Agenda| All times Mountain Daylight Time (UTC -6)

This 1-day course is an introduction to Butler assumptions and mathematical principles and its limitations and also briefly discusses other studies which address steam assisted gravity drainage (SAGD) oil production and SOR evaluation. In this course there will be examples using Excel spreadsheets.

Topics:

• SAGD Operation: The course starts with short definition of SAGD process and how different stage of it will be operated.
• SAGD Heat Transfer: Heat-transfer mechanisms are pivotal to the SAGD process. In this section different temperature models and their consideration with respect to conduction and convection are discussed:
  • Butler Temperature Model
  • Sharma Gates Convection Model
  • Irani and Ghannadi Convection Model
• Butler Production Model: The derivation of oil production using Butler is discussed. Finally assumptions in “Original Butler model”, TANDRAIN and LINDRAIN models will be discussed and evaluated:
  • Butler Plateau Model (i.e., “Original Butler model”, TANDRAIN and LINDRAIN)
  • Butler Ramp-up Model
  • Discussion on Steam Interface Velocity in “Original Butler model”, TANDRAIN and LINDRAIN models, and its comparison to field data.
  • m exponent factor discussion
• SOR Discussion: Heat conservation concept and the basis of different SOR analytical models are presented:
  • Reis SOR Model
  • Edmunds and Peterson (2007)
  • Miura and Wang (2012) Model

Learning Objectives:

• At the end of this course, participants will be able to:
• Understanding heat transfer in SAGD operation.
• Understanding limitations of Butler and other analytical models.
• Assumptions made in SAGD analytical models.
• Optimizations made in forecasting models.

Special Requirements: 

• Personal Computer
• High-Speed Internet Connection
• Search Engine (Google preferred)

0.4 CEUs will be awarded for this half-day course.

Learn more about this course.

Tubular structures in thermal applications are subject to unique design challenges that cannot be addressed with conventional methods. In conventional design, the structure “fails” when thermally induced loads yield the pipe, but thermal wells often must operate under such conditions and industry experience demonstrates that wells can do so reliably. Designing a structure that remains stable requires knowledge of strain-based design: the distinction between strength and stiffness, the effects of variability in strength and stiffness, load path dependencies, post-yield material behavior, and strain localization. Collateral considerations for resistance to environmental effects, geomechanical loads and production management can also challenge intuition developed in conventional well design. In this course, participants will learn to “think strain, not stress” for well structure design and gain exposure to other aspects of thermal well design.

Learning Objectives:

This course provides an introduction to subsurface well design for thermally stimulated wells, including discussion of why design approaches for thermal wells must differ from conventional approaches. The concept of strain-based design will be explored, focusing first on the design needs for a successful thermal well design and second on raising awareness of other situations where casing loads can exceed yield (e.g. wells passing through unstable geology or producing from compacting reservoirs).

Topics:

• Mechanical properties for thermal OCTG
• Casing and Liner design considerations
• Connections for thermal stimulation
• Considerations for environmental exposure
• Production management topics pertinent to liner design

0.4 CEUs will be awarded for this course.

Special Requirements: 

• Personal Computer
• High-Speed Internet Connection
• Search Engine (Google preferred)

Learn more about this course.

Machine learning has been successfully used in various engineering disciplines. Nowadays, large amount of data related to reservoir properties, drilling, completion, and production is routinely collected in the upstream oil and gas operation, which can be further analyzed to optimize the field operations and improve the reservoir performance. This course starts with the basic Machine Learning concepts, application workflows and the supervised and unsupervised learning algorithms. The commonly used algorithms in both categories such as Clustering, Artificial Neural Networks, Decision Trees, Support Vector Machine will be presented, allowing participants to reach a clear understanding on their strengths. Specific examples will be discussed to demonstrate the application of each algorithm in the development of unconventional tight/shale reservoirs. The course is devoted to field applications of this technology with a focus on reservoir characterization, production analysis and prediction, and recovery enhancement.

Topics:

• Introduction of Machine Learning concepts
• A typical workflow to design and develop a Machine Learning project
• Feature selection
• Supervised learning algorithms
• Unsupervised learning algorithms
• Machine learning applications in the reservoir characterization in tight/shale formations
• Machine learning applications in productivity prediction and recovery enhancement in tight/shale reservoirs

Upon completion of this course, participants are expected to have a good understanding of the characteristics of the machine learning approaches and be able to use them to identify potential application domains in the upstream oil and gas industry. They will acquire detailed knowledge of the popularly used machine learning algorithms and the workflow to employ these algorithms to solve petroleum engineering problems. Finally, they will see the demonstrations of different machine learning algorithms to reservoir characterization, production analysis, well productivity forcast, and recovery enhancement in tight/shale reservoirs.

0.4 CEUs will be awarded for this course.

Special Requirements: 

• Personal Computer
• High-Speed Internet Connection
• Search Engine (Google preferred)

Learn more about this course.

Shales and other low-permeability formations require multistage completions, hydraulic fracturing, and horizontal wells to produce at economic rates. This course focuses on the multistage completion systems that are used in these applications, including plug-and-perf, ball-activated systems (frac sleeves), and coiled tubing-activated systems (annular fracturing). Participants will learn the different types of multistage completion options and how they compare in different applications. They will also get an overview of low-permeability plays and learn the basics of hydraulic fracturing and refracturing.

Topics:

• An overview of applications that require multistage hydraulic fracturing
• The basics of hydraulic fracturing and refracturing theory and design
• Evolution of multistage completions
• Wellbore completion options for multistage hydraulic fracturing
  • Plug-and-perf completions
  • Ball-activated completion systems (frac sleeves)
  • Coiled-tubing-activated completions systems (annular fracturing)
• New multistage completion technologies
• Benefits and considerations for each completion system
• Application specific solutions for wellbore completion design
• Refracturing options for existing wells

0.4 CEUs will be awarded for this course.

Special Requirements: 

• Personal Computer
• High-Speed Internet Connection
• Search Engine (Google preferred)

Learn more about this course.

This course covers the essential concepts and techniques applied in evaluating hydrocarbon storage and productivity potential in shale and tight reservoirs, with an emphasis on practical methods uncommon to conventional reservoir analysis. The limitations and advantages of the different techniques will be illustrated through examples compiled from actual evaluation studies in several different basins. The pace and content of the course is intended for those looking to gain a solid understanding of unconventional reservoir concepts who are already very familiar with reservoir evaluation techniques for conventional oil & gas reservoirs.

Topics:

• Unconventional reservoir factors having the largest impact on well productivity and hydrocarbon storage
• Quality control analysis and applications for unconventional core test data
• Well spacing concepts and optimization
• Geochemical and electric log considerations
• Factors generating sweet spots
• Geologic factors impacting the success of frac treatments

0.4 CEUs will be awarded for this course.

Special Requirements: 

• Personal Computer
• High-Speed Internet Connection
• Search Engine (Google preferred)

Learn more about this course.