While significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are interested in learning a fine-grained representation of how users are feeling towards their products or services. In this work, we describe the methods used by the team Emotion Mining in Arabic (EMA), as part of the SemEval-2018 Task 1 for Affect Mining for Arabic tweets. EMA participated in all 5 subtasks. For the five tasks, several preprocessing steps were evaluated and eventually the best system included diacritics removal, elongation adjustment, replacement of emojis by the corresponding Arabic word, character normalization and light stemming. Moreover, several features were evaluated along with different classification and regression techniques. For the 5 subtasks, word embeddings feature turned out to perform best along with Ensemble technique. EMA achieved the 1 st place in subtask 5, and 3 rd place in subtasks 1 and 3.

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In this paper, we discuss the development of an end-to-end waterflood optimization solution that provides monitoring and surveillance dashboards with artificial intelligence (AI) and machine learning (ML) components to generate and assess insights into waterflood operational efficiency in an automated manner. The solution allows for fast screening of waterflood performance at diverse levels (reservoir, sector, pattern, well) enabling prompt identification of opportunities for immediate uptake into an opportunity management process and for evaluation in AI-driven production forecast solution and/or a reservoir simulator. The process starts with the integration of a wide range of production and reservoir engineering data types from multiple sources. Following this, a series of monitoring and surveillance dashboards of key units and elements of the entire waterflood operations are created. The workflows in these dashboards are framed with key waterflood reservoir and production engineering concepts in mind. The optimization opportunity insights are then extracted using automated traditional and AI/ML algorithms. The identified opportunities are consolidated in an optimization action list. This list is passed to an AI-driven production forecast solution and/or a reservoir simulator to assess the impact of each scenario. The system is designed to improve the business-time decision-making cycle, resulting in increased operational performance and lower waterflood operating costs by consolidating end-to-end optimization workflows in one platform. It incorporates both surface and subsurface aspects of the waterflood and provides a comprehensive understanding of waterflood operations from top-down field, reservoir, sector, pattern and well levels. Its AI/ML components facilitate understanding of producer-injector relationships, injector dynamic performance, underperformance of patterns in the sector as well as evaluating the impact of different optimization scenarios on incremental oil production. The data-driven production forecast component consists of several ML models and is tailored to assess their impact on oil production of different scenarios such as changes in voidage replacement ratio (VRR) in reservoir, sector, pattern and well levels. Opportunities are also converted into reservoir simulator compatible format in an automated manner to assess the impact of different scenarios using more rigorous numerical methods. The scenarios that yield the highest impact are passed to the field operations team for execution. The solution is expected to serve as a benchmark, upon successful implementation, for optimizing injection schemas in any field or reservoir. The novelty of the system lies in automating the insights generation process, in addition to integrating with an AI/ML production forecasting solution and/or a reservoir simulator to assess different optimization scenarios. It is an end-to-end solution for waterflood optimization because of the integration of various components that allow for the identification and assessment of opportunities all in one environment.

Mature field operators collect log data for tens of years. Collection of log dataset include various generation and multiple vintages of logging tool from multiple vendors. Standard approach is to correct the logs for various artefacts and normalize the logs over a field scale. Manually conducting this routine is time consuming and subjective. The objective of the study was to create a machine learning (ML) assisted tool for logs in a giant Lower Cretaceous Carbonate Onshore field in Abu Dhabi, UAE to automatically perform data QC, bad data identification and log reconstruction (correcting for borehole effects, filling gaps, cleaning spikes, etc.) of Quad Combo well logs. The study targets Quad Combo logs acquired since mid-60's. Machine learning algorithm was trained on 50 vertical wells, spread throughout the structure of the field. The workflow solution consists of several advanced algorithms guided by domain knowledge and physics based well logs correlation, all embedded in an ML-data-driven environment. The methodology consists of the following steps: oOutliers detection and complete data clustering.oSupervised ML to map outliers to clusters.oRandom Forest based ML training by clusters, by logs combination on complete data.oSaved models are applied back to the whole data including outliers and sections with one or several logs missing.oValidation and Blind test of results.oModels can be stored and re-used for prediction on new data. The ML tool demonstrated its effectiveness while correcting logs for outliers’ like Depth Offsets between logs, identifying Erroneous readings, logs prediction for absent data and Synthetic logs corrections. The tool has a tendency to harmonize logs. First test demonstrated robustness of the selected algorithm for outliers’ detection. It cleaned data from most of contamination, while keeping good but statistically underrepresented logs readings. Clustering algorithm was enhanced to supplement cluster assignment by extraction of the corresponding probabilities that were used as a cut-off value and utilized for a mixture of different ML models results. This application made results more realistic in the intervals where clustering was problematic and at the transition between different clusters. Several intervals of bad and depth shifted logs corrections were noticed. Outliers’ corrections for these logs was performed the way that at Neutron-Density or Neutron-Sonic cross-plots points were moved towards expected lithology lines. Algorithm could pick-up hidden outliers (such as synthetic logs) and edited the logs to make it look intuitively natural to a human analyst. The work successfully demonstrated effectiveness of ML tool for log editing in a complex environment working on a big dataset that was subject of manual editing and has number of hidden outliers. This strong log quality assurance further assisted in building Rock Typing based Static Model in complex and diagenetically altered Carbonates.