Low-salinity water injection is an emerging IOR/EOR technique, applicable to mixed-to-oil-wet sandstone reservoirs. This paper describes the field response for two large fields: Omar (secondary flood) and Sijan (tertiary flood). The data were analyzed using analytical and numerical modelling tools. This included evaluation of scaling numbers, mixing and dispersion and calibration. Insight was obtained on relevant drive mechanisms. The responses to low-salinity flooding differ for the two fields: In Omar, a dual-step water-cut development was observed, which is characteristic for a change in wetting state. Our interpretation is that in this field, viscous forces provide the dominant drive mechanism, which is favorable to low-salinity flooding. We were able to history match the low-salinity response using a simple conceptual model.In Sijan, the low-salinity flood appears to be still immature and breakthrough of low-salinity water has not (yet) been observed. The reasons for the muted response thus far are explored, including a rather strong buoyancy effect caused by the higher permeability of the block, and the significant effect of injectant mixing with the highly saline aquifer. A proposal is made for a workflow on how to apply this analysis to future low-salinity flooding implementation in field cases.
Several general purpose benchmark generators are now available in the literature. They are convenient tools in dynamic continuous optimization as they can produce test instances with controllable features. Yet, a parallel work in dynamic discrete optimization still lacks.In constructing benchmarks for dynamic combinatorial problems, two issues should be addressed: first, test cases that can effectively test an algorithm ability to adapt can be difficult to create; second, it might be necessary to optimize several instances of an NP-hard problem. Hence, this paper proposes a method for generating benchmarks with known solutions without the need to re-optimize. Consequently, the method does not suffer the usual limitations on the problem size or the sequence length.The paper also proposes a general framework for the generation of test problems. It aims to unify existing approaches and to form a basis for designing newer benchmarks. Such a framework can be more appreciated knowing that combinatorial problems tend to assume very distinct structures, and hence, relevant benchmarks are basically too specific to be of interest to the general reader.
MapReduce is becoming a powerful parallel data processing model and is adopted by many cloud services providers to build cloud computing framework. However, in public cloud systems, several service providers may come from different administration domain out of the user control and may be untrustworthy. Hence, security of MapReduce computation is essential in public cloud systems. Additionally, MapReduce dataprocessing services are long-running, which increases the possibility that an attacker is able to compromise some workers and make them misbehave to corrupt the integrity of all computations allocated to these workers. Thus, the computation integrity is a major concern for Mapreduce user in public cloud environment. In this paper, we propose a new mechanism to ensure the computation integrity of MapReduce in public cloud computing environment. By using replication-based voting method and reputation-based trust management system, our mechanism can efficiently detect both collusive and non-collusive malicious workers and guarantee high computation accuracy with an acceptable overhead.
With the advent of cloud service-based applications and Software as a Service (SaaS), new applications have recently known an increasing use of service-oriented architecture (SOA). This model has allowed computer science and associated industries to build new customized applications, by using the available and the existing cloud services bridged together dynamically to form a complex workflow process with more functionalities. However cloud services with similar and compatible functionalities may be offered by multiple providers but may also be offered at different QoS levels. Hence, to build a composite service with a high QoS, a decision should be made based on end-to-end QoS. This work proposes a new approach, for QoS-aware cloud service composition, which addresses a universal model, with end-toend QoS. It also proposes an effective evolutionary method based on Shuffled Frog Leaping Algorithm (SFLA), which is satisfying global and local constraints. Therefore, in order to evaluate the robustness of the proposed approach, we have evaluated the impact of several parameters that are highly significant in evolutionary methods, such as the impact of the population size, number of candidate services per task and number of criteria. The experimental results show that the chosen algorithm performs better than the ones based on Genetic Algorithm (GA).
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