1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China 2. State-Province Joint Engineering Laboratory of Spatial Information Technology of High-Speed Railway Safety, Chengdu 610031, China 3. School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500, China
The inversion of reservoir parameters and production for the oil field can grasp reservoir status and production changes in time and effectively monitor reservoir health and safety. At present, the study of reservoir parameter inversion is very insufficient in China. The authors chose Shuguang Oil Production Plant, the largest oil production plant in the Liaohe Oilfield, as the research object. Using 21 L-band ALOS/PALSAR data obtained from January 2007 to September 2010, the authors employed StaMPS to extract deformation results. Based on these deformation results, the authors used Mogi model and Finite Prolate Spheroidal model to invert and analyze reservoir parameters respectively, with the inversion results compared with those of Okada model. The results are as follows: ① The subsidence of Shuguang Oil Production Plant is remarkable. The maximum subsidence rate is -189.6 mm/year, the maximum cumulative subsidence is about 750 mm, and the subsidence area is about 28 km 2. ② Compared with Okada model and Mogi model, Finite Prolate Spheroidal model has the highest accuracy of reservoir depth inversion, and the simulated deformation results are in the best agreement with the observed deformation results, which shows that the inversion results of Finite Prolate Spheroidal model are more reliable and more suitable for the inversion of reservoir parameters in this oilfield. This study can provide scientific reference for InSAR subsidence monitoring and reservoir parameter inversion in the oilfield.
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Chong YANG,Guoxiang LIU,Bing YU,Bo ZHANG,Rui ZHANG,Xiaowen WANG. Inversion of reservoir parameters in Shuguang Oil Production Plant of the Liaohe Oilfield based on InSAR deformation. Remote Sensing for Land & Resources, 2020, 32(1): 209-215.
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