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: Creation and maintenance of preferred or “gold” data sets are automated using objective, predefined rules or filters. The rules may be applied as part of a data publication workflow when new data becomes available in a database. The rules govern the type of data to be included in a gold data set, the currency of the data, the quality of the data, and the naming of the data. This helps reduce the amount of work required by users to create gold data sets and also ensures that the gold data set are up-to-date and high-value. The disclosed approach is particularly suited for use with data from hydrocarbon exploration and production related operations.

Abstract: Creation and maintenance of preferred or “gold” data sets are automated using objective, predefined rules or filters. The rules may be applied as part of a data publication workflow when new data becomes available in a database. The rules govern the type of data to be included in a gold data set, the currency of the data, the quality of the data, and the naming of the data. This helps reduce the amount of work required by users to create gold data sets and also ensures that the gold data set are up-to-date and high-value. The disclosed approach is particularly suited for use with data from hydrocarbon exploration and production related operations.