Projects

Traditionally, this process involves multiple steps: first, inversion of seismic data to estimate velocities (through methods like acoustic inversion), and then using these velocities to predict petrophysical properties. The main limitation of these methods is that they often do not incorporate uncertainty quantification, leading to suboptimal predictions. Machine learning, and more specifically physics-informed neural networks (PINNs).

TIn the world of geophysics, coordinates are typically the foundation of all measurements and interpretations. They allow geophysicists to map subsurface features, such as faults, reservoirs, and layers, with precision.

The study used machine learning approaches to acquire predictive log Gamma Ray (GR) data by evaluating the window time base and applying machine learning algorithm in the form of Random Forest and K-Nearest Neighbor (KNN), also deep learning Long-Short Term Memory (LSTM) and Bi-LSTM.

In the exploration and exploitation of hydrocarbons, various interdisciplinary studies are conducted and analyzed comprehensively to obtain as much information.