Due to the rapid development of shale gas, a system has been established that can utilize a considerable amount of data using the database system. As a result, many studies using various machine learning techniques were carried out to predict the productivity of shale gas reservoirs. In this study, a comprehensive analysis is performed for a machine learning method based on data-driven approaches that evaluates productivity for shale gas wells by using various parameters such as hydraulic fracturing and well completion in Eagle Ford shale gas field. Two techniques are used to improve the performance of the productivity prediction machine learning model developed in this study. First, the optimal input variables were selected by using the variables importance method (VIM). Second, cluster analysis was used to analyze the similarities in the datasets and recreate the machine learning models for each cluster to compare the training and test results. To predict productivity, we used random forest (RF), gradient boosting tree (GBM), and support vector machine (SVM) supervised learning models. Compared to other supervised learning models, RF, which is applied with the VIM, has the best prediction performance. The retraining model through cluster analysis has excellent predictive performance. The developed model and prediction workflow are considered useful for reservoir engineers planning of field development plan.

A small RFID tag antenna in a UHF band is proposed in this article. Using dual resonance, the antenna has bandwidth‐enhanced characteristics without an additional matching circuit. The meander shape of the proposed antenna helps to reduce the antenna size. The bandwidth of the antenna is enhanced by two radiators that create dual resonance in adjacent frequencies. When the length and location of each radiator are adjusted, the antenna can make dual resonance at an arbitrary impedance point, thereby enabling the antenna to match an RFID tag chip in a wide frequency range. According to the simulated and measured results, the proposed antenna has almost double the bandwidth of a regular antenna. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 2027–2031, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23576

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