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**File Type**

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.

Artificial Intelligence, deviatoric source, elastic full waveform inversion, estimation, full waveform inversion, inversion, isotropic source, machine learning, mechanism, objective function, receiver, Reservoir Characterization, source mechanism, source parameter, source parameter estimation, source wavelet, source-parameter estimation, Upstream Oil & Gas, velocity model, waveform, wavelet

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.84)

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, CO_{2} 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

Location: 361F

Presentation Type: ORAL

amplitude, baseline, baseline fwi, Baseline Survey, baseline velocity model, full-waveform inversion, injection, international exposition, inversion, microbubble injection, microbubble water, p-wave velocity, repeatability, Reservoir Characterization, time-lapse fwi, time-lapse velocity change, Upstream Oil & Gas, velocity change, velocity model, waveform

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

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.

3d-seismic data, Artificial Intelligence, Bayesian Inference, classification, Engineering, hydraulic unit, hydraulic-unit analysis, inversion, machine learning, modeling, permeability, porosity, probability, Probability Cube, probability-density function, reservoir, Reservoir Characterization, reservoir property, seismic inversion, Simulation, SPE Reservoir Evaluation, Upstream Oil & Gas, well location

Country:

- Asia > Middle East (0.93)
- Oceania > Australia (0.88)
- North America > United States (0.68)

Oilfield Places:

- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Stybarrow Field (0.99)
- Oceania > Australia > Western Australia > Carnarvon Basin (0.99)
- North America > United States > Wyoming > Powder River Basin > Hartzog Draw Field > Shannon Reservoir (0.99)
- (12 more...)

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

**Summary**

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.

equation, full wavefield, full wavefield imaging, Imaging, inversion, inversion method, operator, passive seismic imaging, Reservoir Characterization, seg seg denver 2014, seismic data, source location, subsurface, Upstream Oil & Gas, velocity change, Velocity Inversion, velocity model, wavefield, wavefield imaging, Waveform Inversion

**Abstract**

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:

- Which of the common methods to stochastic litho-facies modelling produce reservoir models that best match the baseline 3D seismic data after seismic modelling?
- How are the results affected by the presence of noise in the observable data, and by the low vertical resolution of seismic data compared to logs?
- What is the effect of geostatistical variogram parameters on the results?
- How do these methods perform on object-oriented reservoir models?

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.

Artificial Intelligence, average misfit error, facies, geologic modeling, geological modeling, History, impedance, international petroleum technology conference, Modeling & Simulation, noise, p-wave impedance, probability, reservoir, Reservoir Characterization, reservoir model, seismic data, Upstream Oil & Gas, variogram, variogram parameter, variogram range

Oilfield Places: Africa > Republic of the Congo > Republic of the Congo Offshore > Bilondo Field (0.98)

SPE Disciplines:

Technology:

The effect of single-phase fluid saturation on the seismic bulk modulus of a rock is well understood; however, the behavior becomes more complex when multiple fluids are present. Several fluid mixing theories have been developed (e.g., Voigt, Reuss, and Hill) and each is valid in certain situations; however, in some scenarios it is unclear which theory to select, or indeed whether any are accurate. The critical wave propagation behavior depends on the manner that fluids are spatially distributed within the rock, compared to a seismic wavelength. We apply elastic finite-difference modeling to different rock-fluid distribution scenarios and replicate behavior described by various theoretical, empirical and lab data results. Significantly, our results compare well with observations from lab experiments, yet do not rely on poroelastic or squirt-flow models whose parameters are difficult to estimate in real reservoir settings. Our elastic scattering approach is less computationally expensive than poroelastic modeling and can be more easily applied to actual reservoir rock and fluid distributions. Our results provide us with a powerful new tool to analyze and predict the effects of multiple fluids and ‘patchy’ saturation on elastic moduli and seismic velocities. They also challenge assumptions about the controlling factors on saturated bulk moduli, suggesting they are more strongly affected by the spatial fluid distribution properties and wave scattering, than by pore-scale fluid flow effects.

