Content Tagged: "analytics"

This SPE Live introduces three prominent approaches to generative AI, followed by a presentation of a novel method utilizing two of these approaches t

Whatever your profession. Whatever your field. As a professional, and certainly as a leader, you will be asked to make a decision based on data. This course will introduce the different types of decisions made in an organizational setting, why quantitative analytics is important, and how data quality can affect decision making. Since quantitative analytics is used in various settings, this intermediate-level course also offers insight into how research is used in different sectors. From a management perspective, the course highlights appropriate quantitative methods and ways to ensure quality and accuracy through research design.

When using data analysis to improve organizational performance, it's vital to employ the tools that bring the data to life and keep people engaged in the process. Organizations in both the public and private sectors often use tools and frameworks to deliver the data, and the information the data might suggest, to its staff. This intermediate-level course will explain some of these measures and tools, describe some specific measurements, and explain the relationship between assessment and strategy. Summarizing the data with the correct tool can be the gating factor to reaching staff and effecting changes that spur performance improvement.

How are data-driven decisions put into practice in the real world? How do these decisions differ when applied to different sectors, such as health care, education and government? This intermediate-level course will provide answers to these questions as well as recommendations for decision-making based on data analytics for each sector. The course will begin with an introduction of Big Data, then continue into a deeper dive on its implications within each sector. Industry case studies make the concepts applicable in the real-world.

This intermediate-level course describes, evaluates, and analyzes different statistical techniques and their real-world limitations and benefits. The course features crossover analysis, break-even analysis, cluster analysis, decision tree analysis as well as an introduction to regression.

Join this SPE Live to learn more about the evolution of data and analytics in well construction, and how the lessons learned in traditional drilling a

Join us for this SPE Tech Talk with TGS to discuss the intricacies of subsurface analytics, exploring pressing questions surrounding unconventional resources and the challenges posed by data integration.

In January 2024, SPE Journal will relaunch as the single resource for all of SPE’s peer-reviewed research. The new journal sections include: Drillin

In this SPE Tech Talk, we explore the impact of combining data analytics, automation, sensors, and AI on drilling operations and efficiency.

Data Science is no longer just a mere buzz word for the Energy sector. Recently, there has been tremendous growth and innovations in this arena with m

AI has been around for about five decades. However, in the past few months due to emergence of Generative AI, there is a renewed buzz about Artificial

Join us for an informative webinar showcasing how advanced technologies like big data analytics and machine learning are transforming real-time drilling operations for Rate of Penetration (ROP) optimization.

This presentation aims to introduce you to this exciting and rapidly advancing field. We'll start by outlining the present state of technology development and then discuss some details. We will examine how a quantum algorithm is constructed to help you understand its unique characteristics, limitations, and potential to significantly improve the performance of some applications and generate entirely new applications.

In this Tech Talk, we discuss the subject of Data Validity: the extent to which data accurately reflects reality, and the impact it may have on decisions that are based upon data.

Reservoir engineering is rapidly evolving, and traditional methods alone can no longer meet the demands of today's complex reservoirs and business needs. In this course, you will learn how to leverage cutting-edge data analytics techniques to extract valuable insights from vast amounts of reservoir data. In this course, we will explore current applications of data analytics in reservoir engineering, ensuring you develop a clear understanding of how these techniques can enhance your work. Additionally, we will delve into recent trends and developments that merge data-driven and physics-based methods (hybrid reservoir models), enabling you to stay ahead of the curve in this rapidly evolving field with focus on surveillance, reservoir management and field optimization for unconventional and conventional reservoirs. From understanding the methodology behind model development to exploring machine learning algorithms, you'll gain a solid foundation in data analytics and its relevance in reservoir engineering that will allow you to make more informed decisions and optimize reservoir performance. We will guide you through a hands-on model development process, equipping you with the best practices and helping you navigate potential pitfalls. No prior Python knowledge is required, but we will provide optional code samples for those interested in diving deeper. As we wrap up the course, we will explore future trends in data, models, automation, and the human element in reservoir engineering. You'll gain valuable insights into where the industry is headed, ensuring you stay at the forefront of innovation.