Proof of Private Mineral Assets
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Updated
Jul 4, 2018
Proof of Private Mineral Assets
Exploratory analysis of seismic data using R and RStudio
I worked with OilyGiant, a petroleum mining firm, to find new oil well locations. I created a model to identify high-profit zones and assessed potential earnings and risks using bootstrapping techniques.
In this project we predict fracture position by oil production data and predict optimal prod well position for best oil production
Ajude a OilyGiant a encontrar os melhores locais para novos poços de petróleo. Use análise de dados e regressão linear para prever reservas e tomar decisões de investimento, mantendo o risco de prejuízo abaixo de 2,5%.
Identifying outliers in well logs with machine learning.
This Repository will be destinated to the study of Offshore Well Modeling. The model that will be implemented is called Fast Offshore Wells Model (FOWM) from the paper: Fast Offshore Wells Model (FOWM): A practical dynamic model formultiphase oil production systems in deepwater and ultra-deepwaterscenarios
A data prediction algorithm implemented in Python to infer depth and casing size data of California oil wells missing data using surrounding wells in the same oil field. Python libraries such as pandas for manipulating dataframes and geopy were utilized.
Determining the best oil well for development
Analysis of complaints against Colorado oil wells.
A fast way to check and work with .dlis files.
Web application to store P.S.I records of PDVSA Oil Wells (C.R.U.D + charts)
Conducting a well-test analysis on the volve dataset to test our reservoir for better understanding of some useful reservoir properties
Projects relevant to the oil & gas industry built by Microsoft
Oil and gas production has skyrocketed in Pennsylvania in the past decade as a result of improved techniques with horizontal drilling and hydraulic fracturing. This analysis will look at the waste produced by these wells and how that waste is handled.
Multivariate oil production time series analysis with XGBoost and neural networks
This repository contains the necessary scripts for oil production flow prediction models that make use of spark's MLlib
A Python Module for Outliers Detection, Visualization and Treatment in Oil Well Datasets
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