New EAGE Short Courses

 

Engineering

Engineering

Introduction to Heavy Oil: Genesis, Properties, Distribution, Recovery Technologies and Upgrading

Instructor: Dr Ali Shafiei (Nazarbayev University)

In this course, an overview of heavy oil, extra heavy oil, and bitumen resource development including its genesis, physical and chemical properties, resources, reserves, geographical distribution, production, transport, upgrading, refining, future technology developments, carbon footprint, and environmental impacts is provided.

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Petroleum Engineering for Non-engineers

Instructor: Saad Ibrahim (Petro Management Group Ltd.) 

This course is designed to provide non-engineering petroleum industry staff with a thorough overview of most key aspects of petroleum engineering/technology and its applications. The main objective of the course to enhance the awareness of the support and non-technical staff of the tasks performed by the Petroleum Engineers to improve team efforts which will reflect of the bottom line results. The course addresses engineering issues ranging from initial involvement with exploration, reserves evaluation, field development, production optimization, all the aspects of well drilling, and well/field decommissioning. The sessions will focus on relevant and practical issues; including, real case studies.

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Value of Information in the Earth Sciences

Instructor: Prof. Jo Eidsvik (Norwegian University of Science and Technology)

We constantly use information to make decisions about utilizing and managing natural resources. How can we quantitatively analyze and evaluate different information sources in the Earth sciences? What is the value of data and how much data is enough?
The course covers multidisciplinary concepts required for conducting value of information analysis in the Earth sciences.
Participants will gain an understanding for the integration of spatial statistical modeling, geomodeling and decision analysis for evaluating information gathering schemes. The value of information is computed before purchasing data. It is used to check if data is worth its price, and for comparing various experiments.

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Geology

Geology

Volumes and Risks Assessment for Conventional and Unconventional Plays and Prospects

Instructor: Prof. Dr Alexei Milkov (Colorado School of Mines, USA)

The course enables participants to transform qualitative geological descriptions of plays and prospects into technically robust quantitative success-case and risked volumetric models. Obtained learnings will help participants evaluate probabilities of success (PoS) for exploration plays, segments, prospects, wells and portfolios and to assess the range of petroleum volumes in exploration projects. Examples and case studies come from both conventional and unconventional plays, prospects and wells around the world. The learning objectives are achieved through well-illustrated lectures, numerous hands-on exercises and active class discussions. We will cover:

- Play-based exploration;
- Assessment of success-case volumes;
- Assessment of exploration risks/PoS;
- Biases;
- Post-mortem analysis.

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Carbonate Reservoir Characterization

Instructor: Laura Galluccio or Thomas Haines (Badley Ashton)

This course presents a journey into pore system evolution within carbonate rocks resulting from the complex interaction between the original depositional facies and subsequent diagenesis, while detailing the intricate path that leads to improved prediction of large-scale reservoir potential. Consideration of the type, geometry and origin (ie. primary or secondary) of pores is vital to understanding the storage capacity and subsequent flow potential of carbonate sediments and hence the resultant reservoir quality distribution, which is key to the successful field development and production.

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Carbonate Reservoirs - Sedimentology, Diagenesis and Reservoir Quality Evaluation

Instructors: Laura Galluccio and Thomas Haines (Badley Ashton)

A comprehensive 5-day course introducing participants to the world of carbonate rocks from the microscale thin-section observations to the large-scale sedimentological and sequence stratigraphic evaluation. A full immersion into facies analysis, diagenetic processes and pore evolution with the aim towards the understanding and prediction of vertical and lateral variability in reservoir properties. This course highlights the key components important for consideration when building a fully integrated reservoir model; the establishment of the depositional textures, construction of the sequence stratigraphic framework, the post-depositional diagenetic modifications that carbonate deposits are susceptible to and finally the resultant reservoir characterization.

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Sedimentological Characterization of Carbonate Rocks

Instructor: Thomas Haines or Laura Galluccio (Badley Ashton)

This gentle dive into the carbonate world from the microfacies scale to the regional architecture scale will provide the key to describing and interpreting carbonate rocks. From the basic understanding of a carbonate system to the complexities of sequence stratigraphy, this course will equip the participants with the foundations of carbonate sedimentology. Despite the complexity of carbonate sediments, the concepts illustrated in this course will form the fundamental knowledge required for the assessment and prediction of carbonate reservoir characterization.

