https://github.com/equinor/flownet

FlowNet - Data-Driven Reservoir Predictions

data-assimilation data-driven petroleum-engineering reduced-order-models reservoir-simulation

Last synced: 01 May 2025

https://github.com/frank1010111/pywaterflood

Capacitance resistance models for waterflood connectivity

petroleum-engineering python reservoir rust

Last synced: 01 Dec 2024

https://github.com/f0nzie/zFactor

compressibility-correlations petroleum-engineering r rstats

Last synced: 14 Mar 2025

https://github.com/lukasmosser/geo-deadlines

Repository of upcoming abstract submission deadlines for geoscience conferences

geology geophysics geoscience petroleum-engineering sedimentology stratigraphy

Last synced: 15 Apr 2025

https://github.com/frank1010111/bluebonnet

Scaling solutions for production analysis from unconventional oil and gas wells

oil-and-gas petroleum-engineering python reservoir

Last synced: 01 Dec 2024

https://github.com/volpatto/anp-data-analysis

Repo that contains an exploratory data analysis of pre-salt wells and geo data from ANP data base

exploratory-data-analysis georeferencing maps notebook-jupyter pandas-python petroleum-engineering

Last synced: 03 Dec 2024

https://github.com/clementetienam/ensemble-based-history-matching-with-a-machine-learning-surrogate-reservoir-simulator

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 “1st time step” 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.

datascience petroleum-engineering reservoir-characterization reservoirs-history-matching

Last synced: 26 Mar 2025

https://github.com/maximeguinard/oil-mx

⛽ An addon that allows you to have pretrol as a resource for Garry's mod

addon addons gmod gmod-addon gmod-gamemodes gmod-lua gmod-module gmodaddon gmodlua petrol petroleum petroleum-engineering petrology resources

Last synced: 20 Mar 2025

https://github.com/ser-arthur/knn-machine-learning

a k-Nearest-Neighbors (kNN) classification model to petrophysical well log data from the Lismore Field. Includes a full analysis workflow, classification notebook, and final report.

knn-classification machine-learning petroleum-engineering python reservoir-characterization

Last synced: 10 Apr 2025

https://github.com/leosimoes/uerj-modelagem-de-reservatorios-de-petroleo

Trabalhos desenvolvidos na disciplina de Modelagem de Reservatórios de Petróleo no período 2018.2. Implementação, gráficos e análise de Escoamento Monofásico em Meios Porosos.

engineering petroleum-engineering python

Last synced: 24 Mar 2025