It is common to use the standard gamma ray log (SGR) or the total contribution from all three elements— uranium (U), potassium (K), thorium (Th)] as an indicator of shale content. The linear IGR shaliness indicator model over-estimates the shale volume. The nonlinear models have been developed for specific geographic areas or formation ages. All non-linear relationships are more optimistic in that they produce a shale volume value lower than that from the linear equation. On the other hand, the presence of highly-radioactive black organic material in carbonate or unconventional reservoirs "gas/oil shale", overestimates the shale volume. In all cases, an accurate determination of shale volume impacts in the calculation of formation porosity and water saturation and therefore affects the original oil in place (OOIP) and reserves. The question from engineers and geoscientists: which is the best model to be used? We have found that the sigmoidal function can be used for any kind of reservoir: shaly-sands or carbonates with highly radioactive black organic material or presence of radioactive material in factures. We have tested our new equation for carbonate reservoirs in Venezuela and Mexico; shaly-sands reservoirs in Colombia, Venezuela including deepwaters in the Gulf of Mexico. In all cases, the calculated Vshale volumes have been validated with the X-ray diffraction data or the elemental capture spectroscopy (ECS) logs that quantify the actual clay volume (Vsh) and achieve correlation coefficients, r2, greater than 0.92.

This is the second of a two-part article of a new reservoir classification based of pore types. We discussed in the first article a new methodology developed in order to classify all kinds of reservoirs worldwide: fracture, matrix, vugs, or combinations of these using the cementation factor, m variable , and fuzzy logic concepts. The new classification presented was based on a 2D Model and we identified at least five types of reservoirs according to pore types. This article provides the updated reservoir classification using a ternary diagram based on fractures, vugs and/or intercristaline pore systems. We show in this paper how to use core, log, field data and fuzzy logic to build a confident petrophysical model, for complex reservoirs; and how to determine which kind of reservoir belongs to the new classification; and discuss the best practices of how to exploit and increase hydrocarbon production and reduce the uncertainty of the original oil in place calculation.

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In the past, only four types of reservoirs were defined to characterize matrix and fracture systems. These definitions based on matrix and fracture systems do not cover all the pore systems present in the real world because a great number of reservoir systems are made up of different lithologies and pore types. The pore types could be matrix, fractures, or vugs or combinations of these. One of the potential problems is that engineers have simplified that complex problem and therefore have erroneously produced the reservoirs. If a complete classification were available in the literature, more effort would have been made to recognize all the pore types present in a specific reservoir for better characterization and production. This paper discusses a new methodology to classify all kind of reservoirs in the real world: fracture, matrix, vugs, or combinations of those. We have developed membership functions using fuzzy logic concepts for the cementation factor m variable. We have identified at least five types of reservoirs according to pore types. All types of unconventional or conventional reservoirs are represented in this new classification system. We used core data from Southern Vietnam, Libya, the United States, Argentina, Iran, Iraq, Saudi Arabia, Colombia, and Venezuela to validate our new classification, and we are certain that it will be of great help to the engineers. Better understanding of the behavior of a specific reservoir will help increase the production and the recovery factor. We also discuss how to increase the oil or gas production as a reservoir moves from one class to another as result of hydraulic fracturing.

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