Artificial intelligence apps
Our AI technology – developed to predict depofacies, lithofacies, flow unit classification and assisting well corellation for Oligocene, Miocene sequences in Cuulong basin. It can be loosely described as a designed framework of multiple Machine learning methods include basic analytics and deep learning tools, with few others between each end of this spectrum.
It brings operators significant benefits as below:
• A data driven, consistency interpretation of all wells. It is key to produce good reservoir property mapping for plan new wells.
• An application which can integrate different discipline data such as logs, bio, chemostratigraphy, etc...
• Real time interpretation in big data maner to help in geosteering
Our AI can be loosely described as a designed framework of multiple Machine learning methods include basic analytics and deep learning tools, with few others between each end of this spectrum.
Before building a reservoir model, a subsurface team may see the same set of seismic data, well data, reservoir engineering data a bit differently. Ultimately, they will hone in a single view and start model. But if all the discounted angles are kept for retrospective study, they may prove valuable in helping reduce uncertainty. This objective can afford well in an AI system.
• A data driven, consistency interpretation of all wells. It is key to produce good reservoir property mapping for plan new wells.
• An application which can integrate different discipline data such as logs, bio, chemostratigraphy, etc...
• Real time interpretation in big data maner to help in geosteering
Our AI can be loosely described as a designed framework of multiple Machine learning methods include basic analytics and deep learning tools, with few others between each end of this spectrum.
Before building a reservoir model, a subsurface team may see the same set of seismic data, well data, reservoir engineering data a bit differently. Ultimately, they will hone in a single view and start model. But if all the discounted angles are kept for retrospective study, they may prove valuable in helping reduce uncertainty. This objective can afford well in an AI system.