ABSTRACT In order to provide more accurate reservoir-operating policies, this study attempts to implement effective monthly forecasting models. Seven inflow forecasting schemes, applying discrete wavelet transformation and artificial neural networks are proposed and provided to forecast the monthly inflow of Dez Reservoir. Based on some different performance indicators the best scheme is achieved comparing to the observed data. The best forecasting model is coupled with a simulation-optimization framework, in which the performance of five different reservoir rule curves can be compared. Three applied rules are based on conventional Standard operation policy, Regression rules, and Hedging rule, and two others are forecasting-based regression and hedging rules. The results indicate that forecasting-based operating rule curves are superior to the conventional rules if the forecasting scheme provides results accurately. Moreover, it can be concluded that the time series decomposition of the observed data enhances the accuracy of the forecasting results efficiently.

中文翻译：

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应用小波变换和人工神经网络开发基于预测的油藏运行规律曲线

摘要 为了提供更准确的水库运行政策，本研究尝试实施有效的月度预测模型。提出并提供了七种流入量预测方案，应用离散小波变换和人工神经网络来预测Dez水库的月流入量。基于一些不同的性能指标，与观察到的数据相比，实现了最佳方案。最佳预测模型与模拟优化框架相结合，可以比较五种不同储层规则曲线的性能。三个应用规则基于常规标准操作策略、回归规则和对冲规则，另外两个是基于预测的回归和对冲规则。结果表明，如果预测方案提供准确的结果，则基于预测的操作规则曲线优于常规规则。此外，可以得出结论，观测数据的时间序列分解有效地提高了预测结果的准确性。