The rate of penetration (ROP) is the key parameter to enhance drilling processes as it is inversely proportional to the overall cost of drilling operations. Maximizing the ROP without any limitation can induce drilling dysfunctions such as downhole vibrations. These vibrations are the main reason for bottom hole assembly (BHA) tool failure or excessive wear. This paper aims to maximize the ROP while managing the torque to keep the depth of cut within an acceptable range during the cutting process. To achieve this, machine learning algorithms are applied to build ROP and drilling torque models. Then, a metaheuristic algorithm is used to determine the optimal technical control parameters, the weight on bit (WOB) and revolutions per minute (RPM), that simultaneously enhance the ROP and mitigate excessive vibrations. This paper introduces a new methodology for mitigating drill string vibrations, improving the rate of penetration (ROP), minimizing BHA failures, and reducing drilling costs.
High-performance copper alloys are crucial for integrated circuit lead frames due to their high density, multifunctionality, and low cost. High-performance copper alloys typically address the competing issues of high strength and high electrical conductivity through alloying and…
Pipeline incidents pose significant concerns due to their potential environmental, economic, and safety risks, emphasizing the critical need to understand and manage this vital infrastructure. While existing studies predominantly focus on the causes of pipeline incidents and fail…
Plastic waste accumulation presents critical environmental challenges demanding innovative circular economy solutions. This study developed a comprehensive machine learning framework to systematically identify optimal enzyme combinations for polyester depolymerization. We integra…
The increasing use of electric vehicles has made fault diagnosis in electric drive motors, particularly in variable speed drives (VSDs) using three-phase induction motors, a critical area of research. This article presents a fault classification model based on machine learning (M…
In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we pro…
The accurate prediction of pressure and saturation distribution during the simulation of CO2 injection into saline aquifers is essential for the successful implementation of carbon sequestration projects. Traditional numerical simulations, while reliable, are computationally expe…