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Lithofacies classification using well log data from the Hugoton and Panoma Fields dataset. This project implements various machine learning algorithms including Support Vector Machines, Random Forest, Neural Networks, and others to predict facies groups. The study focuses on improving facies classification accuracy using well log data from 9 well.
Comprehensive analysis and evaluation of well log data for subsurface reservoir characterization. Includes gamma ray tool design, quality control of wireline logs, porosity, permeability, and water saturation calculations using advanced petrophysical methods. Features machine learning models for lithofacies classification and case study.
Petrophysic is a Python library developed to assist in the processing of Nuclear Magnetic Resonance (NMR) data, obtaining parameters such as porosity, T2 components, and permeability, as well as providing tools for data visualization. This project is part of a postgraduate work and aims to provide efficient tools for relaxation data analysis.
Take continuous high-resolution digital core images of the reservoir rock and process these images to define sand vs. shale for Borehole Imagelog calibration and Sand Count
We have used a novel supervised learning, Cluster Classify Regress algorithm (CCR) for approximating 2 phase flow in a synthetic toy reservoir with very high accuracy. We compared the performance of CCR with a single DNN architecture in recovering the evolving pressure and saturation fields. The method consists of creating different surrogate ma…
This project contains machine learning solution to automate reservoir quality prediction process. It's our team submission when joining hackathon held by Schlumberger.
A comprehensive Open Source repository for Petrophysics, providing tools, scripts, and resources for analyzing subsurface data. Ideal for geoscientists, engineers, and researchers working with porosity, permeability, and other petrophysical properties. Join us in advancing the science of subsurface exploration and reservoir characterization.
This R Notebook project illustrates how Artificial Neural Network can be applied to Reservoir Characterization dataset. It illustrates the relationship between a dependent variable and several independent variables using ANN.
Generate a Representative Thin Sections and Capillary Pressure Curves from any poro-perm combination using normalized core data with kNN backed by the Rosetta Stone Arab D Carbonate core database as calibration data.
The repository has the PyTorch codes to reproduce the results for our recently accepted paper, "Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network", in SEG Technical Program Expanded Abstracts, 2019.
The repository has the PyTorch codes to reproduce the results for our recently accepted paper, "Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network", in SEG Technical Program Expanded Abstracts, 2019.