Overview

Introduction to Machine Learning and Data Analytics for Applications in Subsurface Engineering and Geosciences - IMLD

About the Course

 cent developments in machine learning and the increased accessibility of processing power have made it possible to understand rich, heterogeneous, and even real-time data. Utilizing real-time production, drilling, and completions data, SCADA data streams, 3D and 4D seismic, well data such as cores, well-logs, thin-sections, and SEM images, as well as the emergence of newer data types like DTS/DAS measurements, the oil and gas industry is leveraging the power of this data-driven revolution to produce actionable insights.

 Participants will learn about exploratory data analysis, machine learning workflows, and—most importantly—data analytics and machine learning use cases for subsurface applications during this two-day session.

Three modules make up the course's presentation:

 1. A description of data-driven procedures, covering data kinds, data quality assurance, and exploratory data analysis

2. Clustering and unsupervised learning with a focus on use cases and workflows

3. Supervised learning with an emphasis on use cases and workflows (including regression and classification).

Target Audience

Anyone interested in subsurface engineering and geoscience applications of machine learning and data analytics, including geoscientists, petrophysicists, engineers, and others

Course Objectives

•  terms that are essential to understanding data analytics and machine learning Oil and gas industry reporting standards and data types Techniques for assuring and confirming data quality
• exploratory data analyses to spot outliers, quantify correlations, and display
• The fundamental ideas behind popular machine learning methods used in the petroleum industry Unsupervised education
• supervised education reinforcement in education
• Applications of data-driven subsurface geoscience and engineering Recognize and handle data-driven approach issues in the oil and gas sector.

Course Outlines

•  introduction to the language and procedures of machine learning
• Data kinds, quality assurance/quality control, exploratory data analysis, and its relationship to insights from data-driven workflows
• detailed examination of use cases for the major well-known supervised and unsupervised machine learning algorithms
•  Algorithms and applications for unsupervised machine learning, including:
•  Rocktype prediction from core/well log data; Seismic facies identification using 3D seismic characteristics locating hydraulic fractures using seismic or well-log data
• Clustering of wells to find characteristics of profitable wells in common
•   applications and techniques for machine learning under supervision.
• Here is a list of a few examples:
•  linear regressive forecasting production
• Rocktypes obtained from core are scaled up to well scale for 3D geomodelling.
• production of synthetic logs (like sonic logs) from triple-combo data
• recognizing and foreseeing lost-time drilling occurrences, such as stuck-pipe or stick-slip, in advance Real-time analytics for failure prevention and mitigation in applications involving artificial lifts
• determining the important geology, petrophysical, and completions-related factors influencing well productivity
•   With a particular emphasis on the oil and gas sector, data collection, management, storage, and accessibility Data-driven approaches' drawbacks and difficulties in the oil and gas sector