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We estimate saturation for homogeneous distribution of CO2 to be 0 20% and for CO2 as patches of gas as 0 80% of the total porosity within ∼200 m thick reservoir unit.
Although some empirical and mathematical models have been introduced to measure cementation factor, without the need of coring, there is not exist a reliable method to estimate saturation exponent in similar way.
Elsharkawy [8] developed an empirical model to estimate saturation pressure from gas chromatography data.
Several researchers have proposed empirical correlations to estimate saturation pressure from PVT data [3, 4, 5, 6].
Since accurate measurement of saturation pressure is highly expensive and time-consuming, it is more favorable to estimate saturation pressure from empirical models.
High value of correlation coefficient, i.e., 0.97338 proves the robustness of committee machine modeling Open image in new window Fig. 3 Cumulative probability of error distribution for committee machine model meant to estimate saturation pressure.
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Non-linear regression methods were recently proposed to estimate saturation-dependent petrophysical properties from fractional flow-rate measurements acquired with formation testers.
Our estimated saturation indices for ZnS s) confirm the temperature dependence of the ZnS s) solubility.
Elsharkawy (El) empirical model, SRK EOS, and PR EOS are among the most successful models estimating saturation pressure.
For pyrite (FeS2 s)), the estimated saturation indices at 25°C (1 bar) in all the samples were greater than zero, sometimes by several orders of magnitude (Figure 4b).
Committee machine offers the following equation for estimating saturation pressure through optimal linear combination of Elsharkawy, SRK and PR equation of states models.
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