COVID-19 is a contagious disease that has infected over half a billion people worldwide. Due to the rapid spread of the virus, countries are facing challenges to cope with the infection growth. In particular, healthcare organizations face difficulties efficiently provisioning medical staff, equipment, hospital beds, and quarantine centers. Machine and deep learning models have been used to predict infections, but the selection of the model is challenging for a data analyst. This paper proposes an automated Artificial Intelligence-enabled proactive preparedness real-time system that selects a learning model based on the temporal distribution of the evolution of infection. The proposed system integrates a novel methodology in determining the suitable learning model, producing an accurate forecasting algorithm with no human intervention. Numerical experiments and comparative analysis were carried out between our proposed and state-of-the-art approaches. The results show that the proposed system predicts infections with 72.1% less Mean Absolute Percentage Error (MAPE) and 65.2% lower Root Mean Square Error (RMSE) on average than state-of-the-art approaches.
An integrated and collaborative study was required in order to determine the most cost effective field development scenario while ensuring collision risk mitigation, to define and validate the well planning and slot allocation for the wells scheduled for the next ten years as part of the re-development due to a new sub-surface strategic scheme that was later extended to the full lifecycle of a green field offshore Abu Dhabi. The workflow included data, feedback and participation of four main stakeholders: Sub-surface Team, Petroleum Engineering Team, Drilling & Completion Team and Surface Facilities Engineering Team. The process started with the provision of the targets by the Petroleum Engineering Team, previously validated by the Sub-Surface Team to the Drilling & Completion Team. The second step included generation of preliminary trajectories including high-level anti-collision analysis against existing wells as well as other planned wells; this step also included validation of the Completion requirements based on the preliminary drilling schedule and equipment availability. The trajectories were then sent back to the Petroleum Engineering Team for well objectives validation and finally a multi-disciplinary session with the Surface Facilities Engineering Team, Petroleum Engineering Team and Drilling & Completion Team was executed to ensure readiness of surface installations based on the drilling schedule; as part of the outcome of this session multiple iterations occurred until alignment and agreement of all the stakeholders was achieved. The outcome of the workflow was the generation of full field development study including the preliminary trajectories, their respective slot allocation, high-level anti-collisions and estimated Drillex (Drilling Capex) validated and agreed by all stakeholders. This novel approach to the integrated multi-disciplinary collaborative field development well planning provides multiple benefits such as: 1. Fast delivery of scenarios for field development well planning, reducing the cycle time to less than half of the conventional time required. 2. Generation of multiple scenarios instead of a single scenario, allowing further optimization of cost and risk reduction without compromising expected production targets. 3. Early understanding of the completion equipment requirements to ensure availability based on the drilling schedule. 4. Quick response to unplanned changes based on the understanding of the full field scale planning allowing swapping of wells with minimum to impact on cost, risk and operations. 5. Full In-House process that represents a continuous and dynamic project allowing constant fine tuning based on new data and new models instead of a fixed time stamp, static, project with a single report outcome.
Manual drifting of drill pipes from the derick constitutes a potentially serious risk from dropped objects. When drifting in the derrick, traditionally rig crews manually place the drift in the carrier on the bails and transport it to the Derrickman who will usually be around 90ft above the rig floor. He then has to retrieve it and place it in the drill pipe. When the drill pipe is picked up, the drift falls out of the bottom tool joint and the Roughneck retrieves it to begin the process again. There is a potential dropped object risk every time for the Derrickman if he mishandles the drift and if it falls toward the rig floor and potentially strikes a member of the rig personnel. This paper will detail the risk of manual drifting in the derick and also present a novel alternative method using a drift catcher sub that eliminates the dropped objects hazard completely – while also saves significant operational time when utilized.
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