Abstract
For an oil well, we can determine the working conditions of drilling wells and whether there is an accident by logging data. However, it takes a long time to analyze logging curves by traditional manual work. Therefore, this paper proposes a new method combining logging big data (BD) and machine learning (ML), which can train and study a large number of logging curves and form a data base. Then this data base can be used to judge the working conditions of the well being drilled and whether there is an accident automatically.
In the proposed method, firstly, due to the large span of data and different detection methods, there will be some differences in data accuracy, so the data need to be preprocessed to avoid these influences. Secondly, the preprocessed data is input into the artificial intelligence method, and trained by cyclic iterations; meanwhile the rule of judging working conditions is also learned from the data. Thirdly, we get a training set containing all kinds of working conditions and common accidents through artificial intelligence learning method. Finally, the logging data that need to judge is input into the training set after preprocessing, and then we can determine the working conditions and whether there is an accident according to the output of the procedure.
For the proposed method, we used the logging data of Bohai Oilfield to verify that a total of 200 logging data including 4 conditions were preprocessed and input to the artificial intelligence method to randomly generate a training set (160 sets) and the test set (40 groups) is output after the training set. The results show that the relative error of the four kinds of working conditions output by the proposed method is less than 5%, and the average calculation time is faster than traditional method, saving a lot of manual processing time.
This article introduces the methods of big data and artificial intelligence into the field of drilling, which have been tested by actual data. The time for judging drilling conditions by manpower can be greatly saved, and the common accidents during drilling can be found in time, which show that this method is of great significance.
