Aug 2026· SPE Nigeria Annual International Conference and Exhibition· 0 citations· 7 references
Abstract
Artificial intelligence (AI) is increasingly used in upstream production surveillance for real-time anomaly detection, condition monitoring, and predictive maintenance. Recent industry surveys indicate that well over half of global upstream operators now apply AI-based tools to monitor wells and facilities, and that mature predictive-maintenance programs have achieved downtime reductions on the order of 20–30% alongside marked improvements in early-failure detection accuracy. These results show that, when properly integrated into field workflows, AI-enabled surveillance can deliver tangible gains in reliability and maintenance efficiency.
In Nigeria, by contrast, published assessments suggest that fewer than one in five upstream assets currently employ AI-enabled monitoring in routine operations, and many initiatives remain confined to proofs of concept or limited pilots. Fragmented data infrastructures, inconsistent integration between Internet of Things (IoT) devices and legacy Supervisory Control and Data Acquisition (SCADA) systems, low levels of digital maturity, and gaps in analytics skills within production and maintenance teams hinder the transition from experimentation to stable, field-wide deployment. This paper addresses these challenges through a framework-guided review of documented AI deployments in production surveillance and predictive maintenance, supplemented by digital-operations case studies and analyses of digital-transformation efforts in asset-intensive industries. The synthesis reveals recurring barriers, including unreliable and siloed production data, weak data governance, unclear ownership of AI-generated alerts, and low confidence in model outputs among operational staff.
Building on these insights, the paper proposes a three-stage AI-Ready Production Surveillance Roadmap tailored to Nigerian upstream operations. The roadmap combines a concise diagnostic checklist with a phased implementation plan that emphasizes establishing continuous, high-quality data capture from critical wells and equipment, embedding AI-based anomaly and health indicators into the production-surveillance dashboards and workflows already used by asset teams, and providing targeted upskilling and role clarification for production, maintenance, and data personnel so that AI outputs are interpreted correctly, trusted, and systematically acted upon.
The intended contribution is a practical, surveillance-focused guide that upstream operators, service companies, and regulators in Nigeria can apply to move AI-driven monitoring from isolated pilots toward sustained, asset-level use. By following this roadmap, organizations can increase the likelihood that AI investments in production surveillance translate into measurable improvements in equipment reliability, operational safety, and production performance, while progressively aligning Nigerian upstream operations with global digital-transformation trajectories.
The study proposes a layered architecture linking logs, metrics, traces and service dependency graphs with predictive analytics, automation policy and reliability dashboards, and gives a maturity roadmap for organisations that want to transition from reactive monitoring to proactive, self-improving operations.
Mohsin Tahir· Global academic journal of e...· 0 citations
Remote oil and gas assets are increasingly operated through connected instrumentation, supervisory control platforms, historians, cloud analytics and integrated operations centres. This digital shift improves production surveillance, artificial-lift optimisation, reservoir management and pipeline integrity, but it also creates a cyber-physical attack surface in which manipulated telemetry, unauthorised commands, delayed alarms or compromised remote access can produce safety, environmental and production consequences. This review develops an artificial intelligence (AI)-based threat detection and response model for remote wellheads, electric submersible pump (ESP) systems and pipeline monitoring environments. Evidence published between 2020 and 2025 is synthesised across operational technology (OT) security, industrial anomaly detection, petroleum analytics, Permanent Downhole Monitoring Systems (PDHMS), downhole gauges, variable-frequency drive (VFD) monitoring and critical-infrastructure guidance. The review argues that PDHMS should not be treated as a separate topic; bottom-hole pressure, downhole temperature, vibration, intake and discharge pressure, motor current and real-time production data are integrated evidence sources for cyber-physical resilience. The proposed model combines data assurance, process-aware baselines, network intrusion detection, asset criticality, explainability, and operator-approved response playbooks. For wellheads, the key requirement is to detect command and pressure-flow inconsistencies before unsafe valve or choke action occurs. For ESP systems, cyber analytics must distinguish genuine pump degradation from malicious manipulation of VFD settings, vibration, motor current, frequency and downhole gauge data. For pipelines, detection must integrate SCADA anomalies, leak indicators, geospatial evidence and communications integrity. The review therefore prioritises cyber-physical consequence rather than alert volume, linking AI outputs to verification, containment, safe-state operation, recovery and continuous learning.
Shajji Mohiuddin· Global academic journal of e...· 0 citations
Overhead crane operations remain a high-risk activity in heavy manufacturing, yet safety management often relies on static assessments that fail to capture real-time operational dynamics. In developing economies such as Indonesia, a significant digital gap hinders the adoption of high-cost IoT solutions, leaving safety data fragmented and reactive. This study aims to bridge this gap by developing and validating a Business Intelligence (BI) Safety Dashboard that utilizes bridge technologies, defined as cost-effective digital solutions that leverage existing administrative and operational data instead of dedicated IoT infrastructure, to provide real-time predictive risk insights. Following a Design Science Research (DSR) framework, a three-year longitudinal study (2024–2026) was conducted at a metal fabrication facility in West Java. A Weighted Dynamic Risk Score (WDRS) was formulated using Data Analysis Expressions (DAX), integrating incident logs, maintenance records, and operator certification data into a unified star schema model. The results demonstrate a 95% reduction in data processing time and a 30% increase in near-miss reporting. The proposed artifact successfully identified critical risk outliers, such as Crane 08 (WDRS = 8.3), and generated spatiotemporal heatmaps that pinpointed specific risk hotspots within the facility. These findings confirm that the BI Dashboard is a feasible and highly practical solution for resource-constrained environments, providing a scalable blueprint for Indonesian SMEs to achieve Industry 4.0 safety standards by leveraging existing administrative data for predictive maintenance and proactive safety interventions.
