Summary Estimating source parameters (e.g.,wavelet and source mechanisms) is an important aspect of both active-and passive-source elastic full waveform inversion. Waveformbased source-parameter estimation potentially leads deeper insights into the nature of earthquakes and active sources. Accuracy of source parameters affects estimation of elastic parameter distributions, and vice versa as source-path effects (i.e., a tradeoff between velocity and source parameters). We use synthetic and field active-source cross-well datasets, and demonstrate clearly that the source-parameter estimation depends on the quality of the velocity models, geometry of sources and receivers, and available data components. We further demonstrate that incorrect source parameters degrade the quality of elastic models obtained by full waveform inversion.
Kamei, Rie (University of Western Australia) | Jang, U Geun (Korea Polar Research Institute) | Lumley, David (University of Texas–Dallas) | Takanashi, Mamoru (Japan Oil, Gas and Metals National Corporation) | Nakatsukasa, Masashi (Japan Oil, Gas and Metals National Corporation) | Mouri, Takuji (Japan Oil, Gas and Metals National Corporation) | Kato, Ayato (Japan Oil, Gas and Metals National Corporation)
Seismic monitoring is increasingly important to understand time-varying changes in subsurface physical properties for hydrocarbon production, CO2 geosequestration, and near-surface engineering purposes. Since the resulting changes in elastic parameters and then in recorded seismic waveforms tend to be small, full waveform inversion (FWI) can be a powerful method to accurately estimate time-lapse velocity changes by maximizing the use of waveform information. We apply time-lapse FWI to cross-well survey data acquired with highly repeatable pseudo-random sources to monitor microbubble injection into shallow unconsolidated sediments. We use parallel time-lapse inversion, and successfully detect very small time-lapse velocity changes (<1 %) within a thin layer (< 1m) due to highly repeatable data sets, careful data preprocessing, and well-designed inversion procedures. The velocity changes indicate the potential influence of the fluvial depositional environment on the migration of injected microbubble water.
Presentation Date: Wednesday, September 27, 2017
Start Time: 11:00 AM
Presentation Type: ORAL
We present a new method for seismic reservoir characterization and reservoir-property modeling on the basis of an integrated analysis of 3D-seismic data and hydraulic flow units, and apply it to an example of a producing reservoir offshore Western Australia. Our method combines hydraulic-unit analysis with a set of techniques for seismic reservoir characterization including rock physics analysis, Bayesian inference, prestack seismic inversion, and geostatistical simulation of reservoir properties. Hydraulic units are geologic layers and zones characterized by similar properties of fluid flow in porous permeable media, and are defined at well locations on the basis of logs, core measurements, and production data. However, the number of wells available for hydraulic- unit analysis is often extremely limited. In comparison, the lateral coverage and resolution of 3D-seismic data are excellent, and can thus be used to extend hydraulic-unit analysis away from well locations into the full 3D reservoir volume. We develop a probabilistic relationship between optimal 3D-seismic-data attributes and the hydraulic units that we determine at well locations. Because porosity and permeability distributions are estimated for each hydraulic flow unit as part of the process, we can use the 3D seismic probabilistic relationships to constrain geostatistical realizations of porosity and permeability in the reservoir, to be consistent with the flow-unit analysis. Reservoir models jointly constrained by both 3D-seismic data and hydraulic flow-unit analysis have the potential to improve the processes of reservoir characterization, fluid-flow performance forecasting, and production data or 4D-seismic history matching.
We demonstrate full-wavefield imaging and inversion methods to locate passive seismic source events, and estimate subsurface velocity. A source function is imaged by applying the adjoint wave propagation operator to the receiver field. Our experiments show that the origin time and the source location depend on the background velocity model, while a lack of detailed features in the velocity model may introduce artifacts to the image. We apply full waveform inversion to time-lapse passive seismic data to estimate velocity changes over time. Our method estimates small 4D velocity changes surprisingly well, even for a single passive seismic source event.
Stochastic reservoir modeling is a common practice in the energy industry, and is widely used for hydrocarbon reserves estimation, targeting new producer/injector locations, and production profile forecasting with flow simulators. Due to its high spatial coverage, 3D seismic data plays a critical role for defining the reservoir geometry, and for constraining physical property modeling. However, integration of 3D and time-lapse 4D seismic data into the reservoir model history matching process poses a significant challenge due to the frequent mismatch between the initial reservoir model, the reservoir geology, and the pre-production (baseline) seismic. Therefore, a key step in a reservoir performance study is the preconditioning of the initial reservoir model to equally honor both the geological knowledge and the baseline seismic data. In this study, we investigate issues that have a significant impact on the (mis)match of the initial reservoir model with the geological and geophysical data. Specifically, we address the following questions:
The results of this study indicate that a method based on the probability of litho-facies distribution given by P-wave impedance in a stochastic modeling process yields the best match to the reference model, even in the presence of noise in the dataset. The effect of variogram parameters on the seismically-constrained litho-facies modeling process is also demonstrated.
Pevzner, Roman (Curtin University) | Galvin, Robert J. (Curtin University) | Madadi, Mahyar (Curtin University) | Urosevic, Milovan (Curtin University) | Caspari, Eva (Curtin University) | Gurevich, Boris (Curtin University) | Lumley, David (University of Western Australia) | Shulakova, Valeriya (CSIRO) | Cinar, Yildiray (University of New South Wales) | Tcheverda, Vladimir (Trofimuk Institute of Geology)