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1. 发明申请

US20230091421A1 SYSTEMS AND METHODS OF DYNAMIC OUTLIER BIAS REDUCTION IN FACILITY OPERATING DATA 有权
公开(公告)号：US20230091421A1
公开(公告)日：2023-03-23
申请号：US17949743
申请日：2022-09-21
申请人： HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANY
发明人： Richard B. Jones
IPC分类号： G06K9/62 , G06F11/34 , G06F17/18 , G06N7/00 , G06N5/04
摘要： In at least one embodiment, the present description is directed to a computer system, having a processor to at least: electronically receive a model for one or more operating conditions, and facility operating data; iteratively perform one or more iterations of outlier bias reduction in the facility operating data based on the model, including: determining model predicted values, comparing the model predicted values to the facility operating data, removing bias facility operating data from the facility operating data of the plurality of facilities, and constructing, based at least in part on the non-biased facility operating a data, an updated model with one or more updated coefficients; determine, based on non-biased facility operating data, a non-biased performance standard for the one or more operating conditions; and track, based on the no-biased performance standard and the facility operating data, operating performance of each respective facility of the plurality of facilities.

2. 发明授权

US11521278B2 Method of predicting drilling and well operation 有权
公开(公告)号：US11521278B2
公开(公告)日：2022-12-06
申请号：US17395260
申请日：2021-08-05
申请人： THE HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANY
发明人： Richard B. Jones
IPC分类号： G06Q50/02 , G06Q10/06 , E21B47/26 , E21B44/00 , E21B47/08 , G16C10/00
摘要： A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

3. 发明授权

US11514527B2 Computer systems and computer-implemented methods utilizing sensor-driven dynamically adjustable feedback loops to manage equipment based risk on an asset specific level of energy data usage 有权
公开(公告)号：US11514527B2
公开(公告)日：2022-11-29
申请号：US16046976
申请日：2018-07-26
申请人： The Hartford Steam Boiler Inspection and Insurance Company
发明人： Richard B. Jones , Paul A. Cullum
IPC分类号： G06Q40/08 , G06N3/08 , G06N3/04 , G06N20/00
摘要： In some embodiments, the present invention provides for an exemplary inventive system that may include executable program code and a computer processor which, when executing the particular program code, is configured to perform operations of: receiving, for a population of energy consuming physical assets, asset-specific historical data and asset-specific current energy consumption data from utility meter(s), sensor(s), or both; determining, for each respective physical asset category, each respective frequency of breakdowns and each respective average severity of each breakdown; determining, an adjusted breakdown loss value per each physical asset for each respective physical asset category; determining a respective average current energy consumption value per each physical asset for each respective physical asset category; associating each respective energy consuming location to a particular physical asset category; generating, based on usage-based breakdown insurance premium value of the respective energy consuming location, an electronic alert configured to affect the location-specific level of energy usage of the at least one energy consuming physical asset.

4. 发明申请

US20220195860A1 METHOD OF PREDICTING DRILLING AND WELL OPERATION 有权
公开(公告)号：US20220195860A1
公开(公告)日：2022-06-23
申请号：US17395260
申请日：2021-08-05
申请人： THE HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANY
发明人： Richard B. Jones
IPC分类号： E21B44/00 , E21B47/08 , E21B47/26
摘要： A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

5. 发明申请

US20160260036A1 RISK ASSESSMENT FOR DRILLING AND WELL COMPLETION OPERATIONS 审中-公开
标题翻译：钻井和完井操作的风险评估
公开(公告)号：US20160260036A1
公开(公告)日：2016-09-08
申请号：US15062164
申请日：2016-03-06
申请人： The Hartford Steam Boiler Inspection and Insurance Company
发明人： Richard B. Jones
IPC分类号： G06Q10/06
CPC分类号： G06Q50/02 , G06Q10/0635 , G16C10/00
摘要： A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.
摘要翻译：提供了一种用于评估井完井的风险的方法，装置和系统，包括：使用输入界面获得一个或多个计划运行的下旋转小时数和多个井场参数，确定使用至少一个处理器，基于井场参数对应于一个或多个计划的运行的一个或多个非生产时间值，使用至少一个处理器，经由一个或多个处理器开发非生产时间分布和下转动表分布，更多的蒙特卡洛试验; 并且使用图形显示器输出基于从下表旋转表中的总BRT小时的风险转移模型结果和从一个或多个蒙特卡洛试验产生的非生产时间分布。

