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

• URL: http://arxiv.org/abs/2302.11195v1
• Date: Wed, 22 Feb 2023 08:10:25 GMT
• Title: Prediction of single well production rate in water-flooding oil fields driven by the fusion of static, temporal and spatial information
• Authors: Chao Min, Yijia Wang, Huohai Yang and Wei Zhao
• Abstract summary: 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. 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.
• Score: 3.743500951752524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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