Digital projects of STC


Data is the new oil

When it comes to data and its exponential growth in the 21st century, the saying "data is the new oil" can often be heard. However, in the oil and gas industry, this sentence is not just a saying, but a realistic goal – we expect the smart management and the use of data to become the new oil.

The development of the Internet and IT systems accelerated the development of artificial intelligence within which data science is developed. Data science uses advanced analytical techniques to extract valuable information from data for making business decisions, strategic planning, and other purposes.

Data science increases operational efficiency

Insights generated by data science in the oil industry lead to an increase in operational efficiency and the identification of new business opportunities that can lead to competitive advantages in the market. An indispensable part of data science is data engineering, exploratory data analysis, and modelling with machine learning algorithms.

Wide application of machine learning algorithms

In the work of our digital laboratory, machine learning algorithms have wide applications, with the aim to predict future scenarios by learning from the data they receive in training without explicit programming. The best-known ones are classification and regression algorithms, which fall within the scope of supervised learning. Using classification algorithms, we predict categorical target variables, e.g. whether a rock is or is not an oil collector (binary classification) or what the lithological composition of the rock in the well is (multivariable classification). Regression algorithms solve the problem of predicting a continuous target variable, e.g. fluid flow in an oil well or prediction of the reservoir complexity index.

Unsupervised learning deals with finding some structure in the data, starting from the specific structure that is sought, with clustering as perhaps the most recognizable unsupervised learning algorithm (e.g. grouping consumers of NIS loyalty card "Sa nama na putu" users). More complex problems are solved by using autoencoders – e.g. for eliminating image noise.

Machine learning and neural networks

Neural networks are created on the model of the neural network of the human brain, in order to imitate a complex learning process. They are often misinterpreted as synonymous with machine learning. On the contrary, they are used exclusively in the case of a large volume of learning data and when the learning process is based on a raw representation of data. They are used to solve problems in supervised and unsupervised learning. Neural networks consist of computer units ("neurons") that are connected in layers, and if a network has two or more layers, this type of machine learning is called deep learning.