3D Micro- & Macro- Characterization Workflow for Carbonate Reservoirs

Abstract

Summary The field development of carbonate fields arise a high complexity due to the heterogeneity of the rock. The characterization and modelling of carbonates involve the understanding of sedimentation and diagenetic processes. The workflow in reservoir characterization integrates several data types at different scales (i.e. seismic, well data, sedimentological models, analogous models, etc.) with the aim to generate 3D static and dynamic models that attempt to predict the reservoir behaviour In the last decade, the industry has developed methods for characterizing carbonate rocks at microscopic scale using confocal microscopy, Micro- /Nano- X-ray Computed Tomography (CT) or at macroscopic scales using CT-Scan technology. These methodologies allow studying digital data and analysing the distribution of the pore network in three dimensions, in order to determine petrophysical properties. Great emphasis has been placed on these techniques to determine the storage capability and flow of rock cores. However, it is needless to say that such capabilities are affected by components constituting the rock and that the distribution of the components affects the petrophysical properties. We show a methodology for the characterization of carbonate rocks at micro and/or macro scale, inspired by the characterization modelling workflow of reservoirs. Using the techniques developed for digital rocks, we analysed the spatial organization of the components that contribute to the pore network. In this way, we selected the representative samples of each facies and we discretized each main component. This is called “facies model” at high scale resolution. The petrophysical properties like porosity and permeability were modelled using Geostatistical methods as a function of the components that impact the flow. Once the 3D static model was built for each sample, we proceeded with the flow simulation for calibration and validation with laboratory data. Finally, we propagated the samples to core scale using Geostatistical methods. This methodology allowed a better understanding of carbonate rock property distribution at core that later on can be extrapolated to the field later on. The methodology was applied to confidential data of a Repsol asset therefore generic terms are used from now on.