AI Model Proposes New Path for Energy Safety and Efficiency, with Nkese Amos Essien Playing Central Role

A recently published multidisciplinary research study is drawing commendation across academic and industrial circles for advancing a compelling artificial intelligence (AI) framework tailored to the increasingly complex domain of energy project management. With an emphasis on safety enhancement, operational efficiency, and proactive risk mitigation, the research sets forth a unified AI-driven model that experts believe may serve as a catalyst for modernizing how energy projects are conceptualized and executed. Central to this research effort is Nkese Amos Essien, a professional with TotalEnergies EP Nigeria Limited, whose field-based contributions are seen as critical to the study’s real-world applicability and long-term value.

Nkese Amos Essien

The study, published in the International Journal of Advanced Multidisciplinary Research and Studies in December 2024, introduces a model that seamlessly embeds AI capabilities—such as machine learning algorithms, real-time data processing, and predictive analytics—into every phase of the energy project lifecycle. Its ambition is not limited to theoretical abstraction. Rather, the research is methodical and practice-oriented, offering detailed mechanisms by which AI can identify emerging risks, automate safety protocols, optimize resources, and support continuous monitoring in live operations.

While the research is a collaborative endeavor involving international professionals based in Europe, the Middle East, and Nigeria, the voice and vision of Nkese Amos Essien resonate throughout the work. Drawing from his direct involvement in high-stakes energy environments in Nigeria, Essien brought a rare combination of operational insight and systems-thinking to the development of the model. His contributions ensured that the proposed framework was not merely scalable and innovative, but also realistic and implementable in field conditions that often include logistical hurdles, regulatory complexity, and infrastructure gaps.

Essien’s involvement also helped frame the model as more than a technical innovation; it is a practical tool for navigating the daily challenges energy professionals face across geographies. One of the recurring criticisms of AI integration in industrial systems has been the disconnect between model assumptions and operational realities. Essien, however, helped close this gap, advocating for a modular design that adapts to data-limited contexts, allows partial deployment, and respects the nuances of local project conditions.

The model itself spans several critical domains of project execution. In the planning stage, it supports feasibility analysis through AI-powered simulations that take into account environmental data, geological conditions, and market variables. During execution, it facilitates scheduling optimization, anticipates equipment failures, and enables machine-led inspections—drastically reducing downtime and risk exposure. In the monitoring phase, it employs sensor data and edge computing to track system behavior in real time, while the closure phase leverages AI tools to automate reporting, identify lessons learned, and recommend improvements for future projects.

Among the most praised features of the research is its treatment of safety as a dynamic and data-driven process. Rather than depending on conventional checklists and incident-based audits, the model incorporates predictive analytics that enable early detection of abnormal conditions—such as unusual pressure levels, temperature spikes, or vibration patterns in critical equipment. This enables a proactive safety culture, which is particularly important in oil and gas operations where delays in response can result in catastrophic consequences.

Another strength of the model lies in its ability to deliver operational efficiency. It uses intelligent algorithms to optimize crew assignments, reduce material waste, and fine-tune energy production in response to real-time demand and weather conditions. It also provides project managers with intuitive dashboards, ensuring that key performance indicators are monitored continuously and can trigger automated workflows if deviations are detected. These tools, the researchers argue, not only lower project costs but also enhance decision-making by reducing information overload and response time.

The research does not overlook the practical challenges of implementation. It acknowledges barriers such as inconsistent data quality, fragmented infrastructure, workforce resistance to automation, and the ethical complexities of AI deployment. Yet, rather than being limited by these concerns, the study offers clear recommendations—such as investing in workforce upskilling, establishing robust data governance frameworks, and creating incentives for cross-sector collaboration. Essien’s field experience was instrumental in shaping these recommendations, especially in articulating the realities of deploying digital technologies in regions where legacy systems and analog processes still dominate.

Importantly, the model is presented not as a rigid formula but as a strategic framework that organizations can tailor to their unique conditions. This flexibility enhances its potential to be adopted not only by large multinationals with established digital infrastructure but also by mid-sized firms and regional operators seeking to modernize without overhauling their entire systems. In this respect, the research stands out as both inclusive and forward-thinking.

The broader energy community is beginning to take note. Observers see the model as timely, especially as energy companies globally face dual imperatives: increasing output to meet growing demand while simultaneously reducing environmental and operational risk. AI, if thoughtfully implemented, offers a path toward achieving both. And this research—praised for its clarity, relevance, and rigor—makes a persuasive case for how to do so.

Nkese Amos Essien’s role in the study is emblematic of a broader shift in energy innovation—one in which experts based in operational regions are taking center stage in shaping global solutions. His contributions highlight the critical importance of practitioner-informed research and reinforce the value of building AI systems that are designed not just for the field, but from the field. It is a model of collaborative knowledge production that bridges the historical divide between research and operations.

As AI continues to gain traction in energy systems worldwide, the insights and structure presented in this research may become foundational for future implementations. And with contributors like Essien helping to guide its evolution, the field of energy project management appears better positioned to embrace a smarter, safer, and more resilient future.