New insights into transport phenomena involved in carbonated water injection: effective mathematical modeling strategies

Esene, Cleverson Ebeagbor (2019) New insights into transport phenomena involved in carbonated water injection: effective mathematical modeling strategies. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Abstract

Carbonated water injection (CWI) is a promising enhanced oil recovery (EOR) method that provides an efficient and a more environmentally friendly alternative to meet the ever-increasing demand for energy. An additional benefit from the implementation of CWI is the storage of anthropogenic CO₂ and this has made it even more attractive. Over the years, several attempts have been made to model CWI as an EOR process but have been of very little success due to the underlying assumptions used or the modelling strategy. There are several multi-physics involved during CWI and to have an accurate model to investigate CWI, these physics need to be adequately captured. In this thesis, we have attempted to model CWI adequately by using more realistic and practical assumptions to present a novel modeling strategy. This thesis shows our research in a manuscript-based format which is presented in each chapter as major contributions. Firstly, a comprehensive review of CWI where the behavior of fluids, fluid-rock interactions and challenges associated with CWI technique have been thoroughly discussed. Secondly, the modelling investigation to capture the critical salinity which plays an important role in EOR techniques for sandstones and carbonate as well as the solubility of CO₂ during CWI is presented. Thirdly, a 3-D modeling method to investigate CWI which considers important terms such as gravity, non-instantaneous equilibrium, heterogeneity, anisotropy and well orientation is presented. Fourthly, a 1-D core modelling approach which considers the reaction term and rock dissolution in an improved attempt to capture CWI is presented. Finally, a deterministic approach is presented to effectively predict oil recovery factor based on pattern recognition and artificial intelligence. To facilitate this, the use of artificial neural network (ANN), least square support vector machine (LSSVM) modelling and gene expression programming (GEP) are adopted.

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