CO2 flooding recovery strongly depends on the minimum miscibility pressure (MMP). Conventional tests to determine gas–oil MMP such as rising bubble apparatus and slim tube displacement are either costly or time consuming. In order to propose a quick and accurate model to determine MMP, a back-propagation neural network is presented for MMP prediction during pure and impure CO2 injections. Five new variables were screened as input parameters to the network. Next, the network was optimized using five evolutionary algorithms, and this work highlights that three of these evolutionary algorithms (e.g. Mind Evolutionary, Artificial Bee Colony, and Dragonfly) are firstly used to predict MMP. Then, data from the literature were input to the optimized network to train it. Statistical evaluation and graphical analyses were used to evaluate the performance of the proposed models and for comparison with published MMP correlates to obtain the optimal model for predicting MMP. The back-propagation model optimized using the dragonfly algorithm exhibited the highest accuracy among all those considered and MMP correlates; its coefficient of determination, average absolute percent relative error, root mean square error, and standard deviation were 0.965, 5.79%, 206.1, and 0.08, respectively. In addition, reservoir temperature was determined as the strongest MMP predictor (Pearson correlation = 0.63) based on sensitivity analysis.

中文翻译：

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使用优化神经网络估算碳氢化合物油藏 CO2 驱油过程中的最小混相压力

CO2驱采收率很大程度上取决于最小混相压力（MMP）。确定气-油 MMP 的常规测试，例如上升气泡装置和细管置换，要么成本高昂，要么耗时。为了提出一种快速准确的模型来确定 MMP，提出了一种反向传播神经网络，用于在纯和不纯 CO2 注入期间进行 MMP 预测。筛选出五个新变量作为网络的输入参数。接下来，使用五种进化算法对网络进行了优化，这项工作强调了其中三种进化算法（例如 Mind Evolutionary、Artificial Bee Colony 和 Dragonfly）首先用于预测 MMP。然后，将文献中的数据输入到优化的网络中进行训练。使用统计评估和图形分析来评估所提出模型的性能，并与已发布的 MMP 相关性进行比较，以获得预测 MMP 的最佳模型。使用蜻蜓算法优化的反向传播模型在所有考虑的和 MMP 相关的模型中表现出最高的准确度；其决定系数、平均绝对百分比相对误差、均方根误差和标准偏差分别为0.965、5.79%、206.1和0.08。此外，基于敏感性分析，储层温度被确定为最强的 MMP 预测因子（Pearson 相关系数 = 0.63）。使用蜻蜓算法优化的反向传播模型在所有考虑的和 MMP 相关的模型中表现出最高的准确度；其决定系数、平均绝对百分比相对误差、均方根误差和标准偏差分别为0.965、5.79%、206.1和0.08。此外，基于敏感性分析，储层温度被确定为最强的 MMP 预测因子（Pearson 相关系数 = 0.63）。使用蜻蜓算法优化的反向传播模型在所有考虑的和 MMP 相关的模型中表现出最高的准确度；其决定系数、平均绝对百分比相对误差、均方根误差和标准偏差分别为0.965、5.79%、206.1和0.08。此外，基于敏感性分析，储层温度被确定为最强的 MMP 预测因子（Pearson 相关系数 = 0.63）。