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    Systems and Methods of Calibrating Integrated Computational Elements
    21.
    发明申请
    Systems and Methods of Calibrating Integrated Computational Elements 审中-公开
    校准集成计算元素的系统和方法

    公开(公告)号：US20150300945A1

    公开(公告)日：2015-10-22

    申请号：US14362551

    申请日：2013-09-25

    Applicant: Halliburton Energy Services, Inc.

    Inventor： Li Gao ,  David L. Perkins ,  Michael T. Pelletier ,  Dingding Chen

    IPC: G01N21/27

    CPC classification number: G01N21/274 ,  G01N21/314 ,  G01N33/2841 ,  G01N2021/3174

    Abstract: Disclosed are systems and methods for calibrating integrated computational elements. One method includes measuring with a spectrometer sample interacted light comprising spectral data derived from one or more calibration fluids at one or more calibration conditions, the one or more calibration fluids circulating in a measurement system, programming a virtual light source based on the spectral data, simulating the spectral data with the virtual light source and thereby generating simulated fluid spectra corresponding to the spectral data, conveying the simulated fluid spectra to the one or more ICE and thereby generating corresponding beams of optically interacted light, and calibrating the one or more ICE based on the corresponding beams of optically interacted light.

    Abstract translation: 公开了用于校准集成计算元件的系统和方法。 一种方法包括使用光谱仪样品进行测量，所述相互作用的光包括在一个或多个校准条件下从一个或多个校准流体得到的光谱数据，在测量系统中循环的一个或多个校准流体，基于光谱数据编程虚拟光源， 用虚拟光源模拟光谱数据，从而产生对应于光谱数据的模拟流体光谱，将模拟的流体光谱输送到一个或多个ICE，从而产生相应的光学相互作用的光束，并校准一个或多个基于ICE 在相应的光学相互作用的光束上。

    METHOD AND APPARATUS FOR IMPROVING TEMPERATURE MEASUREMENT IN A DENSITY SENSOR
    22.
    发明申请
    METHOD AND APPARATUS FOR IMPROVING TEMPERATURE MEASUREMENT IN A DENSITY SENSOR 审中-公开
    用于改善密度传感器温度测量的方法和装置

    公开(公告)号：US20150253231A1

    公开(公告)日：2015-09-10

    申请号：US14432152

    申请日：2012-12-06

    Applicant: HALLIBURTON ENERGY SERVICES INC.

    Inventor： Li Gao ,  Wei Zhang ,  David P. Perkins ,  Dingding Chen ,  Nestor Rodriquez

    IPC: G01N9/00 ,  G01N9/32

    CPC classification number: G01N9/002 ,  G01F1/00 ,  G01N9/32 ,  G01N2009/006

    Abstract: An apparatus for determining the density of a fluid in a flowstream is disclosed. The apparatus comprises a vibrating tube (12) having a bore and a vibrating region. The apparatus also comprises a housing (16) to support the vibrating region. The apparatus further comprises a vibration source (22) and a vibration detector (24) coupled to the vibrating tube (12), and one or more sensors (26) coupled to the housing (16), said one or more sensors substantially oriented toward the vibrating region of the vibrating tube.

    Abstract translation: 公开了一种用于确定流动流体中的流体密度的装置。 该装置包括具有孔和振动区的振动管（12）。 该装置还包括用于支撑振动区域的壳体（16）。 该装置还包括耦合到振动管（12）的振动源（22）和振动检测器（24）以及耦合到壳体（16）的一个或多个传感器（26），所述一个或多个传感器基本上朝向 振动管的振动区域。

    Discrimination Analysis Used with Optical Computing Devices
    23.
    发明申请
    Discrimination Analysis Used with Optical Computing Devices 审中-公开
    用于光学计算设备的辨别分析

    公开(公告)号：US20150205000A1

    公开(公告)日：2015-07-23

    申请号：US14360214

    申请日：2013-06-07

    Applicant: Halliburton Energy Services, Inc.

    Inventor： David L. Perkins ,  Dingding Chen ,  Christopher Michael Jones ,  Jing Shen

    IPC: G01V8/10

    Abstract: Disclosed are systems and methods that use discriminant analysis techniques and processing in order to reduce the time required to determine chemical and/or physical properties of a substance. One method includes optically interacting a plurality of optical elements with one or more known substances, each optical element being configured to detect a particular characteristic of the one or more known substances, generating an optical response from each optical element corresponding to each known substance, wherein each known substance corresponds to a known spectrum stored in an optical database, and training a neural network to provide a discriminant analysis classification model for an unknown substance, the neural network using each optical response as inputs and one or more fluid types as outputs, and the outputs corresponding to the one or more known substances.

    Abstract translation: 公开了使用判别分析技术和处理以减少确定物质的化学和/或物理性质所需的时间的系统和方法。 一种方法包括使多个光学元件与一种或多种已知物质光学相互作用，每个光学元件被配置为检测一种或多种已知物质的特定特性，从对应于每种已知物质的每个光学元件产生光学响应，其中 每个已知物质对应于存储在光学数据库中的已知光谱，并训练神经网络以提供未知物质的判别分析分类模型，使用每个光学响应作为输入的神经网络和一种或多种流体类型作为输出，以及 对应于一种或多种已知物质的输出。

    Optical sensor adaptive calibration
    26.
    发明授权
    Optical sensor adaptive calibration 有权

    公开(公告)号：US11467314B2

    公开(公告)日：2022-10-11

    申请号：US16464244

    申请日：2018-07-16

    Applicant: Halliburton Energy Services, Inc.

