Abstract: A system can be used to incorporate historical geological data into machine learning techniques. The system can receive historical geological data. The system can pre-process the historical geological data by applying a selected, relative-time pre-processing technique to the historical geological data with respect to time-attributed geological phenomena. The system can train a machine-learning model using the pre-processed historical geological data. The system can apply the trained machine-learning model to generate predictions of geological phenomena. The system can provide a user interface to provide a visualization of the predictions of geological phenomena.

Abstract: A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation.

Abstract: A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.

Abstract: A method comprises sampling, at a first sampling rate for a first time window, data values of at least one operational parameter of equipment. The method comprises sampling, at a second sampling rate for a second time window, the data values of the at least one operational parameter, wherein the second sampling rate is different from the first sampling rate. The method comprises classifying, using a machine learning model and the data values in the first time window and the second time window, an operational mode of the equipment into different failure categories.

Abstract: A method comprises receiving a time series of data values for a time window of each operational parameter of a number of operational parameters of equipment; calculating a time derivative feature that comprises a change of the data values of a first operational parameter of the number of operational parameters over the time window; and classifying, using a machine learning model and based on the time derivative feature, an operational mode of the equipment into different failure categories.

Abstract: A drilling data correction system corrects drilling data entries in high-importance drilling data segments using machine learning and rules-based drilling models. A data importance analyzer identifies high-importance data segments in incoming drilling data. The drilling data correction system inputs features of drilling data into machine learning drilling models and rules-based drilling models trained to predict the high-importance data segments. Predictions from the machine learning drilling models and rules-based drilling models are presented to a user based on drilling data prediction criteria. The machine learning drilling data predictions are used to automatically correct the high-importance data segments, or the user chooses between machine learning drilling data predictions and rules-based drilling data predictions to correct the high-importance drilling data segment.

Abstract: A drilling data analytics engine disclosed herein automatically corrects drilling data with predictive modeling. A drilling data quality analyzer segregates drilling data into good drilling data and bad drilling data that has missing, incomplete, or incorrect entries. For each bad data entry in the bad drilling data, the drilling data analytics engine preprocess drilling data attribute values for the corresponding task not including the drilling data attribute value for the bad data entry and inputs the preprocessed drilling data attribute values into a trained predictive model. The trained predictive model is trained on good drilling data to estimate values for the drilling attribute corresponding to the bad data entry.

Abstract: A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.

Abstract: A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation.