FROM QUALITATIVE OBSERVATIONS TO QUANTITATIVE DATA SETS: NEW QUANTITATIVE PORE-TO-LOG UPSCALING TECHNIQUES FOR RESERVOIR QUALITY PREDICTION

Reservoir quality description and prediction, that includes components such as porosity, permeability, texture, mineralogy, pore networks, and diagenesis are critical elements of reservoir characterization which may support exploration, production, and IOR/EOR projects. Moving away from conceptual reservoir quality models towards models that are supported by quantitative data sets allows a) better prediction of reservoir quality (poroperm) away from well bores; b) upscaling of pore networks into logs, geomodels, and simulators; c) evaluation of the impact of pore networks on flow.

Within ConocoPhillips an integrated reservoir quality team in collaboration with petrophysicists, geomodelers, and reservoir engineers has been developing workflows and techniques that produce quantitative data sets of pore-scale (nano to macro) and core-scale heterogeneities that are readily upscalable to logs, and to the reservoir.

Three different quantitative workflows and techniques are introduced to illustrate their potential applications across geological boundaries. The carbonate example demonstrates a core-to-log technique of quantification of depositional and diagenetic anhydrites in the Permian Basin. Core image segmentation on box-photos was performed using JMicroVision, to quantify the vertical distribution of anhydrite and to produce anhydrite curves as input to log evaluation. The clastic example is related to EOR core-flood experiments. In order to evaluate the efficiency of core-flood experiments, quantitative evaluation of minerals, in particularly clay minerals and their relative distribution in pore throats is necessary before and after core-flood experiments. An integrated XRD, point-count and clay coat measurements in combination with SpecCam mineral analyses have been developed and applied to support EOR projects. The unconventional workflow is introducing a SEM point-count technique on FIB-SEM images combined with flow simulations to evaluate the impact of nanopores (organic and inorganic) on flow behavior in these micro-to-nano-porous systems. SEM point count not only focuses on pore size and types distributions, but also quantifies depositional and diagenetic products, which can be input data to geocellular models and exploration-scale conceptual models.