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Abstract

Summary

We present a workflow (Rejuvenate) that reduces cost by using big data, rule based and expert systems (ES) integrating geology and geophysics datasets for the energy transition such as Carbon Capture and Storage (CCS). The implications are a substantial reduction in risk, cost, and confidence in reservoir properties.

ES derives geology from seismic data itself. The workflow provides resources and intelligence to clients so that green gas with CCS can bridge the gap to sustainable renewable energy towards a net-zero target. By background, a major oil company drilled 17 exploration wells spread over several sub basins. All wells were dry. Using our approach, we found that the information existed in the seismic and ancillary data that could have avoided this expense. ES is based on decades of research into dry wells and associated seismic, well data and geology with patents in place. Proven to increase efficiency by more than fifty percent, anomalies can be identified in geology and directly linked to seismic patterns. This learning can now be migrated to Machine Learning (ML) using risk matrices for the wells of today and the future; in essence a knowledgebase of seismic that did not fit the real geology that adapts.

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/content/papers/10.3997/2214-4609.202332070
2023-03-20
2024-03-29
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References

  1. Gardner, G.H.F., Gardner, L.W., and Gregory, A.R., [1974]. Formation velocity and density – The diagnostic basics for stratigraphic traps. Geophysics., 39, 770–780.
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  2. BP, [2021]. Statistical Review of World Energy. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf
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