The satisfactory recovery of the hydrocarbon gases has made them a reliable choice for gas injection-based enhanced oil recovery (EOR) techniques. The minimum miscibility pressure (MMP) is a pivotal parameter governing the recovery factor during gas injection processes. Therefore, the determination of the authentic MMP is of a crucial importance. Due to the drawback of the experimental techniques (time and cost), empirical correlations are valuable tools in MMP determination. In this study, a multi-gene genetic programming and another software known as LINGO as an optimization tool are applied to offer a dependable MMP formula based on a comprehensive MMP dataset (a total of 108 MMP data). The independent parameters of reservoir temperature, pseudocritical temperature of the injection gas, molecular weight of C₅₊ components of the reservoir fluid and the intermediate (H₂S, CO₂, C₂–C₄)-to-volatile (N₂ and C₁) ratio are considered as input variables. A comprehensive set of experimental data covers wide span of primary parameters. Furthermore, in order to judge the accuracy of the suggested model and assess the precision and compare the predicted MMP by the current model with those estimated by preexisting correlations, the statistical and graphical error analyses have been employed. Based on the results, the proposed model can estimate MMP of the associated gas with an average absolute relative error of 9.86%. Also, the proposed correlation is more trustworthy and precise than the preexisting models in an extensive spectrum of thermodynamic circumstances. Eventually, the relevancy factor has depicted that the pseudocritical temperature of the injected gas has the most severe role in miscibility achievement.

中文翻译：

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估算烃类气体注入过程中的最小混溶压力（MMP）的新相关性

令人满意的烃气采收率使其成为基于注气的强化采油（EOR）技术的可靠选择。最小混溶压力（MMP）是控制气体注入过程中采收率的关键参数。因此，确定真实的MMP至关重要。由于实验技术的缺点（时间和成本），经验相关是确定MM​​P的有价值的工具。在这项研究中，基于全面的MMP数据集（总共108个MMP数据），使用了多基因遗传编程和另一种称为LINGO的软件作为优化工具，以提供可靠的MMP公式。储层温度，注入气的假临界温度，C ₅₊分子量的独立参数储层流体和中间产物（H ₂ S，CO ₂，C ₂ –C ₄）挥发分（N ₂和C ₁）的组分）比率视为输入变量。全面的实验数据集涵盖了广泛的主要参数。此外，为了判断建议模型的准确性并评估精度，并将当前模型预测的MMP与通过预先存在的相关性估计的MMP进行比较，采用了统计和图形误差分析。基于结果，提出的模型可以估计伴生气的MMP，平均绝对相对误差为9.86％。而且，在广泛的热力学环境中，所提出的相关性比现有模型更可靠和更精确。最终，相关因子描述了注入气体的伪临界温度在混溶性实现中具有最严重的作用。