Oil and gas exploration

By combining data science, physics, and numerical analysis, it's now possible to create digital representations of subsoil rocks in order to obtain data on their properties, such as porosity, permeability, and interaction with fluids contained in their pores, therefore minimizing the number of samples needed, reducing the analysis period, and eliminating the need to transport samples to labs.

These 3D rocks allow us to predict the mechanical behavior of rocks, as well as their fractures and faults, throughout the life cycle of an oil field. 

By adding magnetic nanoparticles to liquids, we can alter their temperature and viscosity, therefore simplifying their transport. Another use for magnetic nanoparticles is in Smart Cement, where these particles, along with a sensor system, tell us the status of the material at all times to anticipate possible repairs or maintenance. We are also working on projects so that the cement self-heats, as well as developing solutions using “Nanosqueeze". 

We have developed an advanced system for evaluating biodiversity in the areas where we operate using DNA samples of the terrain.

Organic material (water, fecal samples, etc.) is collected from the area; and the DNA is analyzed using Big Data, Machine Learning, and other algorithms to create conclusive maps of the area's biodiversity. This analysis can tell us about the animals in the area and detect changes or warning signs in their activity, like a shortage of large predators.

With this information, we are able to discover variations or imbalances in the environment and take specific action toward conservation.

One major challenge in Oil and Gas is predicting how the properties of rocks in an oil field will behave and change as production increases over time.

Technology plays an important role in allowing complex processes like seismic interpretation and statistical and dynamic modeling of the fields to be carried out automatically, therefore cutting down the analysis period and allowing for more informed decision making.

We are working to incorporate Artificial Intelligence (AI) into the energy sector as a part of our strategy to make the company more data driven.

AI has applications throughout much of the value chain, for example in learning and forecasting the production of an oil field - key for decision making, in assisting in data collection and analysis, and in proposing the best options for materials, logistics, etc. Thanks to its use in the optimization of wells, we are now able to predict production in real time with adaptive dynamics models.