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
    • 3. 发明申请
    • Sensor Mounting Assembly for Drill Collar Stabilizer
    • 用于钻铤稳定器的传感器安装组件
    • US20130105222A1
    • 2013-05-02
    • US13651864
    • 2012-10-15
    • PRECISION ENERGY SERVICES, INC.
    • Lance C. Pate
    • E21B47/12
    • E21B47/01E21B17/1014
    • A drill collar assembly allows a sensor to be mounted with the same standoff from a borehole wall independent of the size of stabilizer and borehole involved. A sensor component disposes in a receptacle in the drill collar, but does not affix in the receptacle. Instead, a stabilizer fits on the drill collar and covers the receptacle, and the sensor component mounts directly to the underside of the stabilizer so the component “floats” or “suspends” in the receptacle. The sensor component can mount at a stabilizer blade so the sensor can be positioned in closer proximity to the borehole wall to measure parameters of interest. Because the drill collar and sensor component can be used in different sized boreholes, different sized stabilizers may be positioned on the drill collar to account for the different sized boreholes while the sensor still has the same standoff.
    • 钻铤组件允许传感器以与所涉及的稳定器和钻孔的尺寸无关的方式从钻孔壁安装相同的间隙。 传感器部件配置在钻铤中的容器中,但不会固定在容器中。 相反,稳定器适合于钻铤并覆盖容器,并且传感器部件直接安装在稳定器的下侧,使得部件在容器中“漂浮”或“悬挂”。 传感器组件可以安装在稳定器叶片上,使得传感器可以定位成更接近钻孔壁,以测量感兴趣的参数。 因为钻铤和传感器部件可以用于不同尺寸的钻孔中,所以不同尺寸的稳定器可以定位在钻铤上以考虑不同尺寸的钻孔,同时传感器仍然具有相同的间隙。
    • 4. 发明申请
    • Measurement Tool and Method of Use
    • 测量工具及使用方法
    • US20130049773A1
    • 2013-02-28
    • US13661676
    • 2012-10-26
    • Precision Energy Services, Inc.
    • Margaret Cowsar WaidBryan W. KasperskiMichael Andrew Yuratich
    • G01R27/26
    • G01N33/2823G01N27/221
    • This invention relates to a measurement tool and method of use, and in particular to a measurement tool for use in determining a parameter of a stationary or moving fluid. The measurement tool has been designed primarily for use in borehole formation testing. The measurement tool can measure the dielectric constant of a fluid within a pipe or surrounding the tool. The pipe or wall between the tool and the fluid is electrically insulating. The tool has pair of capacitor plates mounted adjacent to the pipe or wall, a signal generator which can deliver an alternating electrical signal to at least one of the capacitor plates, and a detector for measuring a signal dependent upon the electrical capacitance between the capacitor plates. The measurement tool can additionally measure the electrical resistivity of the fluid.
    • 本发明涉及一种测量工具和使用方法,特别涉及一种用于确定静止或移动流体参数的测量工具。 测量工具主要用于钻孔成形测试。 测量工具可以测量管道内或周围工具的流体的介电常数。 工具和流体之间的管或壁是电绝缘的。 该工具具有邻近管道或壁安装的一对电容器板,一个信号发生器,其可将交流电信号传递给至少一个电容器板;以及检测器,用于根据电容器板之间的电容测量信号 。 测量工具还可以测量流体的电阻率。
    • 8. 发明授权
    • Clustering process for analyzing pressure gradient data
    • 分析压力梯度数据的聚类过程
    • US09581015B2
    • 2017-02-28
    • US14550543
    • 2014-11-21
    • Precision Energy Services, Inc.
    • Hamed ChokJeffery J. Hemsing
    • E21B47/12E21B47/06G06K9/62E21B49/10E21B49/00G06N5/04
    • E21B47/06E21B47/12E21B49/003E21B49/10G06K9/6221G06K9/6271G06N5/04
    • Clustering analysis is used to partition data into similarity groups based on mathematical relationships between the measured variables. These relationships (or prototypes) are derived from the specific correlation required between the measured variables (data) and an environmental property of interest. The data points are partitioned into the prototype-driven groups (i.e., clusters) based on error minimization. Once the data is grouped, quantitative predictions and sensitivity analysis of the property of interest can be derived based on the computed prototypes. Additionally, the process inherently minimizes prediction errors due to the rigorous error minimization during data clustering while avoiding overfitting via algorithm parameterization. The application used to demonstrate the power of the method is pressure gradient analysis.
    • 根据测量变量之间的数学关系,使用聚类分析将数据划分为相似组。 这些关系(或原型)源自测量变量(数据)和感兴趣的环境属性之间所需的具体相关性。 基于误差最小化将数据点分割成原型驱动组(即,集群)。 一旦数据分组,可以基于计算的原型来导出感兴趣的属性的定量预测和敏感性分析。 此外,该过程固有地最小化由于在数据聚类期间严格的误差最小化而导致的预测误差,同时通过算法参数化避免过拟合。 用于演示该方法功率的应用是压力梯度分析。