A 3D OBC survey was acquired over an offshore Field in Abu Dhabi, Middle East during 2000. The acquisition geometry of the survey provides data that is well sampled with respect to azimuth and offset range. Sensitivity analysis indicated that the data is suitable for azimuthal Pwave AVO studies. Data processing was designed to preserve offset-azimuth amplitude variations. A subset of the 3D data volume was extracted for azimuthal P-wave AVO analysis. The results suggest that there is a dominant trend of open fractures within the study volume. Acquisition and Data Processing The 3D survey area is approximately 236 km west offshore Abu Dhabi in water depths ranging from 10-40m. The primary geologic zones of interest are the Uweinat reservoir at around 1.7-1.9s TWT, Khuff between 2.0-2.2s and the pre-Khuff at 2.3-2.5s. The 3D OBC survey was acquired over this area during 2000 using a wide azimuth, high-fold patch acquisition geometry in order to resolve the fault-orientation in the area.
In ADNOC Oil and Gas 4.0 mission, we are committed to empower people with the needed capabilities and Artificial Intelligence (AI) technologies to fuel innovation, efficiency and more importantly achieve and sustain a 100% HSE, by transforming the way of handling HSE events by moving from reactive to proactive approach. The ultimate objective is to save lives, empower the vessel Captains to immediately identify and respond to violators, improve the HSE culture of the crew, and automatically generate live data analytics and statistics with the aim of improving safety in operations. The implemented AI use cases are; deviation for not wearing Protective Safety equipment in designated areas, violation of not utilizing safety passages, alert when no watchman in muster station, alarm when man overboard incident, alarm when man fell on stairs, and live Personnel on board each weather-deck. When introduced the Artificial Intelligence cameras, our marine vessels will adopt a smarter automated response and reporting culture, which will in turn, lead to increased safety oversight of our critical offshore operations. Therefore, with the advent of the AI technology, many common business processes have been automated thus enabling personnel to increase their focus on more important tasks while technologies like the AI System can handle many of the time consuming tasks. The solution components consists of Artificial Intelligence platform, high definition cameras, local server, wide-range WiFi access point, network infrastructure and a tablet. On the tablet device, the captain have full coverage of the vessel weather decks, working areas and restricted zones with a feature to generate alerts when detecting an emergency situation. This was provided to empower the vessel Captain to acknowledge and respond to violations as well as take a proactive action to prevent incidents from happening. The Machine Learning algorithm has been trained on actual scenarios and will be continuously improved by adding more recorded event to retrain the initial model. Currently, the prediction model is performing on the vessel operation mode and recording events with high rate of accuracy. In case of automatically detecting an alerting or non-compliance event, the captain would be notified, beacon lights and sound, and log recorded in the local and central system with a photo and a short video clip of the incident. The process of identifying HSE deviations are becoming digitally transformed by deploying AI capabilities on real-time video streams. The AI-based camera system leverages Computer Vision features that enables machines to get and analyze visual information and take action. The whole process of identifying HSE violation events has been digitally transformed by deploying an artificial intelligence solution to perform real time video analytics.
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