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Summary The objective of this study is to develop a new method that leads to diagnostic charts that quantify the pressure response between two interfering wells. Analytical linear flow models for single hydraulic fracture are used to develop a fracture hit model, which is next verified with a numerical model for validity. An analytical two-fracture model is then developed to simulate flowing bottomhole pressure (BHP) of a shut-in well, which interferes with the other well through a fracture hit, during well-testing for a short-term period. From the insight of two-fracture analytical model, a dimensionless pressure scalar, which is proportional to square root of time, is proposed to summarize the interference level between two wells. Utilizing such proportionality between the defined dimensionless pressure scalar and square root of time, a diagnostic chart for quick assessment of the production interference level between wells is developed. Such diagnostic chart is also applied to interference caused by multifracture hits that a multistage fractured horizontal well with history match performed from the Eagle Ford formation is considered as a parent well for production interference quantification. A new identical horizontal well, which is just fractured but is not in production, is assumed parallel to the pre-existing well. The result shows that when the percentage of fracture connection increases, the slope of dimensionless pressure scalar vs. square root of time increases proportionally to the percentage of fracture connection. Because the slope of dimensionless pressure scalar vs. square root of time is between 0 and 1, it can be used to quantify the well production interference level under different situations.

Wind power prediction technology is important to improve the reliability of grid-connected, the common statistic modeling method result is not satisfied because it lacks of effective pretreatment. This paper puts forward wind power prediction based on similar day clustering support vector machine, which catches the training data by similar day and modeling respectively, each model is used to predict specific similar days. Experiment on a wind farm shows the proposed method is effective.

A novel kernel based semi-supervised fuzzy clustering algorithm is proposed, and its iterative formula is given. This new algorithm can effectively improve the efficiency of the clustering algorithm. Combined with Fisher projection algorithm, two principal components are extracted from 7 hue statistics and 11 green value statistics, this new semi-supervised clustering method is applied to recognize the angular leaf spot disease of Bauhinia blakeana. The results showed that the consistent rate is 100% for the labeled leaves, and above 95% for other unlabeled leaves.