Process Safety Management (PSM) is a critical framework for preventing major accidents and ensuring operational integrity in offshore oil and gas facilities. Floating Production Storage and Offloading (FPSO) units, such as FPSO XYZ, present unique challenges due to their complex systems, harsh environmental conditions, and high-risk operations. This paper assesses the effectiveness of PSM implementation on FPSO XYZ by evaluating compliance with key elements such as hazard identification, risk assessment, mechanical integrity, and emergency response planning. Using a mixed-method approach that combines quantitative performance indicators (incident rates, audit scores) and qualitative insights from workforce interviews, the study identifies strengths and gaps in the current PSM framework. Findings reveal that while regulatory compliance and technical safeguards are robust, areas such as management of change and competency assurance require improvement to mitigate human-factor-related risks. The paper concludes by recommending a risk-based performance monitoring system and digital integration of PSM elements to enhance real-time decision-making and operational resilience. This case study provides actionable insights for improving process safety in offshore operations and contributes to industry best practices for FPSO safety management.
D. Abia, D. Kalu, M. Iwegbu· SPE Nigeria Annual Internati...· 0 citations
High-rise buildings present critical fire safety challenges due to their vertical configuration, high occupant density, and complex evacuation dynamics. Fire events in such structures can escalate rapidly, with smoke spread and compromised egress routes significantly increasing risks to life and assets. Conventional fire risk assessment and evacuation strategies are largely static and pre-incident focused, limiting their effectiveness under evolving fire conditions. This paper applies Bow-Tie Analysis (BTA) to systematically identify fire hazards, escalation pathways, and key preventive and mitigative barriers in high-rise buildings, and introduces a novel framework termed Dynamic Bow-Tie Integration with Smart Evacuation Systems (DBT-SES). The proposed approach transforms traditional BTA into a real-time, adaptive risk management tool through integration of IoT-based sensing, AI-driven fire and smoke propagation modelling, and dynamic evacuation algorithms. A representative high-rise fire scenario is analysed to compare static evacuation planning with the DBT-SES framework. Results indicate improved evacuation efficiency, reduced congestion, lower occupant exposure to hazardous conditions, and enhanced barrier effectiveness under dynamic conditions. The study demonstrates the value of intelligent, data-driven barrier management and provides practical insights for improving fire risk management and emergency response in high-rise structures.
D. Abia, D. Kalu, M. Iwegbu et al.· SPE Nigeria Annual Internati...· 0 citations
Sustaining petroleum production targets requires developing marginal, weakly consolidated offshore reservoirs. However, continuous volumetric sand production causes severe borehole collapse and surface facility erosion. Legacy analytical Critical Drawdown Pressure (CDP) baselines assume an ideal post-yield elastoplastic state, ignoring transient rock-fluid degradation and yielding forecasting errors exceeding 20%. To resolve this multi-scale gap, this study presents a coupled simulation framework split into two computational nodes. In Node 1, an offline two-way coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) Digital Core tracks discrete particle kinematics and pore-scale hydrodynamics. A 1,200-run synthetic dataset generated via Latin Hypercube Sampling (LHS) mapped three sanding regimes, capturing wormhole propagation where localized porosity spikes from 0.24 to 0.85. In Node 2, a lightweight Physics-Informed Machine Learning (PIML) neural network is trained on this dataset. Unlike unconstrained black-box models, the PIML surrogate embeds fluid-solid mass conservation and Navier-Stokes partial differential equations (PDEs) directly into its loss function via automatic differentiation. When validated against a 24-month blind historical dataset from the Gulf of Guinea, the PIML surrogate achieved near-perfect fitment with an R2 of 0.97, an RMSE of 1.2 lb/1000 bbl, and an AAPRE of 7.4%, marking a 78.3% error reduction over legacy baselines without non-physical artifacts. Retroactively deployed as a dynamic virtual choke advisor, the surrogate minimized unexpected downhole cleanouts to zero, yielding a net 70.3% ($2.6 million) lifecycle operating expenditure (OPEX) reduction. The framework proves that physics-bounded intelligence successfully replaces reactive workflows to safely optimize drawdowns in unconsolidated assets.
C. I. Okoh, D. Kalu, Medlyne Oragwuncha et al.· SPE Nigeria Annual Internati...· 0 citations
This study evaluates the prediction of flowing bottom-hole pressure (FBHP) in dry gas wells using machine learning techniques, specifically Random Forest and Artificial Neural Network (ANN) models. Unlike earlier work based on PROSPER-generated synthetic data, this study utilizes a real field dataset of 206 samples obtained from the ProBHP repository, originally compiled by Govier and Fogarasi (1975) and Asheim (1986). The dataset comprises 10 input variables, including production rates, well depth, tubing size, temperatures, and wellhead pressure, with measured bottom-hole pressure (MBHP) as the target. Feature importance analysis identified well depth, oil rate, water rate, and wellhead pressure as the most influential parameters. The data were split into 80% training and 20% testing sets, with Z-score-based outlier removal reducing the training data slightly. The Random Forest model showed strong predictive performance, achieving a test R2 of 0.81, MAE of 93.73 psig, and RMSE of 123.39 psig, with a cross-validation R2 of 0.72 ± 0.11. In contrast, the ANN model performed poorly, with a test R2 of 0.05 and MAE of 209.55 psig. Overall, the results highlight the reliability of Random Forest for FBHP prediction using real field data, while also showing the limitations of a simple ANN model on small, noisy datasets. The identified key parameters provide useful insights for well performance monitoring and production optimization in dry gas systems.
Fred Akpososo, V. Aimikhe, D. Kalu et al.· SPE Nigeria Annual Internati...· 0 citations
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