Abstract
This study examined the relationship between the early production data and the long-term performance of shale gas wells, including the estimated ultimate recovery (EUR) and economics. The investigated early production data are peak gas production rate, 3-, 6-, 12-, 18-, and 24-month cumulative gas production (CGP). Based on production data analysis of 485 reservoir simulation datasets, CGP at 12 months (CGP_12m) was selected as a key input parameter to predict a long-term shale gas well’s performance in terms of the EUR and net present value (NPV) for a given well. The developed prediction models were then validated using the field production data from 164 wells which have more than 10 years of production history in Barnett Shale, USA. The validation results showed strong correlations between the predicted data and field data. This suggests that the proposed models can predict the shale gas production and economics reliably in Barnett shale area. Only a short history of production (one year) can be used to estimate the EUR and NPV of various production periods for a gas well. Moreover, the proposed prediction models are consistently applied for young wells with short production histories and lack of reservoir and hydraulic fracturing data.
Highlights
Shale gas has attracted considerable attention worldwide in recent years because of its high potential for current and future clean energy supply
This study examined the relationship between the early production data and whole life shale gas well performance, including estimated ultimate recovery (EUR) and net present value (NPV), through that prediction models were developed for predicting the shale gas well performance
There still existed a gap between the simulation results and the field production data
Summary
Shale gas has attracted considerable attention worldwide in recent years because of its high potential for current and future clean energy supply. An evaluation of the shale gas potential production and economics is essential because the performance is quite different for various reservoir quality and completion designs. Wang et al [2] developed a deep neural network to predict cumulative gas production of shale gas wells in six and eighteen months based on geological and well-hydraulic fracturing design parameters. Various approaches to production data analysis have been proposed to evaluate and predict the performance of shale gas wells and reservoirs. Xu et al [3] used the linear dual porosity type curve analysis technique to estimate the reservoir properties and predict gas production from the Eagle Ford shale reservoir, USA. Ikewun and Ahmadi [4] used simulation models and decline curve analysis (DCA) to predict the production of the Eagle Ford reservoir, USA and other young producing shale reservoirs with short
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