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Third EAGE Workshop on High Performance Computing for Upstream
- Conference date: October 1-4, 2017
- Location: Athens, Greece
- Published: 01 October 2017
21 - 25 of 25 results
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Machine Learning Ecosystem for the O&G Industry
Authors P. Demichel and E. OrlottiSummaryHPC and Big Data are progressing towards new challenges in an era of explosion of machine data. Computing power is a given, data management is the challenge. Memory-driven computing is going to change dramatically the systems architectures, and HPE is working on the building blocks that will enable this new paradigma of computing. The talk will present the innovative technologies and the roadmap that will bring to that new ecosystem.
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The Three Silver Bullets Of HPC For Oil and Gas
By A. JonesSummaryHigh Performance Computing (HPC), or supercomputing, is a powerful tool for research and production use. However, HPC inhabits a complex and rapidly evolving technology landscape. This presents challenges for those seeking to use HPC, in terms of picking the right technologies, and exploiting them effectively. Active research programs and technology companies around the world continue to propose solutions for various aspects of the HPC difficulties.
Many of these proposed solutions are hyped as ‘silver bullets’, hoping to solve major challenges with how we use HPC or deliver disruptive improvements. This talk explores three of these ‘silver bullets’ to examine whether their will live up to their hype and what problems they will solve. The three silver bullets are: GPUs, cloud computing, new programming languages and domain specific languages.
Merely identifying the silver bullets is not enough – this talk will explore how each silver bullet affects oil and gas use cases of HPC. How can oil and gas users of HPC take advantage of these silver bullets? How will they drive the skills needs and software methods used by developers?
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Big Data Role in the Upstream Business Research
Authors S.L. Nimmagadda and A. AseevSummaryAn 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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On a Robust Data Modelling Approach for Managing the Fractured Reservoirs in an Onshore Colombian Oil & Gas Field
Authors S.L. Nimmagadda, L. Chavez, J. Castaneda and A. LoboSummaryNeither the limits of elements and processes of the petroleum systems of Colombian sedimentary basins are known nor interpreted without ambiguities undermining the reservoir complexities and hampering the data integration in the upstream business. Partly it is due to poor understanding of the datasets and poorly articulated data modelling, visualization and interpretation artefacts in complex geological regimes. We propose an ontology based multidimensional warehouse repository approach with ontology constructs and models for various data sources, acquired from multiple domains of upstream business. We choose several data volumes, variety of multidimensional data attributes and their fact instances for interpreting seismically integrated geological horizons. Structure and fracture attribute map views are computed to ascertain the density of fractures and their orientations, calibrating the fracture signatures with production data existing within the interpreted faulted compartments. Field development plans are assessed based on new knowledge, obtained from domain ontology descriptions, exploring connections among multi-stacked fractured reservoirs. Though we find no structural bearing on the accumulations of oil and gas in the study area, the fracture density and orientation appear to have definite bearing on production. Integrated framework minimizes the ambiguity involved in the interpretation of fractures, their density and orientations in the study area.
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Three Dimensional Parallel Sobel Seismic Fault Detection
Authors A. Al-Naeem, S. Al-Dossary and Z. ZhouSummaryThe Sobel filter is a discrete differentiation operator widely used in seismic image processing algorithms for automatic fault detection and extraction. The filter approximates the local gradient by combining derivatives of the amplitude between neighboring traces along the x, y, and z directions. However, 3D-Sobel Seismic Fault Detection algorithm runs very slowly and is computationally intensive, even with dual Sandy Bridge Xeon 8-core 2.6 GHz CPU, it requires more than one minute to process a 560×390×320 seismic volume data.
Herein we present a multiple parallel computing designs leveraging shared memory (OpnMp), distributed memory (MPI) and many-core Graphical processing Unit (GPU) to reduce total execution time of 3D-Soble Seismic Fault Detection; experiments demonstrate a significant speed up over serial CPU version. For a 250 MB poststack seismic volume, the speedup using the parallel algorithm with MPI was six times faster than the serial version of the algorithm.
We first introduce the new 3D-Sobel seismic fault detection algorithm and the parallel implementations, and then show the experimental results.
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