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The present work aims to boost tourism development in China, grasp the psychology of tourists at any time, and provide personalized tourist services. The research object is the tourism industry in Macau. In particular, tourists' experiences are comprehensively analyzed in terms of dining, living, traveling, sightseeing, shopping, and entertaining as per their psychological changes using approaches including big data analysis, literature analysis, and field investigation. In this case, a model of tourism experience formation path is summarized, and a smart travel solution is proposed based on psychological experience. In the end, specific and feasible suggestions are put forward for the Macau tourism industry. Results demonstrate that the psychology-based smart travel solution exerts a significant impact on tourists' tourism experience. Specifically, the weight of secular tourism experience is 0.523, the weight of aesthetic tourism experience is 0.356, and the weight of stimulating tourism experience is 0.121. Tourists prefer travel destinations with excellent urban security and scenic authenticity. They give the two indexes comprehensive scores of 75.14 points and 73.12 points, respectively. The proposed smart travel solution can grasp the psychology of tourists and enhance their tourism experiences. It has strong practical and guiding significances, which can promote constructing smart travel services in Macau and enhancing tourism experiences.

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

Coarse clastic rocks of the upper Sha 4 Member in the northern Bonan Sag of the Bohai Bay Basin in eastern China are important hydrocarbon reservoirs. The deposits are tight reservoirs owing to the low porosity (less than 10%) and low permeability (less than 1 mD). Because of the strong heterogeneity, although the reserve in the northern Bonan Sag is remarkable, only 4.9% of the reserves are recovered. We have studied these tight reservoirs by linking diagenesis to sedimentary facies to help predict the distribution of high-quality tight reservoirs. Petrographic analysis is undertaken based on cores, thin sections, X-ray diffraction and scanning electron microscope, helpful to understand the impacts on tight reservoirs of sedimentary factors and diagenesis factors. Sedimentary microfacies, lithologic characteristics, reservoir property, diagenesis, and diagenetic minerals are studied. Coarse clastic rocks are deposited mainly in nearshore subaqueous fans and fan deltas. The multistage sandstones are the valid reservoirs of coarse clastic rocks and dominated by feldspathic litharenite, lithic arkose, and arkose. The reservoir property is poor principally owing to the strong compaction and cementation. Pores are composed of secondary pores and primary pores. The secondary pore, generated in the dissolution of detrital minerals and/or cements, is the major type of pores and important to porosity improvement. By linking diagenesis to sedimentary facies, it can be concluded that high-quality tight reservoirs of coarse clastic rocks of the upper Sha 4 Member in the northern Bonan Sag of Bohai Bay Basin in eastern China are associated with medium to coarse-grain sandstones, found in the middle part of underwater distributary channel deposits in fan deltas and in the middle part of underwater channel deposits in nearshore subaqueous fans, with abundant secondary porosity but low cement contents, vertically at depths ranging from 3500 to 4100 m.

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