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primary topic: Numerical Investigation of Hybrid Smart Water and Foam Injections in Carbonate Reservoirs

Numerical Investigation of Hybrid Smart Water and Foam Injections in Carbonate Reservoirs
type: AcademicArticle
type: Article
abstract: This contribution is a progressive effort to investigate the effect of the novel hybrid EOR method of Smart Water Assisted Foam (SWAF) technique on oil recovery from carbonates through numerical modeling. In this work, a core-scale model was utilized to provide an insight and a better understanding of the controlling mechanisms behind incremental oil recovery using a new hybrid EOR method consisting of a combination of smart water flooding and foam injection, termed as Smart Water Assisted Foam (SWAF) technology, particularly for carbonate reservoirs. A core-scale model encapsulating the physics of SWAF flooding was used to history-match experimental data and the model was further optimized utilizing the CMG simulator. For extracting the most value from this numerical investigation, a sensitivity analysis was performed to monitor the effect of influential parameters affecting oil recovery depending on the spectrum of the experimental data available. The objective functions used in the sensitivity analysis include minimizing the history-matching global error and maximizing the oil recovery profiles. Three sensitivity analysis approaches were used: Tornado-plot, SOBOL analysis, and MORRIS analysis. For generating the related proxy models, polynomial regression, and radial basis function (RBF) neural networks were investigated. Subsequently, the DECE-based and PSO-based optimization methods were employed to examine the effect of chemical design parameters such as smart water (Mg2+), surfactant aqueous solution (SAS), and foam concentrations along with the liquid production rate on the oil recovery factor during SWAF-flooding. Based on the numerical results, the experimental coreflooding data were accurately history-matched using the proposed model with a minimal error of 4.74 applying the PSO-based optimization method. Furthermore, in terms of the objective function prediction during the sensitivity analysis study, the comparative assessment of both proxy models on the…
date: 2023
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Society of Petroleum Engineers - SPE Reservoir Characterisation and Simulation Conference and Exhibition 2023, RCSC 2023
type: Collection

E.W. Al-Shalabi
type: Person
given name: E.W.
family name: Al-Shalabi

M.A. Ayoub
type: Person
given name: M.A.
family name: Ayoub

M.E. Mohyaldinn
type: Person
given name: M.E.
family name: Mohyaldinn

I.A. Adel
type: Person
given name: I.A.
family name: Adel

B.N. Tackie-Otoo
type: Person
given name: B.N.
family name: Tackie-Otoo

A.M. Hassan
type: Person
given name: A.M.
family name: Hassan

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primary topic: Numerical Investigation of Hybrid Smart Water and Foam Injections in Carbonate Reservoirs