Description

• Operation System: Windows, MacOS or Linux
• Processor: no specific requirements. Any modern processor is sufficient
• Memory: 8 GB (minimal requirement) or higher (16 GB of RAM is recommended)
• Graphical card: no specific requirements
• Display resolution: the recommended resolution is 1920x1080 or higher

Designed for you, if you are...

• A reservoir engineer, geologist or petrophysicist, and keen to obtain a fundamental understanding and practical knowledge on scientific programming, data science and machine learning

• Participants should have strong upstream domain knowledge with a minimum of 5 years' experience.
• Prior programming experience (Python) is of advantage. Recommended pre-reading material on Python can be provided upon request.

How we build your confidence

• The main machine learning methods will be discussed and illustrated with multiple re-usable code examples and real data sets
• Solutions of multiple problems related to reservoir engineering, geology and petrophysics will be demonstrated using state-of-the-art machine learning libraries

The benefits from attending

• Core concepts of machine learning and data science
• Identifying existing bottlenecks for machine learning methods application in your professional domain
• Choosing the most appropriate machine learning methods to solve a particular problem
• Applying the main machine learning methods in practice

Topics

• Introduction to the machine learning ecosystem
• Brief introduction to Python (crash course)
• Data wrangling (using Pandas and SQL)
• Data visualisation

• Production data analysis and visualisation
• Data preparation for material balance calculations
• Reservoir simulation model QC
• Well log data visualisation

• Confidently use Python programming language and the main machine learning libraries to solve different problems from the upstream domain
• Create a powerful and reusable workflow for production data analysis from different sources (local files and production databases) that can be applied for small and large oil and gas fields
• Quickly prepare production and pressure data for material balance calculation for the reservoirs of high-level of complexity (multiple compartments and pressure datums) in the format of industry-standard software (PETEX MBAL)
• Analyse a large number of reservoir simulation runs in an efficient way, quickly getting insights into history matching quality and forecasting results
• Easily create high-quality visualisation of different kinds of field and well data (production, pressure, well log) to simplify the data analysis and get ready-to-use plots for presentations and reports

• Numerical optimisation
• Statistics refresher
• Exploratory data analysis
• Uncertainty evaluation and decision making

• Decline curve analysis
• PVT data preparation for reservoir simulation
• Volume-in-place probabilistic estimation
• Static model upscaling
• Waterflood optimisation

• Apply different numerical optimisation methods to solve practical problems from reservoir engineering domain (fitting rate-time data to understand the reservoir depletion mechanism, matching the reservoir pressure gradient with PVT data for consistent reservoir simulation model initialisation)
• Perform smart upscaling of the fine grid static model into the coarse grid reservoir simulation model with precise control of the upscaling process and finding a trade-off between model dimensionality reduction and the level of geological details preservation
• Perform the probabilistic volume-in-place estimation taking into account the uncertainty of input parameters to quickly evaluate volumetrics without building a full-scale geological model
• Allocate water and gas injection volume between injection wells to maximise oil production using the optimal number of reservoir simulation runs

• Machine learning introduction
• Dimensionality reduction methods
• Clustering methods
• Anomaly detection methods

• Electrofacies identification based on well log data
• Static model realisations screening
• Detection of abnormal behaviours in production and injection wells

• Confidently apply machine learning terminology and identify technical and business requirements for successful application of machine learning methods
• Choose the most suitable machine learning method to solve a particular problem from the upstream domain depending on the type of the problem, data availability, data quality and solution requirements
• Perform screening of static model scenarios to simplify the history matching process, reduce the number of simulation runs and efficiently evaluate the impact of geological uncertainty on production forecast
• Identify the optimal number of electrofacies for a modelling study to guide the distribution of properties in the reservoir model
• Develop fully automated workflows to detect abnormal behaviours in production wells, helping prevent production losses and reduce environmental risks

• Machine learning core concepts
• Regression methods
• Tuning of machine learning models

• Production forecast of unconventional reservoir
• Saturation pressure prediction

• Design and perform machine learning study to ensure the solution quality and reproducibility of the modelling results
• Apply on practice and understand the main concepts of machine learning modelling: train/test split, cross-validation, objective function definition, bias-variance trade-off, hyperparameters tuning
• Predict the performance of a new well and optimise the well completion design for unconventional reservoirs without building a sound physics-based reservoir simulation model
• Develop a powerful data-driven model incorporating available fluid studies and predict the saturation pressure with high accuracy for the reservoirs with missing key PVT experiments
• Automatically find the combination of machine learning model parameters to simplify the model tuning and reduce the amount of manual efforts

• Classification methods
• Neural networks and Deep learning
• Advanced machine learning topics:
  - Imbalanced datasets
  - Interpretability of machine learning models

• Lithofacies identification
• Screening of enhanced oil recovery (EOR) methods

• Explain machine learning modelling results to technical and business audience to perform QA/QC solution and support decision making
• Develop a robust classification model for lithofacies identification based on well logs for wells without core data
• Create enhanced oil recovery screening model that allows incorporating different sources of information (PVT, SCAL, geological data), performing screening of a company's fields portfolio in an efficient way and identifying the most suitable EOR method for a particular field

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