For the exploitation of both conventional and unconventional plays, lithology classification, petrophysical evaluation, pore pressure prediction and geomechanical analysis play critical roles in accurate reservoir characterisation, safe well planning and execution, sweetspot identification etc. In unconventional plays specifically, the ability to predict areas of higher productivity depends on understanding the pressure and stress magnitudes. In general, any pressure-stress-property model must be supported by petrophysically conditioned logs, calibrated to core data. It’s important for the industry to develop safe and innovative methods which keep pace with the drilling activity and harness all data effectively.
Machine learning can play an important role in making sub-surface interpretation workflows faster, more consistent and in certain cases superior. This leads to quicker, more confident results and therefore improved decision making. Its adaptability means that machine learning can be deployed in different subsurface workflows as explained below.