Facies classification using machine learning. However, distinguishing between various reef facies remains challenging, as SOM clusters often overlap, reflecting the difficulty in differentiating these facies using the available seismic attributes. Oct 1, 2016 · These attributes make it possible to accurately distinguish key facies by using only gamma-ray data, both with formulaic calculations and employing machine-learning (ML) algorithms. ”Geomechanics and Geo-Resources 10: 185. This is an image-to-point classification approach, which takes considerable time in the application phase. In this study, we employ facies data in optimizing a numerical model permeability matrix scaling parameter using Monte Carlo Simulation of Markov Switching Dynamic Regression and machine learning. Feb 24, 2026 · As oil and gas exploration increasingly targets highly heterogeneous unconventional reservoirs, conventional well-log-based lithofacies identification methods often fail to adequately characterize local abrupt variations and long-range depositional dependencies within complex geological architectures such as thin interbeds. Therefore, our goal in this research is to employ machine learning to predict seismic facies according to a classified trained model to facilitate the interpretation tasks, such Contribute to mardani72/Facies-Classification-Machine-Learning development by creating an account on GitHub. A. Well Log Facies Classification using Machine Learning This solution incorporates well logs (borehole measurements) from the corporate data center and applies a low-code machine learning (ML) model on the data using Amazon SageMaker Autopilot to obtain a facies (rock type) classification at each well log’s measured depth. This activity demands significant experience, effort, and time. Oct 1, 2016 · In this tutorial, we will demonstrate how to use a classification algorithm known as a support vector machine to identify lithofacies based on well-log measurements. Data pre-processing and preparation involve two processes: data cleaning and feature scaling. In this work, we present an method for automated facies classification using feature engineering and ensemble classifiers (machine learning). 5 days ago · Seismic facies analysis is key for understanding geology in underexplored areas with minimal well control. Aug 22, 2020 · In future work, I will demonstrate how a convolutional neural network can be applied to facies classification, as well as evaluating its effectiveness in terms of accuracy and other metrics. 3 days ago · Seismic facies classification is a challenging task for seismic reflection data interpreters who are unfamiliar with recognizing seismic facies. Ensemble-based machine learning application for lithofacies classification in a pre-salt carbonate reservoir, Santos Basin, Brazil Posted in 24/07/2023by João Lucas Braga Da Silva. Therefore, our goal in this research is to employ machine learning to predict seismic facies according to a classified trained model to facilitate the interpretation tasks, such as Machine learning for facies classification and attribute selection Deep learning-based inversion and rock property prediction Bayesian methods for uncertainty and scenario modeling Foundation models and agentic AI workflows in geoscience The key message: AI is a powerful assistant — not a replacement for experienced interpreters. Nov 26, 2022 · Here we leverage improvements in machine learning and X-ray fluorescence core scanning to develop an improved approach to automatic sediment-facies classification. The dataset we will use comes from a class excercise from The University of Kansas Mar 1, 2025 · Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature (seven well logs and one facies log) were used to classify four facies. In carbonate environments, however, extracting geological insights from seismic data is challenging due to complex depositional and diagenetic processes. 3 days ago · Abstract Seismic facies classification is a challenging task for seismic reflection data interpreters who are unfamiliar with recognizing seismic facies. The outputs are visualized using Amazon QuickSight dashboards. However, traditional waveform clustering methods struggle with variable-length seismic signals due to their reliance on Euclidean distance, which necessitates zero-padding or truncation—procedures that risk distorting SOMs proved use-ful in highlighting carbonate intervals and differentiating them from siliciclastics. To address this challenge, this study proposes a hybrid KNN Next, we trained a LeNet CNN-type of Machine Learning model to classify the 3D seismic response surrounding each labeled position into these 8 classes. Facies logs from several interpreted wells are used to train multiple multiclass machine learning models. Ismail. This notebook demonstrates how to train a machine learning algorithm to predict facies from well log data. “Unsuper-vised Machine Learning-BasedMulti-AttributesAnalysisforEnhancing Gas ChannelDetection and Facies Classification inthe Serpent Field, Offshore Nile Delta, and GeophysicsforGeo-Energy Egypt. This study utilizes newly acquired high-resolution broadband seismic data to better understand a well-known example of Middle Miocene 5 days ago · Enhancing machine learning-based seismic facies classification through attribute selection: application to 3D seismic data from the Malay and Sabah Basins, offshore Malaysia Article Full-text 1 day ago · Waveform clustering serves as an effective technique for seismic facies classification by leveraging waveform similarity. Machine learning techniques have been widely used in the oil and gas industry to improve the qualitative and quantitative characterization of subsurface reservoirs. Machine learning provides opportunities to integrate facies data into the numerical model-building process. 2024. . lzl fxb dse evo oom spb qce ojq ptq ppl hug qvd wvz sfs zrr