Evaluation of enhanced oil recovery from clay-rich sandstone formations

In the last few years, there has been a growing interest in smart water (SW) flooding as economically and environmentally friendly method to Enhanced Oil Recovery (EOR) in sandstone and carbonated reservoirs. Formation damage especially fines migration and clay swelling by lowering salinity and changing the ionic environment, causes the significant decrease in permeability of the sandstone reservoirs. In this study, an experimental study has been undertaken to illuminate the effect of formation damage during smart water injection as the function of clay types. The state of the art procedure has been established in direction of sandpack construction containing favorable clay content. Injection of smart water was performed in sandpacks with different clay types (montmorillonite and kaolinite). The results show that the presence of montmorillonite augments formation damage and enhances oil recovery. Analyzing Interfacial Tension (IFT) experimental data showed that interaction of oil/SW had no great influence on increasing oil recovery. The results have been achieved based on extensive experiments including Differential Pressure (DP) measurements, Zeta potential, and Recovery Factor (RF). Two mechanisms were proposed to interpret permeability reduction and amount of oil produced values which are clay swelling, and detachment/re-attachment for montmorillonite and kaolinite, respectively.Armin Bazyari, Mohammad Jamialahmadi, Bahram Soltani Soulgani and Abbas Zeinijahrom

Cue-based aggregation with a mobile robot swarm: A novel fuzzy-based method

Aggregation in swarm robotics is referred to as the gathering of spatially distributed robots into a single aggregate. Aggregation can be classified as cue-based or self-organized. In cue-based aggregation, there is a cue in the environment that points to the aggregation area, whereas in self-organized aggregation no cue is present. In this paper, we proposed a novel fuzzy-based method for cue-based aggregation based on the state-of-the-art BEECLUST algorithm. In particular, we proposed three different methods: naïve, that uses a deterministic decision-making mechanism; vector-averaging, using a vectorial summation of all perceived inputs; and fuzzy, that uses a fuzzy logic controller. We used different experiment settings: one-source and two-source environments with static and dynamic conditions to compare all the methods. We observed that the fuzzy method outperformed all the other methods and it is the most robust method against noise. © The Author(s) 2014

Multivariate Permutation Tests for Ordered Categorical Data

The main goal of this article is to compare whether different groups with ordinal responses on the same measurement scale satisfy stochastic dominance and monotonic stochastic ordering. In the literature, the majority of inferential approaches to settle the univariate case are proposed within the likelihood framework. These solutions have very nice characterizations under their stringent assumptions. However, when the set of alternatives lie in a positive orthant with more than four dimensions, it is quite difficult to achieve proper inferences. Further, it is known that testing for stochastic dominance in multivariate cases by likelihood approach is much more difficult than the univariate case. This paper intends to discuss the problem within the conditionality principle of inference through the permutation testing approach and the nonparametric combination (NPC) of dependent permutation tests. The NPC approach based on permutation theory is generally appropriate to suitably find exact good solutions to this kind of problems. Moreover, some solutions for a typical medical example are provided