Synergetic effect between in-situ mobility control and micro-displacement for chemical enhanced oil recovery (CEOR) of a surface-active nanofluid

Author(s):  
Rui Liu ◽  
Jiayue Lu ◽  
Wanfen Pu ◽  
Quan Xie ◽  
Yuanyuan Lu ◽  
...  
2020 ◽  
Vol 146 ◽  
pp. 02002
Author(s):  
Zachary Paul Alcorn ◽  
Sunniva B. Fredriksen ◽  
Mohan Sharma ◽  
Tore Føyen ◽  
Connie Wergeland ◽  
...  

This paper presents experimental and numerical sensitivity studies to assist injection strategy design for an ongoing CO2 foam field pilot. The aim is to increase the success of in-situ CO2 foam generation and propagation into the reservoir for CO2 mobility control, enhanced oil recovery (EOR) and CO2 storage. Un-steady state in-situ CO2 foam behavior, representative of the near wellbore region, and steady-state foam behavior was evaluated. Multi-cycle surfactant-alternating gas (SAG) provided the highest apparent viscosity foam of 120.2 cP, compared to co-injection (56.0 cP) and single-cycle SAG (18.2 cP) in 100% brine saturated porous media. CO2 foam EOR corefloods at first-contact miscible (FCM) conditions showed that multi-cycle SAG generated the highest apparent foam viscosity in the presence of refined oil (n-Decane). Multi-cycle SAG demonstrated high viscous displacement forces critical in field implementation where gravity effects and reservoir heterogeneities dominate. At multiple-contact miscible (MCM) conditions, no foam was generated with either injection strategy as a result of wettability alteration and foam destabilization in presence of crude oil. In both FCM and MCM corefloods, incremental oil recoveries were on average 30.6% OOIP regardless of injection strategy for CO2 foam and base cases (i.e. no surfactant). CO2 diffusion and miscibility dominated oil recovery at the core-scale resulting in high microscopic CO2 displacement. CO2 storage potential was 9.0% greater for multi-cycle SAGs compared to co-injections at MCM. A validated core-scale simulation model was used for a sensitivity analysis of grid resolution and foam quality. The model was robust in representing the observed foam behavior and will be extended to use in field scale simulations.


2018 ◽  
Author(s):  
Sandeep Kumar ◽  
Shuaib Ahmed Kalwar ◽  
Ghulam Abbas ◽  
Abdul Quddos Awan

Author(s):  
Xue-Zhi Zhao ◽  
Guang-Zhi Liao ◽  
Ling-Yan Gong ◽  
Huo-Xin Luan ◽  
Quan-Sheng Chen ◽  
...  

2004 ◽  
Author(s):  
George J. Hirasaki ◽  
Clarence A. Miller ◽  
Gary A. Pope ◽  
Richard E. Jackson

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Chuan Lu ◽  
Wei Zhao ◽  
Yongge Liu ◽  
Xiaohu Dong

Oil-in-water (O/W) emulsions are expected to be formed in the process of surfactant flooding for heavy oil reservoirs in order to strengthen the fluidity of heavy oil and enhance oil recovery. However, there is still a lack of detailed understanding of mechanisms and effects involved in the flow of O/W emulsions in porous media. In this study, a pore-scale transparent model packed with glass beads was first used to investigate the transport and retention mechanisms of in situ generated O/W emulsions. Then, a double-sandpack model with different permeabilities was used to further study the effect of in situ formed O/W emulsions on the improvement of sweep efficiency and oil recovery. The pore-scale visualization experiment presented an in situ emulsification process. The in situ formed O/W emulsions could absorb to the surface of pore-throats, and plug pore-throats through mechanisms of capture-plugging (by a single emulsion droplet) and superposition-plugging or annulus-plugging (by multiple emulsion droplets). The double-sandpack experiments proved that the in situ formed O/W emulsion droplets were beneficial for the mobility control in the high permeability sandpack and the oil recovery enhancement in the low permeability sandpack. The size distribution of the produced emulsions proved that larger pressures were capable to displace larger O/W emulsion droplets out of the pore-throat and reduce their retention volumes.


2015 ◽  
Author(s):  
Muhammad Sagir ◽  
Muhammad Mushtaq ◽  
Muhammad Rehan Hashment

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