Rock property prediction for integrated exploration and development, Vaca Muerta formation, Neuquén Basin, Argentina

Abstract

Oil companies have been testing different characteristics related to shale plays in an effort to identify the best zones for a good productive well. Total organic carbon (TOC), pore pressure, natural fractures, thickness, mineralogical composition and geomechanical parameters are used to define those zones. The vertical and lateral heterogeneity of rock properties are critical factors that impact production. The integration of 3D seismic and well data is necessary for the prediction of rock properties and identifies their distribution within the rock, which can also be related to the mechanical response of the rock. This study uses TOC and carbonate content as part of the mineralogical composition of the Vaca Muerta (VM) Formation to identify the best zones with hydrocarbon presence (sweet spots) and rock with brittle characteristics based on carbonate content. For this purpose, the study includes a 3D seismic survey and several vertical wells with full log suites in each well to complete the analysis. The well logs are used to compute a model-based inversion which uses pre-stack seismic data to estimate rock property volumes. An inverse relationship between P-impedance and TOC is observed and quantified. Likewise, a direct relationship between P-impedance and volume of carbonate is identified. Neural network is then used to predict the lateral and vertical heterogeneity of rock properties. TOC behaves as an adequate indicator of possible zones with high potential of hydrocarbon presence. Meanwhile, the carbonate content in the VM Formation is a valid indicator of brittle-ductile rock. The analysis detected zones that have a good presence of hydrocarbons and brittle rock. This model presents a more complete understanding of the reservoir, which can be used as an aid in future exploration and development of the VM Formation.