Drilling Specialist Advocates Real-Time Decision Support Systems to Close Gap Between Data Availability and Operational Intelligence

By Ugo Aliogo

The modern drilling operation generates enormous volumes of data—surface sensors capturing hookload and pump pressure, downhole tools measuring temperature and inclination, mud logging systems tracking lithology and gas composition. Yet despite this wealth of information, many operators struggle with a fundamental paradox: they possess more data than ever before, but lack the integrated systems needed to transform it into timely, high-quality decisions.

Joshua Ozor sees this gap as one of the most consequential challenges facing drilling operations today. In a conceptual paper published in the International Journal of Scientific Research in Science and Technology, Joshua Ozor and his colleagues propose a structured framework for real-time decision support systems tailored specifically to drilling environments. The work synthesizes his many years of experience managing complex operations across Nigeria’s onshore fields with emerging digital technologies that promise to revolutionize well delivery.

“Traditional decision-making relies on retrospective analysis, where field data is interpreted long after critical events have occurred,” Joshua Ozor explains in the paper. “This time lag leads to missed intervention opportunities, escalating operational risk, and financial loss. The gap between event detection and corrective action has become a major source of non-productive time and cost overruns.”
The proposed architecture comprises three core layers. Data acquisition interfaces directly with rig hardware, ensuring continuous ingestion with high fidelity and time synchronization. An analytics layer processes incoming streams using pattern recognition and machine learning to detect anomalies and assess operational states. The decision support layer delivers synthesized insights through logic engines that apply rule sets and probabilistic models, generating prioritized alerts for human operators.

Joshua Ozor’s vision maintains humans firmly in the decision loop—the system augments rather than replaces expert judgment. Functional modules support specific operational challenges: risk scoring quantifies event probability and severity, trajectory management integrates geological targets with real-time inclination data, and collaboration interfaces connect field teams with remote support centers.
This conceptual framework builds directly on achievements from Joshua Ozor’s recent project leadership. His work on OML 26’s 2022 Isoko field production ramp-up—which drilled three wells delivering 8,500 barrels of oil per day—demonstrated the value of integrated planning and execution. The Isoko-8 appraisal well alone increased reserves by 20 million barrels of condensate and 400 billion standard cubic feet of gas, outcomes that depended on precise directional control and formation evaluation.
Joshua Ozor’s 2024 well planning work further illustrates the cost implications of informed decision-making. By aligning casing grade specifications with existing inventory while reviewing designs for Ogini and Aboh field wells, he achieved $12 million in combined savings—the kind of optimization that becomes systematic when supported by decision support infrastructure.

The paper acknowledges significant implementation barriers. Reliable rig-to-cloud connectivity remains challenging in remote locations, cybersecurity concerns require robust encryption and access controls, and organizational readiness demands cross-functional training and governance. Yet Joshua Ozor argues these challenges are surmountable, particularly as edge computing and AI mature.

For Joshua Ozor, real-time decision support represents more than technological advancement—it’s a philosophical shift from reactive troubleshooting to proactive operational resilience. The framework positions drilling organizations to leverage their data infrastructure, moving beyond simple monitoring toward adaptive systems that learn and improve continuously.

As drilling complexity increases and economic margins tighten, the industry faces mounting pressure to modernize decision-making processes. Ozor’s conceptual model provides a structured starting point for operators ready to close the gap between data availability and operational intelligence.