Data Management in Oil and Gas Industry

Implementation of modern data technologies in highly data intensive oil and gas industry

Ratings 3.18 / 5.00

What You Will Learn!

Meta data, data, vintage, geospatial data, operations for data collection/ acquisition
Differentiate between data, information and knowledge, data interpretation and processing, indexing, data life cycle
The lifecycle of surface and subsurface exploration, production data and related information management issues,
GIS and Modern Information Technologies (IOT, AI, Block chain, Machine and Deep learning)
Firm grip on various subsurface data to undergo repository processes in the database.
Thorough, effective and practical flow in handling and managing petroleum data.
Cartography Reference System for loading data at cross over regions of different Datum.
Loading the database i.e. all categories in the well data set in one single database from drilling to production and abandonment.

Description

In this course, the content is going to leverage participants understanding the total data flow principles of surface and subsurface data management by going through golden rules in detail, various data categories in geology, seismic, navigation, E&P database design, knowing G&G software. All the important points covered are also illustrated by figures and tables with examples given.

In all disciplines of oil and gas industry, acquired data is growing exponentially day by day. Currently, storage capacities are in the size of petabytes which is equivalent to ~1000TB (One thousand Terabytes). Thus, the need to perform data governance is cumbersome but inevitable. A strong and consistent data governance ensures operational success, long-term cost and time savings.

This course reveals the value that data generates within E&P companies. It then reviews the most important themes that the areas where improvements are commonly can be found. All E&P companies are generating value with their existing data management, the important question is whether there are compelling business cases to expand on their current capabilities.

This course is focused on the information generated through data analytics related to the subsurface. This data ranges from exploration data, such as seismic surveys to production data, such as hourly flow readings, and from objective measurements, such as raw log readings to interpreted results such as dynamic reservoir models. The key reason that oil companies spends millions on data is in order to reduce the “development uncertainties”.

The final conclusion is that all oil company personnel should carefully review their current data management tasks and responsibilities. In most companies there are opportunities to expand the governance, access, security or quality of data which would significantly increase the total value an organizational profitablity.

Throughout the course, you will find communication medium informal, interactive and the topics are covered by implementation of practical case studies/hands on exercises. Every time in video recordings I ask questions, try to write your own reply on a pieces of paper and check yourself.

Instructor

Mr. Serdar Kaya is a senior consultant with an extensive experience in geoscience, data analytics, reservoir characterization, geological modeling and various high tech applications. He has published several journal and conference papers about innovative data modeling approaches for challenging issues. He has also successfully trained, mentored and coached many geoscientists, geologist and engineers. He holds both MSc and BSc degrees in Petroleum Engineering. His achievements and high level of technical competence are a reflection not only his engineering knowledge but also high level of personnel commitment and drive.