As part of our Research and Development efforts, we are currently developing new tools based on state-of-the-art AI algorithms to optimise identification of reservoir intervals from 1D (well) and 2D-3D (well and seismic integration) data environments.

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TerraPROBE – Well-based reservoir presence and property prediction

Using existing logs, petrophysical parameters, and core data, machine learning models can be trained to predict reservoir presence, properties and heterogeneities near and far from selected well datasets. Training datasets commonly use wells that have drilled known reservoirs, through Q&A and multiple parameter (log) correlation such as GR, density, porosity, acoustic impedance, etc.


TerraFLAG – From well to seismic and from seismic to well

3D propagation and prediction of reservoir presence and properties can go both ways: from a set of wells building out to the 3D using available seismic and attributes; or from seismic data being used to target future wells where no reservoir information is presently available. An iterative loop between seismic data and wells can also be established for cross-checking and quality control purposes. Synthetic wells and ties can be created to maximise communication between wells and seismic data and to ensure appropriate labelling of parameters.
The workflow that we currently have under development aims to assist in the automated identification of geological bodies on seismic volumes, such as sand bodies, channels, reefs, etc., using a combination of seismic attributes and input coming from wells.

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