eng-rsmy Goto Github PK

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eng-rsmy's Projects

learnedfm

A numerical code to solve flow and transport in fractured porous media by the embedded discrete fracture method

This is a web based project for managing employees leave. Here employees can apply for leave and application is verified by admin. In the project, the main language of programming which we are using is PHP, JavaScript. MySQL server is used to maintain system database. For designing the view for both frontend and backend, HTML, CSS, Bootstrap are used.

li-s_ecn_model

Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries

library-management-php

This is a Library Management System built in plain PHP. This project was for my university's library and according to the requirements of our library. When new book is added to the database then this system would automatically built a barcode for a book, then you can print it for a book.Link: https://github.com/Naveedali1234/library-management-php This project is built in plain PHP and i implemented it for my university's library. This project is only for data management of the students , books and staff. we wanted to replace the file system into automated system. In this project we have 2 aspects, one is for admin side which is managed by library's assistant and one aspect is of the student side in which every registered user can see their library record of returned and issued books by giving credentials into login panel. Student can search the type of books just to check how many books are left of that name. On the admin side , the admin can register new students and books, the admin can see the records of every student. When book is issued to student, then an email would be sent to student just for confirmation. We used the barcode technology in our project and when an admin scans a book through barcode scanner then the admin can know the student name which issued the book and after scanning the book returned/issued to student. A barcode can be generated when a new record of book added to the database.

lithology_prediction_deep_learning

In this work, we will use Force2020 well data to employ Deep Learning for lithology prediction

lsmlib

Level Set Method Library

machine-learning

Machine learning techniques applied to subsurface data.

machine-learning-competition-2020

SPWLA PDDA’s 1st Petrophysical Data-Driven Analytics Contest -- Sonic Log Synthesis

machine-learning-in-oil-and-gas

This is a repository that groups a diverse set of machine learning models that I have created, tackling problematics in the oil and gas industry using real data.

machine-learning-oil-gas-industry

Source Code for 'Machine Learning in the Oil and Gas Industry' by Yogendra Narayan Pandey, Ayush Rastogi, Sribharath Kainkaryam, Srimoyee Bhattacharya, and Luigi Saputelli

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