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How to Coach Featured Image

How to Coach

Optimize reservoir management and control decisions using surveillance techniques, information systems, technical indicators and financial guidelines. Upon completion of this module, the participant should be able to do the following: manage available resources (e.g., reserves, assets, personnel, budget) in order to maximize hydrocarbon reserves and minimize recovery cost and make appropriate reservoir management and control decisions with the aid of surveillance techniques, information systems and technology applications to generate pressure and production histories, maps and other key reservoir data.

Data Acquisition Featured Image

Data Acquisition

Acquire and analyze the necessary data for optimizing reservoir surveillance. Upon completion of this module, the participant should be able to acquire and analyze pressure data, PVT reports, production records, injection records, production tests, fluid sampling, injectivity tests, and other information for the purpose of monitoring reservoir behavior.

Geologic/Seismic Integration Featured Image

Geologic/Seismic Integration

Integrate multidimensional seismic information and geological data to optimize the processes of reservoir monitoring and exploitation. Upon completion of this module, the participant should be able to establish a basis for integrating geophysical and geological data and incorporating them into the reservoir surveillance program.

Reservoir Management and Control Featured Image

Reservoir Management and Control

Optimize reservoir management and control decisions using surveillance techniques, information systems, technical indicators and financial guidelines. Upon completion of this module, the participant should be able to do the following: manage available resources (e.g., reserves, assets, personnel, budget) in order to maximize hydrocarbon reserves and minimize recovery cost and make appropriate reservoir management and control decisions with the aid of surveillance techniques, information systems and technology applications to generate pressure and production histories, maps and other key reservoir data.

Reservoir Surveillance and Control Featured Image

Reservoir Surveillance and Control

Understand and apply reservoir surveillance and control techniques to confirm the materialization of the proposed strategies in the reservoir exploitation scheme. Upon completion of this module, the participant should be able to compare actual reservoir behavior to various performance prediction tools and account for differences, evaluate the effectiveness of the exploitation strategy, and explain reasons why actual reservoir performance deviated from initial predictions.

ESP Smart Alarms for Real Time Diagnostics Featured Image

ESP Smart Alarms for Real Time Diagnostics

Continuous monitoring of electrical submersible pumps (ESPs) ensures optimal working operating conditions and avoids deferred oil production. With the increased population of ESPs deployed worldwide, a comprehensive alarm triggering system is at the center of modern oilfield production surveillance systems.

Flow Network Based Hybrid Models for Reservoir Applications Featured Image

Flow Network Based Hybrid Models for Reservoir Applications

In this talk, we will discuss a new generation of reservoir modeling tools, referred to as reservoir graph network (RGNet). It combines physics and machine learning that can be built using routinely collected field measurements for practical reservoir model calibration, characterization, forecasting and optimization applications.

Transforming Legacy Production Operations into Smart Asset Operations in Ecuador - (Webinar in Spanish) Featured Image

Transforming Legacy Production Operations into Smart Asset Operations in Ecuador - (Webinar in Spanish)

To start the process of digital transformation in the oil production operations carried out in the Ecuadorian Oriente Basin, the methodology proposed was based on "MIT Sloan School of Management" and established for all the processes of innovation and product creation, called RWW, “Real, Win and Worth”. Real case studies in Ecuador will be discussed including not only production engineering analysis but also production operations in the field with a major focus on asset surveillance.

Data Analytics in Reservoir Engineering Featured Image

Data Analytics in Reservoir Engineering

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.