Seis2Rock

Seis2Rock: A Data-Driven Approach to Direct Petrophysical Inversion of Pre-Stack Seismic Data
Corrales M.¹, Hoteit H.¹, Ravasi M.^1
1 King Abdullah University of Science and Technology (KAUST)

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 Synthetic dataset

The following notebooks are provided:

• 📙 01_Synthetic_Benchmark.ipynb: The notebook assesses and benchmarks the method's applicability using a synthetic dataset that has been constructed based on the reservoir model of the Smeaheia Field.
• 📙 02_Synthetic_4D_Benchmark.ipynb: The notebook aims to evaluate the feasibility of tracking changes in water saturation using well logs that lack information about the new water-oil contact in the subsurface. It seeks to simulate 4D changes in the subsurface.
• 📙 03_Synthetic_Stacking_Wells.ipynb: This notebook compares the inversion results obtained by varying the number of well logs used for training.

Notebooks for Volve dataset

The following notebooks are provided:

• 📘 01_Wavelet_Estimation_Well_NO_15_9_19_BT2.ipynb: This notebook demonstrates the procedure for extracting a statistical wavelet estimate along the Well NO 15-9 19 BT2 fence. This wavelet is subsequently utilized in the inversion step.
• 📘 02_Wavelet_Estimation_Well_NO_15_9_19_A.ipynb: This notebook demonstrates the procedure for extracting a statistical wavelet estimate along the Well NO 15-9 19 A fence. This wavelet is subsequently utilized in the inversion step.
• 📘 03_Benchmark_Inversion_Synthetic_Well_NO_15_9_19_BT2.ipynb: This notebook conducts a benchmark of the method using a synthetic gather constructed from the well log information of well NO 15-9 19 BT2.
• 📘 04_Benchmark_Inversion_Synthetic_Well_NO_15_9_19_A.ipynb: This notebook conducts a benchmark of the method using a synthetic gather constructed from the well log information of well NO 15-9 19 A.
• 📘 05_Inversion_Well_NO_15_9_19_BT2.ipynb: This notebook presents the inversion results obtained just on the well 15-9 19 BT2..
• 📘 06_Inversion_Well_NO_15_9_19_A.ipynb: This notebook presents the inversion results obtained just on well NO 15-9 19 A.
• 📘 07_Inversion_Fence_Well_NO_15_9_19_BT2.ipynb: This notebook presents the inversion results obtained along the 2D fence of well NO 15-9 19 BT2.
• 📘 08_Inversion_Fence_Well_NO_15_9_19_A.ipynb: This notebook presents the inversion results obtained along the 2D fence of well NO 15-9 19 A.
• 📘 09_Inversion_Fence_Well_NO_15_9_19_BT2_stacking_wells.ipynb: This notebook presents the inversion results obtained along the 2D fence using the well logs from NO 15-9 19 BT2 and NO 15-9 19 A for the training.
• 📘 10_Inversion_Fence_Well_NO_15_9_19_A_stacking_wells.ipynb: This notebook presents the inversion results obtained along the 2D fence using the well logs from NO 15-9 19 BT2 and NO 15-9 19 A for the training.
• 📘 11_Inversion_3D.ipynb: This notebook presents the inversion in 3D.

Getting started 👾 🤖

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

Simply run:

./install_env.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 seis2rock

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
All experiments have been carried on a Intel(R) Xeon(R) W-2245 CPU @ 3.90GHz equipped with a single NVIDIA Quadro RTX 4000 GPU. Different environment configurations may be required for different combinations of workstation and GPU.