    Inventor： Dingding Chen ,  Christopher Michael Jones ,  Bin Dai ,  Megan Pearl ,  James M. Price

    IPC: G01V8/10 ,  E21B49/08 ,  G01N21/25 ,  G01V13/00 ,  G01V99/00

    Abstract: The subject disclosure provides for a method of optical sensor calibration implemented with neural networks through machine learning to make real-time optical fluid answer product prediction adapt to optical signal variation of synthetic and actual sensor inputs integrated from multiple sources. Downhole real-time fluid analysis can be performed by monitoring the quality of the prediction with each type of input and determining which type of input generalizes better. The processor can bypass the less robust routine and deploy the more robust routine for remainder of the data prediction. Operational sensor data can be incorporated from a particular optical tool over multiple field jobs into an updated calibration when target fluid sample compositions and properties become available. Reconstructed fluid models adapted to prior field job data, in the same geological or geographical area, can maximize the likelihood of quality prediction on future jobs and optimize regional formation sampling and testing applications.

    MACHINE LEARNING MUD PULSE RECOGNITION NETWORKS
    27.
    发明申请
    MACHINE LEARNING MUD PULSE RECOGNITION NETWORKS 有权

    公开(公告)号：US20220195868A1

    公开(公告)日：2022-06-23

    申请号：US17665999

    申请日：2022-02-07

    Applicant: Halliburton Energy Services, Inc.

    Inventor： Dingding Chen ,  Li Gao ,  Joni Polili Lie ,  Paravastu Badrinarayanan ,  Faisal Farooq Shah ,  Bipin K. Pillai ,  Murali Krishna Thottempudi

    IPC: E21B47/18 ,  G06N20/00

    Abstract: This disclosure presents a process for communications in a borehole containing a fluid or drilling mud, where a conventional mud pulser can be utilized to transmit data to a transducer. The transducer, or a communicatively coupled computing system, can perform pre-processing steps to correct the received data using an average of a moving time window of the received data, and then normalize the corrected data. The corrected data can then be utilized as inputs into a machine learning mud pulse recognition network where the data can be classified and an ideal or clean pulse waveform can be overlaid the corrected data. The overlay and the corrected data can be fed into a conventional decoder or decoded by the disclosed process. The decoded data can then be communicated to another system and used as inputs, such as to a well site controller to enable adjustments to well site operation parameters.

    METHODS FOR PREDICTING PROPERTIES OF CLEAN FORMATION FLUID USING REAL TIME DOWNHOLE FLUID ANALYSIS OF CONTAMINATED SAMPLES
    29.
    发明申请
    METHODS FOR PREDICTING PROPERTIES OF CLEAN FORMATION FLUID USING REAL TIME DOWNHOLE FLUID ANALYSIS OF CONTAMINATED SAMPLES 有权

    公开(公告)号：US20210054738A1

    公开(公告)日：2021-02-25

    申请号：US16336172

    申请日：2018-06-27

    Applicant: Halliburton Energy Services, Inc.

    Inventor： Bin Dai ,  Christopher Michael Jones ,  Dingding Chen

    IPC: E21B49/08 ,  G01N21/31 ,  G01N33/28

    Abstract: A method, including disposing a probe of a sensor system in a wellbore to interact with a formation fluid that includes a mud filtrate and a clean fluid that includes one of a formation water, or a formation hydrocarbon fluid including at least one hydrocarbon component. The method includes collecting multiple measurements of a formation fluid from a wellbore, the formation fluid comprising a mud filtrate and a clean fluid, is provided. The clean fluid includes at least one hydrocarbon component, and the method also include identifying a concentration of the mud filtrate and a concentration of the clean fluid in the formation fluid for one of the measurements, and determining at least one hydrocarbon composition and at least one physical property of the clean fluid based on a measurement fingerprint of the hydrocarbon components. A sensor system configured to perform a method as above is also provided.

    FLUID OPTICAL DATABASE RECONSTRUCTION METHODS AND APPLICATIONS THEREOF
    30.
    发明申请
    FLUID OPTICAL DATABASE RECONSTRUCTION METHODS AND APPLICATIONS THEREOF 审中-公开

    公开(公告)号：US20200257654A1

    公开(公告)日：2020-08-13

    申请号：US16347243

    申请日：2018-07-03

    Applicant: Halliburton Energy Services, Inc.

    Inventor： Dingding Chen ,  Bin Dai ,  Christopher Michael Jones ,  Jing Shen ,  Anthony Van Zuilekom

    IPC: G06F16/11 ,  G01V8/10 ,  G01N21/3577 ,  G01N33/28 ,  G01N21/94 ,  E21B49/08 ,  G06N3/08 ,  G06N3/04

    Abstract: Mutual-complementary modeling and testing methods are disclosed that can enable validated mapping from external oil and gas information sources to existing fluid optical databases through the use of forward and inverse neural networks. The forward neural networks use fluid compositional inputs to produce fluid principal spectroscopy components (PSC). The inverse neural networks apply PSC inputs to estimate fluid compositional outputs. The fluid compositional data from external sources can be tested through forward models first. The produced PSC outputs are then entered as inputs to inverse models to generate fluid compositional data. The degree of matching between reconstructed fluid compositions and the original testing data suggests which part of the new data can be integrated directly into the existing database as validated mapping. The applications of using PSC inputs to reconstruct infrared spectra and estimate oil-based-mud (OBM) contamination with endmember spectral fingerprints are also included.

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