Abstract: Methods and systems for drilling a well into a subsurface formation are configured for performing a downhole measurement to generate measured logging-while-drilling (LWD) data; retrieving a machine learning model that is trained using labeled LWD data, the labeled LWD data representing one or more environmental effects each causing a respective data signature in the LWD data, each respective data signature being associated with a corresponding label identifying the environmental effect; inputting the LWD data into the machine learning model; generating, by the machine learning model based on the inputting, a classification output representing at least one environmental effect represented in the LWD data; and generating, based on the classification output, corrected LWD data that removes a change to the LWD data caused by the environmental effect. The systems and methods are configured for drilling a well into the subsurface based on the corrected LWD data.

Abstract: Methods for determining a rock testability index (RTI) include receiving petrophysical data of a geological formation at a particular rate. The petrophysical data is measured at each depth of a plurality of depths from a surface of the Earth. For each depth of the plurality of depths, an RTI is determined for the geological formation. The RTI indicates a probability of success for performing a hydrocarbon fluid formation test at each depth. The RTI is generated by normalizing a rock type of the geological formation. The RTI is adjusted based on a correspondence of the petrophysical data to hydrocarbon productivity. A display device generates a visual representation of the RTI at the particular rate. The visual representation indicates a potential hydrocarbon productivity of the geological formation.