# Explainable Machine Learning for Hydrocarbon Prospect Risking

arXiv: https://arxiv.org/abs/2212.07563 · HTML: https://mlsanity.com/paper/2212.07563/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 66 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 29 of 29)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Gradients as a Measure of Uncertainty in Neural Networks](https://mlsanity.com/paper/2008.08030/) (2020)
- [Backpropagated Gradient Representations for Anomaly Detection](https://mlsanity.com/paper/2007.09507/) (2020)
- [A Machine Learning Benchmark for Facies Classification](https://mlsanity.com/paper/1901.07659/) (2019)
- [Contrastive Explanations in Neural Networks](https://mlsanity.com/paper/2008.00178/) (2020)
- [Novelty Detection Through Model-Based Characterization of Neural Networks](https://mlsanity.com/paper/2008.06094/) (2020)
- [Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics](https://mlsanity.com/paper/1908.11262/) (2019)
- [Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation](https://mlsanity.com/paper/2206.08158/) (2022)
- [Semi-supervised Sequence Modeling for Elastic Impedance Inversion](https://mlsanity.com/paper/1908.07849/) (2019)
- [Structure Label Prediction Using Similarity-Based Retrieval and Weakly-Supervised Label Mapping](https://mlsanity.com/paper/1905.06528/) (2019)
- [Implicit Saliency in Deep Neural Networks](https://mlsanity.com/paper/2008.01874/) (2020)
- [Relative Afferent Pupillary Defect Screening through Transfer Learning](https://mlsanity.com/paper/1908.02300/) (2019)
- [A Gating Model for Bias Calibration in Generalized Zero-shot Learning](https://mlsanity.com/paper/2203.04195/) (2022)
- [Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network](https://mlsanity.com/paper/1906.02684/) (2019)
- [Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset](https://mlsanity.com/paper/2003.07725/) (2020)
- [Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation](https://mlsanity.com/paper/2006.15472/) (2020)
- [Joint Learning for Seismic Inversion: An Acoustic Impedance Estimation Case Study](https://mlsanity.com/paper/2006.15474/) (2020)
- [On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption](https://mlsanity.com/paper/2009.00817/) (2020)
- [A comparative study of texture attributes for characterizing subsurface structures in seismic volumes](https://mlsanity.com/paper/1812.08263/) (2018)
- [Self-Supervised Annotation of Seismic Images using Latent Space Factorization](https://mlsanity.com/paper/2009.04631/) (2020)
- [Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning](https://mlsanity.com/paper/1812.11109/) (2018)
- [Multiresolution Analysis and Learning for Computational Seismic Interpretation](https://mlsanity.com/paper/1901.08539/) (2019)
- [Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor](https://mlsanity.com/paper/2002.01154/) (2020)
- [Robustness and Overfitting Behavior of Implicit Background Models](https://mlsanity.com/paper/2008.09306/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behavior](https://mlsanity.com/paper/2505.08585/) (2025)

## Abstract (arXiv)

Hydrocarbon prospect risking is a critical application in geophysics predicting well outcomes from a variety of data including geological, geophysical, and other information modalities. Traditional routines require interpreters to go through a long process to arrive at the probability of success of specific outcomes. AI has the capability to automate the process but its adoption has been limited thus far owing to a lack of transparency in the way complicated, black box models generate decisions. We demonstrate how LIME -- a model-agnostic explanation technique -- can be used to inject trust in model decisions by uncovering the model's reasoning process for individual predictions. It generates these explanations by fitting interpretable models in the local neighborhood of specific datapoints being queried. On a dataset of well outcomes and corresponding geophysical attribute data, we show how LIME can induce trust in model's decisions by revealing the decision-making process to be aligned to domain knowledge. Further, it has the potential to debug mispredictions made due to anomalous patterns in the data or faulty training datasets.