A petrophysics python package for geoscience python computing of conventional and unconventional formation evaluation. Reads las files and creates a pandas dataframe of the log data. Includes a basic petrophysical workflow and a simple log viewer based on XML templates.

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.

Notebooks on production optimisation and history matching

Estimate Core-based Permeability from NMR well log data

Geological Reservoir Virtualization

We have used Mihai's PetroGG and modified the program to be used with our shaley-sand Gulf Coast data. In this version we are using Vshale and not Vclay, and we have added Waxman-Smits and Dual-Water saturation models appropriate for these data.

We are using Altair to Select Samples from a Poro-Perm Cross Plot and the respective Pc Curves or other data are then shown for the selected samples

Calculate a Chart Book type of Neutron Density log analysis Porosity using Python's KNN

Predict Petrophysical Rock Types (PRT) using KNN

Descriptive statistics about BHI fractures; Critically stressed faults

Mihai's PetroGG modified to be used with our shaly-sand Gulf Coast NMR data.

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…

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.

In this repository we provide the python code used to Closure correct and model HPMI data employing the Thomeer hyperbola for up to two pore systems.

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

Assess discrete depth interval to estimate the Petrophysical properties for that interval

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.

Codes associated with PhD thesis titled "Structural and Shape construction using inverse problems and machine earning techniques"

This project implements a method to balance the injection rate between multiple injectors in a CCS project in aquifers.