​DATA
​
ENERGY​
Energy Data & Tech Consulting
Empowering the oil and gas industry to seamlessly integrate cutting-edge technology, driving sustainable growth through innovation and expertise
Our mission is to bridge the gap between traditional oil and gas operations and modern technology. We equip industry professionals with the knowledge and tools to leverage the latest advancements in tech, fostering a culture of innovation and ensuring our clients stay ahead in a rapidly evolving landscape.
​
​​​
At DataEnergy, we transform traditional approaches in the oil, gas, energy, and mining sectors by integrating advanced machine learning and data science to unlock the full potential of your data. We go beyond conventional methods to uncover deeper insights, delivering innovative solutions that drive sustainable growth. Our expertise in cutting-edge technology enables us to identify hidden patterns and trends across extensive datasets, revealing critical information that might otherwise remain unnoticed. By empowering your team with these insights, we enhance decision-making processes and position your business at the forefront of industry innovation.
How GeoData Science & Machine Learning Works
Problems
When traditional geoscience methods fall short, AI and machine learning can redefine problem-solving, offering robust, innovative solutions.
Pilot test
Start with small but representative datasets. Some AI/ML models will be computationally expensive in larger datasets.
Data Science & Geoscience
Domain expertise is crucial. All model predictions, trends, and patterns must be backed by solid geoscientific evidence. Despite the inherent noise in geoscience data, meaningful relationships between parameters often emerge.
Data is wealth
Data is a valuable asset that gains significant worth when leveraged strategically. While data quality is crucial, it's not advisable to wait for perfect data. Instead, data science offers effective strategies to maximize data utility efficiently.
Model
Data visualization, statistical analysis, feature engineering, model training, and evaluation form a flexible, iterative process tailored uniquely for each geoscientific scenario, allowing for necessary adaptations and refinements.
Production
Our outcomes include recognized patterns, optimized parameters, predicted results, and deployed models. Even when results diverge from traditional geoscientific principles, they provide a nuanced perspective on data complexities where conventional methods falter.