Abstract: Systems, processes, and computer-readable media for determining lithology porosity of a formation rock from surface drilling parameters using a lithology porosity machine learning model without the use of wireline logging. Lithology porosity at different depths in existing may be determined from the wireline logs. The lithology porosity may be shaly sand, tight sand, porous gas, or porous wet. The lithology porosity machine-learning model may be trained and calibrated using the data from a structured data set having surface drilling parameters from the existing wells and lithology porosity classifications from the wells. The lithology porosity machine learning model may then be used to determine a lithology porosity classification for a new well without the use of wireline logging.

Abstract: Systems, processes, and computer-readable media for determining lithology porosity of a formation rock from surface drilling parameters without the use of wireline logging. Lithology porosity at different depths in existing may be determined from the wireline logs. The lithology porosity may be shaly sand, tight sand, porous gas, or porous wet. A lithology porosity machine-learning model may be trained and calibrating using the data from a structured data set having surface drilling parameters from the existing wells and lithology porosity classifications from the wells. The lithology porosity machine learning model may then be used to determine a lithology porosity classification for a new well without the use of wireline logging.