The prediction of shale gas well production rate based on grey system theory dynamic model GM(1, N)
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ORIGINAL PAPER-PRODUCTION ENGINEERING
The prediction of shale gas well production rate based on grey system theory dynamic model GM(1, N) Xiaohui Luo1 · Xiaoqing Yan1 · Yusong Chen2 · Ming Yue2 · Jingwei Li2 Received: 18 April 2020 / Accepted: 6 July 2020 © The Author(s) 2020
Abstract The prediction of production volumes from shale gas wells is important in reservoir development. The physical parameters of a reservoir are uncertain and complex, and therefore, it is very difficult to predict the production capability of a shale gas well. An improved GM(1, N) model for shale gas well productivity prediction, focused upon the causes of prediction errors from the existing traditional GM(1, N) method, was established. By processing a data series related to the predicted data, the improved GM(1, N) model takes into account the fluctuations of the original production data, reflects the trend of the original data under the influence of relevant factors, and hence predicts more accurately the fluctuation amplitude and direction of the original data. Additionally, the proposed method has higher accuracy than the conventional GM(1, N), GM(1, 1), and MEP models. The prediction accuracy increases gradually and the relative error decreases gradually from bottom data (casing pressure at well start-up, etc.) to top data (shale gas production). Accordingly, a step-by-step prediction method could be effective in improving prediction accuracy and reflects the typical fluctuation characteristics of shale gas production. Keywords Shale gas production · Prediction · Improved GM(1, N) · Background value · Data smoothness · Prediction accuracy evaluation
Introduction The prediction of shale gas production rates and volumes is an important part of oilfield development (Elmabrouk et al. 2014). Whether the production of shale gas wells can be predicted effectively in the future, based on the historical data, is related to the real-time adjustment of the shale gas well working schedule, thus playing the role of an assistant during decision-making (Mohammadpoor and Torabi 2018). There are three methods to predict production rate. First is the reservoir engineering method based on basic percolation theory, for example, production decline analysis (Bahadori 2012; Miao et al. 2020; Wang 2017). This method takes into account the effects of reservoir properties, well conditions, and production control parameters on shale gas production. It is a common mathematical statistics method for predicting * Xiaohui Luo [email protected] 1
Business School, Ningbo University, Ningbo 315211, Zhejiang, China
CCDC Shale Gas Exploration and Development Department, Chengdu 610000, China
2
and analysing reservoir production performance and is one of the typical representations used in shale gas field. However, the reservoir engineering criteria are obtained for an ideal percolation environment, which will not reflect fully the phenomena and laws of actual shale gas field percolation. Furthermore, during operation, shale gas production is con
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