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Data Science

Machine Learning for Geoscientists with Hands-on Coding

Instructor: Dr. Ehsan Naeini (Ikon Science)

The objectives of this short course are: 1) show various Geoscience examples in which machine learning algorithms have been implemented, 2) teach the basic principles of machine learning and deep learning, 3) demonstrate the flexibility of coding machine learning in Python. Trainees will code a classification and a regression algorithm during the class using freely available Python libraries. We use RokDoc (Ikon Science's proprietary software - educational licenses / laptops will be provided) to facilitate this process. The course is for entry level practitioners and involves hands-on coding, hence having some Python skills is an advantage but not essential.

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New Applications of Machine Learning to Oil & Gas Exploration and Production

Instructor: Dr. Bernard Montaron (Fraimwork SAS)

The course introduction will attempt to answer the question: How will A.I. change the way we work in the Oil and Gas industry in the coming years? Looking at what is underway in other industries and guessing what type of projects are under development in R&D departments in our industry will help answer that question.

Oil and Gas examples will be presented corresponding to each of the terms A.I., Machine Learning, and Deep Learning, allowing participants to reach a clear understanding on how they differ.
The course will then focus on Deep Learning (DL) and address all key aspects of developing and applying the technology to Oil and Gas projects.

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Reservoir Characterization

Reservoir Characterization

Interpretation of Natural Gases

Instructor: Prof. Dr Alexei Milkov (Colorado School of Mines)

This course enables participants to add value in E&P projects through interpretation of natural gases. Examples and case studies come from conventional and unconventional petroleum systems around the world. The instructor will transfer his practical knowledge of most important and relevant theories, interpretation tools and applications used in the industry. The learning objectives are achieved through well-illustrated lectures, numerous hands-on exercises and active class discussions.

We will cover:
• Natural gas composition and properties;
• Sampling and analytical techniques;
• Hydrocarbon gases: origin and processes;
• Prediction of non-hydrocarbon gases;
• Using gas data/models to solve business problems in exploration, appraisal/development, production and environmental projects.

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Petroleum Fluids and Source Rocks in E&P Projects

Instructor: Prof. Dr Alexei Milkov (Colorado School of Mines)

The course enables participants to interpret fluids and source rock data to add value to E&P projects from exploration to environmental remediation. Examples and case studies come from both conventional and unconventional petroleum systems around the world. The learning objectives are achieved through well-illustrated lectures, numerous hands-on exercises and active class discussions.

We will cover the following topics:
• Petroleum composition and properties;
• Sampling and analytical techniques;
• Characterization of source rocks, prediction of fluid properties in exploration prospects;
• Assessing reservoir compartmentalization;
• Geochemical surveillance of oil/gas production;
• Use of geochemical data to locate producing intervals and allocate petroleum production;
• Petroleum spills and leaks.

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Rock Physics for Quantitative Seismic Reservoir Characterization

Instructor: Prof. Tapan Mukerji (Stanford University)

This course covers fundamentals of Rock Physics ranging from basic laboratory and theoretical results to practical “recipes” that can be immediately applied in the field. Application of quantitative tools for understanding and predicting the effects of lithology, pore fluid types and saturation, saturation scales, stress, pore pressure and temperature, and fractures on seismic velocity. We will present case studies and strategies for quantitative seismic interpretation using statistical rock physics work flows, and suggestions for more effectively employing seismic-to-rock properties transforms in Bayesian machine learning for reservoir characterization and monitoring, with emphasis on seismic interpretation and uncertainty quantification for lithology and subsurface fluid detection.

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Training and Development

Training and Development

Mitigating Bias, Blindness and Illusion in E&P Decision Making

Instructor: Mr Marc Bond and/or Mr Creties Jenkins (Rose & Associates)

The oil and gas industry consistently underperforms relative to expectations. We regularly overestimate discovered volumes, production forecasts, and present value in spite of our expertise, experience, and increasingly sophisticated technologies.
Why is this? The answer, to a large extent, is the influence of cognitive errors. These skew our thinking towards the more optimistic scenarios and ranges of uncertainty that are too narrow.
This course addresses these cognitive errors through lectures, awareness exercises and examples. Most importantly, the course provides case studies and mitigation exercises that provide participants with tools to lessen their impact in their interpretations and decisions.

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