# Groningen: Spatial Prediction of Rock Gas Saturation by Leveraging Selected and Augmented Well and Seismic Data with Classifier Ensembles

arXiv: https://arxiv.org/abs/2410.10371 · HTML: https://mlsanity.com/paper/2410.10371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [PyTorch Tabular: A Framework for Deep Learning with Tabular Data](https://mlsanity.com/paper/2104.13638/) (2021)
- [Reservoir Prediction by Machine Learning Methods on The Well Data and Seismic Attributes for Complex Coastal Conditions](https://mlsanity.com/paper/2301.03216/) (2023)
- [Generalization with Reverse-Calibration of Well and Seismic Data Using Machine Learning Methods for Complex Reservoirs Predicting During Early-Stage Geological Exploration Oil Field](https://mlsanity.com/paper/2304.03048/) (2023)
- [Gas trap prediction from 3D seismic and well test data using machine learning](https://mlsanity.com/paper/2401.12717/) (2024)

## Abstract (arXiv)

This paper presents a proof of concept for spatial prediction of rock saturation probability using classifier ensemble methods on the example of the giant Groningen gas field. The stages of generating 1481 seismic field attributes and selecting 63 significant attributes are described. The effectiveness of the proposed method of augmentation of well and seismic data is shown, which increased the training sample by 9 times. On a test sample of 42 wells (blind well test), the results demonstrate good accuracy in predicting the ensemble of classifiers: the Matthews correlation coefficient is 0.7689, and the F1-score for the "gas reservoir" class is 0.7949. Prediction of gas reservoir thicknesses within the field and adjacent areas is made.