Technical Session 3: Big Data Analytics - I
Big Data Analytics is revolutionising the upstream sector of the oil and gas industry, where exploration, drilling, and production operations generate vast volumes of structured and unstructured data. By leveraging advanced analytics, Artificial Intelligence (AI), and Machine Learning (ML), companies can extract actionable insights from seismic data, well logs, drilling records, and reservoir performance. These insights improve decision-making, operational efficiency, and asset utilisation, while minimising risks and reducing costs.
In the exploration phase, big data analytics helps identify potential hydrocarbon reserves with greater accuracy by analyzing geological and geophysical data. In drilling, real-time data from sensors and equipment allow predictive maintenance, reducing downtime and equipment failure. It also enables the optimisation of drilling parameters, minimising non-productive time and increasing wellbore stability. Similarly, in production operations, analytics aids in optimising reservoir management by predicting production trends, monitoring well performance, and improving recovery rates.
Big data analytics also enhances safety and environmental sustainability. By monitoring sensor data, it can predict hazardous conditions, enabling proactive interventions that prevent accidents and reduce emissions. Furthermore, it supports regulatory compliance by streamlining data management and reporting processes. However, the integration of big data analytics in upstream oil and gas faces challenges such as data silos, legacy systems, and the need for skilled personnel. Addressing these issues through digital transformation strategies, cloud computing, and cross-disciplinary collaboration is crucial to realising the full potential of big data.
In conclusion, big data analytics is a critical enabler for the upstream oil and gas industry, driving efficiencies, reducing costs, enhancing safety, and supporting more sustainable operations. Its continued adoption is expected to play a key role in meeting the growing global energy demand while navigating the challenges of the energy transition.
Primary | 15:00 – 15:30 | Using advanced computer vision and image segmentation to enhance geological core image datasets Siddharth Barua, SLB |
Primary | 15:30 – 16:00 | Revolutionizing Maintenance: Predictive Insights through Real-Time Frac Operation Implementation Ignatius Emmanuel, NESR |
Primary | 16:00 – 16:30 | 3D Reservoir Connectivity Mapping Using AI James Martin, Aramco |
Alternate / ePoster | TBC | Leveraging Data-Driven Approach in Reservoir Surveillance for Effective Planning Alaa Shbair, ADNOC Onshore |
Alternate / ePoster | TBC | Leveraging Time Series Analysis and Machine Learning Techniques to correlate Petrophysical and Seismic Attributes with Production Data Dipesh Chopra, Cairn Oil & Gas vertical of Vedanta Limited |
Alternate / ePoster | TBC | AI-Enhanced Log Data Analysis and Rock Typing Solutions for Sweet Spot Identification and Well Drilling Locations Sayani Kumar, SLB |
Alternate / ePoster | TBC | Reviving legacy wells for oil and gas prospecting: predicting missing well logs using an AI-Driven approach Yacine Meridji, Aramco |