Extended exponential decline curve analysis
Introduction
The development of unconventional hydrocarbons has become a significant resource leading to material reserve growth worldwide. In the US, one of the important contributions in our industry came from the development of shale gas and oil during the past decade. However, forecasting production and estimating shale hydrocarbon reserves is still not fully understood because of the limited knowledge of flow mechanics in the ultra-low permeability rock. This paper presents a concept of “growing drainage volume” and develops an empirical formula to forecast production.
Standard reserve evaluation methods include volumetric calculations, material balance, decline curve analysis (DCA), analogy, and numerical simulation. An evaluation process typically involves a combination of two or more methods. Among them, numerical simulation is generally believed to be the most rigorous and accurate method. The drawback for using simulation, though, is the significant data requirements. For shale reservoirs, data requirements are even more demanding and uncertain because multi-stage fracture stimulation and horizontal completions increase significantly the data needed, or the assumptions that must be applied. It is very challenging for the simulation engineer to properly take into account the interference between fractures, which requires reliable estimates of the fracture half-length, width, and fracture permeability. This method also encounters other modeling problems like relative permeability effects and extremely heterogeneous rock properties, to name a few. On the other hand, when properly applied, DCA can play an effective role because it accommodates all of these factors, which may have influenced the historical production performance. Further, it has the exclusive advantages of speed, simplicity, and the inherent reasonableness in the forecasts generated.
We note that as with any new technique, there may be wells, reservoirs, or plays (formations) that might not be suitable to this technique. In some cases, we have seen that this new technique might provide overly optimistic or pessimistic results when compared to other methods. These cases are typically associated with situations where it is early in the life of the well(s). Because only additional data and time will truly validate the methodology presented herein, we caution the user to take into consideration the prior understanding of the reservoirs, the methodology employed, and the experience of the evaluator, when applying this method to previously evaluated entities.
Section snippets
Discussion
Traditional DCA is based on the Arps equation (Arps, 1945). Recently, this equation has been adopted (with some controversy) for shale production forecasts. At the time Arps published this method, shale gas and oil were not even considered viable for development. Thus, by applying the traditional DCA method to model production in shale reservoirs, engineers commonly encounter the difficulties of simultaneously matching the high initial production rate, extremely sharp decline rate in the
Conclusions
This paper presented a new form of DCA with three empirical coefficients for shale reservoirs. The authors have validated this method by field production data and numerical simulation. This model can capture both transient and BDF flow in the same equation. Further:
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Although this new method provides similar results when compared to the combination methods found in literature, the model is simpler and requires less effort;
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The presented model does not require a switch from a transient model to a
Acknowledgement
The authors thank Ryder Scott Company for allowing us to publish this paper.
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2023, Petroleum ScienceCitation Excerpt :Thus, the empirical decline model has been improved (Fetkovich, 1973; Carter, 1985; Palacio and Blasingame, 1993; Agarwal et al., 1999). Novel empirical decline models for tight/shale gas reservoirs have been developed since 2008, including four categories (i.e., related to Arps' exponential decline models (Ilk et al., 2008; Johnson et al., 2009; Mattar and Moghadam, 2009; Valko, 2009; Yu and Miocevic, 2013; Zhang et al., 2016), related to Arps' hyperbolic decline models (Fulford and Blasingame 2013; Maraggi et al., 2016), related to the rate decline models during the linear flow in fracture-dominated reservoirs (Josh and Lee, 2013; Ali et al., 2014; Wang et al., 2017a), and other methods (e.g., logistic growth curves (Clark et al., 2011), fractional decline curves (Zuo et al., 2016), and other complicated methods (Makinde and Lee, 2017; Mishra, 2012). Many parameters are required to be calculated by trial and error or should be adjusted using the corresponding dimensionless curves for the above-mentioned models, causing them inconvenient for field application (Wang et al., 2020).
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2022, Journal of Natural Gas Science and EngineeringCitation Excerpt :However, owing to the difficulty of obtaining precise fracture and reservoir parameters, this method does not always provide sufficient accuracy. Another prevalent forecast approach is the mathematics-based methods, also called the empirical methods (Zhang et al., 2016; Zuo et al., 2016), which matches the field data to a certain curve with simple information related to the history of gas production. Jang (Jang et al., 2019) employed decline curve analysis, flow regime analysis and flowing material balance to analyze the gas production of a CBM horizontal fractured well and then predicted its production performance.