6. 发明授权

US11334645B2 Dynamic outlier bias reduction system and method 有权
公开(公告)号：US11334645B2
公开(公告)日：2022-05-17
申请号：US15963817
申请日：2018-04-26
申请人： HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANY
发明人： Richard B. Jones
IPC分类号： G06F17/10 , G06F17/18 , G01N33/00 , G06N5/04 , G06F7/58
摘要： A system and method is described herein for data filtering to reduce functional, and trend line outlier bias. Outliers are removed from the data set through an objective statistical method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line calculations. Outlier data records are removed when the error values are greater than or equal to the user-supplied criteria. For optimization methods or other iterative calculations, the removed data are re-applied each iteration to the model computing new results. Using model values for the complete dataset, new error values are computed and the outlier bias reduction procedure is re-applied. Overall error is minimized for model coefficients and outlier removed data in an iterative fashion until user defined error improvement limits are reached. The filtered data may be used for validation, outlier bias reduction and data quality operations.

7. 发明授权

US10409891B2 Future reliability prediction based on system operational and performance data modelling 有权
公开(公告)号：US10409891B2
公开(公告)日：2019-09-10
申请号：US14684358
申请日：2015-04-11
申请人： Hartford Steam Boiler Inspection and Insurance Company
发明人： Richard B. Jones
IPC分类号： G06Q10/06 , G06F17/18
摘要： Systems, methods, and apparatuses for improving future reliability prediction of a measurable system by receiving operational and performance data, such as maintenance expense data, first principle data, and asset reliability data via an input interface associated with the measurable system. A plurality of category values may be generated that categorizes the maintenance expense data by a designated interval using a maintenance standard that is generated from one or more comparative analysis models associated with the measureable system. The estimated future reliability of the measurable system is determined based on the asset reliability data and the plurality of category values and the results of the future reliability are displayed on an output interface.

8. 发明申请

US20150294048A1 Future Reliability Prediction Based on System Operational and Performance Data Modelling 审中-公开
标题翻译：基于系统运行和性能数据建模的未来可靠性预测
公开(公告)号：US20150294048A1
公开(公告)日：2015-10-15
申请号：US14684358
申请日：2015-04-11
申请人： Hartford Steam Boiler Inspection and Insurance Company
发明人： Richard B. Jones
IPC分类号： G06F17/50 , G06F17/18
CPC分类号： G06F17/18 , G06F2217/16 , G06Q10/0635 , Y02P90/845
摘要： Systems, methods, and apparatuses for improving future reliability prediction of a measurable system by receiving operational and performance data, such as maintenance expense data, first principle data, and asset reliability data via an input interface associated with the measurable system. A plurality of category values may be generated that categorizes the maintenance expense data by a designated interval using a maintenance standard that is generated from one or more comparative analysis models associated with the measureable system. The estimated future reliability of the measurable system is determined based on the asset reliability data and the plurality of category values and the results of the future reliability are displayed on an output interface.
摘要翻译：通过经由与可测量系统相关联的输入接口接收诸如维护费用数据，第一原理数据和资产可靠性数据的操作和性能数据来改进可测量系统的未来可靠性预测的系统，方法和装置。可以生成多个类别值，其使用从与可测量系统相关联的一个或多个比较分析模型生成的维护标准来将维护费用数据分类指定间隔。基于资产可靠性数据和多个类别值确定可测量系统的估计的未来可靠性，并且将未来可靠性的结果显示在输出接口上。

9. 发明授权

US11636292B2 Dynamic outlier bias reduction system and method 有权
公开(公告)号：US11636292B2
公开(公告)日：2023-04-25
申请号：US16145544
申请日：2018-09-28
申请人： HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANY
发明人： Richard B. Jones
IPC分类号： G06N5/04 , G06K9/62 , G06F11/34 , G06F17/18 , G06N7/00
摘要： In at least one embodiment, the present description is directed to a computer system, having at least components of a server, including a processor and a non-transient storage subsystem, storing a computer program including instructions that, when executed by the processor, cause the processor to at least: electronically receive a model for one or more operating conditions, one or more threshold criteria, and facility operating data for each respective facility of a plurality of facilities; validate the one or more threshold criteria to be one or more acceptable bias criteria; iteratively perform one or more iterations of outlier bias reduction in the facility operating data based on the model; determine, based on non-biased facility operating data, a non-biased performance standard for the one or more operating conditions; and track, based on the non-biased performance standard and the facility operating data, operating performance of each respective facility of the plurality of facilities.

10. 发明授权

US11599740B2 Computer-based systems, computing components and computing objects configured to implement dynamic outlier bias reduction in machine learning models 有权
公开(公告)号：US11599740B2
公开(公告)日：2023-03-07
申请号：US17741417
申请日：2022-05-10
申请人： HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANY
发明人： Richard B. Jones
IPC分类号： G06K9/62 , G06N20/20 , G06V10/75
摘要： Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activity.

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