identifier: Saeed, A. (2014). Time-lapse AVO inversion (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/28453

title: Time-lapse AVO inversion

contributor:
Lines, Laurence
Margrave, Gary

subject: Geophysics AVO Inversion time-lapse

abstract:
A reservoir characterization workflow for time-lapse study requires integrating seismic data vintages and well logs information into a single consistent model to delineate changes of reservoir parameters. The research work in this thesis is divided into four parts: 1) pre-stack inverse problems for time-lapse seismic surveys; 2) prediction of shear sonic logs for wells that are not available; 3) a processing flow for optimum seismic repeatability and imaging of the production related change; 4) interpretation of seismic inversions and seismic differences results from time-lapse seismic surveys.
In this thesis, we present three new time-lapse AVO inversion algorithms: 1) total inversion of the differences; 2) inversion of seismic differences only; 3) sequential reflectivity-constrained inversion. The proposed methods were implemented using synthetic data that simulate reservoir conditions at pre- and post-production of a heavy oil reservoir after depletion. The time-lapse AVO inversion schemes simultaneously invert the P-P & P-S seismic data of the baseline and monitor line surveys to estimate the change of elastic impedances and density model parameters. The proposed algorithms have proven their robustness in terms of computation time and stability in the presence of noise.
Predictions of shear-wave logs in wells that do not have dipole sonic are challenging, particularly in heavy oil reservoirs. The linear-regression, robust locally weighted scattering and smoothing (LOWESS), and several other approaches of iteratively re-weighted linear least-squares inversion (IRLS) techniques were implemented to estimate shear-wave sonic logs. The developed computer codes were applied using well logs from three different types of reservoirs (conventional oil, heavy oil, and tight shale oil) in WCSB. The proposed methods guard against outliers, and have shown improvements in predicting shear-wave sonic logs compared to empirical linear relationships.
The time-lapse processing and imaging flow designed for seismic data vintages of Pikes-Peak oil field has overcome many difficulties related to differences in acquisition parameters and seismic noise. The proposed flow managed to track production related changes in the reservoir and clearly imaged induced amplitude changes at the Waseca reservoir channel.
The dimming zones observed in the stack section of the monitor line reveal that the steam pressures have caused fluid move through the top seal into the overlying formation. This finding also correlates with the cloud of amplitude change in colony formation shown in the amplitude differences of the time-lapse stack section. The impedances differences show that the interbedded silty sands provide a pathway for steam movements within the Waseca sand channel. The elastic attributes from the AVO inversion of time-lapse seismic surveys successfully map the hydrocarbon movement at the Waseca reservoir channel.
Conditioning of pre-stack seismic migrated gathers of the Pikes Peak time-lapse surveys remains a challenge and is not covered in this dissertation. Therefore the implementation of proposed time-lapse inversion schemes is left as future research work.


identifier: Powell, H. M. (2008). Riverine community structure, biotic and abiotic drivers, and the modifying impact of impoundment (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/1636

publisher:
University of Calgary
Calgary

format:
xiv, 206 leaves : ill. ; 30 cm.
application/pdf

rights: University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

abstract: Bibliography: p. 155-200


identifier: Lopes Queiroz, G. (2019). Remote Sensing Boreal Coarse Woody Debris (Unpublished master's thesis). University of Calgary, Calgary, AB.

publisher:
Arts
University of Calgary

contributor:
McDermid, Gregory J.
McDermid, Gregory J.
Else, Brent G. T.
Reid, Mary L.

subject:
coarse woody debris
snag
log
CWD
boreal forest
GEOBIA
random forest
machine learning
LiDAR
Forestry and Wildlife
Ecology
Physical Geography
Remote Sensing
Artificial Intelligence
Geotechnology

abstract: Coarse woody debris (CWD) are vital components of forested environments, affecting the physical structure and biochemistry of forests, supplying habitats, nutrients and food for many organisms. Additionally, CWD is an especially important element in boreal forest management in Alberta, Canada. Large quantities of CWD can configure a fire hazard, whereas moderate quantities of CWD in linear disturbances can aid newly planted seedlings to survive and hinder the hunting effectiveness of predators of endangered caribou herds. Forest managers and ecologists can benefit from large-scale high-accuracy quantitative mapping of CWD in complex boreal environments. This thesis represents the first high-resolution remote sensing study of CWD within the context of Alberta’s boreal forest. The research conducted here tested the effectiveness of a geographical object-based image analysis (GEOBIA) workflow with random forest classification for mapping CWD logs and snags in a 4300-hectare study area in northeastern Alberta, Canada. Additionally, zero-adjusted models were selected for accurate estimation of CWD volume in the study area using Akaike’s information criterion. The developed models successfully mapped (up to 93.4% completeness and 94.5% correctness) and estimated volume of CWD (0.623 R2, 0.224 RMSE) with good accuracies. Light detection and ranging (LiDAR) data improved the distinction between logs and snags in CWD maps (~6% better distinction; significant at α 0.05), and multispectral LiDAR data improved the estimation of CWD volume occluded by superimposed vegetation (~ 0.1 higher R2 and ~0.018 lower RMSE). Models developed in a calibration area could be applied to a verification area 4 km distant from all training data without substantial differences in accuracy (average 9% drop in mapping accuracy, no decrease in R2 or increase in RMSE when estimating volume). Given the potential of emerging multispectral LiDAR technologies, it is likely that future improvements to sensors will make ever more accurate CWD predictions possible. Site managers, as well as ecologists and foresters interested in studying the spatiality of CWD can make use of the developed workflows to obtain accurate and extensive map products in forested areas.