Production monitoring using joint inversion of marine controlled‐source electromagnetic data and production data

2011 ◽  
Author(s):  
L. Liang ◽  
A. Abubakar ◽  
T. M. Habashy
Keyword(s):  
Joint Inversion ◽  
Production Data ◽  
Controlled Source ◽  
Electromagnetic Data ◽  
Production Monitoring

scholarly journals Bayesian joint inversion of controlled source electromagnetic and magnetotelluric data to image freshwater aquifer offshore New Jersey

2019 ◽  
Vol 218 (3) ◽  
pp. 1822-1837 ◽  
Author(s):  
Daniel Blatter ◽  
Kerry Key ◽  
Anandaroop Ray ◽  
Chloe Gustafson ◽  
Rob Evans
Keyword(s):  
Parameter Uncertainty ◽  
Joint Inversion ◽  
Data Sets ◽  
Magnetotelluric Data ◽  
Model Parameter ◽  
Controlled Source ◽  
Low Salinity ◽  
Electromagnetic Data ◽  
Model Parameter Uncertainty ◽  
The Individual

SUMMARY Joint inversion of multiple electromagnetic data sets, such as controlled source electromagnetic and magnetotelluric data, has the potential to significantly reduce uncertainty in the inverted electrical resistivity when the two data sets contain complementary information about the subsurface. However, evaluating quantitatively the model uncertainty reduction is made difficult by the fact that conventional inversion methods—using gradients and model regularization—typically produce just one model, with no associated estimate of model parameter uncertainty. Bayesian inverse methods can provide quantitative estimates of inverted model parameter uncertainty by generating an ensemble of models, sampled proportional to data fit. The resulting posterior distribution represents a combination of a priori assumptions about the model parameters and information contained in field data. Bayesian inversion is therefore able to quantify the impact of jointly inverting multiple data sets by using the statistical information contained in the posterior distribution. We illustrate, for synthetic data generated from a simple 1-D model, the shape of parameter space compatible with controlled source electromagnetic and magnetotelluric data, separately and jointly. We also demonstrate that when data sets contain complementary information about the model, the region of parameter space compatible with the joint data set is less than or equal to the intersection of the regions compatible with the individual data sets. We adapt a trans-dimensional Markov chain Monte Carlo algorithm for jointly inverting multiple electromagnetic data sets for 1-D earth models and apply it to surface-towed controlled source electromagnetic and magnetotelluric data collected offshore New Jersey, USA, to evaluate the extent of a low salinity aquifer within the continental shelf. Our inversion results identify a region of high resistivity of varying depth and thickness in the upper 500 m of the continental shelf, corroborating results from a previous study that used regularized, gradient-based inversion methods. We evaluate the joint model parameter uncertainty in comparison to the uncertainty obtained from the individual data sets and demonstrate quantitatively that joint inversion offers reduced uncertainty. In addition, we show how the Bayesian model ensemble can subsequently be used to derive uncertainty estimates of pore water salinity within the low salinity aquifer.

Joint inversion of controlled-source electromagnetic and production data for reservoir monitoring

Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. ID9-ID22 ◽  
Author(s):  
Lin Liang ◽  
Aria Abubakar ◽  
Tarek M. Habashy
Keyword(s):  
Finite Difference ◽  
Water Saturation ◽  
Joint Inversion ◽  
A Priori ◽  
Recovery Process ◽  
Water Flooding ◽  
Electromagnetic Response ◽  
Production Data ◽  
Controlled Source ◽  
Permeability Distribution

We present a fluid-flow constrained inversion approach for integrating controlled-source electromagnetic data and production data. In this approach, we assumed that the reservoir model has been well defined from a priori knowledge obtained from other independent measurements such as seismic and/or well-logs. Our objective was to reconstruct the permeability distribution and the shape and location of the flooding front. A finite-difference reservoir simulator was used to model the water-flooding process to simulate the time-dependent production data as well as the temporal and spatial distributions of water saturation and salt concentration, which were then transformed into the reservoir resistivity distribution using a petrophysical relationship. A finite-difference frequency-domain electromagnetic forward-modeling code was then employed to simulate the controlled-source electromagnetic response. The permeability distribution was reconstructed using a multiplicative-regularized Gauss-Newton algorithm for jointly inverting controlled-source electromagnetic and production data. From the water saturation distribution, we can identify the shape and location of the fluid-front resulting from the recovery process.

Interpretation of controlled-source electromagnetic data from iron ores under rough topography

2016 ◽  
Vol 124 ◽  
pp. 106-116 ◽  
Author(s):  
Seokmin Oh ◽  
Kyubo Noh ◽  
Soon Jee Seol ◽  
Joongmoo Byun ◽  
Myeong-Jong Yi
Keyword(s):  
Iron Ores ◽  
Controlled Source ◽  
Electromagnetic Data
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