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Using machine learning to reduce ensembles of geological models for oil and gas exploration

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https://ieeexplore.ieee.org/document/8955121
Original languageEnglish
Title of host publication2019 IEEE/ACM 5th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-5)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages42-49
Number of pages8
DOIs
Publication statusE-pub ahead of print - 17 Nov 2019
EventThe 5th International Workshop on Data Analysis and Reduction for Big Scientific Data - Denver, United States
Duration: 17 Nov 201917 Nov 2019
https://web.njit.edu/~qliu/drbsd5.html

Workshop

WorkshopThe 5th International Workshop on Data Analysis and Reduction for Big Scientific Data
Abbreviated titleDRBSD-5
CountryUnited States
CityDenver
Period17/11/1917/11/19
Internet address

Abstract

Exploration using borehole drilling is a key activity in determining the most appropriate locations for the petroleum industry to develop oil fields. However, estimating the amount of Oil In Place (OIP) relies on computing with a very significant number of geological models, which, due to the ever increasing capability to capture and refine data, is becoming infeasible. As such, data reduction techniques are required to reduce this set down to a smaller, yet still fully representative ensemble. In this paper we explore different approaches to identifying the key grouping of models, based on their most important features, and then using this information select a reduced set which we can be confident fully represent the overall model space. The result of this work is an approach which enables us to describe the entire state space using only 0.5\% of the models, along with a series of lessons learnt. The techniques that we describe are not only applicable to oil and gas exploration, but also more generally to the HPC community as we are forced to work with reduced data-sets due to the rapid increase in data collection capability.

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