Three‐dimensional inversion of electromagnetic data

1993 ◽  
Author(s):  
Louise Pellerin ◽  
Jeffery M. Johnston ◽  
Gerald W. Hohmann
Keyword(s):  
Three Dimensional ◽  
Electromagnetic Data

scholarly journals Inversion of time domain three-dimensional electromagnetic data

2007 ◽  
Vol 171 (2) ◽  
pp. 550-564 ◽  
Author(s):  
Eldad Haber ◽  
Douglas W. Oldenburg ◽  
R. Shekhtman
Keyword(s):  
Time Domain ◽  
Three Dimensional ◽  
Electromagnetic Data

scholarly journals Three-Dimensional Inversion of Controlled-Source Electromagnetic Data in Fanchang Region, Anhui Province

2021 ◽  
Vol 660 (1) ◽  
pp. 012037
Author(s):  
Zikun Zhou ◽  
Xueqiu Wang ◽  
Jian Yin ◽  
Jinhui Zhang ◽  
Aihua Weng ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Anhui Province ◽  
Controlled Source ◽  
Electromagnetic Data

Accuracy and effectiveness of three-dimensional controlled source electromagnetic data inversions

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. E83-E95
Author(s):  
Richard T. Houck ◽  
Adrian Ciucivara ◽  
Scott Hornbostel
Keyword(s):  
Three Dimensional ◽  
Rock Physics ◽  
Hydrocarbon Reservoirs ◽  
Rock Properties ◽  
Data Sets ◽  
Controlled Source ◽  
Electromagnetic Data ◽  
Fluid Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling

Unconstrained 3D inversion of marine controlled source electromagnetic data (CSEM) data sets produces resistivity volumes that have an uncertain relationship to the true subsurface resistivity at the scale of typical hydrocarbon reservoirs. Furthermore, CSEM-scale resistivity is an ambiguous indicator of hydrocarbon presence; not all resistivity anomalies are caused by hydrocarbon reservoirs, and not all hydrocarbon reservoirs produce a distinct resistivity anomaly. We have developed a method for quantifying the effectiveness of resistivities from CSEM inversion in detecting hydrocarbon reservoirs. Our approach uses probabilistic rock-physics modeling to update information from a preexisting prospect assessment, based on uncertain resistivities from CSEM. The result is an estimate the probability of hydrocarbon presence that accounts for uncertainty in the resistivity and in rock properties. Examples using synthetic and real CSEM data sets demonstrate that the effectiveness of CSEM inversion in identifying hydrocarbon reservoirs depends on the interaction between the uncertainty associated with the inversion-derived resistivity and the range of rock and fluid properties that were expected for the targeted prospect. Resistivity uncertainty that has a small effect on hydrocarbon probability for one set of rock property distributions may have a large effect for a different set of rock properties. Depending on the consequences of this interaction, resistivities from CSEM inversion might reduce the risk associated with predictions of hydrocarbon presence, but they cannot be expected to guarantee a specific well outcome.

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