(685f) Prediction of Production Performance of Unconventional Reservoirs Using Data-Driven Methods
AIChE Annual Meeting
Monday, November 15, 2021 - 4:42pm to 5:00pm
This study presents a data-driven approach to forecast the production from unconventional reservoirs with limited production histories using multivariate statistics and traditional decline curve analysis. We use an unsupervised machine learning algorithm to identify the patterns and regularities on a large production data set. This algorithm is then applied to large sets of field data to create an uncorrelated weighted sum of ârepresentative curves,â the truncated summation of which yields an approximation of the original data.
The method presented is also applicable to estimate the gas to oil ratio (GOR) for liquid-rich shale reservoirs with complex phase behavior. As the underlying data capture the effect of multiphase flow automatically, this method presents a reliable alternative to estimate the secondary phase from volatile oil and gas condensate reservoirs.