Authors: Osamah Alomair, Adel Malallah, Adel Elsharkawy, and Maqsood Iqbal
Miscible gas injection nowadays has become an imperative enhanced oil recovery (EOR)approach for increasing oil recovery. Due to the massive cost associated with this approach a high degree of accuracy is required for predicting the outcome of the process. Such accuracy includes, the preliminary screening parameters for gas miscible displacement; the “minimum miscibility pressure”(MMP) and the availability of the gas.All conventional and stat-of-the-art MMP measurement methods are either time consuming or decidedly cost demanding processes. Therefore, in order to address the costly (industry) demands a non parametric approach Alternating Conditional Expectation (ACE) is employed in this study to estimate an important parameter MMP. ACE algorithm (Breiman and Friedman, 1985), actually correlates optimal transforms of a set of predictors with an optimal response transform. Finally, the proposed model has produced a maximum linear effect between these transformed variables.More than 100 data points are considered both from the relevant published literature and the experimental work. A few (five) MMP measurements are experimentally accomplished for Kuwaiti Oil are also a part of the testing data. The proposed model is validated using detailed statistical analysis and it reveals that the results are more reliable than the existing correlations for pure CO2 injection to enhance oil recovery. Addition to this, the approach is more powerful, quick and can handle a huge data.
Keywords: Enhanced Oil Recovery (EOR); CO2Minimum MiscibilityPressure (MMP); Alternating Conditional Expectation (ACE)