Abstract
Decline curve analysis (DCA) is one of the most common tools to estimate hydrocarbon reserves. Recently, many decline curve models have been developed for unconventional reservoirs because of the complex driving mechanisms and production systems of such resources. DCA is subjected to some uncertainties. These uncertainties are mainly related to the data size available for regression, the quality of the data, and the selected decline curve model/s to be used. In this research, first, 20 decline curve models were summarized. For each model, the four basic equations were completed analytically. Second, 16 decline curve models were used with different data sizes and then a machine learning (ML) algorithm was used to detect the outlier from shale gas production data with different thresholds of 10, 15, and 20%. After that, the 16 models were compared based on different data sizes and the three levels of data quality. The results showed differences among all models' performances in the goodness of fitting and prediction reliability based on the data size. Also, some models are more sensitive to removing the outlier than others. For example, Duong and Wang's models seemed to be less affected by removing the outlier compared to Weng, Hesieh, stretched exponential production decline (SEPD), logistic growth (LGM), and fractional decline curve (FDC) models. Further, the extended exponential decline curve analysis (EEDCA) and the hyperbolic-exponential hybrid decline (HEHD) models tended to underestimate the reserves, and by removing the outlier, they tended to be more underestimators. This work presented a comparative analysis among 16 different DCA models based on removing the outlier using ML. This may motivate researchers for further investigations to conclude which combination of the outlier removers and DCA models could be used to improve production forecasting and reserve estimation.