skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: SITE CHARACTERIZATION USING JOINT RECONSTRUCTIONS OF DISPARATE DATA TYPES

Abstract

Potential CO{sub 2} reservoirs are often geologically complex and possible leakage pathways such as those created. Reservoir heterogeneity can affect injectivity, storage capacity, and trapping rate. Similarly, discontinuous caprocks and faults can create risk of CO{sub 2} leakage. The characteristics of potential CO{sub 2} reservoirs need to be well understood to increase confidence in injection project success. Reservoir site characterization will likely involve the collection and integration of multiple geological, geophysical, and geochemical data sets. We have developed a computational tool to more realistically render lithologic models using multiple geological and geophysical techniques. Importantly, the approach formally and quantitatively integrates available data and provides a strict measure of probability and uncertainty in the subsurface. The method will characterize solution uncertainties whether they stem from unknown reservoir properties, measurement error, or poor sensitivity of geophysical techniques.

Authors:
; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
883537
Report Number(s):
UCRL-PROC-218592
TRN: US200615%%106
DOE Contract Number:
W-7405-ENG-48
Resource Type:
Conference
Resource Relation:
Conference: Presented at: CO2 Site Characterization Symposium, Berkeley, CA, United States, Mar 20 - Mar 22, 2006
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; CAPACITY; PROBABILITY; SENSITIVITY; SITE CHARACTERIZATION; STORAGE; TRAPPING

Citation Formats

Ramirez, A, Friedmann, J, Dyer, K, and Aines, R. SITE CHARACTERIZATION USING JOINT RECONSTRUCTIONS OF DISPARATE DATA TYPES. United States: N. p., 2006. Web.
Ramirez, A, Friedmann, J, Dyer, K, & Aines, R. SITE CHARACTERIZATION USING JOINT RECONSTRUCTIONS OF DISPARATE DATA TYPES. United States.
Ramirez, A, Friedmann, J, Dyer, K, and Aines, R. Tue . "SITE CHARACTERIZATION USING JOINT RECONSTRUCTIONS OF DISPARATE DATA TYPES". United States. doi:. https://www.osti.gov/servlets/purl/883537.
@article{osti_883537,
title = {SITE CHARACTERIZATION USING JOINT RECONSTRUCTIONS OF DISPARATE DATA TYPES},
author = {Ramirez, A and Friedmann, J and Dyer, K and Aines, R},
abstractNote = {Potential CO{sub 2} reservoirs are often geologically complex and possible leakage pathways such as those created. Reservoir heterogeneity can affect injectivity, storage capacity, and trapping rate. Similarly, discontinuous caprocks and faults can create risk of CO{sub 2} leakage. The characteristics of potential CO{sub 2} reservoirs need to be well understood to increase confidence in injection project success. Reservoir site characterization will likely involve the collection and integration of multiple geological, geophysical, and geochemical data sets. We have developed a computational tool to more realistically render lithologic models using multiple geological and geophysical techniques. Importantly, the approach formally and quantitatively integrates available data and provides a strict measure of probability and uncertainty in the subsurface. The method will characterize solution uncertainties whether they stem from unknown reservoir properties, measurement error, or poor sensitivity of geophysical techniques.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 31 00:00:00 EST 2006},
month = {Tue Jan 31 00:00:00 EST 2006}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • We describe a stochastic inversion method for mapping subsurface regions where CO{sub 2} saturation is changing. The technique combines prior information with measurements of injected CO{sub 2} volume, reservoir deformation and electrical resistivity. Bayesian inference and a Metropolis simulation algorithm form the basis for this approach. The method can (a) jointly reconstruct disparate data types such as surface or subsurface tilt, electrical resistivity, and injected CO{sub 2} volume measurements, (b) provide quantitative measures of the result uncertainty, (c) identify competing models when the available data are insufficient to definitively identify a single optimal model and (d) rank the alternative modelsmore » based on how well they fit available data. We use measurements collected during CO{sub 2} injection for enhanced oil recovery to illustrate the method's performance. The stochastic inversions provide estimates of the most probable location, shape, volume of the plume and most likely CO{sub 2} saturation. The results suggest that the method can reconstruct data with poor signal to noise ratio.« less
  • The purpose of this project is to develop a computer code for joint inversion of seismic and electrical data, to improve underground imaging for site characterization and remediation monitoring. The computer code developed in this project will invert geophysical data to obtain direct estimates of porosity and saturation underground, rather than inverting for seismic velocity and electrical resistivity or other geophysical properties. This is intended to be a significant improvement in the state-of-the-art of underground imaging, since interpretation of data collected at a contaminated site would become much less subjective. Potential users include DOE scientists and engineers responsible for characterizingmore » contaminated sites and monitoring remediation of contaminated sites. In this three-year project, we use a multi-phase approach consisting of theoretical and numerical code development, laboratory investigations, testing on available laboratory and borehole geophysics data sets, and a controlled field experiment, to develop practical tools for joint electrical and seismic data interpretation.« less
  • The authors demonstrate the reconstruction of a 3D, time-varying bolus of radiotracer from first-pass data obtained at the dynamic SPECT imager, FASTSPECT, built by the University of Arizona. The object imaged is a CardioWest Total Artificial Heart. The bolus is entirely contained in one ventricle and its associated inlet and outlet tracts. The model for the radiotracer distribution is a time-varying closed surface parameterized by 162 vertices that are connected to make 960 triangles, with uniform intensity of radiotracer inside. The total curvature of the surface is minimized through the use of a weighted prior in the Bayesian framework. MAPmore » estimates for the vertices, interior intensity and background scatter are produced for diastolic and systolic frames, the only two frames analyzed.« less
  • The Bayes Inference Engine (BIE) has been used to perform a 4D reconstruction of a first-pass radiotracer bolus distribution inside a CardioWest Total Artificial Heart, imaged with the University of Arizona's FastSPECT system. The BIE estimates parameter values that define the 3D model of the radiotracer distribution at each of 41 times spanning about two seconds. The 3D models have two components: a closed surface, composed of hi-quadratic Bezier triangular surface patches, that defines the interface between the part of the blood pool that contains radiotracer and the part that contains no radiotracer, and smooth voxel-to-voxel variations in intensity withinmore » the closed surface. Ideally, the surface estimates the ventricular wall location where the bolus is infused throughout the part of the blood pool contained by the right ventricle. The voxel-to-voxel variations are needed to model an inhomogeneously-mixed bolus. Maximum a posterior (MAP) estimates of the Bezier control points and voxel values are obtained for each time frame. We show new reconstructions using the Bezier surface models, and discuss estimates of ventricular volume as a function of time, ejection fraction, and wall motion. The computation time for our reconstruction process, which directly estimates complex 3D model parameters from the raw data, is performed in a time that is competitive with more traditional voxel-based methods (ML-EM, e.g.).« less