Data Analytics for Oil and Gas Exploration and Production
One of the most important components is data from the oil and gas industry. Data is likewise crude. It's valuable, but it can't be used if it's not purified. Every day, most organizations deal with a large volume of data. They evaluate them and try to come up with new solutions. Big Data analytics has piqued the interest of the oil and gas industry. A successful oil company will forecast potential information promptly and maintain costs low in order to achieve success without losing any discrepancy in the data set's appraisal. The quality of the data and an insight of the problem's complexity are two more difficult aspects to overcome when using Big Data.
- Structured, Unstructured & Semi-Structured Data
- Big Data Analytics
Committee Members
Selim Sanad Shaker
Geopressure Analysis Services, United States
Saim Memon
Sanyou London Pvt Ltd, United Kingdom
Cleveland M Jones
Fronteira Energia Ltda, Brazil GOPE 2026 Speakers
Jia Min Jin
Shanghai Research Institute of Materials, China
Matthew Godfrey
Enventure Global Technology, United States
Javier Garrigos
Tecnicas Reunidas, Spain
Rukiye Oztekin
Dokuz Eylül University, TurkeySubmit your abstract Today
Title : The Vacuum Insulated Heatable Curtain (VIHC): From conceptual invention to market deployment as a cost-effective dual solution for window heat loss reduction and localised radiant comfort
Saim Memon, Sanyou London Pvt Ltd, United Kingdom
Title : Hydrogen production from depleted or unproductive oil and gas reservoirs
Cleveland M Jones, Fronteira Energia Ltda, Brazil
Title : Predicting drilling challenges and hazards due to subsurface pressure’s drifting
Selim Sanad Shaker, Geopressure Analysis Services, United States
Title : Transforming waste plastic into hydrogen: Progress, challenges, and future directions in pyrolysis-based integrated pathways
Nur Hassan, Central Queensland University, Australia
Title : Novel expandable liner hanger platform for advanced liner drilling and reaming
Matthew Godfrey, Enventure Global Technology, United States
Title : From empirical decline to intelligent forecasting: A hybrid deep learning framework embedding arps physics for unconventional tight-gas reservoir production prediction
Emmanuel Chibueze Obasi, University of Wyoming, United States