At-bit sensing of rock lithology

Abstract

A method is described for determining a measure of geo-mechanical strength of subterranean rock at the cutting surface of a drill bit during drilling using a single measurement related to mechanical power at the drill bit. This measurement in combination with a measure of volumetric rate of rock failure defines an expression for mechanical specific energy (SMSE). SMSE is the mechanical power at the drill bit divided by volumetric rate of rock failure over any time interval. A method is described for predicting geo-mechanical and physical properties (lithology) of subterranean rock at the cutting surface of a drill bit during drilling operations using machine learning (data analytical) model(s). Said models are driven by a set of subterranean measurements related to the structural, physical response of subterranean rock mechanical failure in combination with the measure of SMSE. The geo-mechanical and physical properties of subterranean rock may include measures of rock geo-mechanical strength, unconfined compressive strength (UCS), porosity, density, natural gamma ray, and/or borehole natural fracture network. Said machine learning models are developed (or trained) using historical drilling data sets of subterranean sensors through correlation to an accepted or accurate measure of subterranean rock properties. Said predicted measures may be further processed along with other data from oilfield development operations to provide vital information for drilling performance, well bore placement and engineered completion design.