Training for the Future


Upstream Data Analytics and Information Optimization in Oil and Gas Industry

Course Code: UDAIO

Dates: 21 Nov - 25 Nov | 13 Mar - 17 Mar | 5 Jun - 9 Jun | 4 Sep - 8 Sep | 20 Nov - 24 Nov

Price: £2950 Plus VAT

Duration: 1 Week /s

Course Objectives:

With the exponentially increasing amount of data and information available to optimize oil and
gas exploration and production activities, particularly with regards to the emerging
unconventional plays and resources, the need to address best practices related to data
management and analysis is a top priority for industry leaders. Especially in upstream oil and
gas, business efficiency and ensuing value relies heavily on a sound decision-making based
upon accurate and accessible data. The objective of this course is to help Oil and Gas business
leaders understand why big data is important to their industry and how some of those leaders
are already using it to gain market advantage. It also offers guidance on how big data can be
used to gain valuable operational insight and to assist in decision- making.

Who Should Attend?

The course is aimed for upstream oil and gas industry professionals who desire better
understanding of data and information management including. These among others may include
upstream oil and gas executives, Data managers, Project managers, Operational leaders,
Information technology professionals and upstream consultants and contractors.

Prerequisite Courses

None

Course Overview

  • Current Landscape in Upstream Data Analysis

  • Understanding the Appeal of Big Data for Oil & Gas

  • Three Tenets of Upstream Data

  • Data Management Platform

  • Seismic Attribute Analysis

  • Time-Lapse Seismic Exploration

  • Reservoir Characterization and Simulation

  • Drilling and Completion Optimization

  • Production Forecasting in Oil and Gas

  • Exploratory and Predictive Data Analysis

  • Data Visualization

  • Big Data: Structured and Unstructured

  • Deploying Big Data Solution in Oil & Gas

  • Hybrid Expert and Data-Driven Systems

  • Case Study


  • Course Materials

    Course notes, handouts and exercise materials
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