SepAlex

SepAlex's Stars

• koalaverse/homlr

  Supplementary material for Hands-On Machine Learning with R, an applied book covering the fundamentals of machine learning with R.

  Language:CSS231247388

• salmansust/Machine-Learning-TSF-Petroleum-Production

  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

  Language:Python330117

• FracThePermian/Reservoir-Simulation-Part-B

  3D Graphical Output of well producers and injectors in oil reservoir.

  Language:MATLAB17203

• Philliec459/ChartBook-Neutron-Density-Porosity-Estimations-using-KNN

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

  Language:Jupyter Notebook13209

• volpatto/anp-data-analysis

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

  Language:Jupyter Notebook11303