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For the thick sandstone reservoir, due to gravity differentiation, water drive mainly uses the bottom oil of the reservoir, while gas drive mainly drives the oil along the top of the reservoir. Water-alternating gas (WAG) injection can effectively combine the advantages of water flooding and gas injection, so that gas and water can work in synergy, thus further expanding swept volume and enhancing oil recovery. Over the past half century, the technology has been successfully applied to more than 60 oilfields worldwide, but its mechanism remains to be further studied. In this paper, a total of three tests were conducted through conventional water-flooding (WF), water flooding followed by gas flooding and WAG in sand-packed 2d-model. These experimental processes are recorded by video with time so that the saturation distribution of each phase in porous media can be observed，migration law of gas and water is studied, and the mechanism of WAG displacement is analyzed. The results show that gravity and capillary force have great influence on the process of gas water alternation, thus increasing the overall swept volume of water and gas; The water/gas alternating injection has the highest oil recovery factor (RF) of 75.45% in test 3, in comparison with water flooding followed by gas flooding (70.85% in test 2) and water-flooding (66.7%in test 1); Increase in cycles of WAG tends to reduce residual oil saturation.

Accurate and timely traffic flow prediction is important for the successful deployment of intelligent transportation systems. Most of existing methods have not made good use of information from adjacent sections to analyse the trends of the object section. A new method for traffic flow prediction of highway network, namely network-constrained Lasso (Least absolute shrinkage and selection operator) and Neural Networks, was proposed. Unlike existing methods, our approach incorporated all the spatial and temporal information available in a highway network to carry our short-term traffic flow prediction for the objective section. To capture the spatial correlation of traffic network, the Laplacian matrix was introduced to describe the highway network structure. Subsequently, a network-constrained Lasso method was applied for sparse variable selection. With the extracted historic and real-time data, the back propagation neural networks were implemented to predict traffic flow at different time intervals in future. The experimental results verified that the proposed method could achieve above 90% average accuracy in the 30-minutes speed predictions for 78 road sections.