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Artificial Neural Systems in Petroleum Engineering

October 30 8:00 AM - October 31 5:00 PM CDT

$2,125.00
Home 9 Event 9 Artificial Neural Systems in Petroleum Engineering
Sia Agah
INSTRUCTOR:
Robert F. Shelley, PE
DISCIPLINE:
Engineering
COURSE LENGTH:
2 Days (October 30-31, 2024)
CEUS:
1.6
AVAILABILITY:
Public
 
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SAMPLE TOPIC FROM THE CLASS: "Artificial Neural Systems Provide Wolfcamp Completion Design Insight"
WHO SHOULD ATTEND: Experienced oil and gas professionals that are receptive to applying alternative approaches to physics-based modeling and problem solving. Predictive Neural Network Models (ANN) developed from data provide quick answers, offer a fresh perspective, test physics-based modeling assumptions and can quantify the impact of geology, chemistry, operations and other factors on well production and recovery.

This course is intended for the experienced professional charged with solution development, reservoir assessment, completion design, hydraulic fracture assessment, prospect evaluation, recompletion candidate selection, or forecasting outcomes such as sand-outs, well production, recovery, etc. Potential participants include engineers, geologists, geophysicists, managers, data scientists, technicians, chemists, etc.

Participants in this class will obtain the knowledge needed to build models to make predictions and quantify the impact of cause-and-effect relationships. Programming skills are not required for successful completion of this course.

COURSE DESCRIPTION: An Artificial Neural Network is a form of Artificial Intelligence (AI) that can develop a relationship between known outputs and a desired outcome. Participants will learn the fundamentals of using AI techniques to build models to solve problems. ANN modeling case histories will be studied to help participants understand why this approach was used and provide examples in which significant cost savings and value has been created. These examples include predicting well production, recovery, completion design, hydraulic fracturing, reservoir classification and prospect evaluation. Additionally, participants will obtain a working knowledge about building models from data and be able to identify potential applications for ANN modeling technology.

All students will be required to have a laptop (with admin rights) with Windows and Excel installed. As a learning aid, a demo version of ANN model development software will be installed during class. This course does not include a software license for students.

Although this is not a programming course, proficiency with Excel is recommended.

LEARNING OUTCOMES:

  • Basic understanding of ANN modeling concepts.
  • Identify potential applications and for the ANN modeling approach.
  • Model development strategies to address specific needs or problems.
  • Techniques to mitigate real world uncertainty.
  • Strategies to minimize the impact of data issues such as quantity, quality, completeness and consistency.
  • Hands on exposure with ANN model development.

COURSE CONTENT:

Day 1
AI Concepts and Theory

  • Predictive Model Development
    • Feed Forward Neural Network (ANN)
      • Model Training and Fitness
      • Predictor Evaluation, Genetic Algorithms
      • Model Selection
      • Use of Testing Data
      • Strategies and Application
      • Model Development Experiments
    • Self-Organizing Mapping
      • Model Training
      • Strategies and Application

AI Case Histories

  • Evaluate Completion and Frac Design, Granite Wash Case; SPE-39814
    • Double Well Production for Lower Cost
  • Completion Design Evaluation, Marcellus Case; SPE 171003
    • Facilitate Increasing PUD Recovery to 10 BCF/Well
  • Evaluate Reservoir Proxy Data, Bakken Case; URTeC-2667653
    • Use Measurements Taken During Horizontal Drilling Operations as Proxies for Reservoir Quality
  • Predicting Sand-Outs, Mission Canyon Case; SPE 19191
    • Predict Frac Treatment Sand-Out to improve Operational Efficiency
  • Reservoir Classification, Wolfcamp B Case; SPE-210383
    • Evaluate the Usefulness of Reservoir Characteristics

Day 2

  • Installation of Demo Software
  • Modeling Orientation
  • Hands-on Model Development and Experiments
    • Data Quantity
    • Use of Testing Data
    • Predictor Evaluation and Model Selection
    • Modeling to Reduce the Impact of Real-World Uncertainty

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Artificial Neural Systems in Petroleum Engineering (October 30-31, 2024 / Houston, TX)
$ 2,125.00
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Details

Start:
October 30 8:00 AM CDT
End:
October 31 5:00 PM CDT
Cost:
$2,125.00
Event Category:
Event Tags:

Organizer

SCA
Phone
713-789-2444
Email
training@scacompanies.com
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Venue

Houston, Texas Training Center
10700 Richmond Ave, Ste 325
Houston, TX 77042 United States + Google Map
Phone
7137892444
View Venue Website

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REGISTRATION POLICY

Registration should be made at least one month before the start of a course. It is recommended that participants register early due to limited seating. However, we will accept paid registrations up to the last business day before the class, provided there are seats available. Registrants will receive a confirmation email within 48 hours of registration and will receive complete venue information two weeks prior to the first day of class.

REMINDER: your seat in a course is NOT confirmed until payment is received.

 

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