Advanced hybrid neural network Pretrained Models on more than 100 000 data samples including multiple seismic attributes
Apply your knowledge Intelligent ML Workflow Customisation aimed at retaining your competences and experience
Reduced operational time Intuitive ML workflow building that significantly decreases processing time and delivers fast and accurate prediction results
Data model at a new level Robust prediction of the most complex geological features based on varying seismic quality and complexity of geological strata
Years of ML research and Development
Leading Academic Institutions
Participating in the R&D program
Successful consultancy projects
Completed by the Geoplat team
Up to 70
Percent productivity improvement
Geoscientists have already applied Geoplat on their data
Data samples are included
in the AI model
Our ML software Geoplat AI is here to significantly speed up the processes for your business challenges and to give you valuable data insights
ML Fault Interpretation
Fault interpretation is one of the most difficult tasks within a general structural interpretation workflow.
Geoplat developed the technology which 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 Horizon Interpretation
Horizon interpretation is the core process in understanding the structural features of a geological cross section and conduct reliable dynamic seismic analysis.
Geoplat offers a new approach to address automatic horizon tracing. You can trace a single horizon or the whole set, preserving complex fault structures and regional geological features.
ML Seismic Data Conditioning
Low quality seismic datasets often make it difficult to build a structural framework and predict pay zone properties.
Machine learning algorithms developed by Geoplat provide a powerful workflow for interactive and intelligent data conditioning of post stack seismic data. It enables getting instant results considerably saving time on defining functions and workflows.
ML Salt Bodies Delineation
Common interpretation methods are usually unable to determine the exact positioning of salt body’s boundaries including its top and base.
Our innovative machine learning approach helps to generate a unique volume attribute that predicts the distribution areas of salt layers.
ML Channels and Sand Bodies Detection
The problem of extracting subtle changes from the reflectivity data is often associated with various types of sedimentation responses. Sometimes there is insufficient detail to detect the entire object within the interval formed in the same geological period.
Geoplat AI can generate a complete probabilistic model to detect channels and other geological objects in the entire seismic volume.
Check out an effective application of the Geoplat technologies on real seismic data