Develop an AI-Driven Fault Detection Model to Autonomously Troubleshoot Electrical Power Grids in High-Risk Offshore Oil Platforms (Published)
Offshore oil platforms represent some of the most challenging operational environments in the world, where electrical power grids are the lifeline for critical functions such as drilling, production, safety systems, and crew support. These isolated microgrids must maintain exceptional reliability amidst harsh marine conditions—saltwater corrosion, extreme weather, and high humidity—while facing logistical constraints like limited maintenance access and the absence of external power backups. Electrical faults in these settings, such as cable insulation failures, circuit overloads, and power fluctuations, pose severe risks: production downtime costing millions of dollars per day, environmental disasters like oil spills, and safety hazards that threaten human lives. Traditional fault management approaches—reliant on scheduled maintenance, manual inspections, and reactive troubleshooting—fall short in these high-stakes conditions. They often fail to detect incipient faults early enough to prevent escalation, depend heavily on scarce human expertise, and expose personnel to hazardous interventions. This research introduces a groundbreaking AI-driven fault detection model designed to autonomously troubleshoot electrical power grids on offshore oil platforms, integrating deep learning, IoT sensor networks, self-healing mechanisms, and reinforcement learning to deliver a robust, proactive solution tailored to these unique challenges. The cornerstone of this framework is the use of deep learning models to achieve early detection of critical electrical anomalies. Cable insulation failures, a prevalent issue due to saltwater exposure and mechanical stress, are identified using Convolutional Neural Networks (CNNs) that analyze high-frequency waveform data sampled at 25.6 kHz. Trained on a dataset of 127,500 labeled samples, including both normal and faulted conditions, the CNN achieves a detection accuracy of 98.2%, identifying degradation up to 62.8 days before critical failure—over eight times earlier than conventional methods. Circuit overloads, driven by variable loads from drilling and processing equipment, are predicted using Long Short-Term Memory (LSTM) networks, which process multi-sensor time-series data to forecast overload conditions with a mean squared error of 0.002 and an average lead time of 4.3 hours. Power fluctuations, often caused by generator instability or harmonic distortions, are detected by a hybrid CNN-LSTM model with 97.3% accuracy, enabling proactive adjustments to mitigate equipment damage and maintain power quality. These models collectively transform fault detection from a reactive process to a predictive one, offering substantial lead times for planned interventions and reducing reliance on emergency repairs. Beyond detection, the system incorporates self-healing electrical grids powered by AI-driven automated rerouting mechanisms. A reinforcement learning (RL) agent, implemented with a Double Deep Q-Network (DDQN), models the grid as a multi-agent environment where components like generators, switchgear, and transformers are nodes with dynamic states. Trained in a high-fidelity digital twin simulating real-world fault scenarios, the agent learns optimal switching sequences to isolate faults and reroute power in just 120 milliseconds—over 100 times faster than traditional automated systems and dramatically outpacing manual responses averaging 12.7 minutes. During a 12-month pilot on a North Sea oil platform, this self-healing system mitigated 37 potential disruption events, reducing mean time to resolution by 68% (from 8.2 seconds with conventional automation to 3.1 seconds) and boosting critical load availability from 99.92% to 99.98%. By prioritizing safety-critical loads and balancing generator output, it minimized production interruptions, achieving zero electrical-related shutdowns compared to three in the prior year. This autonomous capability not only enhances grid stability but also reduces personnel exposure to hazardous troubleshooting tasks. IoT-based fault localization forms another critical pillar, enabling precise predictive diagnostics through a network of 867 ruggedized sensors deployed across the platform’s electrical infrastructure. These sensors—measuring voltage, current, temperature, partial discharge, vibration, and environmental factors—are designed to IP68 standards for durability in offshore conditions. Edge computing nodes preprocess data with wavelet denoising and adaptive sampling, reducing bandwidth demands while providing high-resolution insights during anomalies. The system achieves a fault localization accuracy of 0.9 meters—a 23-fold improvement over the 20.8-meter average of traditional methods—using a fusion of traveling wave analysis (pinpointing faults within ±2 meters) and impedance-based techniques. Predictive diagnostics extend this capability, with component health models estimating remaining useful life and identifying failure modes with 87.6% accuracy, based on trends like gradual insulation degradation or overheating. In practice, this precision cut average repair time by 