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In order to develop, maintain and deplete reservoirs economically around the globe, various measurements are needed with a high demand on natural core samples. The next stage in the life of every reservoir is a secondary or tertiary method to enhance productivity. However, to tailor the available methods and technologies to the reservoir, several screening processes, feasibility studies and pilot experiments are needed. As an aid to these, like a sensitivity analysis, continuous measurements are set up to study fluid flow, chemical reactions, additional recovery and much more, but for all of these, core samples are needed. The lack and high value of natural core samples yield that the demand cannot be satisfied from this source alone. The aim of the study was to create an artificially consolidated stone core sample, a model material, which can be suitable for being the subject of these experiments, with additional benefits in mass production and reservoir parameter-based quality control. In this article the authors wish to present partial results of a big study, this time with comparing the porosity, permeability, connate water and capillary pressure parameters of the core samples used with different after-cure techniques. The process of compaction was the same, but the overburden pressures and the effect of CO2 rich curing were examined. For this, part of the samples was prone to high CO2 environment for different timespans during the after treatment of the samples. The petrophysical parameters were then measured on all of the groups, including a control group and the CO2 affected cores. The focus was on porosity, permeability, connate water saturation/wettability and capillary pressure measurements and the common features and differences in the yielded pore space’s structure are summarized in this article.

This article is about establishing an artificial modeling environment using real data, then creating special formulae in order to estimate the recovery factor of the depletion of the reservoir model. The key concept is that these aids in the form of formulae can be very useful, economic and fast methods to help reservoir simulation. Waterflooding was examined in previous years and a sensitivity analysis was conducted several times. First the parameters were screened for impact and importance, then the structure of the resulting proxy model was chosen, along with the accuracy, both based on the focus of investigation itself, which is the recovery factor. The artificial reservoir environment is a seven-spot water flooded well pattern in an initially undersaturated oil reservoir. The results in this particular study are three formulae which are capable of predicting the recovery factor with a satisfactory error margin after five, ten, and fifteen years of production as a function of the initial produced fluid rate target.