Revolutionizing Oil Lift Operations: Harnessing Data Analytics for Esp Energy Management, Cost Savings, Co2 Emissions Reduction, and Enhanced Pump Runlife

Abstract

Objectives/Scope In light of the global transition towards the 1.5-degree pathway, the need for alternative power sources becomes increasingly crucial. However, the rapid growth in the number of wells has led to a substantial rise in power consumption. Currently, ESP systems account for a significant portion, approximately 19%, of the company's power consumption, with an upward trend. To address this challenge, this study aims to employ data analytics and Six Sigma tools to identify and implement methods for optimizing power consumption and enhancing energy efficiency within ESP systems. Methods, Procedures, Process The study commenced by gathering data for approximately 20 ESP parameters along with corresponding power consumption readings from a comprehensive sample of 250 wells. The data underwent meticulous verification and validation processes, ensuring technical and analytical accuracy. To gain insights into the relationship between power consumption and ESP parameters, graphical tools were employed for in-depth analysis. The analysis revealed two primary parameters that consistently contributed to higher power consumption in ESP systems when compared to their design specifications. The team then embarked on developing models aimed at concurrently reducing these identified parameters, namely voltage and tubing head pressure, without compromising production levels. The models demonstrated a promising reduction in power consumption, ranging from 8% to 24%, based on a sample of wells. Subsequently, the team implemented the identified optimizations on the selected wells, which resulted in tangible reductions in power consumption. Encouraged by these positive outcomes, the exercise was replicated across other wells within the field, yielding significant improvements in energy efficiency. Results, Observations, Conclusions The impact of the power consumption optimization measures implemented across the entire company's ESP systems, encompassing approximately 1,000 units equipped with variable speed drives, has yielded remarkable results. Calculations based on electricity tariffs indicate an annual cost savings of $3.0 million. This substantial reduction in operational expenses not only enhances the financial performance of the company but also reinforces its commitment to sustainability. In terms of environmental impact, the power reduction initiatives have led to a significant reduction of approximately 5,500 tons of CO2 emissions annually. The successful implementation of optimizations yielded significant financial savings and environmental benefits. This study establishes a foundation for future energy management strategies, emphasizing the potential for widespread adoption in the oil industry. Novel/Additive Information The study's noteworthy aspect is the successful integration of Six Sigma tools and data analytics, providing a structured framework for optimizing ESP systems. This approach enhanced clarity and direction, making the comprehensive analysis of parameters manageable. Critical factors influencing power consumption were identified, leading to accurate models and significant energy savings. The synergy between data analytics and Six Sigma showcases a novel and additive approach for optimizing energy management in ESP systems, with potential for broader application in complex systems across industries.