Data Management Transformation to Drive Subsurface Autonomy

Richard Mohan; Dr Sumeet Gupta

doi:10.3997/1365-2397.fb2024107

ISSN: 0263-5046
E-ISSN: 1365-2397

Data Management Transformation to Drive Subsurface Autonomy
Authors Richard Mohan¹ and Dr Sumeet Gupta¹
View Affiliations Hide Affiliations

¹ University of Petroleum & Energy Studies
Publisher: European Association of Geoscientists & Engineers
Source: First Break, Volume 42, Issue 12, Dec 2024, p. 75 - 78
DOI: https://doi.org/10.3997/1365-2397.fb2024107
- Published online: 01 Dec 2024

Abstract

The digital transformation of subsurface processes, powered by AI and ML shall enable a deeper understanding of reservoir dynamics and efficient subsurface decisions. Subsurface autonomy has significant potential to transform the way upstream companies with limited human intervention in conducting reservoir characterisation, field development planning, well planning, and production management.

To realise the full potential of AI in the subsurface domain, it requires a paradigm shift in current data management practices. This paper explores key components in transforming data management to enable AI-ready subsurface data. This addresses key challenges to facilitate AI-assisted subsurface interpretation, data-driven reservoir modelling, and the creation of subsurface digital twins. The paper highlights the emergence of industry standards such as Open Subsurface Data Universe (OSDU) in liberating data from proprietary, multivendor petrotechnical applications, ensuring seamless integration across disciplines for better decision-making. Finally, we discuss strategic actions that upstream companies must take to prepare their data to power AI and subsurface autonomy, enabling a more agile, efficient, and sustainable future for the energy industry.

EAGE Publications BV

Article metrics loading...

/content/journals/10.3997/1365-2397.fb2024107

2024-12-01

2025-11-14

From This Site

/content/journals/10.3997/1365-2397.fb2024107

dcterms_title,dcterms_subject,pub_keyword

-contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution

10

5

Full text loading...

References

Amini, S. and Mohaghegh, S. [2019]. Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media. Fluids, 4(3), 126. https://doi.org/10.3390/fluids4030126.
[Google Scholar]
Brulé, M. R. [2015]. The Data Reservoir: How Big Data Technologies Advance Data Management and Analytics in E…P. SPE Digital Energy Conference and Exhibition, held in The Woodlands, Texas, USA, Expanded Abstracts. SPE-173445-MS.
[Google Scholar]
Melo, D. [2019]. Data management evolves, but challenges persist. Data Science and Digital Engineering in Upstream Oil and Gas.
[Google Scholar]
Mohan, R. [2017]. Upstream Data Architecture and Data Governance Framework for Efficient Integrated Upstream Workflows and Operations. SPE ADIPEC, held in Abu Dhabi, UAE. SPE-185440188962-MS
[Google Scholar]
Perrons, R.K. and Jensen, J.W. [2015]. Data as an Asset: What the Oil and Gas Sector Can Learn From Other Industries about Big Data. Energy Policy, 81, 117–121.
[Google Scholar]
Popa, A. and Cassidy, S. [2012]. Implementing i-Field-Integrated Solutions for Reservoir Management: A San Joaquin Valley Case Study. SPE Econ & Mgmt, 4(1), 58–65. SPE-143950PA.
[Google Scholar]
RogerM.S. [2013]. Stratigraphic Reservoir Characterization for Petroleum Geologists, Geophysicists, and Engineers. Developments in Petroleum Science, 61, 229–281.
[Google Scholar]
Sankaran, S., Matringe, S., Sidahmed, M., Saputelli, L., Wen, X.H., Popa, A., and Dursun, S. [2020]. Data Analytics in Reservoir Engineering. Richardson, Texas: PetroBriefs Series, Society of Petroleum Engineers.
[Google Scholar]
Venkataraman, A. [2021]. Data Management: Establishing a foundation for digital transformation in the upstream energy industry. IHS Markit, IDC White Paper.
[Google Scholar]

/content/journals/10.3997/1365-2397.fb2024107

Data Management Transformation to Drive Subsurface Autonomy

First Break 42, 75 (2024); https://doi.org/10.3997/1365-2397.fb2024107

/content/journals/10.3997/1365-2397.fb2024107

Data & Media loading...

Article Type: Research Article

Most Cited This Month Most Cited RSS feed

- Curvature attributes and their application to 3D interpreted horizons
  
  Authors A. Roberts
- Distributed Acoustic Sensing – a new tool for seismic applications
  
  Authors Tom Parker, Sergey Shatalin and Mahmoud Farhadiroushan
- Thematic Set: Full waveform inversion: the next leap forward in imaging at Valhall
  
  Authors L. Sirgue, O.I. Barkved, J. Dellinger, J. Etgen, U. Albertin and J.H. Kommedal
- 2D resistivity surveying for environmental and engineering applications
  
  Authors T. Dahlin
- Effects of igneous intrusions on the petroleum system: a review
  
  Authors Kim Senger, John Millett, Sverre Planke, Kei Ogata, Christian Haug Eide, Marte Festøy, Olivier Galland and Dougal A. Jerram
- Thematic Set: Sequence stratigraphy: common ground after three decades of development
  
  Authors O. Catuneanu, J.P. Bhattacharya, M.D. Blum, R.W. Dalrymple, P.G. Eriksson, C.R. Fielding, W.L. Fisher, W.E. Galloway, P. Gianolla, M.R. Gibling, K.A. Giles, J.M. Holbrook, R. Jordan, C.G.St.C. Kendall, B. Macurda, O.J. Martinsen, A.D. Miall, D. Nummedal, H.W. Posamentier, B.R. Pratt, K.W. Shanley, R.J. Steel, A. Strasser and M.E. Tucker
- Virtual outcrop models of petroleum reservoir analogues: a review of the current state-of-the-art
  
  Authors J. K. Pringle, J. A. Howell, D. Hodgetts, A. R. Westerman and D. M. Hodgson
- Seismic Inversion Methods and some of their constraints
  
  Authors P.C.H. Veeken and M. Da Silva
- Two-dimensional resistivity mapping with a computer-controlled array
  
  Authors D.H. Griffiths, J. Turnbull and A.I. Olayinka
- Automatic seismic prediction ahead of the tunnel boring machine
  
  Authors G. Kneib, A. Kassel and K. Lorenz
More Less

Data Management Transformation to Drive Subsurface Autonomy

Abstract

Most Read This Month

Most Cited This Month Most Cited RSS feed