Mechanistic Modeling of Water-Alternating-Gas Injection and Foam-Assisted Chemical Flooding for Enhanced Oil Recovery

Abstract

History-matching of core-flood experimental data through numerical modeling is a powerful tool to get insight into the relevant physical parameters and mechanisms that control fluid flow in enhanced oil recovery processes. We conducted a mechanistic numerical simulation study aiming at modeling previously performed water-alternating-gas and foam-assisted chemical flooding core-flood experiments. For each experiment, a one-dimensional model was built. The obtained computed tomography scan data was used to assign varying porosity, and permeability, values to each grid block. The main goal of this study was to history-match measured phase saturation profiles along the core length, pressure drops, produced phase cuts, and the oil recovery history for each of the experiments conducted. The results show that, to obtain a good match for the water-alternating-gas experiment, gas relative permeability needs to be reduced as a function of injection time due to gas trapping. The surfactant phase behavior, for the aid of foam-assisted chemical flooding, was successfully simulated and its robustness was verified by effectively applying the same phase behavior model to the two different salinity conditions studied. It resulted in the oil mobilization, through the injection of a surfactant slug, being properly modeled. The mechanistic simulation of foam using the steady-state foam model built in UTCHEM proved inadequate for the mechanistic modeling of a foam drive in the presence of oil. An alternative heuristic approach was adopted to overcome this limitation.

Numerical simulation of enhanced oil recovery studies for aqueous gemini surfactant‐polymer‐nanoparticle systems

“…The effectiveness of numerical simulation studies on chemical flooding showed far‐reaching consequences during oilfield applications, as evident from the findings of earlier papers 37,40 . This kind of grid‐based model helps in understanding fluid flow behavior prior to injection; as well as achieve a sufficiently robust model 36,38,39 . Table 1 presents the core and fluid properties employed in STARS.…”

“…Druetta and co‐workers developed a flooding simulator to investigate EOR properties of different chemical fluid compositions 38 . Janssen et al found that CMG simulation model is useful to determine displacement phenomena governing flooding behavior, depending on injection fluid type 39 . The effectiveness of numerical simulation studies on chemical flooding showed far‐reaching consequences during oilfield applications, as evident from the findings of earlier papers 37,40 .…”

Numerical Simulation of Enhanced Oil Recovery (EOR) studies for aqueous Gemini Surfactant-Polymer-Nanoparticle systems

“…The effectiveness of numerical simulation studies on chemical flooding showed far-reaching consequences during oilfield applications, as evident from the findings of earlier papers [34,37]. This kind of grid-based model helps in understanding fluid flow behavior prior to injection; as well as achieve a sufficiently robust numerical model [33,35,36]. Table 1 presents the core and fluid properties employed in STARS model.…”

“…Druetta and co-workers [35] developed a flooding simulator to investigate EOR properties of different chemical fluid compositions. Arhuoma et al [36] found that CMG simulation model is useful to determine displacement phenomena governing flooding behavior, depending on injection fluid type. The effectiveness of numerical simulation studies on chemical flooding showed far-reaching consequences during oilfield applications, as evident from the findings of earlier papers [34,37].…”

Evaluation of the use of Water Alternated Gas Injection for Enhanced Oil Recovery

“…Mechanistic numerical simulation also can be carried out to model previous WAG and foam-assisted chemical flooding core-flood experiments (Janssen et al, 2020). In addition, a 1-D model was built to obtain tomography scan data and assign varying porosity and permeability to each grid.…”