Predictive Analytics for Mental Well-Being: An AI Approach to Suicide Prevention at Remote Oil & Gas Facilities

Abstract The urgency of mental well-being in high-risk environments has gained momentum post-pandemic, with the World Health Organization and International Labor Organization emphasizing mental health as a fundamental component of occupational safety. In 2024, studies reported a 3.5x increase in depression symptoms among offshore workers compared to 2019 baselines. The integration of generative AI for voice and sentiment analysis has further empowered predictive models, now capable of detecting early warning signs with up to 87% accuracy. The oil & gas industry, particularly in remote and offshore facilities, presents unique challenges for workforce mental well-being. Long working hours, isolation, high-pressure environments, and exposure to hazardous conditions contribute to elevated stress levels, anxiety, and, in extreme cases, suicide risks among employees. Mental health in oil & gas operations is further affected by psychological stressors, including workplace harassment, bullying, and a lack of psychological support systems on-site. In many cases, employees working in remote and offshore environments face social isolation, performance pressure, and lack of access to mental health professionals, leading to increased anxiety, depression, and even suicidal ideation. One of the critical gaps in current Fitness-to-Work (FTW) medical assessments is the absence of psychological profiling. Traditional FTW assessments primarily focus on physical health indicators such as cardiovascular fitness, substance use screening, and musculoskeletal health, often overlooking mental health risks. Employees experiencing chronic stress, anxiety, PTSD, or workplace bullying may remain undetected under conventional assessment protocols, increasing the risk of severe mental health deterioration. Key concerns included: Undiagnosed psychological distress leading to safety incidents and absenteeism.Lack of on-site counselors or mental health first aiders, particularly in offshore or remote locations.Inadequate response to workplace harassment and bullying, which can exacerbate mental health issues. Absence of mandatory psychological screenings in FTW medical assessments, leaving employees vulnerable to undetected conditions. The study evaluates a data-driven approach that discusses machine learning algorithms, biometric monitoring, behavioral analytics, and Natural Language Processing (NLP) to detect early warning signs of mental distress. It suggests collection of anonymized data from employee surveys, voice analysis, and work performance metrics to build predictive models capable of identifying stress, fatigue, and emotional instability. The methodology involves: Data Collection: Gathering real-time biometric, physiological, and behavioral data from employees.AI & Machine Learning Models: Developing predictive models trained on historical mental health data from ADNOC and global oil & gas companies.Benchmarking & Comparative Analysis: Evaluating ADNOC's initiatives alongside global leaders such as BP, ExxonMobil, Shell, and Saudi Aramco.Validation & Testing: Assessing AI-driven predictions against real-world case studies toBenchmarking ADNOC's approach with international best practices revealed that companies integrating AI-based predictive mental health tools could potentially result in:30-40% reduction in stress-related incidents.25% improvement in employee engagement and retention.20% decrease in lost-time incidents (LTIs) due to mental health-related absences.Faster detection of at-risk employees, allowing for early intervention through counseling and wellness programs. Looking ahead, the integration of AI-powered mental health monitoring with wearable IoT devices, anonymized peer reporting tools, and secure tele-counseling platforms can further strengthen early intervention pathways. Partnerships between oil and gas companies, technology providers, and mental health professionals will be essential to create industry-wide standards for digital mental health safety nets. By combining advanced analytics with a human-centered approach, the industry can move toward a future where mental well-being is not treated as a secondary concern, but as a core pillar of operational excellence, safety performance, and ESG responsibility. The adoption of such systems not only safeguards lives but also reinforces the sector's commitment to holistic workforce health in some of the world's most challenging work environments.