Minimum miscibility pressure (MMP) is a very critical parameter to design any enhanced oil recovery affiliated with carbon dioxide (CO2) gas injection methodology. MMP can be computationally estimated using the Peng Robinson Cubic Equation of State (PR-EOS). In this paper, an association term was incorporated into the equation to account for covalent bonds between oxygen and carbon atoms in a CO2 compound for accurate MMP estimation. During the CO2 gas injection process, interactions between the oil multicomponent system and injected CO2 are in place where strong electrostatic force is exhibited between oxygen and carbon atoms. This attractive force cannot be neglected. Nevertheless, a Cubic Equation of State, such as Peng Robinson, accounts only for physical forces such as repulsion and attraction forces only. For this, an association term is introduced to account for electrostatic forces. Cubic plus Association EOS (CPA-EOS) was assimilated with Ahmed Tarek's methodology to estimate MMP rigorously in consideration of oil system and CO2 compositions. MMP was estimated using both PR-EOS and CPA-EOS, and compared against the experimental value with a very minimal absolute error. Therefore, the results showed a close agreement between calculated and experimental MMP. The uncertainty was immensely reduced when utilizing CPA-EOS proposed by Ahmed Tarek for MMP estimation. Three correlations were applied to estimate MMP with a slightly high deviation from the experimental MMP values. This high error is due to the ignorance of the intermolecular forces exhibited between molecules among these correlations. It is worth mentioning that this proposed method is highly appreciating the intermolecular bonding exhibited in CO2 and hydrocarbon multicomponent mixture, which results in a very reliable and accurate estimation of MMP. In other words, integrating conventional EOS with the association term provides accurate estimation of MMP to ensure effective modeling of an enhanced oil recovery (EOR) design with CO2 injection.
Multilateral wells are considered to be an advancement revolution in the petroleum industry. The employment of multilateral wells ensured higher drainage and productivity of reservoirs through the utilization of diverse configurations. Achieving higher productivity and maximizing the reach from a multilateral well has highly improved inflow performance relationship (IPR) compared to that of a conventional horizontal well under certain conditions. Several analytical models have been developed to estimate the average oil flow rate of multilateral wells by utilizing reservoir parameters to come up with decent correlations for better accuracy. These models are accompanied with uncertainties and limitations due to the complexity of multilateral wells. Artificial Intelligence (AI) techniques have been proven to predict various parameters associated with high uncertainties in the oil industry. One of these methodologies is Artificial Neural Networks (ANN) which was utilized in this paper as new approach to predict the average oil flow rate of multilateral wells though the use of some reservoir parameters along with flowing wellhead data. As a comparable method, an analytical model was used to calculate the flow rate from several multilateral wells to quantify the value of utilizing ANN against other methods or correlations. Borisov's correlation that was developed for estimating the productivity of multilateral wells of planar configuration was used to calculate the oil flow rate of the multilateral wells and compared the results against actual average oil flow rates. Additionally, PROSPER software was utilized to estimate some wells' parameters including Productivity Index (PI) and flowing bottomhole pressure (FBHP) for oil rate calculations. Rigorous statistical error analyses have been obtained from ANN method and Borisov's correlation. The overall regression correlation coefficient was calculated to be 0.97 for ANN which shows a strong matching between predicted and actual field values with an overall absolute error of 7.85%. High divergence was found between oil rate calculated from Borisov's correlation and the actual average oil rate with an error greater than 50%. This indicates the actual advantage of the ANN method against other correlations. This paper discussed a new method for predicting average oil flow rates for multilateral wells using surface and reservoir parameters obtained from field data via the employment of Artificial Intelligence modeling. A model was constructed for enhancing the prediction of oil flow rate for multilateral wells and resulted in a great prediction accuracy proved by field data comparison.
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