Analysis of the Grammatical Errors in Chinese Undergraduate Students’ Online English Writing

Learners’ language data present an interesting phenomenon that can be used to explain the processes that learners go through in the continuum of learning a second or foreign language. The aim of this study was to examine the grammatical errors in the English writing of the Chinese undergraduate students. The sample was drawn from the second-year students studying Communication in Shandong Normal University. All the second-year students studying Communication were asked to write one composition online from which a random sample of 90 scripts was selected. The study was based on the following objectives: (a) to identify and categorize the most common types of grammatical errors in the second-year undergraduate students’ English writing; (b) to find out the frequency of these errors; (c) to infer, with the help of available literature on error analysis, the possible causes of these errors; (d) to extract from the available literature on error analysis pedagogic strategies to reduce these errors.The Interlanguage Theory (Selinker, 1972) guides the interpretation and description of the phenomenon observed in the study data. Using the “Let the Error Determine the Categories” approach the errors in the following grammatical categories were identified: Noun Phrase, Verb Phrase, Preposition, Adjective, Adverb, Complementation, Word order, Concord, Negation and Clause Link.The identified errors were then described using the Error Analysis Method (Corder, 1974). The errors were determined through a consideration of the deviations of the students’ grammar from the norms of the target language (English) as described for example in Quirk et al. (1985). The data analysis showed that Verb Phrase related errors were the most frequent and the word order errors were least frequent.On the basis of the available literature on error analysis, the study discusses some causes of the errors observed and identifies some pedagogic strategies that can be used to alleviate these errors. After considering various causes, it was evident that overgeneralization was the main cause of the grammatical errors found in the English writing of these second-year undergraduate students

A Machine Learning Method for the Risk Prediction of Casing Damage and Its Application in Waterflooding

During the development of oilfields, casings in long-term service tend to be damaged to different degrees, leading to poor development of the oilfields, ineffective water circulation, and wasted water resources. In this paper, we propose a data-based method for predicting casing failure risk at both well and well-layer granularity and illustrate the application of the method to GX Block in an eastern oilfield of China. We first quantify the main control factors of casing damage by adopting the F-test and mutual information, such as that of the completion days, oil rate, and wall thickness. We then select the top 30 factors to construct the probability prediction model separately using seven algorithms, namely the decision tree, random forest, AdaBoost, gradient boosting decision tree, XGBoost, LightGBM, and backpropagation neural network algorithms. In terms of five evaluation indicators, namely the accuracy, precision, recall, F1-score, and area under the curve, we find that the LightGBM algorithm yields the best results at both granularities. The accuracy of the prediction model based on the preferred algorithm reaches 87.29% and 92.45% at well and well-layer granularity, respectively

A Machine Learning Method for the Risk Prediction of Casing Damage and Its Application in Waterflooding

During the development of oilfields, casings in long-term service tend to be damaged to different degrees, leading to poor development of the oilfields, ineffective water circulation, and wasted water resources. In this paper, we propose a data-based method for predicting casing failure risk at both well and well-layer granularity and illustrate the application of the method to GX Block in an eastern oilfield of China. We first quantify the main control factors of casing damage by adopting the F-test and mutual information, such as that of the completion days, oil rate, and wall thickness. We then select the top 30 factors to construct the probability prediction model separately using seven algorithms, namely the decision tree, random forest, AdaBoost, gradient boosting decision tree, XGBoost, LightGBM, and backpropagation neural network algorithms. In terms of five evaluation indicators, namely the accuracy, precision, recall, F1-score, and area under the curve, we find that the LightGBM algorithm yields the best results at both granularities. The accuracy of the prediction model based on the preferred algorithm reaches 87.29% and 92.45% at well and well-layer granularity, respectively