A novel method for predicting spatial distribution of thermal properties and oil saturation of steam injection well from temperature logs

• Published in: Energy (Oxford) 2014-03, Vol.66, p.898-906
• Main Authors: Cheng, Wen-Long, Nian, Yong-Le, Li, Tong-Tong, Wang, Chang-Long
• Format: Article
• Language: English
• Subjects: Applied sciences; Crude oil, natural gas and petroleum products; Crude oil, natural gas, oil shales producing equipements and methods; Energy; Enhanced oil recovery methods; Exact sciences and technology; Fuels; Inversion method; Inversions; Logs; Mathematical models; Oil saturation; Prospecting and production of crude oil, natural gas, oil shales and tar sands; Reservoirs; Saturation; Spatial distribution; Spreads; Temperature logs; Thermal properties
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2013.12.074

Formation and reservoir of steam injection well thermal properties are important parameters for evaluating thermal efficiency of thermal recovery process. Oil saturation of reservoir is also a crucial criterion for evaluating exploitation value of oil field. This study firstly presents a layered inversion method for estimating the spatial distributions of formation and reservoir thermal properties from temperature logs, and then the distribution of oil saturation in reservoir is predicted by a semi-empirical oil saturation model with the inversion thermal properties distributions. The proposed method was based on the heat transfer in steam injection well. Sensitivity analysis was firstly conducted to investigate the sensitivity of uncertain parameters, determining the inversion sequence. Then the spatial distributions of thermal properties were obtained by the layered inversion method, which reflected the diversity in physical properties of different wells and depth variability. Consequently, the present method not only could estimate effectively the thermal properties distributions which showed variability over the whole spread in depth; but also was able to predict reservoir depth by the significant change in thermal properties distributions between formation and reservoir. Above all, the estimated oil saturation distribution agreed well with the field data with relative error below 10%. •A novel method predicting oil saturation based on temperature logs was presented.•A method estimating spatial thermal properties distributions was developed.•Reservoir depth could be predicted by spatial thermal properties distributions.