Topic: petroleum-engineering Goto Github
Some thing interesting about petroleum-engineering
Some thing interesting about petroleum-engineering
petroleum-engineering,Gas compressibility z-factor calculator package in Python
User: aegis4048
Home Page: https://github.com/aegis4048/GasCompressibiltiy-py
petroleum-engineering,This Python script creates a Streamlit web application for a quick IPR Calculation for both an oil and gas reservoir. It collects user input test data and with it performs a curve fitting using scipy.optimize, fitting the Inflow Performance Relationship curve to the data.
User: aggg97
Home Page: https://ipr-calculation.streamlit.app/
petroleum-engineering,My RE (reservoir engineering) Python code
User: alex-kalinichenko
petroleum-engineering,A repo containing Jupyter notebooks where ensemble algorithms are investigated to attempt predicting the downhole concentration of cuttings in oil wells using surface data.
User: awojinrin
petroleum-engineering,capacitance resistance model for waterflood in matlab
User: billalaslam
petroleum-engineering,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
petroleum-engineering,R package for Arps decline curve analysis.
User: derrickturk
petroleum-engineering,A "simple" decline-curve analysis example in Python
User: derrickturk
petroleum-engineering,Interactive aRpsDCA demo with Shiny
User: derrickturk
petroleum-engineering,Programming (& Excel Automation) webinar series for SPE YP
User: derrickturk
Home Page: https://derrickturk.github.io/yp-excel-automation/
petroleum-engineering,FlowNet - Data-Driven Reservoir Predictions
Organization: equinor
petroleum-engineering,A reproducible platform to improve paper search and selection in OnePetro
User: f0nzie
Home Page: https://f0nzie.github.io/petro.One/.
petroleum-engineering,R interface to Eclipse parser of binary files
User: f0nzie
petroleum-engineering,Volve dataset. Reservoir model. Analyze steps and field cumulatives from Eclipse PRT file
User: f0nzie
petroleum-engineering,
User: f0nzie
Home Page: https://f0nzie.github.io/zFactor/
petroleum-engineering,Compares analytical/numerical results for the drainage of a single well.
User: fracthepermian
petroleum-engineering,Elastic-Plastic Deformation on a 2-D Plane
User: fracthepermian
petroleum-engineering,3D Graphical Output of well producers and injectors in oil reservoir.
User: fracthepermian
petroleum-engineering,Scaling solutions for production analysis from unconventional oil and gas wells
User: frank1010111
petroleum-engineering,Capacitance resistance models for waterflood connectivity
User: frank1010111
petroleum-engineering,In this repository, you will find resources related to the oil, gas and energy industry.
User: freddyecu-ch
petroleum-engineering,use lasio to analyze and plot digital well log files
User: hydrospanner
petroleum-engineering,Discounted cash flow analysis for oil and gas
User: jshumway0475
petroleum-engineering,Repository of upcoming abstract submission deadlines for geoscience conferences
User: lukasmosser
petroleum-engineering,The objective of this GitHub repository is to develop code that can effectively display well log parameter plots and their animations.
User: maribickpostanes
petroleum-engineering,This open-source code repository provides a Python implementation for generating an interactive horizon surfaces plot using Plotly. The focus of this code is to visualize the Volve horizons in a 3D surface plot.
User: maribickpostanes
petroleum-engineering,This repository contains a Python script that uses the Plotly library to create an interactive web-based application for plotting seismic data sections from SEG-Y files.
User: maribickpostanes
petroleum-engineering,This open-source code repository provides a Python implementation for generating an interactive well trajectory plot using Plotly. The focus of this code is to visualize the trajectory of Well 15/9-F-5 from the Volve dataset.
User: maribickpostanes
petroleum-engineering,GEOLOGY GRAD STUDENT JUST SHARING MY ALGORITHMS TO GET FEEDBACKS FROM SOFTWARE ENGINEERS AND COMPETITIVE PROGRAMMERS.πππ
User: moyinolorunpreciousadegbie
petroleum-engineering,Open source petroleum engineering projects, useful scripts, functions, and jupyter notebooks
User: mwentzww
petroleum-engineering,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
petroleum-engineering,The objective of this project is to interactively interrogate oil field production data using Altair
User: philliec459
petroleum-engineering,Calculate a Chart Book type of Neutron Density log analysis Porosity using Python's KNN
User: philliec459
petroleum-engineering,Estimate Core-based Permeability from NMR well log data
User: philliec459
petroleum-engineering,Utilized Tensorflow to estimate the Mode of a Pore Throat Distribution based on Carbonate Core Data
User: philliec459
petroleum-engineering,Carbonate Reservoir Characterization workflow using Clerkeβs carbonate Arab D Rosetta Stone calibration data to provide for a full pore system characterization with modeled saturations using Thomeer Capillary Pressure parameters for an Arab D complex carbonate reservoir
User: philliec459
petroleum-engineering,Shaley-Sand Log Analysis Tutorial using Waxman-Smits and Dual-Water
User: philliec459
petroleum-engineering,Mihai's PetroGG modified to be used with our shaly-sand Gulf Coast NMR data.
User: philliec459
petroleum-engineering,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
petroleum-engineering,Use of Sklearn to predict Petrophysical Rock Types (PRT) in an Arab D carbonate based on Clerke's Rosetta Stone Calibration data
User: philliec459
petroleum-engineering,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
petroleum-engineering,Load cases for tubulars
Organization: pro-well-plan
Home Page: https://lnkd.in/g2Yr5B9
petroleum-engineering,Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this research, we applied machine learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions and showed comparison of different machine learning models. For evaluation purpose, a case study from the petroleum industry domain is carried out using the production data of an actual gas field of Bangladesh. Toward a fair evaluation, the performance of the models were evaluated by measuring the goodness of fit through the coefficient of determination (R2 ) and Root Mean Square Error (RMSE), Mean Squared Error (MSE) , Mean Absolute Error(MAE) and model Accuracy
User: salmansust
petroleum-engineering,
User: shmeleved
petroleum-engineering,Machine learning framework for reservoir simulation
Organization: skoltech-chr
petroleum-engineering,Modeling two-phase flow and transport in porous media using MATLAB.
User: tstran155
petroleum-engineering,Educational Vibration programs. Intended for undergraduate and early graduate students.
Organization: vibrationtoolbox
Home Page: http://vibrationtoolbox.github.io/vibration_toolbox/
petroleum-engineering,Repo that contains an exploratory data analysis of pre-salt wells and geo data from ANP data base
User: volpatto
petroleum-engineering,An open-source Python package for writing DLIS files.
Organization: well-id
Home Page: https://well-id-widcdliswriter.readthedocs-hosted.com/index.html
petroleum-engineering,Python Automatic Decline Curve Analysis (DCA) For Petroleum Producing wells
User: yous3ry
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