Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a first data stream from a sensor of a network of sensors monitoring well-site parameters. Obtaining a first feature vector associated with the first data stream. Determining a potential well-site event by identifying, among a stored set of well-site event models, a second feature vector from an event model that correlates with the first feature vector, where the event model includes the potential well-site event. Then, sending an alert to a user device, where the alert informs a user of the potential well-site event.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method executed by one or more processors, the method comprising: receiving, by the one or more processors, a data stream from a sensor of a network of sensors monitoring well-site parameters; obtaining, by the one or more processors, a feature vector from the data stream; determining, by the one or more processors, the feature vector correlates with a well-site event; and storing, by the one or more processors, the feature vector with data indicating the well-site event in an event model. 2. The method of claim 1, further comprising: receiving a second data stream; and obtaining a second feature vector from the second data stream, wherein determining that the feature vector correlates with the well-site event comprises: determining that the feature vector correlates with the second feature vector, and determining that both the feature vector and the second feature vector correlate with the well-site event, and wherein storing the feature vector with data indicating the well-site event in the event model comprises storing the correlated feature vector and second feature vector in the event model. 3. The method of claim 1, wherein the event models is stored in a database of event models. 4. The method of claim 1, wherein obtaining the feature vectors includes extracting features from the data streams using an applied method of a Karhunen-Loéve theorem. 5. The method of claim 1, wherein obtaining the feature vectors includes extracting features from the data streams using an applied method of a Hilbert-Huang transform. 6. The method of claim 1, wherein obtaining the feature vectors includes extracting features from the data streams using at least one of Singular Spectrum Analysis, Fourier Analysis, Wavelet Decomposition, or Empirical Mode Decomposition. 7. The method of claim 1, wherein determining that feature vectors correlate with the well-site event is performed using a machine learning model. 8. The method of claim 1, wherein the data stream includes data related to at least one of an equipment parameter, an environmental parameter, a pipeline parameter, an operational parameter, or a material parameter. 9. The method of claim 1, further comprising determining a confidence value associated with the event model. 10. A computer-implemented method executed by one or more processors, the method comprising: receiving, by the one or more processors, a first data stream from a sensor of a network of sensors monitoring well-site parameters; obtaining, by the one or more processors, a first feature vector associated with the first data stream; determining a potential well-site event by identifying, by the one or more processors from a stored set of well-site event models, a second feature vector from an event model that correlates with the first feature vector, the event model including the potential well-site event; and sending an alert to a user device, the alert informing a user of the potential well-site event. 11. The method of claim 10, further comprising: obtaining a second data stream by applying an estimation model to the data stream, the second data stream being a prediction of future data in the data stream; and obtaining a third feature vector from the second data stream, and wherein determining the potential well-site event comprises determining the potential well-site event by identifying that the second feature vector from an event model correlates with the third feature vector. 12. The method of claim 10, further comprising determining a confidence value associated with the generated second data stream and third feature vector. 13. The method of claim 10, further comprising determining a confidence value of the correlation between the first feature vector and the second feature vectors is within a confidence threshold. 14. The method of claim 10, wherein the alert is an e-mail, an SMS message, or a notification in a computing device application. 15. The method of claim 10, wherein the event model includes an action to address the potential well-site event, and wherein the alert includes a recommendation to perform the action. 16. The method of claim 10, wherein the event model includes an action, and wherein the method further comprises sending a signal to a control device to automatically perform the action. 17. The method of claim 10, wherein the steps of receiving, obtaining, identifying and sending are performed before parameter conditions measured by the sensor change appreciably. 18. A system comprising: one or more processors; and a data store coupled to the one or more processors having instructions stored thereon which, when executed by the one or more processors, causes the one or more processors to perform operations comprising: receiving, by the one or more processors, a first data stream from a sensor of a network of sensors monitoring well-site parameters; obtaining, by the one or more processors, a first feature vector associated with the first data stream; determining a potential well-site event by identifying, by the one or more processors among a stored set of well-site event models, a second feature vector from an event model that correlates with the first feature vector, the event model including the potential well-site event; and sending an alert to a user device, the alert informing a user of the potential well-site event. 19. The system of claim 18, wherein the operations further comprise: obtaining a second data stream by applying an estimation model to the data stream, the second data stream being a prediction of future data in the data stream; and obtaining a third feature vector from the second data stream, and wherein determining the potential well-site event comprises determining the potential well-site event by identifying, from the stored set of well-site event models, that the second feature vector from an event model that correlates with the third feature vector. 20. The system of claim 18, wherein the event model includes an action, and wherein the operations further comprise sending a signal to a control device to automatically perform the action.