Application of structural interpretation and simultaneous inversion to reservoir characterization of the Duvernay Formation, Fox Creek, Alberta, Canada

2019 ◽  
Vol 38 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Ronald Weir ◽  
Don Lawton ◽  
Laurence Lines ◽  
Thomas Eyre ◽  
David Eaton
Keyword(s):  
Reservoir Characterization ◽  
Lateral Displacement ◽  
Rock Properties ◽  
S Wave ◽  
Amplitude Variation With Offset ◽  
Structural Interpretation ◽  
Simultaneous Inversion ◽  
Prestack Inversion ◽  
Unconventional Resource ◽  
Depth Relationship

Simultaneous prestack inversion of multicomponent 3D seismic data integrated with structural interpretation can provide an effective workflow to maximize value for unconventional plays. We outline an integrated workflow for characterizing the Duvernay play in western Canada, an emerging world-class low-permeability unconventional resource fairway. This workflow includes the determination of a time-depth relationship using synthetic seismograms, generation of seismic-derived time- and depth-converted structural maps, and calculation of inversion-based parameters of density and P- and S-wave velocity. The model-based procedure includes poststack (acoustic) inversion, amplitude variation with offset prestack inversion, and joint PP-PS inversion. With these rock properties determined, calculations are made to determine Young's modulus, Poisson's ratio, and brittleness. Faults are mapped based on time slices, isochrons, and correlatable vertical displacements of stratigraphic marker reflections. Significant strike-slip movements are identified by lateral displacement on interpreted geologic features, such as channels and reef edges. Seismic-derived attributes, combined with structural mapping, highlight zones that are conducive to hydraulic fracturing as well as areas unfavorable for development. Mapping of structural discontinuities provides a framework for understanding zones of preexisting weakness and induced-seismicity hazards.

Simultaneous inversion of PP and PS seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R1-R10 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Model Building ◽  
Joint Inversion ◽  
P Wave ◽  
Inversion Method ◽  
System Of Equations ◽  
S Wave ◽  
Data Set ◽  
Simultaneous Inversion

Elastic parameters derived from seismic data are valuable input for reservoir characterization because they can be related to lithology and fluid content of the reservoir through empirical relationships. The relationship between physical properties of rocks and fluids and P-wave seismic data is nonunique. This leads to large uncertainties in reservoir models derived from P-wave seismic data. Because S- waves do not propagate through fluids, the combined use of P-and S-wave seismic data might increase our ability to derive fluid and lithology effects from seismic data, reducing the uncertainty in reservoir characterization and thereby improving 3D reservoir model-building. We present a joint inversion method for PP and PS seismic data by solving approximated linear expressions of PP and PS reflection coefficients simultaneously using a least-squares estimation algorithm. The resulting system of equations is solved by singular-value decomposition (SVD). By combining the two independent measurements (PP and PS seismic data), we stabilize the system of equations for PP and PS seismic data separately, leading to more robust parameter estimation. The method does not require any knowledge of PP and PS wavelets. We tested the stability of this joint inversion method on a 1D synthetic data set. We also applied the methodology to North Sea multicomponent field data to identify sand layers in a shallow formation. The identified sand layers from our inverted sections are consistent with observations from nearby well logs.

Computation of dry-rock VP/VS ratio, fluid property factor, and density estimation from amplitude-variation-with-offset inversion

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R669-R679 ◽  
Author(s):  
Gang Chen ◽  
Xiaojun Wang ◽  
Baocheng Wu ◽  
Hongyan Qi ◽  
Muming Xia
Keyword(s):  
Reservoir Characterization ◽  
Synthetic Data ◽  
Pore Fluid ◽  
Inversion Method ◽  
S Wave ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Fluid Identification ◽  
Fluid Property ◽  
Avo Inversion

Estimating the fluid property factor and density from amplitude-variation-with-offset (AVO) inversion is important for fluid identification and reservoir characterization. The fluid property factor can distinguish pore fluid in the reservoir and the density estimate aids in evaluating reservoir characteristics. However, if the scaling factor of the fluid property factor (the dry-rock [Formula: see text] ratio) is chosen inappropriately, the fluid property factor is not only related to the pore fluid, but it also contains a contribution from the rock skeleton. On the other hand, even if the angle gathers include large angles (offsets), a three-parameter AVO inversion struggles to estimate an accurate density term without additional constraints. Thus, we have developed an equation to compute the dry-rock [Formula: see text] ratio using only the P- and S-wave velocities and density of the saturated rock from well-logging data. This decouples the fluid property factor from lithology. We also developed a new inversion method to estimate the fluid property factor and density parameters, which takes full advantage of the high stability of a two-parameter AVO inversion. By testing on a portion of the Marmousi 2 model, we find that the fluid property factor calculated by the dry-rock [Formula: see text] ratio obtained by our method relates to the pore-fluid property. Simultaneously, we test the AVO inversion method for estimating the fluid property factor and density parameters on synthetic data and analyze the feasibility and stability of the inversion. A field-data example indicates that the fluid property factor obtained by our method distinguishes the oil-charged sand channels and the water-wet sand channel from the well logs.

