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 and expertise needed to identify potential applications for an AI approach to problem solving and the requirements needed for the AI approach. Programming skills are not required for successful completion of this course.

COURSE DESCRIPTION: Participants will learn the fundamentals of using AI techniques to solve problems. In addition, actual Petroleum Engineering case histories will be presented and there will be discussion to help participants understand why an AI approach was used in these instances. The AI approach in these case histories resulted in better reservoir understanding, significant cost savings and/or higher well production/recovery.

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

LEARNING OUTCOMES:

• Basic understanding of artificial neural network (ANN) modeling concepts.
• Identify potential applications for an AI modeling approach.
• Model development strategies to address specific needs or problems.
• Techniques to mitigate real world uncertainty in data.
• Strategies to minimize the impact of data issues such as quantity, quality, completeness, and consistency.