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Causality analysis and prediction of 2-methylisoborneol production in a reservoir using empirical dynamic modeling

机译：基于经验动力学模型的油藏2-甲基异冰片产量成因分析及预测

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摘要

2-Methylisobornel (MIB) is one of the most widespread and problematic biogenic compounds causing taste-and-odor problems in freshwater. To investigate the causes of MIB production and develop models to predict the MIB concentration, we have applied empirical dynamic modeling (EDM), a nonlinear approach based on Chaos theory, to the long-term water quality dataset of Kamafusa Reservoir in Japan. The study revealed the dynamic nature of MIB production in the reservoir, and determined causal variables for MIB production, including water temperature, pH, transparency, light intensity, and Green Phormidium. Moreover, EDM established that the system is three-dimensional, and the approach found elevated nonlinearity (from 1.5 to 3) across the whole study period (1996-2015). By taking only one or two candidate predictors with varying time lags, multivariate models for predicting MIB production (best model: r = 0.83, p < 0.001, root mean squared error = 3.1 ng/L) were successfully established. The modeling approach used in this study is a powerful tool for causality identification and odor prediction, thus making important contributions to reservoir management. (C) 2019 Elsevier Ltd. All rights reserved.
机译：2-甲基异冰片（MIB）是引起淡水味道和气味问题的最广泛和成问题的生物化合物之一。为了调查产生MIB的原因并开发模型来预测MIB的浓度，我们将经验混沌模型（EDM）（一种基于混沌理论的非线性方法）应用于日本的Kamafusa水库的长期水质数据集中。该研究揭示了水库中MIB产生的动态性质，并确定了MIB产生的因果变量，包括水温，pH，透明度，光强度和绿色Green。此外，EDM确定该系统为三维系统，并且该方法发现在整个研究期间（1996-2015年）内非线性度从1.5升高到3。通过仅采用一个或两个具有不同时滞的候选预测变量，成功建立了用于预测MIB产生的多元模型（最佳模型：r = 0.83，p <0.001，均方根误差= 3.1 ng / L）。本研究中使用的建模方法是进行因果关系识别和气味预测的强大工具，从而为储层管理做出了重要贡献。 （C）2019 Elsevier Ltd.保留所有权利。

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