63.2% (from 8.7 to 3.2 hours) and shifted the planned-to-emergency maintenance ratio from 1.8:1 to 7.3:1, empowering crews with actionable insights to address issues before they escalate. Reinforcement learning further enhances grid resilience, adapting to the dynamic and unpredictable nature of offshore operations. A Proximal Policy Optimization (PPO) algorithm optimizes protection settings, load shedding, and preemptive control actions within a simulation environment reflecting real platform data and fault histories. The RL framework improves robustness by 38.1% (from 6.3 to 8.7 on a 10-point scale), accelerates recovery by 76.3% (from 17.3 to 4.1 minutes), and boosts overall resilience by 61.1%. It anticipates cascading failures, leverages redundancy (utilization up from 64.2% to 93.6%), and adjusts strategies based on environmental stressors like temperature spikes or humidity surges. Integration with a digital twin synchronizes real-time data, enabling operators to visualize fault locations, simulate interventions, and preserve institutional knowledge—an invaluable asset as experienced personnel retire. Field deployment validated these advancements: fault detection time dropped by 87% (from hours to minutes), downtime decreased by 73% (from 27.3 to 7.4 hours annually), and grid reliability rose by 64% (SAIFI from 4.8 to 1.7 interruptions per year). Economically, the system delivered a net annual benefit of $5.24 million per platform, with a 551% return on investment, driven by $3.75 million in avoided production losses and $1.23 million in maintenance savings. Safety benefits were equally profound, with electrical incidents falling 78.4% (from 3.7 to 0.8 per year) and personnel exposure hours reduced by 76.7% (from 1,872 to 437 annually). Challenges include sensor reliability (3.2% failure rate in harsh conditions), limited historical fault data for rare events, and cybersecurity risks from expanded connectivity, necessitating robust encryption and anomaly detection. This AI-driven model integrates deep learning, IoT, reinforcement learning, and smart grid technologies into a cohesive, autonomous solution, addressing the offshore industry’s pressing needs for safety, reliability, and efficiency. It offers a scalable framework with potential applications in other high-risk infrastructures, setting a new standard for power grid management in extreme environments.
Keywords: AI-driven fault detection model, electrical power grids, high-risk, offshore, oil platforms, troubleshoot
Smart Grid Integration for Offshore Oil Platforms (Published)
The successful pilot operation of Equinor’s floating Hywind 2.3 MW wind turbine has validated the potential of new technology for capturing wind energy in deep water environments. This innovation shows promise for harnessing the excellent wind resources near offshore oil and gas platforms, where water depths range from 100 to several hundred meters. Offshore oil and gas platforms, which include numerous energy-consuming facilities such as drilling, accommodation, processing, exporting, and injection units, have significant electrical power demands ranging from 10 MW to several hundred MW on the Norwegian Continental Shelf (NCS). As the NCS is a mature petroleum province, energy consumption per produced unit is expected to increase, posing environmental challenges. Currently, most platforms on the NCS generate their own electrical power using gas turbines, which also directly drive compressors and pumps. These gas turbines are responsible for approximately 80% of the total CO2 and NOx emissions from offshore installations. Integrating smart grid technology with renewable energy sources like floating wind turbines could significantly reduce these emissions and enhance the sustainability of offshore oil and gas operations.
Keywords: Integration, offshore, oil, platforms, smart grid
Economic Analysis of Wind Turbines in Oil and Gas Sector (Published)
Diesel generators or gas turbines located in the platforms usually do electricity production in oil and gas platforms. Using the above equipment, taking into account safety issues to prevent explosions and fires, as well as fueling them offshore, will be very expensive. In addition, the mentioned devices emit a significant amount of CO2 and NOx. Therefore, the use of renewable energy instead of fossil fuels can be much more economical and environmentally friendly. This study attempts to investigate the potential of using wind energy in the Persian Gulf and the feasibility and detailed economic analysis of using wind turbines on oil and gas platforms to provide part of the energy required. For this purpose, wind speed data are extracted from measurements at 10 meters, 30 meters and 40 meters above the ground. Average wind speed for the mentioned levels as 4.45 m/s, 4.99 m/s and 5.34 m/s respectively. In addition, it gives the annual power density as 128.36 W/m2, 173.80 W/m2 and 210.42 W/m2. The results obtained by using RETScreen software show that in this project with a life span of 30 years, the economic output of wind turbine systems is profitable, so that after 8.2 years, the entire cost of the project will return.
Keywords: Marine Platform, RETScreen, Renewable Energy, Wind Turbine, offshore