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Home 9 Training 9 Course Listing 9 Data Analytics Workflows for Artificial Lift, Production, and Facility Engineers
Data Analytics Workflows for Artificial Lift, Production and Facility Engineers
Rajan Chokshi, PhD
INSTRUCTOR: Rajan N. Chokshi, PhD
DISCIPLINE: Engineering
COURSE LENGTH: 1 Day (Classroom) / 8-hours (Live Online)
CEUS: 1.0
AVAILABILITY: Public, In-House, & Live Online

 

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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 the facility with different data analysis techniques applied on large data sets. Reservoir engineers and data scientists have also benefitted from this class.

Prerequisites for the course are summarized below.

  • Understanding of petroleum production concepts.
  • Knowledge of Python is NOT A MUST but preferred to get more benefit.
  • We will use the Google Collaboratory environment available in Google-Cloud for hands-on exercises.
  • Trainees will need to bring a computer with a Google Chrome browser and a Google email account (available for free).

BUSINESS IMPACT: The main aim is to provide insight and understanding of data analytics and machine learning principles through applications. Field data is used to explain data-analysis workflows. Using easy to follow solution scripts, the participants will assess and extract value from the data sets. Hands-on solution approach will give them confidence to try out applicable techniques on data from their field assets.

COURSE DESCRIPTION: Data analysis means cleaning, inspecting, transforming, and modeling data with the goal of discovering new, useful information and supporting decision-making. In this hands-on course, the participants learn some data analysis and data science techniques and workflows applied to petroleum production (specifically artificial lift) while reviewing code and practicing. The focus is on developing data-driven models while keeping our feet closer to the underlying oil and gas production principles. After completing the course, participants will have a set of tools and some pathways to analyze and manipulate their data in the cloud, find trends, and develop data-driven models.

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

  • Gas-Lift optimization: Single point gas-lift injection for gas wells in tight formation using simulated data.
  • Choke flow rate Estimation for high-volume wells using offshore dataset.
  • Rod Pump Diagnosis (card classification) using onshore field data.
  • Multiphase Flow Meter Prediction using three-phase measured dataset.

    Customization

  • The course content is for one-day classroom or two virtual half-day sessions. The training can be presented as a 2-days or four half-day long virtual sessions with expanded content.
  • Client’s dataset-based examples are optionally incorporated in the class discussions. This option requires discussions with the client about the problem, two-days of consulting effort, and access to the client dataset at least 4 weeks before the class.

LEARNING OUTCOMES:
After completing the course, participants will have a set of tools and some pathways to model and analyze their data in the cloud, find trends, and develop data-driven models.

COURSE CONTENT:

1. Digital Oil Field Data Explorations/Workflows
1.1. Digital Transformation and Oilfields
1.2. Key technologies for digital oilfields
1.3. Oilfield System Data Verification and Management
2. A Brief/Incomplete Primer on ML/AI
2.1. Data Science versus Data Analytics
2.2. AI, ML and Deep Learning
2.3. Data Analytics Lifecycle
2.4. Bias-Variance-Complexity Tradeoff
2.5. Data Preparation
2.6. Model Types
2.7. Role of Domain Knowledge
2.8. Training & Evaluating Model
2.9. Toolsets
3. System Setup & Checks
3.1. Google CoLab – Why do we need it?
3.2. Pull datasets & codebase from the GitHub repository.
4. Data Workflows & Best Practices in Data Exploratory Analysis
4.1. Data types in Production Domain: Streaming (Real-time or time-series) vs. Static
(non-streaming)
4.2. Data Processing Challenges
4.3. Data Basics: Cleaning, filtration, and regulation
4.4. Best practices on data exploratory analysis
5. Choke Flow Rate Study
Provide a brief description of the data set/problem use case and expected outcome
5.1. Problem, input & output variables
5.2. Hands On Exercise: Multiple ML models & comparison
6. Rod Pump Dynamometer Card Classification
Provide a brief description of the data set/problem use case and expected outcome
6.1. The problem, input, and output variables definition – SPE paper
6.2. Data set
6.3. Hands On Exercise: Model development & testing
7. Multiphase Flow Meter
Provide a brief description of the data set/problem use case and expected outcome
7.1. Problem, input & output variables – SPE Paper
7.2. Hands On Exercise: Multiple ML models & comparison