A comparative study on outlier detection techniques for noisy production data from unconventional shale reservoirs

Abstract

Decline curve analyses (DCA) and rate transient analyses (RTA) are widely used to characterize the fluid flow through porous media and forecast future production. Oil and gas production data are routinely analyzed for history matching and optimizing the well stimulation methods in hydrocarbon exploration and production lifecycle. However, outliers add significant uncertainty and non-uniqueness to results from production data analysis. This study provides a structured and comprehensive overview of five widely used outlier detection (OD) techniques for identifying and removing outliers in production data. Each OD technique measures deviation differently and, therefore, has a different outcome even when applied to the same dataset, creating the need to test several methods and find the optimal technique for identifying and removing outliers from production data. First, we generated production data from a typical multi-fractured horizontal well using a numerical reservoir simulator and added random noise to the data. Then, we used five different OD techniques to identify the prelabeled outliers from the synthetic production data. Finally, we identified the best-performing OD algorithm by comparing the various evaluation metrics such as the mean absolute error (MAE), precision, sensitivity, and F1 score. Results showed that the angle-based OD (ABOD) had the best MAE, precision, sensitivity, and F1 score of 8%, 85%, 98%, and 0.90, respectively. The next best-performing OD technique was distance-based OD (DBOD), with MAE, precision, sensitivity, and F1 score of 16%, 71%, 100%, and 0.83, respectively. We tested the ABOD method on several field production datasets by assuming different outlier thresholds (fraction of the data points likely to be outliers). Visual inspection of the processed data showed that the ABOD method effectively identified and removed the outliers from a relatively clean dataset (outlier threshold of 20%) and a highly noisy dataset (outlier threshold of 80%). This algorithm is intuitive and can effectively identify and remove outliers from the field production data to improve production forecasting, reserves estimation, and rate transient analysis for multi-fractured horizontal oil and gas reservoirs.