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Optimizing compaction calculation for improved probabilistic 2D mapping
Author(s) -
Babak Olena,
Liu Kun,
Gallop Jeremy
Publication year - 2019
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/1365-2478.12749
Subject(s) - regional geology , probabilistic logic , measure (data warehouse) , reservoir modeling , computer science , data mining , differential (mechanical device) , geology , economic geology , environmental geology , engineering geology , characterization (materials science) , compaction , quality (philosophy) , well control , hydrogeology , petroleum engineering , artificial intelligence , seismology , telmatology , geotechnical engineering , volcanism , drilling , philosophy , materials science , aerospace engineering , engineering , tectonics , epistemology , nanotechnology , mechanical engineering
ABSTRACT Integration of all available data in reservoir characterization is critically important. 2D mapping is a reliable and robust technique that allows integration of multiple secondary data, including geological and geophysical surfaces and maps, to generate realistic summaries of reservoir quality at each location in an area of interest with an associated measure of uncertainty. This is achieved in 2D mapping with a more straightforward implementation, requiring significantly less time and fewer resources than three‐dimensional modelling. In this paper, we propose an approach for the empirical calculation and optimization of differential compaction maps by leveraging existing well control for the use in 2D mapping. Success of the proposal is demonstrated through tests of accuracy, precision and fairness of the local uncertainty distributions for 100 new stratigraphical wells drilled in the Christina Lake and Kirby East area.