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Optimization and Performance Evaluation of Oil Drilling Operations Using Data Envelopment Analysis Models and Metaheuristic Algorithms: Application in Productivity Improvement and Cost Reduction
Volume 5, Issue 1, 2023-2024, Pages 37 - 64
1 Islamic Azad University, Najafabad Branch
Abstract :
The oil industry faces multiple challenges, including low productivity, high costs, and the need for performance improvement. Optimizing drilling operations plays a crucial role in increasing efficiency and reducing operational expenses. In this study, the efficiency of drilling rigs is evaluated using DEA models, including CCR and BCC, to identify efficient and inefficient units. This evaluation is conducted based on input indicators such as drilling time, utilized equipment, and consumed materials, as well as output indicators like drilling productivity, environmental satisfaction, and Human Resources Productivity. In the next phase, metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are employed to optimize the operational parameters of inefficient rigs. These algorithms optimize input parameters to reduce time and operational costs, enhance productivity, and improve environmental performance, thereby improving the efficiency of the evaluated units. The findings of this study demonstrate a significant enhancement in the efficiency of drilling rigs and a reduction in drilling operation costs. This integrated approach can serve as an effective and practical method for increasing productivity in the oil industry. Future research can utilize larger datasets and more diverse variables to enhance the generalizability of the results. Additionally, the application of hybrid optimization methods can be explored to achieve more accurate and practical outcomes.
The oil industry faces multiple challenges, including low productivity, high costs, and the need for performance improvement. Optimizing drilling operations plays a crucial role in increasing efficiency and reducing operational expenses. In this study, the efficiency of drilling rigs is evaluated using DEA models, including CCR and BCC, to identify efficient and inefficient units. This evaluation is conducted based on input indicators such as drilling time, utilized equipment, and consumed materials, as well as output indicators like drilling productivity, environmental satisfaction, and Human Resources Productivity. In the next phase, metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are employed to optimize the operational parameters of inefficient rigs. These algorithms optimize input parameters to reduce time and operational costs, enhance productivity, and improve environmental performance, thereby improving the efficiency of the evaluated units. The findings of this study demonstrate a significant enhancement in the efficiency of drilling rigs and a reduction in drilling operation costs. This integrated approach can serve as an effective and practical method for increasing productivity in the oil industry. Future research can utilize larger datasets and more diverse variables to enhance the generalizability of the results. Additionally, the application of hybrid optimization methods can be explored to achieve more accurate and practical outcomes.
Keywords :
Optimization, Drilling Operations, Data Envelopment Analysis, Metaheuristics
Optimization, Drilling Operations, Data Envelopment Analysis, Metaheuristics