Big data role in the upstream business research

An offshore petroleum is the main focus of the current research with Big Data and high performance computing motivations in the study area. We undertake a joint exploration study focusing on Romanian offshore Black Sea (Western) basin, using volumes and varieties of datasets in a Big Data scale. The...

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Main Authors: Nimmagadda, Shastri, Aseev, A.
Format: Conference Paper
Published: 2017
Online Access:http://hdl.handle.net/20.500.11937/81172
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author Nimmagadda, Shastri
Aseev, A.
author_facet Nimmagadda, Shastri
Aseev, A.
author_sort Nimmagadda, Shastri
building Curtin Institutional Repository
collection Online Access
description An offshore petroleum is the main focus of the current research with Big Data and high performance computing motivations in the study area. We undertake a joint exploration study focusing on Romanian offshore Black Sea (Western) basin, using volumes and varieties of datasets in a Big Data scale. The exploration datasets include more than 175 2D seismic lines, 30 km2 of 3D seismic data, information on more than 8 exploratory drilled wells including check-shot and VSP data including existing petrophysical and production data. Big Data opportunities are explored in the current upstream business research by proposing data modelling, visualization and data interpretation schemes. In spite of the data quality issues in the study area, several isochrones, isochores and other geological information are integrated and made based on which depositional models are drawn for risk minimizing the exploration and field development plans. The conclusions are based on structural, strati-structural interpretation, organic geochemistry and identification of new opportunity areas. Several data models, visualization and interpretation artefacts can handle the volumes and varieties in Big Data scale minimizing the risk involved in the upstream business in the investigating area. Several new opportunities are identified in the shelf, slope and deep marine areas.
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spelling curtin-20.500.11937-811722021-02-12T03:52:44Z Big data role in the upstream business research Nimmagadda, Shastri Aseev, A. An offshore petroleum is the main focus of the current research with Big Data and high performance computing motivations in the study area. We undertake a joint exploration study focusing on Romanian offshore Black Sea (Western) basin, using volumes and varieties of datasets in a Big Data scale. The exploration datasets include more than 175 2D seismic lines, 30 km2 of 3D seismic data, information on more than 8 exploratory drilled wells including check-shot and VSP data including existing petrophysical and production data. Big Data opportunities are explored in the current upstream business research by proposing data modelling, visualization and data interpretation schemes. In spite of the data quality issues in the study area, several isochrones, isochores and other geological information are integrated and made based on which depositional models are drawn for risk minimizing the exploration and field development plans. The conclusions are based on structural, strati-structural interpretation, organic geochemistry and identification of new opportunity areas. Several data models, visualization and interpretation artefacts can handle the volumes and varieties in Big Data scale minimizing the risk involved in the upstream business in the investigating area. Several new opportunities are identified in the shelf, slope and deep marine areas. 2017 Conference Paper http://hdl.handle.net/20.500.11937/81172 10.3997/2214-4609.201702336 restricted
spellingShingle Nimmagadda, Shastri
Aseev, A.
Big data role in the upstream business research
title Big data role in the upstream business research
title_full Big data role in the upstream business research
title_fullStr Big data role in the upstream business research
title_full_unstemmed Big data role in the upstream business research
title_short Big data role in the upstream business research
title_sort big data role in the upstream business research
url http://hdl.handle.net/20.500.11937/81172