annual meeting, Computational rock physics, elastic property, fluid distribution, frequency, gas saturation, heterogeneity, modeling, patchy, patchy curve, patchy saturation, propagation, Reservoir Characterization, Reuss, saturation, seismic velocity, Upstream Oil & Gas, Voigt, wave propagation, wave propagation velocity

In some areas, seismic data can exhibit the effects of strong azimuthal anisotropy (AA). One of the major causes of AA can be anomalous horizontal stress regimes, which can be modeled as horizontally transverse isotropy (HTI). The Stybarrow field, located offshore NW Australia in the Carnarvon sedimentary basin, is one such area, where strong horizontal stress conditions have been present throughout the basin’s tectonic history. We find evidence for AA in repeat 3D seismic data acquired at two separate azimuths over the Stybarrow field. AA is observed in amplitude versus offset (AVO) reflection amplitude difference maps and cross plots, and is consistent with dipole shear logs and borehole breakout data in the area. We model azimuthal AVO responses using Ruger’s HTI AVO equation, using the anisotropy parameters derived from dipole shear logs, and compare the results with AVO data from the two 3D seismic surveys. Certain fault blocks (but not all) exhibit the same AAVO trend in the seismic data as those modeled from log data, consistent with a stress-induced HTI anisotropic model interpretation.

Oilfield Places: Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Stybarrow Field (0.99)

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)

A key objective of Stage 2 of the CO2CRC Otway Project is to explore the ability of geophysical methods to detect and monitor injection of greenhouse gas into a saline formation. For this purpose, injection of some 10,000 – 30,000 t of gas mixture (80/20% CO

Oilfield Places:

- Oceania > Australia > Victoria > Ottway Basin > Naylor Field (0.99)
- Oceania > Australia > South Australia > Otway Basin (0.99)
- Oceania > Australia > Victoria > Otway Basin > Paaratte Field (0.98)

Knowledge of the pressure dependence of elastic rock properties is useful for time-lapse monitoring of hydrocarbon, groundwater, and CO

bulk modulus, constraint, critical porosity, elastic property, Fluid Dynamics, grain size distribution data, log analysis, low effective pressure, new velocity-pressure-compaction model, porosity, prediction, pressure sensitivity, Reservoir Characterization, sediment, structural geology, uncemented sediment, unconsolidated sample, unconsolidated sand, Upstream Oil & Gas, well logging

SPE Disciplines:

- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.70)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Integration of geomechanics in models (0.61)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (0.48)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (0.47)

Shragge, Jeffrey (CPGCO2) | Lumley, David (CPGCO2)

Time-lapse analysis of 4D seismic data acquired at different stages of hydrocarbon production or fluid/gas injection has been very successful at capturing detailed reservoir changes (e.g., pressure, saturation, fluid flow). Conventionally, 4D seismic analysis is performed in the time-migrated domain assuming a fixed migration velocity model; however, this scenario is violated when the subsurface velocity is significantly altered by production/injection effects resulting in large time-shift anomalies and complex 4D wavefield coda. For these scenarios we argue that one should use a robust 4D analysis procedure involving iterative wave-equation prestack depth migration and time-lapse velocity analysis. We adapt 3D image-domain wave-equation migration velocity analysis (WEMVA) to such time-lapse scenarios to backproject discrepancies (residuals) in migrated baseline, monitor, and time-lapse images to estimate 4D velocity model perturbations. We highlight the differences between the 3D and 4D WEMVA inversion problems, and how we constrain 4D perturbation estimates to preferentially be updated within the reservoir zone. We demonstrate the benefits of various 4D WEMVA strategy in a set of synthetic experiments that involve estimating time-lapse model perturbations arising from a thin layer (<20m) of injected CO

Oilfield Places:

- Europe > Norway > North Sea > Northern North Sea > Northern North Sea Basin > Block 15/9 > Sleipner Field (0.99)
- Europe > Norway > North Sea > Northern North Sea > Northern North Sea Basin > Block 15/8 > Sleipner Field (0.99)
- Europe > Norway > North Sea > Northern North Sea > Northern North Sea Basin > Block 15/6 > Sleipner Field (0.99)

Technology:

Thank you!