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The accurate characterization of the lithology porosity is critical for geological interpretation and decision making in petroleum exploration. For this, wireline logging (including sonic, neutron porosity, and density, among other logs) is often used for the characterization of geophysical data performed as a function of wellbore depth. The common practice in the oil and gas industry is to perform the wireline logging for every new well, which is a lengthy and expensive operation. Therefore, the objective of this study is to use the historical logging data and surface drilling parameters to derive machine-learning (ML) models able to identify the different lithology classifications.

We used historical logging data and surface drilling parameters to derive ML models to predict the following lithology classification: 1) porous gas, 2) porous wet, 3) tight sand, and 4) shaly …