Inverse and forward uncertainty quantification of models for foam–assisted enhanced oil recovery

The traditional Enhanced Oil Recovery (EOR) processes allow improving the performance of mature oilfields after waterflooding projects. Chemical EOR processes modify different physical properties of the fluids and/or the rock in order to mobilize the oil that remains trapped. Furthermore, combined processes have been proposed to improve the performance, using the properties and synergy of the chemical agents. This paper presents a novel simulator developed for a combined surfactant/polymer flooding in EOR processes. It studies the flow of a two-phase, five-component system (aqueous and organic phases with water, petroleum, surfactant, polymer and salt) in porous media. Polymer and surfactant together affect each other’s interfacial and rheological properties as well as the adsorption rates. This is known in the industry as Surfactant-Polymer Interaction (SPI). The simulations showed that optimum results occur when both chemical agents are injected overlapped, with the polymer in the first place. This procedure decreases the surfactant’s adsorption rates, rendering higher recovery factors. The presence of the salt as fifth component slightly modifies the adsorption rates of both polymer and surfactant, but its influence on the phase behavior allows increasing the surfactant’s sweep efficiency.

Abstract. Runoff is a critical component of the terrestrial water cycle and Earth System Models (ESMs) are essential tools to study its spatio-temporal variability. Runoff schemes in ESMs typically include many parameters so model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using an uncertainty quantification framework. First, the Polynomial Chaos Expansion machinery with Bayesian Compressed Sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5° × 0.5° for 1991–2010. The main methodological advance in this work is the construction of surrogates for the error metric between ELM and the benchmark data, facilitating efficient calibration and avoiding the more conventional, but challenging, construction of high-dimensional surrogates for ELM itself. Second, the Sobol index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that in most regions ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multi-model ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

The Surfactant-Polymer (SP) process is a type of Chemical Enhanced Oil Recovery (CEOR) method. They are still a challenge for the petroleum oil industry mainly because of the difficulty in designing and forecasting the process behavior on the field scale. Therefore, understanding of the phenomena associated with a CEOR process is of vital importance. For these reasons, this work discusses the benefits of Computed Tomography (CT) uses for the experimental assessment of a SP process. The research includes a literature review that allows identifying the main CT usages for petroleum engineering and a discussion concerning the effectiveness of mathematic expressions proposed for the tomography images treatment of two-phase flow displacement. The conducted experimental methodology can be reproduced to assess the benefits of any chemical Enhanced Oil Recovery (EOR) process with CT. Thus, this paper assesses the conventional waterflooding (WF) and SP flooding as secondary and tertiary oil recovery methods. The developed study allowed us to evaluate through CT images the porosity and the saturation profiles along the rock sample. Also, CT processed data enabled checking the volumetric material balance and determine the oil Recovery Factor (RF). The doubled checked SP data showed an RF increase of 17 and 10 percentage points for secondary and tertiary chemical injection schemes respect to conventional waterflooding. Finally, comparative results of the water cut (Wcut) evidenced the mobility ratio improvement and reduction on the remaining oil saturation.

Nowadays, nanotechnology has become a very attractive subject in Enhanced Oil Recovery (EOR) researches. In the current study, a carbonate system has been selected and first the effects of nanoparticles on the rock and fluid properties have been experimentally investigated and then the simulation and numerical modeling of the nanofluid injection for enhanced oil recovery process have been studied. After nanofluid treatment, experimental results have shown wettability alteration. A two-phase flow mathematical model and a numerical simulator considering wettability alteration have been developed. The numerical simulation results show that wettability alteration from oil-wet to water-wet due to presence of nanoparticles can lead to 8–10% increase in recovery factor in comparison with normal water flooding. Different sensitivity analyses and injection scenarios have been considered and assessed. Using numerical modeling, wettability alteration process and formation damage caused by entrainment and entrapment of nanoparticles in porous media have been proved. Finally, the net rate of nanoparticles’ loss in porous media has been investigated.