It is very difficult to forecast the production rate of oil wells as the output of a single well is sensitive to various uncertain factors, which implicitly or explicitly show the influence of the static, temporal and spatial properties on the oil well production. In this study, a novel machine learning model is constructed to fuse the static geological information, dynamic well production history, and spatial information of the adjacent water injection wells. There are 3 basic modules in this stacking model, which are regarded as the encoders to extract the features from different types of data. One is Multi-Layer Perceptron, which is to analyze the static geological properties of the reservoir that might influence the well production rate. The other two are both LSTMs, which have the input in the form of two matrices rather than vectors, standing for the temporal and the spatial information of the target well. The difference of the two modules is that in the spatial information processing module we take into consideration the time delay of water flooding response, from the injection well to the target well. In addition, we use Symbolic Transfer Entropy to prove the superiorities of the stacking model from the perspective of Causality Discovery. It is proved theoretically and practically that the presented model can make full use of the model structure to integrate the characteristics of the data and the experts' knowledge into the process of machine learning, greatly improving the accuracy and generalization ability of prediction.
翻译:很难预测油井的产量,因为单井的产量对各种不确定因素十分敏感,这些因素隐含或明确显示静态、时空和空间特性对油井生产的影响。在本研究中,设计了一个新型机器学习模型,将静态地质信息、动态良好的生产历史和相邻注入水井的空间信息结合起来。在这个堆叠模型中有三个基本单元,这些单元被视为从不同类型数据中提取特征的编码器。一个是多环 Perceptron,用来分析储油层的静态地质特性,这可能对油井生产率产生影响。另外两个是LSTMS,它们以两个基质而不是矢量的形式提供投入,为目标的时空和空间信息站立。这两个单元的区别在于:在空间信息处理模块中,我们考虑到从注入到目标的延迟反应。此外,我们使用“多环导传输”模型来证明堆叠模型的优越性能影响良好的生产率。另外两个单元都是LSTMS,它们以两种基质的形式提供,而不是矢量的矢量,用以了解目标的时空特性。我们从一般模型的角度将模型和机变现数据转化为数据。它可以证明,从实际的准确性学学学学的精度的精度的精度的精度的精度的精度的精度的精度的精度的精度的精度的精度的精度。