Time‐lapse joint inversion of DC and seismic data

2011 ◽  
Author(s):  
M. Karaoulis ◽  
A. Revil ◽  
D. D. Werkema
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Time Lapse

scholarly journals Joint Inversion of Time-Lapse Seismic Data

2019 ◽  
Author(s):  
Cesar Barajas-Olalde ◽  
Donald Adams ◽  
Lu Jin ◽  
Jun He ◽  
Nicholas Kalenze ◽  
...  
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Time Lapse

Time-lapse joint inversion of crosswell DC resistivity and seismic data: A numerical investigation

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. D141-D157 ◽  
Author(s):  
M. Karaoulis ◽  
A. Revil ◽  
J. Zhang ◽  
D. D. Werkema
Keyword(s):  
Oil Recovery ◽  
Seismic Data ◽  
Seismic Velocity ◽  
Joint Inversion ◽  
Time Lapse ◽  
Dc Resistivity ◽  
Data Sets ◽  
Time Step ◽  
First Case ◽  
Over Time

Time-lapse joint inversion of geophysical data is required to image the evolution of oil reservoirs during production and enhanced oil recovery, [Formula: see text] sequestration, geothermal fields during production, and to monitor the evolution of contaminant plumes. Joint inversion schemes reduce space-related artifacts in filtering out noise that is spatially uncorrelated, and time-lapse inversion algorithms reduce time-related artifacts in filtering out noise that is uncorrelated over time. There are several approaches that are possible to perform the joint inverse problem. In this work, we investigate the structural crossgradient (SCG) joint inversion approach and the crosspetrophysical (CP) approach, which are appropriate for time-lapse problems. In the first case, the inversion scheme looks for models with structural similarities. In the second case, we use a direct relationship between the geophysical parameters. Time-lapse inversion is performed with an actively time-constrained (ATC) approach. In this approach, the subsurface is defined as a space-time model. All the snapshots are inverted together assuming a regularization of the sequence of snapshots over time. First, we showed the advantage of combining the SCG or CP inversion approaches and the ATC inversion by using a synthetic problem corresponding to crosshole seismic and DC-resistivity data and piecewise constant resistivity and seismic velocity distributions. We also showed that the combined SCG/ATC approach reduces the presence of artifacts with respect to individual inversion of the resistivity and seismic data sets, as well as with respect to the joint inversion of both data sets at each time step. We also performed a synthetic study using a secondary oil recovery problem. The combined CP/ATC approach was successful in retrieving the position of the oil/water encroachment front.

TIME-LAPSE JOINT INVERSION OF CROSS-WELL DC RESISTIVITY AND SEISMIC DATA: A NUMERICAL INVESTIGATION

2013 ◽  
Author(s):  
Marios Karaoulis ◽  
Andre Revil ◽  
Junwei Zhang ◽  
Dale Werkema
Keyword(s):  
Numerical Investigation ◽  
Seismic Data ◽  
Joint Inversion ◽  
Time Lapse ◽  
Dc Resistivity

Elastic inverse scattering for fluid variation with time-lapse seismic data

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. WA61-WA67 ◽  
Author(s):  
Zhaoyun Zong ◽  
Xingyao Yin ◽  
Guochen Wu ◽  
Zhiping Wu
Keyword(s):  
Shear Modulus ◽  
Inverse Scattering ◽  
Seismic Data ◽  
Scattering Theory ◽  
Time Lapse ◽  
Monitoring Data ◽  
Prestack Inversion ◽  
Fluid Factor ◽  
Reference Medium ◽  
The Difference

Elastic inverse-scattering theory has been extended for fluid discrimination using the time-lapse seismic data. The fluid factor, shear modulus, and density are used to parameterize the reference medium and the monitoring medium, and the fluid factor works as the hydrocarbon indicator. The baseline medium is, in the conception of elastic scattering theory, the reference medium, and the monitoring medium is corresponding to the perturbed medium. The difference in the earth properties between the monitoring medium and the baseline medium is taken as the variation in the properties between the reference medium and perturbed medium. The baseline and monitoring data correspond to the background wavefields and measured full fields, respectively. And the variation between the baseline data and monitoring data is taken as the scattered wavefields. Under the above hypothesis, we derived a linearized and qualitative approximation of the reflectivity variation in terms of the changes of fluid factor, shear modulus, and density with the perturbation theory. Incorporating the effect of the wavelet into the reflectivity approximation as the forward solver, we determined a practical prestack inversion approach in a Bayesian scheme to estimate the fluid factor, shear modulus, and density changes directly with the time-lapse seismic data. We evaluated the examples revealing that the proposed approach rendered the estimation of the fluid factor, shear modulus, and density changes stably, even with moderate noise.

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. C81-C92 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Hilde Grude Borgos ◽  
Martin Landrø
Keyword(s):  
Stochastic Model ◽  
Seismic Data ◽  
Synthetic Data ◽  
Time Lapse ◽  
Bayesian Framework ◽  
Reservoir Modeling ◽  
Data Set ◽  
The North ◽  
Fluid Saturation ◽  
Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

Joint Inversion of Electromagnetic and Seismic Data Based on Structural Constraints Using Variational Born Iteration Method

2018 ◽  
Vol 56 (1) ◽  
pp. 436-445 ◽  
Author(s):  
Tian Lan ◽  
Hai Liu ◽  
Na Liu ◽  
Jinghe Li ◽  
Feng Han ◽  
...  
Keyword(s):  
Seismic Data ◽  
Iteration Method ◽  
Joint Inversion ◽  
Structural Constraints

Lithological classification assisted by the joint inversion of electrical and seismic data at a control site in northeast Mexico

2010 ◽  
Vol 70 (2) ◽  
pp. 93-102 ◽  
Author(s):  
Victor Infante ◽  
Luis A. Gallardo ◽  
Juan C. Montalvo-Arrieta ◽  
Ignacio Navarro de León
Keyword(s):  
Seismic Data ◽  
Joint Inversion ◽  
Control Site

Acquisition of 3D, 4C seismic data for offshore Gulf of Mexico time lapse studies

1998 ◽  
Author(s):  
Dan Ebrom ◽  
Paul Krail ◽  
Larry Scott
Keyword(s):  
Gulf Of Mexico ◽  
Seismic Data ◽  
Time Lapse
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