Reservoir quality prediction in carbonate reservoirs based on integration of core and petrophysical well data: application to the Pre-Salt Santos Basin

Tese de mestrado em Geologia (Estratigrafia, Sedimentologia e Paleontologia), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012This work aimed at potentially improving reservoir quality prediction in a heterogeneous carbonate reservoir from the Santos Basin pre-salt section, addressing a methodology for core and petrophysical data integration based on data from five wells in this basin. This was developed bearing in mind that one of the greatest challenges in reservoir characterization is to perform an appropriate and geologically sound distribution of the properties that characterize the reservoir in such a way that it allows reservoir engineering studies and dynamic simulations to properly account for the heterogeneities and flow characteristics of each specific reservoir. The results from a thorough study on core data from four wells in the Santos Basin pre-salt, carried on by the Research & Development Project dedicated to the Study of Depositional Environments and Diagenetic Processes in Carbonate Reservoirs (DEDPCR), were used for a geostatistical analysis and further understanding of the depositional environment, cyclicity and complex diagenetic overprints that occur in this area and which ultimately control reservoir quality distribution. Furthermore, core descriptions and laboratory measurements from Routine Petrophysical Core Analysis (RPCA) and Special Core Analysis (SCAL) were used for Reservoir Rock Typing (RRT) and to establish a Hydraulic Flow Unit (HFU) classification based on pore throat size distributions that are considered to control pore transmissibility, and hence, permeability. Core data information was then integrated with wireline log data to generate core calibrated petrophysical information to improve porosity and permeability estimations using Neutron Magnetic Resonance (NMR) logs allowing for the extrapolation of the RRT methodology and HFU identification in uncored wells. Obtained HFUs were confronted with dynamic data from temperature logs, and due to encouraging results this methodology was considered valid and applicable for the study area. The results were incorporated into a static geocelular RRT model for reservoir quality prediction in the study area