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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications
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Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications

机译：基于人工神经网络开发鲁棒的化学驱替模型，以提高采收率

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Application of chemical flooding in petroleum reservoirs turns into hot topic of the recent researches. Development strategies of the aforementioned technique are more robust and precise when we consider both economical points of view (net present value, NPV) and technical points of view (recovery factor, RF). In current study many attempts have been made to propose predictive model for estimation of efficiency of chemical flooding in oil reservoirs. To gain this end, a couple of swarm intelligence and artificial neural network (ANN) is employed. Also, lucrative and high precise chemical flooding data banks reported in previous attentions are utilized to test and validate proposed intelligent model. According to the mean square error (MSE), correlation coefficient, and average absolute relative deviation, the suggested swarm approach has acceptable reliability, integrity and robustness. Thus, the proposed intelligent model can be considered as an alternative model to predict the efficiency of chemical flooding in oil reservoir when the required experimental data are not available or accessible.
机译：化学驱在油藏中的应用成为近年来研究的热点。当我们同时考虑经济角度（净现值，NPV）和技术角度（回收率，RF）时，上述技术的开发策略更加稳健和精确。在当前的研究中，已经进行了许多尝试来提出用于估计油藏中的化学驱油效率的预测模型。为了达到这个目的，采用了群体智能和人工神经网络（ANN）。而且，以前关注的利润丰厚的高精度化学驱数据库也被用来测试和验证所提出的智能模型。根据均方误差（MSE），相关系数和平均绝对相对偏差，建议的群体方法具有可接受的可靠性，完整性和鲁棒性。因此，当所需的实验数据不可用或不可访问时，建议的智能模型可以被视为预测油藏中化学驱油效率的替代模型。

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