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Production forecasting methods for different types of gas reservoirs
- Source :
- Energy Geoscience, Vol 5, Iss 3, Pp 100296- (2024)
- Publication Year :
- 2024
- Publisher :
- KeAi Communications Co., Ltd., 2024.
-
Abstract
- Hydrocarbon production in oil and gas fields generally progresses through stages of production ramp-up, plateau (peak), and decline during field development, with the whole process primarily modeled and forecasted using lifecycle models. SINOPEC's conventional gas reservoirs are dominated by carbonates, low-permeability tight sandstone, condensate, volcanic rocks, and medium-to-high-permeability sandstone. This study identifies the optimal production forecasting models by comparing the fitting coefficients of different models and calculating the relative errors in technically recoverable reserves. To improve forecast precision, it suggests substituting exponential smoothing method-derived predictions for anomalous data caused by subjective influences like market dynamics and maintenance activities. The preferred models for carbonate gas reservoir production forecasts are the generalized Weng's, Beta, Class-I generalized mathematical, and Hu-Chen models. The Vapor pressure and Beta models are optimal for forecasting the annual productivity of wells (APW) from gas-bearing low-permeability tight sandstone reservoirs. The Wang-Li, Beta, and Yu QT tb models are apt for moderate-to-small-reserves, single low-permeability tight sandstone gas reservoirs. The Rayleigh, Hu-Chen, and generalized Weng's models are suitable for condensate gas reservoirs. For medium-to-high-permeability sandstone gas reservoirs, the lognormal, generalized gamma, and Beta models are recommended.
Details
- Language :
- English
- ISSN :
- 26667592
- Volume :
- 5
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Energy Geoscience
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.8485e116b4984ffa9829ecd7f556d9ce
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.engeos.2024.100296