Capturing intrinsic features from field data for predicting the production of natural gas

Abstract

Production prediction for gas wells is a popular topic in reservoir engineering as it plays a crucial role in the formulation of development plans. Most traditional techniques can be categorized into two types, i.e., numerical simulation methods and decline curve analysis, while none of them can precisely capture the varying trends of gas production, which leads to poor prediction results. To tackle the issue, we propose a comprehensive approach that works in a pipeline manner to learn intrinsic features from data for production prediction. (1) We propose to group wells with a clustering algorithm which does not need the pre-specified cluster number. To group wells even better, two parameters, i.e., dynamic volatility and static volatility of productions are introduced and involved for clustering. (2) We devise a technique that is based on the maximum likelihood estimation, for well matching. (3) We develop an encoder–decoder model for learning varying trends of well productions, by considering geological, engineering and production data simultaneously. (4) On real-life data, we conduct intensive experiments and find that our approach achieves superior performance and substantially outperforms its counterparts.