seismic Fault interpretation Geoplat AI 2023

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Geoplat AI 22.0 seismic Fault interpretation Geoplat AI 2023

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Geoplat AI enables the conditioning of seismic data using machine learning techniques. The training algorithm of the convolutional neural network closely resembles the earlier-described approach for structural noise reduction. In this neural network, a substantial amount of unique synthetic data was also used for training. An important distinction lies in the intermediate step – the construction of the specified seismic horizon interpretation volume under the hood – LGT volume – and hence helps to improve fault visibility.

By incorporating this volume into the neural network training, we’ve managed to preserve and accentuate structural features during calculations. This normalization of amplitudes and enhancement of fault zones are achieved. This conditioning algorithm significantly reduces the time required for identifying fault trajectories and facilitates both manual and automatic correlation of seismic horizons.

ML Fault Interpretation
Fault interpretation is one of the most difficult tasks within a structural interpretation workflow. Geoplat developed the technology can help to significantly reduce time and resources spent on building a geological model. The use of machine learning based on deep neural networks allows to calculate fault probability distribution, extract surfaces, and eliminate interpretation uncertainties.
ML High Resolution Seismic Conditioning
One of the issues that can come with seismic data beside poor quality is also low resolution of the data. It is crucial to resolve reservoir intervals and be able to obtain detailed stratigraphic models in order to improve quality of final reservoir models. New powerful neural network made by Geoplat AI allows to enhance seismic data resolution along with improving overall data quality
The Challenge

During the construction of structural and stratigraphic models under complex geological conditions, there’s often a need for a more detailed interpretation of the section and the delineation of boundaries of thinner bodies. However, the resolution of seismic data frequently falls short of facilitating this.

Presently, there are a limited number of analytical methods for addressing this challenge. However, they all come with constraints and do not consistently provide a significant enhancement in resolution. Moreover, these methods tend to require substantial computation time.

Our Solution

Geoplat AI enables the conditioning of original seismic data using machine learning methods. The algorithm, like other our conditioning techniques, was trained on synthetic data. However, a distinctive feature here is the utilization of data with varying frequencies.

This trained neural network is capable of enhancing the resolution of seismic data, enabling a more detailed observation of thin layers. These thin layers become more pronounced through this conditioning, resulting in a more detailed representation of the structural characteristics of the surveyed area.