scholarly journals Inversion and interpretation of seismic-derived rock properties in the Duvernay play

2018 ◽  
Vol 6 (2) ◽  
pp. SE1-SE14 ◽  
Author(s):  
Ronald M. Weir ◽  
David W. Eaton ◽  
Larry R. Lines ◽  
Donald C. Lawton ◽  
Eneanwan Ekpo
Keyword(s):  
Joint Inversion ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Data Set ◽  
Structural Mapping ◽  
Amplitude Variation With Offset ◽  
Multicomponent Seismic ◽  
Depth Relationship ◽  
And Inversion

We have developed an interpretive seismic workflow that incorporates multicomponent seismic inversion, guided by structural mapping, for characterizing low-permeability unconventional reservoirs. The workflow includes the determination of a calibrated time-depth relationship, generation of seismic-derived structural maps, poststack inversion, amplitude-variation-with-offset analysis, and PP-PS joint inversion. The subsequent interpretation procedure combines structural and inversion results with seismic-derived lithologic parameters, such as the Young’s modulus, Poisson’s ratio, and brittleness index. We applied this workflow to a 3D multicomponent seismic data set from the Duvernay play in the Kaybob area in Alberta, Canada. Subtle faults are discernible using isochron maps, horizontal time slices, and seismic stratal slices. Fault-detection software is also used to aid in the delineation of structural discontinuities. We found that seismic-derived attributes, coupled with structural mapping, can be used to map reservoir facies and thus to highlight zones that are most favorable for hydraulic-fracture stimulation. By imaging structural discontinuities and preexisting zones of weakness, seismic mapping also contributes to an improved framework for understanding the induced-seismicity risk.

Rock properties of smectite- and ooze-rich claystones

Geophysics ◽  
2015 ◽  
Vol 80 (1) ◽  
pp. D89-D98 ◽  
Author(s):  
Bente Øygarden ◽  
Helge Løseth ◽  
Sigurd Njerve
Keyword(s):  
Wave Velocity ◽  
Rock Properties ◽  
Environmental Scanning ◽  
S Wave ◽  
Amplitude Variation With Offset ◽  
Lower Miocene ◽  
Lower Oligocene ◽  
Upper Oligocene ◽  
Northern North Sea ◽  
New Equation

Parameters measured by well logs define rock properties and seismic reflections at lithology interfaces. Parameters from standard embedding claystones are normally used when calculating the top sand amplitude variation with offset (AVO) response, but this might give erroneous results when the real claystone rock properties deviate from the standard. Using cuttings and high-quality wireline logs from well 34/8-A-33 H above the Visund field in the northern North Sea, we evaluated rock properties of the Pleistocene glaciomarine claystones, Lower Miocene and Upper Oligocene oozy claystones, Lower Oligocene and Eocene smectite-rich claystones, and two interbedded sands. Glaciomarine claystones fit best with the Greenberg-Castagna equation and have the highest measured velocities even though they are the shallowest buried sediments. Environmental scanning electron microscope analysis proves the Lower Miocene and Upper Oligocene claystones to be oozy. The amount of low-density oozy material causes significant shifts in the log curves and makes the ooze-rich claystones plot far off the trend given by the Greenberg-Castagna equation. We, therefore, developed a new equation for S-wave velocity prediction for ooze-rich claystones with average densities between [Formula: see text] and [Formula: see text]. The [Formula: see text] ratios increase with depth in the Lower Oligocene and Eocene claystones of the Hordaland Group, and we interpreted this to reflect a downward increase in the amount of smectite, which existence was proven by X-ray diffraction analysis. We modeled how the seismic response at the top of a sand changes with embedding claystone type, saturation fluid, and offset. In glaciomarine claystones, the top of a brine-saturated sand corresponds to a negative trough reflection, in ooze-rich claystones to a positive peak reflection, and in smectite-rich claystones the reflection amplitude is close to zero. The predicted AVO response of sands in oozy claystones is highly dependent on whether the measured or calculated S-wave velocity has been used in the modeling.

