0 days left until Latin America and Caribbean Region IBA 2020 Semifinal Competition
Download Consider Donating a Dataset to IBA Program PDFUpdated 29 August, 2018
Download Sponsorship Levels and Details Including Corporate Sponsorship Form PDFUpdated 08 October, 2019
The most critical aspect for the future of the program is the availability of petroleum industry datasets. If the IBA is to be sustainable, relevant and exciting in the long term, new data sets are needed to keep the program fresh. Contributed data sets are used for educational applications only, and will be used by student teams in universities around the world. Industry support is now urgently needed to ensure the long-term sustainability of the Imperial Barrel Award Program.
The IBA Committee would like to develop a library of regional subsurface datasets that are representative of different basin types worldwide, with variable types of petroleum plays. The typical IBA dataset consist of one or more 3D data sets or a regional set of 2D survey(s), wells, and additional information supplied by the donor. (See the PDF link for detailed suggestions.)
The key requirements for suitable IBA datasets are that they be regional in scope and, ideally, relatively poorly known in terms of their subsurface geology and hydrocarbon potential. Datasets from already discovered, major hydrocarbon accumulations are much less suitable than those from under-explored or frontier basins. The datasets should be technically challenging and geologically interesting.
With over 100 participant schools from every part of the world, the AAPG Imperial Barrel Award tests a team’s ability to analyze and promote their understanding of play and prospect elements using real industry data. The AAPG IBA Committee strives to keep each team’s assigned dataset fresh every year (not one seen or used in the recent past) and from someplace unfamiliar. As a result, the committee will retire some datasets and is always in need of fresh data sets to keep the program going.
Please consider donating a dataset that you own.
Steven Ray Wooden