Topic: reservoir-characterization Goto Github
Some thing interesting about reservoir-characterization
Some thing interesting about reservoir-characterization
reservoir-characterization,PhazeOpt is a set of tools for Reservoir Fluid Characterization.
User: aelghattas
reservoir-characterization,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.
User: amustafa9
reservoir-characterization,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.
User: amustafa9
reservoir-characterization,Geological Reservoir Virtualization
User: apirsal
Home Page: https://georevi.com/
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.
User: chuksoo
reservoir-characterization,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 machines equivalent to the number of time-steps (dynamic pressure and saturation snapshots). The inputs to the machine are the x,y,z spatial pixel (grid) location, the absolute permeability at each grid, effective porosity at each grid and the pressure and saturation field for each grid, for the previous time step. The outputs are the pressure and saturation field for the current time step Prediction is computationally cheap as each pressure and saturation map (for each time step) is recovered from each of the machines. The initial pressure and saturation field (Time 0) is fixed and set in the ECLIPSE data file. Learning of the function is first initiated by running eclipse once for the β1st time stepβ alone to get the preceding pressure and saturation field, CCR and DNN was then used to construct the different machines for each of the snap shots. CCR attained R2 accuracies of greater than 96% for both the recovery of the pressure and saturation field and Structural similarity index metric (SSIM) value of greater than 90% to the true pressure and saturation fields. We also use this newly constructed surrogate model in an ensemble based history matching frame-work. We show the overall frame work gives an acceptable history match (avoiding an inverse crime) to the synthetic true reservoir model. Finally we show the wall cock performance time of CCR in prediction (9.25 seconds on a standard personal laptop computer) compared to the full fidelity ECLIPSE reservoir solver to be 19.34 seconds. This is crucial in an ensemble based uncertainty quantification (UQ) task where the size of the ensemble ranges from 100 to 500 for full field reservoir history matching problems.
User: clementetienam
reservoir-characterization,Codes associated with PhD thesis titled "Structural and Shape construction using inverse problems and machine earning techniques"
User: clementetienam
reservoir-characterization,This project implements a method to balance the injection rate between multiple injectors in a CCS project in aquifers.
User: jalal-alali
Home Page: https://github.com/equinor/ecl
reservoir-characterization,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.
Organization: olivesgatech
reservoir-characterization,Notebooks on production optimisation and history matching
User: patnr
reservoir-characterization,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
User: philliec459
reservoir-characterization,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.
User: philliec459
reservoir-characterization,Predict Petrophysical Rock Types (PRT) using KNN
User: philliec459
reservoir-characterization,Calculate a Chart Book type of Neutron Density log analysis Porosity using Python's KNN
User: philliec459
reservoir-characterization,Assess discrete depth interval to estimate the Petrophysical properties for that interval
User: philliec459
reservoir-characterization,Estimate Core-based Permeability from NMR well log data
User: philliec459
reservoir-characterization,Mihai's PetroGG modified to be used with our shaly-sand Gulf Coast NMR data.
User: philliec459
reservoir-characterization,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.
User: philliec459
reservoir-characterization,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.
User: philliec459
reservoir-characterization,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
User: philliec459
reservoir-characterization,Descriptive statistics about BHI fractures; Critically stressed faults
User: shubhodip-konar
reservoir-characterization,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.
User: toddheitmann
Home Page: https://toddheitmann.github.io/PetroPy/
reservoir-characterization,This project contains machine learning solution to automate reservoir quality prediction process. It's our team submission when joining hackathon held by Schlumberger.
User: zakkipuar23
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