Abstract: The present disclosure relates to systems and/or computer-implemented methods that can utilize machine learning models to monitor exploratory hydrocarbon wells for gas reading anomalies. One or more embodiments described herein can regard a method comprising collecting well feature data characterizing operation of an exploratory hydrocarbon well. The well feature data includes a gas measurement value. The method can also comprise applying a machine learning model to predict gas reading values associated with the exploratory hydrocarbon well. The method can further comprise comparing the gas measurement value with the gas reading value predicted by the machine learning model to detect a gas reading anomaly.

Abstract: Systems and methods include techniques for predicting hydrocarbon show indicators classifying a presence of hydrocarbons at a pre-determined distance ahead of a drilling bit. Input data is received that identifies, for different depths of a well that is being drilled, a drill bit location, a depth, a weight on bit, rotations per minute, a rate of penetration, lagged lithology percentages, and real-time mud gas logs. Data cleaning is performed on the input data using an isolation forest algorithm to remove outliers. A sequence of attributes for the well being drilled is identified from the input data, where the sequence of attributes includes the input data measured at a sequence of depths in the well. Hydrocarbon show indicators classifying a presence of hydrocarbons at a pre-determined distance ahead of a drilling bit are predicted in real time using machine learning on the sequence of attributes received while drilling the well.