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公开(公告)号：US20200257015A1
公开(公告)日：2020-08-13
申请号：US16860183
申请日：2020-04-28
Applicant: Halliburton Energy Services, Inc.
Inventor： David Perkins , Dingding Chen , Christopher Michael Jones , Jing Shen
Abstract: A model can be trained for discriminant analysis for substance classification and/or measuring calibration. One method includes interacting at least one sensor with one or more known substances, each sensor element being configured to detect a characteristic of the one or more known substances, generating an sensor response from each sensor element corresponding to each known substance, wherein each known substance corresponds to a known response stored in a database, and training a neural network to provide a discriminant analysis classification model for an unknown substance, the neural network using each sensor response as inputs and one or more substance types as outputs, and the outputs corresponding to the one or more known substances.
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公开(公告)号：US10430542B2
公开(公告)日：2019-10-01
申请号：US14435394
申请日：2012-11-09
Applicant: Halliburton Energy Services, Inc.
Inventor： Dingding Chen , Christopher Michael Jones , David L. Perkins , Li Gao
Abstract: A system for integrated computational element (“ICE”) design optimization and analysis utilizes a genetic algorithm to evolve layer thickness of each fixed ICE structure using a constrained multi-objective merit function. The system outputs a ranked representative group of ICE design candidates that may be used for further fabricability study, ICE combination selection, efficient statistical analysis and/or feature characterization.
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公开(公告)号：US10429541B2
公开(公告)日：2019-10-01
申请号：US15736570
申请日：2015-07-29
Applicant: Halliburton Energy Services, Inc.
Inventor： Bin Dai , Chris Jones , Dingding Chen
Abstract: Two or more Integrated Computational Element (“ICE”) structures are designed and utilized in an optical computing device to combinatorily reconstruct spectral patterns of a sample. To design the ICE structures, principal component analysis (“PCA”) loading vectors are derived from training spectra. Thereafter, two or more ICE structures having spectral patterns that match the PCA loading vectors are selected. The selected ICE structures may then be fabricated and integrated into an optical computing device. During operation, the ICE structures are used to reconstruct high resolution spectral data of the samples which is utilized to determine a variety of sample characteristics.
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公开(公告)号：US10329900B2
公开(公告)日：2019-06-25
申请号：US15348718
申请日：2016-11-10
Applicant: Halliburton Energy Services, Inc.
Inventor： Dingding Chen , Syed Hamid , Michael C. Dix
Abstract: Dimensionality reduction systems and methods facilitate visualization, understanding, and interpretation of high-dimensionality data sets, so long as the essential information of the data set is preserved during the dimensionality reduction process. In some of the disclosed embodiments, dimensionality reduction is accomplished using clustering, evolutionary computation of low-dimensionality coordinates for cluster kernels, particle swarm optimization of kernel positions, and training of neural networks based on the kernel mapping. The fitness function chosen for the evolutionary computation and particle swarm optimization is designed to preserve kernel distances and any other information deemed useful to the current application of the disclosed techniques, such as linear correlation with a variable that is to be predicted from future measurements. Various error measures are suitable and can be used.
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公开(公告)号：US20180371905A1
公开(公告)日：2018-12-27
申请号：US15557049
申请日：2016-11-04
Applicant: HALLIBURTON ENERGY SERVICES, INC.
Inventor： Dingding Chen , Bin Dai , Christopher Michael Jones , John Andrew Quirein
Abstract: System and methods for downhole fluid classification are provided. Measurements are obtained from one or more downhole sensors located along a current section of wellbore within a subsurface formation. The measurements obtained from the one or more downhole sensors are transformed into principal spectroscopy component (PSC) data. One or more fluid types are identified for the current section of the wellbore within the subsurface formation, based on the PSC data and a fluid classification model. The fluid classification model is refined for one or more subsequent sections of the wellbore within the subsurface formation, based at least partly on the one or more fluid types identified for the current section of the wellbore.
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Patent Agency Ranking
36.发明申请公开(公告)号：US20180216441A1
公开(公告)日：2018-08-02
申请号：US15505576
申请日：2015-09-02
Applicant: HALLIBURTON ENERGY SERVICES, INC.
Inventor： Ming Gu , Deepak Gokaraju , John Andrew Quirein , Dingding Chen
CPC classification number: E21B41/0092 , E21B43/26 , E21B2041/0028
Abstract: A method for shale fracturing includes determining dynamic-elastic properties of a shale deposit in a geological formation. A training database is generated by three-dimensional fracture modeling. A neural network is generated in response to output results of the training database. The shale fracturing may then be performed based on the neural network.
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37.发明授权公开(公告)号：US10012763B2
公开(公告)日：2018-07-03
申请号：US14427939
申请日：2013-11-07
Applicant: Halliburton Energy Services, Inc.
