Abstract: The invention relates to using autoencoder-derived features for predicting well failures (e.g., rod pump failures) using a machine learning classifier (e.g., a Support Vector Machine (SVMs)). Features derived from dynamometer card shapes are used as inputs to the machine learning classifier algorithm. Hand-crafted features can lose important information whereas autoencoder-derived abstract features are designed to minimize information loss. Autoencoders are a type of neural network with layers organized in an hourglass shape of contraction and subsequent expansion; such a network eventually learns how to compactly represent a data set as a set of new abstract features with minimal information loss. When applied to card shape data, it can be demonstrated that these automatically derived abstract features capture high-level card shape characteristics that are orthogonal to the hand-crafted features.

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: A computer-implemented artificial lift detection system, method, and software are provided for failure detection for artificial lift systems, such as sucker rod pump systems. The method includes providing artificial lift system data from an artificial lift system. Attributes are extracted from the artificial lift system data. Data mining techniques are applied to the attributes to determine whether the artificial lift system is detected to fail within a given time period. An alert is output indicative of impending artificial lift system failures.

Abstract: A computer-implemented reservoir prediction system, method, and software are provided for failure prediction for artificial lift well systems, such as sucker rod pump systems. The method includes providing well data from a production well. Attributes are extracted from the well data. Data mining is applied to the attributes to determine whether the production well is predicted to fail within a given time period. An alert is output indicative of impending production well failures.

Abstract: A computer-implemented reservoir prediction system, method, and software are provided for failure prediction for artificial lift systems, such as sucker rod pump systems. The method includes a production well associated with an artificial lift system and data indicative of an operational status of the artificial lift system. One or more features are extracted from the artificial lift system data. Data mining is applied to the one or more features to determine whether the artificial lift system is predicted to fail within a given time period. An alert is output indicative of impending artificial lift system failures.

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: A computer-implemented artificial lift detection system, method, and software are provided for failure detection for artificial lift systems, such as sucker rod pump systems. The method includes providing artificial lift system data from an artificial lift system. Attributes are extracted from the artificial lift system data. Data mining techniques are applied to the attributes to determine whether the artificial lift system is detected to fail within a given time period. An alert is output indicative of impending artificial lift system failures.

Abstract: A computer-implemented reservoir prediction system, method, and software are provided for failure prediction for artificial lift systems, such as sucker rod pump systems. The method includes a production well associated with an artificial lift system and data indicative of an operational status of the artificial lift system. One or more features are extracted from the artificial lift system data. Data mining is applied to the one or more features to determine whether the artificial lift system is predicted to fail within a given time period. An alert is output indicative of impending artificial lift system failures.

Abstract: A computer-implemented reservoir prediction system, method, and software are provided for failure prediction for artificial lift well systems, such as sucker rod pump systems. The method includes providing well data from a production well. Attributes are extracted from the well data. Data mining is applied to the attributes to determine whether the production well is predicted to fail within a given time period. An alert is output indicative of impending production well failures.