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This paper presents the models submitted by Ghmerti team for subtasks A and B of the Of-fensEval shared task at SemEval 2019. Offen-sEval addresses the problem of identifying and categorizing offensive language in social media in three subtasks; whether or not a content is offensive (subtask A), whether it is targeted (subtask B) towards an individual, a group, or other entities (subtask C). The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model for subtask A is 77.93% macro-averaged F 1-score.

This paper presents an adaptive approach to optimize field injection strategies using streamline-based well allocations coupled with fuzzy logic. The strength of our approach comes from the fact that streamlines are generated by running full-physics in-house reservoir simulator. Streamlines provide great insights about well pattern connectivity and well allocation factors allowing the injection efficiency (IE) for each pattern to be determined. This enables us to assess and improve the performance of injectors highlighting patterns with low IEs, high voidage replacement ratio (VRR), and high water production to name a few. Once streamline-based IEs are evaluated, a fuzzy logic system is developed and run to rank the injectors according to their injection performance. Three parameters: IE, water cut, and amount of injected water lost to the aquifer constitute the input to the fuzzy logic system. The fuzzy system categorizes the pattern IEs into four classes: poor, acceptable, good, and excellent. Similarly, it classifies the pattern water cut and the loss of injection into three categories: low, medium, and high. The fuzzy system then outputs an injection-ranking index (IRI) for each injection pattern to recommend a new injection strategy. The new injection rates are then fed into our in-house simulator and results are evaluated to quantify improvements in reservoir sweep efficiency. This approach has been tested on large-scale simulation models with hundreds of wells and tens of millions of grid blocks. Results show noticeable improvements in injection efficiency for most wells resulting in reducing injection requirements, ensuring better sweep and pressure support without compromising oil production.