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It is very difficult to forecast the production rate of oil wells as theoutput of a single well is sensitive to various uncertain factors, whichimplicitly or explicitly show the influence of the static, temporal and spatialproperties on the oil well production. In this study, a novel machine learningmodel is constructed to fuse the static geological information, dynamic wellproduction history, and spatial information of the adjacent water injectionwells. There are 3 basic modules in this stacking model, which are regarded asthe encoders to extract the features from different types of data. One isMulti-Layer Perceptron, which is to analyze the static geological properties ofthe reservoir that might influence the well production rate. The other two areboth LSTMs, which have the input in the form of two matrices rather thanvectors, standing for the temporal and the spatial information of the targetwell. The difference of the two modules is that in the spatial informationprocessing module we take into consideration the time delay of water floodingresponse, from the injection well to the target well. In addition, we useSymbolic Transfer Entropy to prove the superiorities of the stacking model fromthe perspective of Causality Discovery. It is proved theoretically andpractically that the presented model can make full use of the model structureto integrate the characteristics of the data and the experts' knowledge intothe process of machine learning, greatly improving the accuracy andgeneralization ability of prediction.

Prediction of single well production rate in water-flooding oil fields driven by the fusion of static, temporal and spatial information