Abstract
The development of prediction tools for production performance and the lifespan of shale gas reservoirs has been a focus for petroleum engineers. Several decline curve models have been developed and compared with data from shale gas production. To accurately forecast the estimated ultimate recovery for shale gas reservoirs, consistent and accurate decline curve modelling is required. In this paper, the current decline curve models are evaluated using the goodness of fit as a measure of accuracy with field data. The evaluation found that there are advantages in using the current DCA models; however, they also have limitations associated with them that have to be addressed. Based on the accuracy assessment conducted on the different models, it appears that the Stretched Exponential Decline Model (SEDM) and Logistic Growth Model (LGM), followed by the Extended Exponential Decline Model (EEDM), the Power Law Exponential Model (PLE), the Doung’s Model, and lastly, the Arps Hyperbolic Decline Model, provide the best fit with production data.
Highlights
In recent years, shale gas reservoirs (SGR) or unconventional reservoirs have steadily become the main bases of natural gas production around the world [1]
Kenomore et al [47] in their production decline study of the Barnett shale found that either the Arps hyperbolic or Doung’s model can be used only if the historical data exceeds 10 months. They used root mean square error (RMSE) analysis and the results indicated that the Arps hyperbolic model showed better forecasting compared to the Doung’s model for the top three longest production histories
Shale gas reservoirs have become an essential source for providing natural gas globally and the process of hydraulic fracking has been used in the extraction of shale gas
Summary
Shale gas reservoirs (SGR) or unconventional reservoirs have steadily become the main bases of natural gas production around the world [1]. Wang [2] notes that shales and sediments are the richest sedimentary rocks in the Earth’s crust and, according to recent activities, shale gas will constitute the largest component in gas production globally, as conventional reservoirs continually decrease. It is further mentioned by Wang [2] that SGR, unlike conventional reservoirs, tend to be more costly to develop and require special tools to enable the gas to be produced at a cost-effective rate due to their extremely low matrix permeability and porosity [3]. The main ideas are (a) to characterise and evaluate the current decline curve models used to explain shale gas reservoir forecasting and (b) use the goodness-of-fit regression test to assess the sensitivity of the decline curve models in (a)
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