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Fossilytics Engineered Solutions is pleased to announce a collaborative partnership with the Australian Institute for Machine Learning (AIML), University of Adelaide.



The project will aim to accelerate the work already undertaken by Fossilytics for augmenting reservoir and production-based analytics developed in our Advanced Flow Analytics suite such as flowing material balance analysis, pressure/rate type curve analysis for conventional and unconventional wells, and potentially 2-Phase coal seam gas.

We are currently engaged in pilot projects in both the Cooper Basin (South Australia) and as far abroad as the Montney Shale in Canada. Our workflow has been designed to automate reservoir and production analyses. We have the ability to connect directly to SCADA Historians, various commercial databases and more allowing for automated real time data updates, data processing, and interpretation using Machine Learning and Neural Network principles.

Our system is cloud-based using secure Amazon Web Services (AWS) and can be accessed from any device, or it can be installed within the customer’s network. All our modules are built on the Wolfram language, one of the world’s leading mathematical and physics-based platform.

Fossilytics Engineered Solutions is pleased to announce it has signed an MOU with a major Australian operator. The MOU, a pilot between the two organizations which uses Fossilytics Advanced Flow Analytics platform to automate data collection from the field, through to analysis.


The pilot will focus on automation of oil and gas well analysis using Machine Learning and other techniques on the famous Flowing Material Balance as well conventional and unconventional decline models (such as Stretched exponential and Power-Law models).


Our system is cloud-based using secure Amazon Web Services (AWS) and can be accessed from any device, or it can be installed within the customer’s network. All our modules are built on the Wolfram language, one of the world’s leading mathematical and physics-based platform.


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