流体注入井中的注入歧管和使用方法。 显示该装置的几个实施例。 在每个实施例中,旁路阀用于控制泵的排出压力,并且使用节流阀来响应于由流量计测量的流量来控制到井的流体喷射流量。 在第一实施例中,手动进行节流。 在第二实施例中,通过将来自仪表的流量信号与预定流量设定点进行比较的控制器自动执行节流,并将相应的输出信号发送到节流阀的致动器。 第三实施例是组合歧管,其也可以用于来自井的回流测试。 在该第三实施例中,电子控制器还可以用于响应于来自流量计的流量信号来控制回流测试期间的流量。 还公开了使用该装置将流体注入井的方法。
    • 4. 发明授权
    • Fluid separator with smart surface
    • 具有智能表面的流体分离器
    • US08211284B2
    • 2012-07-03
    • US12266293
    • 2008-11-06
    • Syed HamidBeegamudre N. MuraliHarry D. Smith, Jr.
    • Syed HamidBeegamudre N. MuraliHarry D. Smith, Jr.
    • C02F1/46
    • B03C9/00B03C5/02B03C2201/02
    • A separating system for separating a fluid mixture incorporates a smart surface having reversibly switchable properties. A voltage is selectively applied to the smart surface to attract or repel constituents of a fluid mixture, such as oil and water produced from a hydrocarbon well. The smart surface can be used in a conditioner to increase droplet size prior to entering a conventional separator, or the smart surface and other elements of the invention can be incorporated into an otherwise conventional separator to enhance separation. In a related aspect, a concentration sensor incorporating smart surfaces senses concentration of the fluid mixture's constituents.
    • 用于分离流体混合物的分离系统包括具有可逆切换特性的智能表面。 选择性地将电压施加到智能表面以吸引或排斥诸如由烃井产生的油和水的流体混合物的成分。 智能表面可以在调节剂中用于在进入常规分离器之前增加液滴尺寸,或者将本发明的智能表面和其它元件并入另外常规的分离器中以增强分离。 在相关方面,包含智能表面的浓度传感器感测流体混合物组分的浓度。
    • 6. 发明申请
    • Neural-Network Based Surrogate Model Construction Methods and Applications Thereof
    • 基于神经网络的代理模型构建方法及应用
    • US20080228680A1
    • 2008-09-18
    • US12048045
    • 2008-03-13
    • Dingding ChenAllan ZhongSyed HamidStanley Stephenson
    • Dingding ChenAllan ZhongSyed HamidStanley Stephenson
    • G06F15/18
    • 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。