ABSTRACT

Carbonate rocks are responsible for a considerable amount of hydrocarbon reservoirs worldwide. In Brazil, a huge part of petroleum reserves is associated with carbonatic facies formed during Aptian and Albian times, specially on offshore basins of east coast. These sedimentary textures have great complexity, due to many possibilities of pore allocation, resulted from the interrelation of different geological processes (depositional and diagenetic), on specific timings and intensities. Beyond quantification, the whole characterization of the porosity on carbonates needs geometrical pore structure data for a better understanding of how fluids could be stored and how efficient they could flow.

The Tartaruga Verde field, located at the southern part of Campo Basin, 127km far from Macaé/RJ, is an important player of this basin, producing hidrocarbons since 2018.

The reservoir is composed of carbonate sediments formed under high to moderate energy conditions during Albian times, represented by reworked facies forming metric to decametric shoals. Facies formed under low energy conditions, like packstones and wackestones, are interpreted as the distal parts of these shoals, or the sedimentary record of paleo-lows generated by tectonical movements. Diagenesis is also important, mainly as dolomitization and carstification, identified on different scales.

The methodology presented here focus on the characterization of the solid matrix and porosity, in terms of mineralogical composition and geometrical features, aiming on the impact of these on properties like Accoustic Impedance, Porosity, Seismic Velocity and their relations. Using automatic segmentation deep learning algorithms, trained on a large dataset of AxioScan and QEMScan acquisitions of carbonate samples, several geometric features of the pore geometry of the reservoir facies of the Tartaruga Verde field were extracted and analyzed. The main goal of this study is to evaluate how the geometric features of the porous systems can influence reservoir properties. Different data are used, from multiple scales and origins.

As results, the characterization of porous system and solid matrix, and their relationship with the genetic pathways of the facies identified, could provide better geological models and production previsibility. This methodology has potential to be widely applied under different geological contexts, due to data avaliability and efficient tools to provide and analize these informations.

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