Abstract: A computing system receives an indication of an energy project. The computing system monitors satellite image data of the energy project. The computing system determines that a first activity has been initiated for the energy project based on a change in satellite image data. The computing system monitors telemetry data associated with a location corresponding to the energy project and the satellite image data of the energy project. The computing system converts the telemetry data associated with the location corresponding to the energy project to a telemetry activity index. The computing system converts the satellite image data associated with the energy project to a satellite activity index. The computing system determines that the first activity has ended for the energy project based on changes in at least one of the telemetry activity index and the pad activity index.

Abstract: Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

Abstract: Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

Abstract: Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

Abstract: Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

Abstract: Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

Abstract: A computing system receives an indication of an energy project. The computing system monitors satellite image data of the energy project. The computing system determines that a first activity has been initiated for the energy project based on a change in satellite image data. The computing system monitors telemetry data associated with a location corresponding to the energy project and the satellite image data of the energy project. The computing system converts the telemetry data associated with the location corresponding to the energy project to a telemetry activity index. The computing system converts the satellite image data associated with the energy project to a satellite activity index. The computing system determines that the first activity has ended for the energy project based on changes in at least one of the telemetry activity index and the pad activity index.

Abstract: Embodiments disclosed herein generally relate to a method and system for oil and gas predictive analytics. A computer system receives a set of production information for a well located in a region. The computing system generates a set of general reference groups comprising one or more reference wells for the region. The computing system determines whether the set of production information for the well includes the threshold amount of production information. The computing system selects a subset of reference wells from the general reference groups based on one or more traits of the well. The computing system generates a reference curve based on the set of production information associated with each reference well in the subset of reference wells. The computing system fits a decline curve to the reference curve, to determine an estimated ultimate recovery of the well.

Abstract: Methods and systems for predicting failures in an artificial lift system are disclosed. One method includes extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems. The method also includes forming a learning model based on identified pre-failure signatures in the extracted features, the learning model configured to predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system.

Abstract: Methods and systems for predicting failures in an artificial lift system are disclosed. One method includes extracting one or more features from a dataset including time sampled performance of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems. The method also includes forming a learning model based on identified pre-failure signatures in the extracted features, the learning model configured to predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system.