Inventor： David L. Perkins , Li Gao , Christopher Michael Jones , Dingding Chen , Michael T. Pelletier
Abstract: Various embodiments include apparatus and methods to utilize fluid phase behavior interpretation to increase sensor measurement accuracy. In some embodiments, a downhole sensor measures detector responses within a measurement sample of a downhole fluid. The measured detector responses are acquired from the downhole sensor. A correlation factor is generated as a scalar product between a vector of the measured detector responses and a vector of known responses. The correlation factor has a value within a fixed interval, one end of the fixed interval corresponding to a high correlation status and an opposite end of the fixed interval corresponding to an uncorrelated status. The correlation factor is compared to a correlation threshold for a specified material and sets of data having correlation factors greater than the correlation threshold are identified.
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公开(公告)号：US09612361B2
公开(公告)日：2017-04-04
申请号：US14780780
申请日：2014-12-12
Applicant: Halliburton Energy Services, Inc.
Inventor： Dingding Chen , David L. Perkins , William Soltmann , Darren Gascooke , Jing Shen
CPC classification number: G01V13/00 , E21B47/06 , E21B47/065 , E21B47/102 , E21B49/082 , G01N21/274 , G01V8/02
Abstract: An example method includes performing validation testing on a tool using a plurality of reference fluids, the tool having a calibrated optical sensor installed therein that includes one or more optical elements. One or more tool sensor responses from the calibrated optical sensor may be obtained and pre-processed, and the one or more tool sensor responses may be compared with calibrated optical sensor responses derived from the calibrated optical sensor during calibration and thereby detecting one or more optical sensor anomalies. The one or more optical sensor anomalies may be evaluated through performance analysis with one or more candidate models, and an alternative candidate model may be selected to mitigate the one or more optical sensor anomalies. One or more remedial options may be pursued when the alternative candidate model fails to mitigate the one or more optical sensor anomalies.
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公开(公告)号：US20160320527A1
公开(公告)日：2016-11-03
申请号：US14783522
申请日：2014-12-29
Applicant: HALLIBURTON ENERGY SERVICES, INC.
Inventor： Dingding Chen , Bin Dai , Christopher M. Jones , Darren Gascooke , Tian He
CPC classification number: G01V13/00 , E21B7/00 , E21B47/06 , E21B47/065 , E21B47/123 , E21B49/08 , G01V8/10
Abstract: A method includes obtaining a plurality of master sensor responses with a master sensor in a set of training fluids and obtaining node sensor responses in the set of training fluids. A linear correlation between a compensated master data set and a node data set is then found for a set of training fluids and generating node sensor responses in a tool parameter space from the compensated master data set on a set of application fluids. A reverse transformation is obtained based on the node sensor responses in a complete set of calibration fluids. The reverse transformation converts each node sensor response from a tool parameter space to the synthetic parameter space, and uses transformed data as inputs of various fluid predictive models to obtain fluid characteristics. The method includes modifying operation parameters of a drilling or a well testing and sampling system according to the fluid characteristics.
Abstract translation: 一种方法包括在一组训练流体中获得与主传感器的多个主传感器响应，并获得训练流体组中的节点传感器响应。 然后，针对一组训练流体找到补偿的主数据集和节点数据集之间的线性相关，并且在一组应用流体上从补偿的主数据集在工具参数空间中生成节点传感器响应。 基于整套校准流体中的节点传感器响应获得反向变换。 反向变换将每个节点传感器响应从工具参数空间转换为合成参数空间，并且使用变换数据作为各种流体预测模型的输入以获得流体特性。 该方法包括根据流体特性修改钻井或井测试和采样系统的操作参数。
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公开(公告)号：US09256701B2
公开(公告)日：2016-02-09
申请号：US13735756
申请日：2013-01-07
Applicant: Halliburton Energy Services, Inc.
Inventor： Dingding Chen , David L. Perkins , Christopher Michael Jones , Li Gao , Lucas Fontenelle , Johanna Haggstrom
CPC classification number: G06F17/5009 , E21B43/26 , G06F2217/16 , G06N3/086
Abstract: Techniques for modeling a wellbore fluid that includes a base fluid and one or more fluid additives includes identifying a target viscosity profile of the wellbore fluid; determining an initial set of values of the fluid additives that are based at least in part on the target viscosity profile; determining, with one or more non-linear predictive models, a computed viscosity profile of the wellbore fluid and a computed set of values of the fluid additives based, at least in part, on the initial set of values of the fluid additives; comparing the computed viscosity profile and at least one of the computed set of values with a specified criteria of the wellbore fluid; and preparing, based on the comparison, an output including the computed viscosity profile and at least one of the computed set of values of a resultant wellbore fluid.
Abstract translation: 用于建模包括基础流体和一种或多种流体添加剂的井筒流体的技术包括识别井筒流体的目标粘度分布; 确定至少部分基于目标粘度分布的流体添加剂的初始值值; 使用一个或多个非线性预测模型，至少部分地基于所述流体添加剂的初始值来确定所述井筒流体的计算粘度分布和所计算的流体添加剂的值的集合; 将所计算的粘度分布与所计算的一组值中的至少一个与井筒流体的特定标准进行比较; 并且基于该比较，准备包括所计算的粘度分布和所得到的井筒流体的所计算的一组值中的至少一个的输出。
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