Abstract: 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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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

Abstract: 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 measurable 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.

Abstract: 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

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: 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

Abstract: 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 measurable 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.

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: 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.