A novel decline curve regression procedure for analyzing shale gas production

Abstract

Accurate prediction of gas production is critical for the shale gas development. Compared with the complicated physics-based simulation, the decline curve analysis (DCA) models based on parameter linearization or direct curve regression are much more straightforward and efficient. However, the linearization method might be time consuming in obtaining the parameters, and the direct regression might lead to large errors in production forecast as it assigns equal weight to the historical data. In this paper, we develop a new DCA procedure combining a data transformation method which convert the production data into logarithmic space, and a nonlinear regression algorithm. The new procedure can effectively capture the trend of late-time production history. The efficiency of the new procedure is verified with the production data of 550 gas wells in the Barnett and Marcellus shales. Based on these field data, we also analyze the performance of seven popular DCA models, i.e. Arps, power law exponential (PLE) decline, stretched exponential production decline (SEPD), Duong, Wang , variable decline modified Arps (VDMA) and logistic growth models. The performances of these models are compared and the tuned parameters for each model are provided. It is shown that the accuracy of the production forecast by the above models can be significantly improved by our new regression method, which is beneficial to the prediction and optimization of shale gas production.