MODELING

This project investigates the effect of key drilling parameters on Rate of Penetration (ROP) using real-world field data from a selected well. The parameters analyzed include Weight on Bit (WOB), Rotational Speed (RPM), and mud properties such as Plastic Viscosity, Yield Point, and Gel Strength. The study aims to understand how variations in these parameters influence ROP and to identify combinations that could
enhance drilling efficiency. Microsoft Excel was used for organizing, calculating, and analyzing the data, with additional tools such as Solver applied for basic optimization. By focusing on a practical, data-driven approach, this work contributes to ongoing efforts in optimizing drilling operations, especially in regions where advanced software and models may be inaccessible. The findings provide insight into the practical relationships between operational parameters and ROP, and highlight opportunities for performance improvement in similar field environments

Corporate mortality modeling refers to the process of predicting the likelihood of a company in a specific sector going out of business or experiencing financial distress. In the manufacturing sector, understanding and accurately predicting corporate mortality is highly important due to the complex and volatile nature of the industry. This work focuses on the analysis of corporate mortality in the manufacturing sector. The manufacturing sector plays a vital role in the global economy, employing a significant number of individuals and contributing to GDP. However, it also faces numerous challenges, such as intense competition, technological advancements, changing consumer demands, and economic fluctuations. The objective of this study is to develop a robust corporate mortality model specifically designed for the manufacturing sector. The model will incorporate various financial and non-financial factors that may influence the likelihood of a company going out of business. Financial factors such as profitability, liquidity, leverage, and solvency will be considered, along with non-financial factors such as industry dynamics, management quality, and market conditions. Data will be collected from a sample of manufacturing companies over a specific period of observation. This data will be used to build a predictive model using advanced statistical techniques such as logistic regression, survival analysis, and machine learning algorithms. The model will be validated using historical data and tested for its predictive accuracy. The results of this study will provide valuable insights into the factors that contribute to corporate mortality