An integrated deep learning solution for petrophysics, pore pressure, and geomechanics property prediction

Abstract

Pore pressure prediction plays a critical role in the ability to predict areas of high overpressure and fracture behavior for the exploitation of unconventional plays, which are both correlated with production. Shales in these plays have variable clay content and complex multi-mineral fractions that require a detailed petrophysical assessment reinforced with rock physics modelling as needed. For example, changes in total organic content have a similar elastic response to changes in porosity. Therefore, any pressure-stress property model for unconventional plays must be supported by petrophysically conditioned elastic logs and accurate multi-mineral volume sets calibrated to core data. A supervised deep neural network approach is introduced as an alternative innovative tool for petrophysical, pore pressure and geomechanics analysis enabling the use of all the previously collected and interpreted data to devise solutions which simultaneously integrate wide ranging well bore and wireline logs. We implement three neural networks, all with similar structure, as each of these networks had a different objective and the outputs from one were the inputs for the other. The first network was trained to predict petrophysical volume logs (shale, sand, dolomite, calcite, kerogen and also porosity) simultaneously from compressional velocity (Vp), Gamma ray, density (rho), resistivity and Neutron logs. The second neural network, cascaded from the first, was then designed to match the manually predicted pore pressure. The inputs were Vp and shear velocity (Vs), Rho, resistivity, Neutron logs as well as the results of the first network. The third network focused on predicting various properties of interest, in this case pore pressure, minimum horizontal stress (Shmin), maximum horizontal stress (SHmax), and volume of kerogen, based on only Vp, Vs, and Rho logs which is an example building a neural network capable of predicting key rock properties directly from seismic inversion results to produce meaningful 3D interpretations. The volumetric pore pressure model was also positively correlated to cumulative production values from blind long horizontal wells. The results show a promising outlook for the application of deep learning in integrated studies such as those shown in this paper.