Improving the Shale Gas Production Data Using the Angular- Based Outlier Detector Machine Learning Algorithm

Production history is essential for any reservoir engineering study. It used for history matching in reservoir simulation study, rate transient analysis and decline curve analysis (DCA). The quality of the production data is important. Better quality of the production data reduces the uncertainties during modeling the reservoir, characterizing it and forecasting the future production. Shale gas reservoirs have been developed heavily in last two decades. They have huge reserves but there are challenges in evaluating them economically. Transient flow that could last for long time, liquid loading causing successful shut ins and controlling the bottom hole flowing pressure cause the production data to fluctuate heavily. The noisy production profile makes it difficult to detect the different flow regimes precisely and affects analysis such DCA. In this paper, we used a machine learning algorithm called angular- based outlier detector (ABOD) to improve the production data of 4 shale gas wells. It was assumed that 20% of the production data is noise and the algorithm is asked to determine the points with the highest potential to be detected as noise. After that, the different flow regimes were determined before and after improving the data quality. The results show that the ABOD algorithm removed the noise from the production data efficiently. The production profile was smoothed without any bias and without removing any significant event. Detecting the different flow regimes was much clear after removing the noise. Moreover, we determined the masked flow regimes after improving the production data quality in some cases.