Ferrylxz's starred repositories
TimeSeries_ForeCasting_on_NIFTY50-stockmarket_data
using various time series forecasting techniques like simple moving averages,weighted moving average,ARIMA,Univariate time-series forecasting:LSTM'S and Multi-variate & single-step forecasting:LSTM'S used to predict future stock price
ML-for-Well-Log-Data
In this notebook I used Naive Bayes, Random Forest, KNN, SVM, and Decision Tree to classify rock types in the oil well based on well log data.
LylesCoalbedMethane2
Analysis for Chris Lyles's coalbed & methane production study
Oil-production-time-series-forecasting
Develop the time series recurrent neural network algorithms using LSTM and ARIMA in order 1) to forecast oil production of existing wells in complex fractured reservoir using input data like well's production and injection data; and 2) to compare which of this tool (LSTM or ARIMA) is better
Oil-production-prediction-using-LSTM-based-on-Keras
Oil production prediction using LSTM(Long short-term memory neural network) based on Keras
Compressive-Sensing-and-Deep-Learning-Framework
We propose Compressive Sensing and Deep Learning framework (CS-DL) for multiple satellite sensor based data fusion. It’s aims to improve spatial and temporal resolution for long term analysis. Compressive Sensing is used as an initial guess to combine data from multiple sources. Deep Learning model, using Long Short Term Memory Neural Network (LSTM/RNN) refines and further improves the resulting data fusion output from CS. Our CS-DL framework has been tested to fuse CO2 from the NASA Orbiting Carbon Observatory-2 (OCO-2) and the JAXA Greenhouse gases from Orbiting Satellites (GOSAT). It achieves lower errors and high correlation compared with the original data. This work demonstrates the use of CS-DL for fusing CO2 from NASA Orbiting Carbon Observatory-3 and GOSAT2 at higher resolution.
Oil-Gas-well-production-Profiling
Model the production profiles of oil and gas wells using LSTMs and ARIMA