Information from cores and wireline logs, processed according to different methodologies, were integrated to provide a consistent facies classification, petrophysical characterization and 3D geological model of a deep-water turbidite reservoir in the Gulf of Mexico.

A number of sedimentological facies (sed-facies), either heterolithics or single-lithology, were first identified on cores and used for generating a conceptual depositional model of the reservoir. At the same time, the conventional wireline log recordings from both cored and uncored wells were processed using a multivariate statistical technique (cluster analysis) to provide a Net-to-Gross (or Vshale)-based log-facies classification. The relationship between sed-facies and log-facies is not straightforward, as some of the latter include different amounts of different sed-facies, either heterolithics or not.

The petrophysical characterization of the log-facies was carried out using a Process-Oriented Modeling approach. Realistic fine-scale, 3D digital models of the different sed-facies were generated. These models were populated at the lamina scale using the statistics of porosity measurements from a selected subset of the core plugs and the statistics of permeability measurements from a mini-permeameter, both parameters having been overburden-corrected in advance. Next, the fine-scale 3D models of the sed-facies were stacked according to their observed amount in each log-facies, and several porosity and permeability grids for each log-facies were stochastically generated. Eventually, the 3D log-facies models were analytically and numerically upscaled to respectively provide effective porosity and permeability statistics for use in the property modeling phase of the 3D geological model of the reservoir.

This workflow has the great advantage of allowing the use of data of types acquired at different scales (mini-permeameter, core plugs and wireline logs) in a consistent manner with respect to their volumes of investigation, and also allows for honoring the conceptual sedimentological model of the reservoir.

Livio Ruvo, SPE, Eni E&P; James Doyle, Eni Petroleum; Mauro Cozzi, Simone Riva, Paolo Scaglioni, and Giuseppe Serafini, Eni E&P

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