In unconventional plays, wells are drilled at an unprecedented rate. This, together with technical challenges in terms of complex stratigraphy, multiple play types, variable rock properties, and various elements of pore pressure, geomechanics, fracturing, and diagenesis, calls for more sophisticated, faster, consistent, and wider ranging analytical tools. Given the scale of the work — i.e., the number of wells — performing classical workflows for petrophysics, pore pressure, and geomechanics prediction can be impractical (if not impossible) due to turnaround considerations. Also such workflows might not use any preexisting regional studies efficiently. In principle, a machine learning approach can mitigate these shortcomings. We show that a supervised deep neural network approach can be an alternative innovative tool for petrophysical, pore pressure, and geomechanics analysis enabling the use of all the previously interpreted data to devise solutions that simultaneously integrate wide-ranging wellbore and wireline logs. Beyond that, a similar approach is taken to predict a certain number of attributes solely from seismically derived properties, which allows one to compute volumetric models. The application of such an algorithm is shown on a Permian case study in which the automatic neural-network-based algorithms achieve reasonable accuracy in a fraction of the time.
Introduction
In unconventional resource plays, pore pressure prediction plays a critical role in the ability to predict areas of high overpressure and fracture behavior for the exploitation of these plays, which are both correlated with production. Traditional pore pressure prediction focuses exclusively on clay-rich shales and assumes that all shales have a porosity/effective stress relationship that can be used to link the mechanical compaction of the rock to the pore pressure via the vertical stress (overburden). Unconventional shales are uplifted and are affected by chemical processes and diagenetic alteration of the elastic properties such that porosity is not typically relatable to effective stress, resulting in a more complex pore pressure prediction workflow.
