Rotliegend aeolian sandstones are the main gas-bearing reservoir in Southern Permian basin and North Sea basin. It is capped by thick layer of Zechstain evaporates, which makes imaging and resolution of underlying reflections challenging.

Our new High Resolution Seismic conditioning tool allows you to not just boost the quality of sub-salt reflections imaging by removing incoherent noise, but also to improve overall resolution of the seismic data. Now you can obtain more detailed information about low frequency intervals of your seismic data, clean the data suppressing noise, restore attenuated reflections, and make seismic interpretation more precise.

Opunake 3D seismic data covers a large scale extensional fault system. It is complicated by presence of incoherent noise and attenuated reflections. Poor quality of the seismic data in the middle and deep parts of the section makes it challenging to trace the faults especially when it comes to interpreting their tails in the deepest intervals.

Our new Mean Seismic conditioning tool is able to boost fault imaging and accelerate obtaining a structural framework from seismic data improving its details and precision. It also helps to remove noise and restore attenuated reflections which results in overall improving of S\N ratio of the seismic data.

Regular noise is presented in Waka Square, as well as areas with faults that are not fully and clearly traced. Post-stack functions are normally used to improve signal to noise ratio. Commonly post-stack processing involves a long choice of workflows and procedures and it takes time to select the optimal settings.

Post-processing workflows can be complicated and time consuming, so Geoplat AI offers simple and more sophisticated functionality to improve the post-stack seismic data quality, based on the application of Machine Learning techniques.

Our algorithms allow you to improve the fault zones display, apply structure-oriented smoothing, restore attenuated reflections and suppress seismic incoherent noise. You will be able to generate noise attenuated seismic volume after running Geoplat AI’s workflow.

Seismic pattern analysis on F3 benchmark volume shows that it is highly complicated due to intensive development of faults and disjunctives in addition to the major salt signatures present in the area. The upper layers of the salt structure are flexed into a significant anticline structure.

The goal was to trace salt bodies within the entire survey area using our advanced Machine Learning techniques as a salt body detection tool. Below you can see the salt body attribute volume (salt dome’s structural model) which was generated using Geoplat AI's salt delineation workflow.

Using our ML algorithms in Geoplat AI we were able to build a detailed model of the salt body in the entire study area considerably saving time and processing resources compared to the conventional auto-tracking techniques.

The Opunake area has varying fault structure complicated by a considerable decrease in the seismic data quality in the lower part of the profile. Due to these challenges, the surface tracking results could potentially be ambiguous and the conventional horizon tracking methods could have taken months to complete.

In order to interpret horizons across the entire area, we used our ML reflective horizon tracing tool. It generates a Local Geological Time volume (LGT) which is a colour-coded attribute with each colour representing a separate horizon. This attribute also preserves fault zones and can be useful for tracking faults on the same survey as well.

Below you can see a selection of individual stratigraphic horizons on the LGT volume. With Geoplat AI we have generated and extracted all stratigraphic horizons within a few hours compared to the conventional horizon tracking techniques that could have taken much longer time to complete.

Key channel elements of this area have been already identified and located at different depths. They are consisted of sandstone with clay injections. The goal was to clarify the spatial position and build the model of the detected channels.

We used Geoplat AI geobody detection tool to detect paleo channels within the entire volume. Most of the channel systems located at different depths were identified using our 3D attribute volume. Our innovative paleo channel interpretation workflow allowed us to obtain a detailed structural model of the Upper Jurassic channel bodies quickly and efficiently using a standard workstation. The obtained level of detail and calculation speed were much more sophisticated compared to the conventional attribute-based interpretation workflow.

Canning Area has significantly complex tectonic structure. One of the most challenging parts of the interpretation workflow was the presence of noise in the lower part of the profile. The conventional ways of interpreting faults on this data are difficult and it's hard to get an accurate result as most of the seismic structural attributes are sensitive to stratigraphic features and noisy areas of the profile. We have generated fault probability attribute based on our ML technology to minimise these factors. The goal was to build a structural model with detailed fault surface identification.

The fault probability attribute was generated for the area which allowed us to successfully track the entire network of sub-meridional and sub-latitudinal faults in detail. Geoplat AI’s ML fault extraction workflow demonstrated good results in the low signal-to-noise ratio areas as well, and highlighted all major fault structures on vertical cross sections and time slices as demonstrated below.

You can automatically track and extract fault surfaces for the whole volume once the fault probability attribute is generated. Geoplat AI identifies faults according to the given probability values. You can also determine fault directions, their spatial extent and dip angles. It took just several hours to generate structural fault model for the entire seismic volume including the calculation of fault probability attribute (to highlight major faults). This was a significant reduction in time compared to the conventional fault interpretation workflows that could have taken months to complete.