Air injection is an effective technique for improved oil recovery in light oil reservoirs. It is speculated that the main mechanism of the process is via spontaneous low-temperature oxidation (LTO) to consume oxygen and generate “flue gas” that displaces oil out of the reservoir. In this study, laboratory experiments have been conducted to study the effects of oil composition and main reservoir parameters on the kinetics of LTO, in a range of reservoir temperatures from 70 to 150 °C. Saturates, aromatics, resins, and asphaltenes (SARA) analysis and experiments using pure oil components were preformed to study the oxidation activity of different oil compounds and components. Reaction rates of typical light and heavy oil samples were also measured for comparison. Effects of temperature, pressure, water saturation, sand type, and residence time on reaction rates and products were investigated under static and dynamic conditions. The results indicate that different oil components exhibit different reaction activity under the LTO conditions. Heavy oils can be more readily oxidized than light oils at low temperatures. The data shed more light on the mechanisms of LTO reactions and can provide guidelines for reservoir selection and air injection process design.
SUMMARYWe consider a technique to estimate an approximate gradient using an ensemble of randomly chosen control vectors, known as Ensemble Optimization (EnOpt) in the oil and gas reservoir simulation community. In particular, we address how to obtain accurate approximate gradients when the underlying numerical models contain uncertain parameters because of geological uncertainties. In that case, 'robust optimization' is performed by optimizing the expected value of the objective function over an ensemble of geological models. In earlier publications, based on the pioneering work of Chen et al. (2009), it has been suggested that a straightforward one-to-one combination of random control vectors and random geological models is capable of generating sufficiently accurate approximate gradients. However, this form of EnOpt does not always yield satisfactory results. In a recent article, formulate a modified EnOpt algorithm, referred to here as a Stochastic Simplex Approximate Gradient (StoSAG; in earlier publications referred to as 'modified robust EnOpt') and show, via computational experiments, that StoSAG generally yields significantly better gradient approximations than the standard EnOpt algorithm. Here, we provide theoretical arguments to show why StoSAG is superior to EnOpt.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.