Training

Surveillance, Optimization, Data Analytics and Machine Learning for Gas Lift Engineers

• 1 day / 8 hours
• Level: Intermediate

Synopsis

Business impact: this course/workshop aims to give an understanding of surveillance, data analytics and machine learning workflows as they relate to gas lift wells through practical applications. Field data is employed to clarify workflows related to surveillance, optimization and data analysis. Participants assess and extract value from the data sets with the help of easy-to-follow solution scripts. The practical, hands-on approach enables them to confidently test these techniques on their naturally flowing and gas lifted wells’ data.

To ensure profitable gas lift wells with minimal NPT and failure rates, robust surveillance and optimization are crucial. Real-time data streams from gas lift wells can be challenging to manage with traditional methods. The course bridges gaps by using data analytics and machine learning approaches facilitated by generative AI. In this hands-on course, participants learn about surveillance, optimization, data analysis and data science techniques and workflows applied to gas lift wells while reviewing code and practicing. The focus is on developing data-driven models while keeping close to the underlying oil and gas production principles.

The following use cases are discussed, covering their business impact, code walkthroughs and solutions:

• Gas lift well state identification
• Virtual flow meters
• Single-point gas lift
• Reservoir productivity assessment for unconventional wells

Learning outcomes

• After completing the course, participants will have a set of tools and some pathways to model and analyze their naturally flowing and gas lift wells’ data in the cloud, find trends, and develop data-driven models.

Who should attend

This Intermediate level course is primarily intended for artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness, and improve facility with different data analysis techniques applied to large data sets.

Prerequisites

• Understanding of petroleum production concepts and specifically gas lift design and operations
• Knowledge of Python is not a must but is preferred to get the full benefit
• Trainees need to bring a computer with a Google Chrome or Edge browser and a Google email account (available for free). The Google Colaboratory environment, available in Google Cloud, is used for hands-on exercises.

Course outline

• Digital Oilfield
  • Digital transformation and oilfields
  • Key technologies for digital oilfields
  • Oilfield system data verification and management
• Digitalization in Gas Lift and production optimization
  • Gas lift surveillance approaches
  • Optimization approaches
  • AI/ML-infused gas lift management
• Data types in the production domain: streaming (real-time or time-series) vs. static (non-streaming)
  • Data processing challenges
  • Data basics: cleaning, filtration and regulation
  • Best practices on data exploration analysis
• AI, ML and Deep Learning: brief and incomplete primer
  • Data analytics lifecycle
  • Bias-variance-complexity tradeoff
  • Data preparation
  • Model types
  • Role of domain knowledge
  • Training and evaluating model
  • Toolsets
• System setup and checks
  • Google CoLab: why do we need it?
  • Pull datasets and codebase from the GitHub repository
• Case studies
  • Problem statement
  • Data requirement
  • Using generative AI in Colab
  • Case study 1: Gas Lift state identification with downhole data / gradient curve predictions
  • Case study 2: VFM using choke pressure drop from offshore gas lift asset
  • Optional: Case study 3: VFM with pressure drop across tubing (PDP to WHPf)
  • Case study 4: reservoir productivity assessment for unconventional well
  • Review Case study 5 (no hands-on): multi-well optimization from Alaska

Formats and customization

• Level: Intermediate. One day (8 hours), in person or virtual.
• Training method: the course can be taught virtually, though the best experience and results are achieved through mutual interaction in a classroom setting, which is also conducive to workshop-type outcomes due to increased interaction. For each use case, the instructor shows the solution using a data analysis technique, with the Python code deployed in Google Cloud. Trainees solve a problem and tweak their solution.
• Software: Python code in Google Colaboratory (Google Cloud), with generative AI in Colab; datasets and code pulled from a GitHub repository.

Request this course in-house