Updated EOR screening criteria and modeling the impacts of water salinity changes on oil recovery
type: CreativeWork
description: "Developing technologies for enhanced oil recovery (EOR) from existing oil fields would supply the world's energy needs for several decades. The application of EOR in many major oil-producing countries remains in its conceptual stage. Thermal and gas EOR methods achieve high incremental rates; however, their application range has not broadened significantly as they have matured, while the rate at which new, promising EOR methods such as Low-salinity water flooding (LSWF) are being implemented is alarming. Despite, the potential of LSWF its development and application has been hindered by the lack of consensus concerning its recovery mechanism(s). Every oil reservoir has a unique ionic environment that changes naturally and by human intervention, which makes it difficult to identify recovery mechanism(s) in EOR methods such as LSWF. This study updates the EOR selection criteria and presents new EOR screening tools based on dataset distribution, incremental recovery and deterministic modeling. LSWF recovery mechanisms are investigated by statistical analysis and numerical solutions. Furthermore, an up-scaled multi-dimensional model is developed for LSWF under various reservoir wetting conditions. Finally, a risk analysis case study is included. The results in this study include an incremental recovery prediction model for miscible CO₂ flooding. The use of statistical analysis and reservoir simulation identifies different LSWF recovery mechanism(s) based on the initial and final wetting state in conjunction with injection brine chemistry. Three dimensional models of LSWF outline the importance of sweep efficiency and the potential incremental recovery in oil-wet reservoirs. On a separate note, hydrogen sulfide (H₂S) risk in anthropogenic CO₂ transportation is highlighted"--Abstract, page iii.
abstract: "Developing technologies for enhanced oil recovery (EOR) from existing oil fields would supply the world's energy needs for several decades. The application of EOR in many major oil-producing countries remains in its conceptual stage. Thermal and gas EOR methods achieve high incremental rates; however, their application range has not broadened significantly as they have matured, while the rate at which new, promising EOR methods such as Low-salinity water flooding (LSWF) are being implemented is alarming. Despite, the potential of LSWF its development and application has been hindered by the lack of consensus concerning its recovery mechanism(s). Every oil reservoir has a unique ionic environment that changes naturally and by human intervention, which makes it difficult to identify recovery mechanism(s) in EOR methods such as LSWF. This study updates the EOR selection criteria and presents new EOR screening tools based on dataset distribution, incremental recovery and deterministic modeling. LSWF recovery mechanisms are investigated by statistical analysis and numerical solutions. Furthermore, an up-scaled multi-dimensional model is developed for LSWF under various reservoir wetting conditions. Finally, a risk analysis case study is included. The results in this study include an incremental recovery prediction model for miscible CO₂ flooding. The use of statistical analysis and reservoir simulation identifies different LSWF recovery mechanism(s) based on the initial and final wetting state in conjunction with injection brine chemistry. Three dimensional models of LSWF outline the importance of sweep efficiency and the potential incremental recovery in oil-wet reservoirs. On a separate note, hydrogen sulfide (H₂S) risk in anthropogenic CO₂ transportation is highlighted"--Abstract, page iii.
author: Aladasani, Ahmad
keywords: Low-salinity water flooding (LSWF), Petroleum Engineering
date published: 2016-02-10T08:00:00Z
date modified: 2026-07-24T03:19:04Z
provider: Missouri University of Science and Technology
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Missouri University of Science and Technology