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Time series analysis is an important data mining task in areas such as the stock market and petroleum industry. One interesting problem in knowledge discovery is the detection of previously unknown frequent patterns. With the existing types of patterns, some similar subsequences are overlooked or dissimilar ones are matched. In this paper, we define patterns with weak-wildcard gaps to represent subsequences with noise and shift, and design efficient algorithms to obtain frequent and strong patterns. First, we convert a numeric time series into a sequence according to the data fluctuation. Second, we define the pattern mining with weak-wildcard gaps problem, where a weak-wildcard matches any character in an alphabet subset. Third, we design an Apriori-like algorithm with an efficient pruning technique to obtain frequent and strong patterns. Experimental results show that our algorithm is efficient and can discover frequent and strong patterns.

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity (MDI) method. To establish productivity prediction models, linear regression (LR), random forest regression (RF), support vector regression (SVR), backpropagation (BP), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms are used. Their performance is evaluated using the coefficient of determination (R²), explained variance score (EV), mean squared error (MSE), and mean absolute error (MAE) metrics. The LightGBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query, oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development. [ABSTRACT FROM AUTHOR]