Revolutionising Offshore Seismic Acquisition: Mr. Elemele Ogu’s Cutting-Edge Approach to Data Optimization and Sustainability

Offshore oilfields have long been the battleground for seismic data acquisition, with companies facing the dual challenge of ensuring high-quality data while managing the complexities of dynamic environments. In a bold leap forward, Mr. Elemele Ogu, an esteemed researcher from TotalEnergies Exploration & Production Nigeria Limited, has unveiled a groundbreaking approach to seismic monitoring.

His paper, A Novel Approach to Overcoming Time-Lapse Seismic Monitoring Challenges: Enhancing Repeatability and Data Quality in Offshore Oilfields, published on October 29, 2022, in the World Journal of Advanced Engineering Technology and Sciences, promises to redefine how the energy sector approaches seismic surveys in challenging offshore environments.

In collaboration with co-authors Peter Ifechukwude Egbumokei, Ikiomoworio Nicholas Dienagha, and Wags Numoipiri Digitemie, Mr. Ogu addresses a long-standing issue in offshore oil exploration: the difficulty in maintaining data consistency and repeatability over extended periods. Offshore seismic operations often contend with uncontrollable environmental factors such as ocean currents, seabed motion, and temperature fluctuations, which can compromise data quality and hinder decision-making.

“Seismic monitoring in offshore environments, particularly over time, is complex. The challenge is maintaining the integrity and repeatability of seismic data amid ever-changing environmental conditions,” Mr. Ogu states. “Our approach brings a new paradigm to the table—one that guarantees both reliable and high-quality seismic data without compromising environmental or economic efficiency.”

At the heart of Mr. Ogu’s model is the integration of permanent ocean-bottom seismic (OBS) systems and fiber-optic sensing technologies. Unlike traditional methods that rely on temporary sensors that must be repeatedly repositioned, these permanent systems remain fixed on the seabed, enabling continuous monitoring of the subsurface over long periods. This static setup eliminates the usual inaccuracies caused by sensor movement, providing seismic data that remains reliable and accurate over time.

“Permanent OBS systems equipped with fiber optics are game-changers. They allow us to continuously monitor temperature, pressure, and seismic activity without the need for constant equipment redeployment,” explains Mr. Ogu. “This ensures consistency in the data we collect and improves the overall reliability of long-term monitoring, which is crucial for making informed decisions in reservoir management.”

Taking the model a step further, Mr. Ogu introduces machine learning algorithms that facilitate real-time data calibration and anomaly detection. These algorithms continuously analyze incoming data, flagging any inconsistencies or environmental factors that might skew the results, thus ensuring the highest level of data integrity. With this approach, energy companies are equipped with a powerful tool to adapt their seismic strategies in real-time, adjusting their surveys based on data insights as they are collected.

“Machine learning plays a critical role in ensuring that seismic data is continuously calibrated and adjusted for any environmental factors. It’s like having an intelligent system that adapts on the fly, ensuring we get the best possible data in any condition,” says Mr. Ogu. “The ability to detect anomalies instantly and make quick adjustments in the field dramatically improves the overall quality of the seismic monitoring process.”

Central to the innovative nature of this research is the model’s commitment to eco-sustainability. By incorporating renewable energy sources, such as solar-powered OBS systems, Mr. Ogu and his team offer a solution that not only enhances the accuracy of seismic surveys but also minimizes the environmental footprint of offshore operations. This eco-conscious design aligns with the global shift toward sustainable energy practices, ensuring that the oil and gas industry takes a more responsible approach to its environmental obligations.

“The energy sector is under increasing pressure to reduce its environmental impact. By using renewable energy to power seismic systems, we are not only improving data quality but also making offshore operations more environmentally friendly,” Mr. Ogu adds. “This sustainable approach enables energy companies to achieve operational goals without compromising on environmental responsibility.”

The research also highlights the significant economic advantages of the proposed model. By improving the repeatability and reliability of seismic data, energy companies can reduce exploration costs and improve the efficiency of their resource targeting. The model’s ability to enhance data quality means that companies can make better-informed decisions, optimizing their exploration strategies and maximizing their returns on investment.

“Our approach offers tangible cost savings by reducing the need for repeated surveys and optimizing data collection techniques,” Mr. Ogu explains. “By improving data consistency and reducing the environmental impact, we help companies make smarter, more cost-effective decisions that drive profitability and sustainability in the long run.”

One of the most striking features of Mr. Ogu’s research is its emphasis on collaborative innovation. The study advocates for a cross-industry collaboration among energy companies, technology providers, and regulatory bodies to facilitate the widespread adoption of these advanced seismic technologies. The paper suggests that such collaboration, combined with government support and policy incentives, will be critical in overcoming the barriers to implementing this new approach across the energy sector.

“Achieving widespread adoption of these innovative seismic technologies will require collective effort and partnership,” says Mr. Ogu. “Governments, industry stakeholders, and technology developers need to work together to create a sustainable framework that supports the integration of these technologies while promoting industry growth and environmental stewardship.”

In conclusion, Mr. Elemele Ogu’s revolutionary approach to seismic monitoring marks a significant milestone in the energy sector. By combining permanent OBS systems, fiber-optic sensing, machine learning, and sustainable technologies, this conceptual model is poised to enhance offshore seismic data acquisition, making it more reliable, efficient, and environmentally friendly. Through this work, Mr. Ogu provides the offshore energy sector with a path forward—one that not only addresses today’s challenges but also ensures a more sustainable, cost-effective future for offshore exploration.

“Our model is designed to make seismic acquisition more sustainable, accurate, and cost-efficient,” concludes Mr. Ogu. “By integrating cutting-edge technologies and fostering collaboration, we can pave the way for a more responsible and profitable energy future.”