Topological data analysis to solve big data problem in reservoir engineering: Application to inverted 4D seismic data

Abdulhamed Alfaleh, Yuhe Wang, Bicheng Yan, John Killough, Hongqing Song, Chenji Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Data analysis is one of the most important topics in any industry. In petroleum engineering, the large, complex, and multi-dimensional reservoir data sets (big data) presents a challenge for engineers to study the masses of unstructured information and make decisions. A new approach to analyze complex data is called Topological Data Analysis (TDA) which aims to extract meaningful information from such data. TDA relies on the concept that complex data has shapes where shape has meanings. It analyzes the shape of complex data, identifying clusters and their statistical significance. The objective of this paper is to introduce TDA to reservoir engineering using an example of inverted 4D seismic data for studying reservoir connectivity and compartmentalization. In this paper, we introduce the principles of TDA and discuss its potential in reservoir engineering, which could allow identification of reservoir engineering data behavior, recognition of new opportunities, detection of anomalies and events, and minimizing uncertainties. The TDA procedures are introduced using inverted 4D seismic data set to study reservoir connectivity and compartmentalization. The process to generate and process the data set is explained. Similarity distance function and lenses are defined and used to create TDA graphs for feature identification and analysis. It is shown that TDA is able to predict the compartmentaliztion of the reservoir models with various process configurations. Variance normalized Euclidean and topological neighborhood function are used successfully to compartmentalize the reservoir model. Using normalized input dataset, correlation and principle component analysis also create similar compartments. The success of TDA in discovering meaningful patterns is attributed to the similarity distance function representing the objective of study, one or more lenses exposing the data, and the right combination of input data, similarity distance function and lenses. A promising big data analysis method, TDA, is introduced to reservoir engineering application with principles, procedures and examples. It has been shown that TDA can automatically discover critical intelligence within the 4D seismic data set for studying reservoir connectivity and compartmentalization, which are essential to the accuracy of forecasts and development plans, the validity of reservoir simulation, and the success of performance diagnostics and optimization.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - SPE Annual Technical Conference and Exhibition, ATCE 2015
PublisherSociety of Petroleum Engineers (SPE)
Pages3547-3581
Number of pages35
Volume2015-January
ISBN (Electronic)9781510813229
Publication statusPublished - 2015
EventSPE Annual Technical Conference and Exhibition, ATCE 2015 - Houston, United States
Duration: 28 Sep 201530 Sep 2015

Other

OtherSPE Annual Technical Conference and Exhibition, ATCE 2015
CountryUnited States
CityHouston
Period28/9/1530/9/15

Fingerprint

Lenses
Big data
Petroleum engineering
Engineers
Industry
Uncertainty

ASJC Scopus subject areas

  • Fuel Technology
  • Energy Engineering and Power Technology

Cite this

Alfaleh, A., Wang, Y., Yan, B., Killough, J., Song, H., & Wei, C. (2015). Topological data analysis to solve big data problem in reservoir engineering: Application to inverted 4D seismic data. In Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition, ATCE 2015 (Vol. 2015-January, pp. 3547-3581). Society of Petroleum Engineers (SPE).

Topological data analysis to solve big data problem in reservoir engineering : Application to inverted 4D seismic data. / Alfaleh, Abdulhamed; Wang, Yuhe; Yan, Bicheng; Killough, John; Song, Hongqing; Wei, Chenji.

Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition, ATCE 2015. Vol. 2015-January Society of Petroleum Engineers (SPE), 2015. p. 3547-3581.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Alfaleh, A, Wang, Y, Yan, B, Killough, J, Song, H & Wei, C 2015, Topological data analysis to solve big data problem in reservoir engineering: Application to inverted 4D seismic data. in Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition, ATCE 2015. vol. 2015-January, Society of Petroleum Engineers (SPE), pp. 3547-3581, SPE Annual Technical Conference and Exhibition, ATCE 2015, Houston, United States, 28/9/15.
Alfaleh A, Wang Y, Yan B, Killough J, Song H, Wei C. Topological data analysis to solve big data problem in reservoir engineering: Application to inverted 4D seismic data. In Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition, ATCE 2015. Vol. 2015-January. Society of Petroleum Engineers (SPE). 2015. p. 3547-3581
Alfaleh, Abdulhamed ; Wang, Yuhe ; Yan, Bicheng ; Killough, John ; Song, Hongqing ; Wei, Chenji. / Topological data analysis to solve big data problem in reservoir engineering : Application to inverted 4D seismic data. Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition, ATCE 2015. Vol. 2015-January Society of Petroleum Engineers (SPE), 2015. pp. 3547-3581
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