Regularization strategies for Seis2Rock-based petrophysical inversion of pre-stack seismic data

Corrales M.¹, Romero J.¹, Luiken N.¹, Hoteit H.¹, Ravasi M.^1
1 King Abdullah University of Science and Technology (KAUST)
https://doi.org/10.1190/image2024-4093717.1

Project structure

This repository is organized as follows:

• 📂 assets: images and figures of the project.
• 📂 seis2rock: python library containing routines for seis2rock.
• 📂 data: folder containing data (and instructions on how to retrieve the data).
• 📂 notebooks: set of jupyter notebooks reproducing the experiments in the paper (see below for more details).

Notebooks for Volve dataset

The following notebooks are provided:

• 📙 01_Regularization_Comparison_Well_NO_15_9_19_BT2_stacking_wells.ipynb: Regularization comparison for fence along well NO_15_9_19_BT2.
• 📙 02_Regularization_Comparison_Well_NO_15_9_19_A_stacking_wells.ipynb: Regularization comparison for fence along well NO_15_9_19_A.

Getting started 👾 🤖

To ensure reproducibility of the results, we suggest using the environment.yml file when creating an environment.

Simply run:

./install_env_gpu.sh

It will take some time, if at the end you see the word Done! on your terminal you are ready to go. Αctivate the environment by typing:

conda activate rseis2rock_gpu

After that you can simply install your package: (double check your new environment is active to proceed as follows)

pip install .

or in developer mode:

pip install -e .

Note
For computer time, this research used the resources of the Supercomputing Laboratory at KAUST in Thuwal, Saudi Arabia. All experiments have been carried on > a Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz equipped with a single NVIDIA TESLA V100. Different environment configurations may be required for different > combinations of workstation and GPU

Cite us

@incollection{corrales_regularization_2024,
	series = {{SEG} {Technical} {Program} {Expanded} {Abstracts}},
	title = {Regularization strategies for {Seis2Rock}-based petrophysical inversion of prestack seismic data},
	url = {https://library.seg.org/doi/10.1190/image2024-4093717.1},
	urldate = {2024-12-18},
	booktitle = {Fourth {International} {Meeting} for {Applied} {Geoscience} \& {Energy}},
	publisher = {Society of Exploration Geophysicists and American Association of Petroleum Geologists},
	author = {Corrales, Miguel and Romero, Juan and Luiken, Nick and Hoteit, Hussein and Ravasi, Matteo},
	month = dec,
	year = {2024},
	doi = {10.1190/image2024-4093717.1},
	keywords = {inversion, machine learning, prestack},
	pages = {26--30},
}