It is true to say the oil & gas industry is transforming – in practice, in operations, and economics. But the physics have not changed – the elemental challenge flowing hydrocarbons from a rock matrix to the surface remains the same. Despite advanced numerical techniques including now various forms of advanced Machine Learning and Artificial Intelligence we struggle to accurately simulate and understand physical processes in the reservoir and surrounding subsurface: fluid flow, transient and
Webinar Hosts: Energy in Data
Building an End to End Solution using Machine Learning – Part 2
The online course is a continuation of Part 1. It will implement an End-to-End Upstream E&P Workflow Solution using Machine Learning (Python knowledge is a pre-requisite). The training session will focus on a Machine Learning workflow in the upstream Oil and Gas domain to generate synthetic Gamma-Ray Logs by applying Artificial Intelligence (AI) Techniques, learning the various aspects of deploying this workflow in an end-to-end solution that a Geoscientist can use. The course is split
Machine Learning Implementations in O&G – Success Stories and Challenges Ahead
Today’s push behind machine learning has led to some pretty high expectations for performance and deliverables. When it comes to business value and ROI, can it really live up to the claims? Evaluating the value of a full machine learning project requires moving past the hype and managing the realities of this evolving technology, one example at the time. We’ll look at the realities of a pure machine learning approach through the lens of applied
Building an End to End Solution using Machine Learning – Part 1
The online course will implement an End-to-End Upstream E&P Workflow Solution using Machine Learning (Python knowledge is a pre-requisite). The training session will focus on a Machine Learning workflow in the upstream Oil and Gas domain to generate synthetic Gamma-Ray Logs by applying Artificial Intelligence (AI) Techniques, learning the various aspects of deploying this workflow in an end-to-end solution that a Geoscientist can use.The course is split in two webinar sessions. Part 1: Identify use
Python for Beginners
Python is one of the most popular programming languages in the world. It is considered easy to learn, test, and is capable of building a wide variety of programs. There are a number of Python courses being taught around the world online and in person with a wide range in price and depth. This is meant to be a gentle practical introduction. This webinar will cover what Python can accomplish, what is needed to set
Subsurface Data Engineering
Subsurface data is quite complex and preparing data for comprehensive analysis is quite challenging. The presentation will provide an overview of how the cloud and edge computing is significantly helping subsurface data management, governance, and faster data analysis. We will introduce you to various concepts of subsurface data engineering workflows including: data aggregation, data cleansing, data mapping and correlation, data governance for field data (i.e. seismic, geobodies), well level data (logs, operational data), and different
Machine Learning Changes Borehole Geophysics
This talk will focus on “old problems and new solutions” in borehole geophysics with machine learning (ML) from data acquisition, quality control, pre-processing, inversion, interpretation, and multi-well studies. Some of these can be achieved with ML as an automation tool to improve the efficiency and some can be done with ML as a discovery tool to enhance the quality. Data sets for Labels, training and testing are critical in the applications. I will start with
Update structural Models in Real Time using Machine Learning
This presentation and demonstration will focus on a machine learning workflow in the upstream oil and gas domain to predict formation tops by applying artificial intelligence and machine learning techniques to learn the well logs signatures. This deep learning model provides high quality predictions to aid the geologists in picking lithology markers consistently and in an accelerated fashion thus boosting their operational efficiency.
Simple applications of machine learning in subsurface characterization
Dr. Misra will present case studies on the use of machine learning techniques. In the first case study, neural network models generate NMR T2 distribution in the absence of an NMR logging tool. In the second case study, simple data-driven models generate compressional and shear travel time logs in the absence of a sonic logging tool. In the third case study, machine learning assisted the segmentation of SEM images of shale samples. This segmentation method
You can build your own models: Why you don’t need to be scared of doing your own data science
There are two ends of the “AI” spectrum that are often presented. On one end, AI is going to solve the world’s problems one slide deck at a time. On the other, a PhD physicist will give you a “quick” run-through of a four-hour deep learning with tensorflow in Python tutorial. In this session, we aim to land right in the middle of those two and provide a layman’s view to getting started with data