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GeoData Science Consulting Service Company

Our vision is to support AI-driven resolutions in the energy industry.


To make a difference in people's life, our planet, and businesses, intelligent data-driven decisions are revolutionizing the oil and gas industry.

In addition to conventional integrated reservoir studies, we help the oil and gas, energy, and mining companies to apply machine learning and data science to put their valuable data to more efficient use. We are aware of your data's importance and believe in extra value extraction for business problems and opportunities. Machine learning methods have found solutions to problems that no human has been able to solve.  We let the machines learn from GeoData to discover patterns and trends embedded in vast volumes of data that are not apparent to the geologists, geophysicists, or other Earth science analysts. 


How GeoData Science & Machine Learning Works



Problems that are not solved by conventional geoscience approaches or solutions that do not satisfy experts, can be examined with AI/ML.  

Pilot test


Start with small but representative datasets. Some AI/ML models will be computationally expensive in larger datasets.

Data Science & Geoscience


Domain knowledge is extremely valuable. All model predictions, trends, and patterns should have geoscientific support and confirmation. Although geoscience data are always noisy, in most cases there is a meaningful relationship between parameters.

Data is wealth


Data is a high-priced commodity and will be highly valued if opportunity extraction proceeds professionally. Quality is important but it is not recommended to wait for perfect data while data science can suggest efficient strategies.



Data visualization, statistical analysis, feature engineering, model training, and model evaluation are the standard iterative process that can be subject to modification and adaption. This workflow can be unique for each geoscientific case.



Recognized patterns, optimized parameters, predicted results and deployed models are outcomes. The optimal results are consistent with geoscientific principles, even if not, there is created a generalized view on data complexity where conventional approaches failed to answer problems. 

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