Márquez-Codina et al . 142
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 3, September-December, 2021.
Introduction
To estimate the STOOIP, it is required to know the Sw at the initial reservoir conditions. Well logs
(resistivity) are often affected by fluids drainage of the reservoir; additionally, old resistivity curves had problems of
not being focused and having a poor vertical resolution (Rider and Kennedy, 2011), for which laboratory
experiments are convenient to represent the reservoir saturation history or the hysteresis phenomenon, being the
special core analysis, such as Pc drainage tests, capable of simulating the initial reservoir conditions.
According to Valenti et al. (2002), when the Pc curves are observed together, different shapes of these are
appreciated, as well as dispersion of data, representing the heterogeneity of the reservoir. This behavior suggests that
the data should be classified according to the sample rock quality (Obeida et al., 2005; Xu y Torres, 2012).
The purpose of this research was to determine the Swi model, based on Pc by rock type, of a siliciclastic
reservoir in the Maracaibo basin, to improve the estimation of the STOOIP. Results are based on core and log data
processing and analysis; these consisted on the description of the rock types present in the reservoir, classification of
Pc curves by rock type, selection of the model that best fit and represented the reservoir data, generation of water
saturation equations, comparison of the Sw curves of the proposed model with the log-derived in the first drilled
wells, as well as the contrast of the STOOIP in an area of the reservoir, obtained from the Sw model, with the log-
derived Sw (Obeida et al., 2005; Paradigm and Epos, 2011; Xu and Torres, 2012).
Materials and Methods
Phase I: information gathering and validation
Data were collected and validated from the reservoir (due to confidentiality rules of the PDVSA company, the
original names of the reservoir, study area and wells have been changed), cored wells, among which stand out:
routine or conventional core analysis (RCA) to determine rock types and special core analysis (SCAL) such as Pc
drainage tests to determine the Swi model, as well as conventional logs. A robust database was generated using a
petrophysical software.
Phase II: description of rock types based on statistical parameters
It was used the Flow Zone Indicator (FZI) methodology of Amaefule et al. (1993), based on porosity () and
permeability (k) data, corrected by overburden pressure, in accordance with Jones (1988). The FZI was calculated for
all the samples using Equations 1, 2 and 3, and results were analyzed using statistical tools, which allowed
identifying the rock types present in the reservoir.
Reservoir Quality Index:
(1)
Where, : effective porosity (fraction); k: permeability (md)
Normalized Porosity Index:
- (2)
Flow Zone Indicator:
(3)
Phase III: preparation of Pc data and their relationship with the core-derived petrophysical properties
In this phase, the data obtained from the drainage Pc tests were classified by rock type; previously, corrections were
made to the data obtained from the laboratory Pc tests and converted to reservoir conditions.
The equations to correct data by overburden pressure indicated by Paradigm and Epos (2011) are detailed below:
Pc corrected by overburden pressure:
(4)
Where, : capillary pressure at laboratory conditions (psi); : porosity at initial reservoir conditions (fraction);
: porosity at laboratory conditions (fraction).