Ridwan Kurniaji, Nur Azizah, Mohamad Rakhmansyah et al.· IAIC Transactions on Sustain...· 0 citations
AI technologies are becoming operational components of urban infrastructure systems, including transport networks, structural health monitoring platforms, water utilities, energy systems, and public facilities. These systems support prediction, diagnosis, control, maintenance planning, and asset-management decisions across long service periods. However, much of the existing literature still evaluates infrastructure AI at the model-design or deployment-performance stage, with limited attention to post-deployment validity, operational degradation, update control, and end-of-life management. This survey examines AI applications in urban infrastructure from a lifecycle-management perspective, covering deployment, runtime monitoring, maintenance and adaptation, governance, and retirement. The review applies a PRISMA-guided search and screening protocol to classify retained studies by lifecycle phase, infrastructure domain, deployment evidence, monitoring strategy, adaptation mechanism, governance control, and benchmark support. The cross-domain analysis indicates that deployment-stage accuracy alone is not sufficient for long-term reliability assessment, because sensor wear, environmental variation, asset aging, data drift, maintenance intervention, topology change, and operating-regime shifts can alter model behavior after deployment. The findings further show that current research provides limited support for linking model outputs to maintenance actions, validating model updates under operational constraints, documenting governance evidence, estimating lifecycle cost, defining retirement criteria, and building shared lifecycle benchmarks. The survey concludes that urban infrastructure AI should be managed as a long-term socio-technical asset, with continuous validation, model-health monitoring, controlled adaptation, audit-ready governance, and retirement planning integrated into infrastructure operations.
SRE has come a long way since its start, from monitoring infrastructure health to reactive events. Modern SRE is an AI-driven observability platform providing real-time visibility into complex distributed systems. As multi-cloud use grows, operational complexity increases and it becomes increasingly complicated to provide dependability, performance and security across a broad range of cloud platforms. Artificial Intelligence (AI), Machine Learning (ML) and AIOps technologies are changing Service Availability and Incident Response (SRE) with intelligent anomaly detection, predictive analytics, automated root-cause investigation and autonomous remediation in response to such. The effort aims at investigating the future of service-oriented architecture (SRE) in the age of AI-based observability and autonomous operations in multi-cloud environments. Through a review of current technologies, industry practice and upcoming trends in research. The objective of this research is to investigate the viability of the application of AI-based solutions to enhance system dependability, minimize operational overhead and optimize incident response efficiency. The study will also address issues of scalability, interoperability and governance. The results indicate that AI and ML in observability systems can deliver automated operational workflows that help enable proactive reliability management, minimize downtime and accelerate decision making. This makes AIOps powered autonomous operations a rapidly growing important enabler for cloud native infrastructures with self repairing systems. This paper describes the convergence of AI with software defined networking (SRE) and strategic implications for enterprises seeking strong, scalable and efficient cloud operations. The results show intelligent automation is becoming more important in shaping the future of cloud-native reliability management and operational excellence.
Srichandra Boosa· International Journal of Eme...· 0 citations
Artificial intelligence for information technology operations has progressed from alert correlation and anomaly
detection to root-cause analysis, mitigation recommendation, and agents that can invoke infrastructure tools. This progression
creates an assurance problem: analytical performance does not establish authority to change a live cloud system. This critical
narrative review examines the evidence required before AI output may influence or execute failure-management action in
mission-critical cloud infrastructure. Searches through 24 July 2026 covered scholarly databases, major systems venues,
standards sources, and authoritative production reports. Forty-four sources were coded by operational task, evidence setting,
authority, safety control, rollback, and outcome verification. Production evidence is substantial for detection, triage, diagnosis,
and several narrowly bounded mitigation systems, but remains weak for general-purpose autonomous action. Recent agentic
AIOps surveys emphasize contracts, bounded tools, canary deployment, and rollback; the unresolved need is an assessment
method that separates what a system can infer, what it may do, and what evidence shows that the action remained safe. The
proposed Operational Decision-Readiness and Verification framework represents a deployment claim through analytical
capability, operational authority, and assurance maturity. Twelve domains use explicit 0-3 evidence anchors, while endpoint
validity, evidence integrity, intervention risk, authorization, reversibility, and recovery verification operate as non-compensatory
gates. Worked assessments of six systems illustrate distinctions among analytical support, testbed execution, and narrow
production autonomy. The framework is conceptual and requires prospective and inter-rater validation; it structures an actionspecific readiness case rather than certifying a model or product
Adepegba Akindayomi Akintade· International Journal for Re...· 0 citations