Establishing a 3D model for the Rhine-Ruhr region based on the geology and property distribution.

2020 ◽  
Author(s):  
Gabriela de los Angeles Gonzalez de Lucio ◽  
Martin Balcewicz ◽  
Erik H. Saenger
Keyword(s):  
Reservoir Characterization ◽  
Geothermal Gradient ◽  
Geological Structure ◽  
Rock Properties ◽  
Low Carbon ◽  
S Wave ◽  
Heterogeneous Model ◽  
Property Distribution ◽  
Geological Models ◽  
Ruhr Region

<p>The Rhine-Ruhr region is located in the state of North Rhine-Westphalia (NRW) in western Germany. Due to the transition from coal to low-carbon heat sources, potential locations in NRW must be explored regarding to their geothermal potential. The Bavarian area has shown for the last 20 years, that deep geothermal energy is both feasible and economical in Germany. Compared to the mentioned Molasse basin in south Germany, the geological setting is much more complex in the Rhine-Ruhr region. Based on a typical geothermal gradient of 30 °C/km, the optimal depth of a reservoir should be between 3000 m to 5000 m. In this depth, carbonate layers from Devonian times were identified in NRW. Due to the lack of accessibility, minor reservoir characterization was done, yet. Therefore, a geological model which reflects local lithological properties is essential for further geothermal projects. The model of the Rhine-Ruhr region is based on field surveys, top formations, geological sections and maps, respectively. The geometrical model is supplemented by rock properties, like density, porosity, and P- respectively S-wave velocities. These properties are derived from well logs, laboratory measurements and literature, transferring the derived properties to the grid require an analysis of upscaling techniques and distribution of such properties in the model. The result is a heterogeneous model representing the geological structure and rock property distribution of the Rhine-Ruhr region. Representative lithological units like Ruhrsandstone or interbedded coal, clay, and sandstone strata are also implemented as dominant fracture orientations. In this work we are considering several parameters to find a balance between the resolution of the model, property scaling and computational efficiency. One key aspect is that geological models are built with irregular grids while for our wave propagation simulations a regular and cartesian grid with equal grid spacing is required. Of course, such regular grids can be used for several modelling techniques and can be used as a basis for different studies. Overall goal is to evaluate local geological models to assess the feasibility of geothermal projects in the area.</p>

Seismic reservoir characterization of a class-1 amplitude variation with offset turbiditic system located offshore Cote d’Ivoire, West Africa

2018 ◽  
Vol 6 (2) ◽  
pp. SD115-SD128
Author(s):  
Pedro Alvarez ◽  
William Marin ◽  
Juan Berrizbeitia ◽  
Paola Newton ◽  
Michael Barrett ◽  
...  
Keyword(s):  
West Africa ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Rock Properties ◽  
Well Log ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Fluid Content ◽  
Class 1

We have evaluated a case study, in which a class-1 amplitude variation with offset (AVO) turbiditic system located offshore Cote d’Ivoire, West Africa, is characterized in terms of rock properties (lithology, porosity, and fluid content) and stratigraphic elements using well-log and prestack seismic data. The methodology applied involves (1) the conditioning and modeling of well-log data to several plausible geologic scenarios at the prospect location, (2) the conditioning and inversion of prestack seismic data for P- and S-wave impedance estimation, and (3) the quantitative estimation of rock property volumes and their geologic interpretation. The approaches used for the quantitative interpretation of these rock properties were the multiattribute rotation scheme for lithology and porosity characterization and a Bayesian lithofluid facies classification (statistical rock physics) for a probabilistic evaluation of fluid content. The result indicates how the application and integration of these different AVO- and rock-physics-based reservoir characterization workflows help us to understand key geologic stratigraphic elements of the architecture of the turbidite system and its static petrophysical characteristics (e.g., lithology, porosity, and net sand thickness). Furthermore, we found out how to quantify and interpret the risk related to the probability of finding hydrocarbon in a class-1 AVO setting using seismically derived elastic attributes, which are characterized by having a small level of sensitivity to changes in fluid saturation.

Effect of VS predictors on amplitude variation with offset response

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. N1-N13
Author(s):  
Humberto S. Arévalo-López ◽  
Uri Wollner ◽  
Jack P. Dvorkin
Keyword(s):  
Seismic Data ◽  
Poor Quality ◽  
Rock Properties ◽  
Specific Situation ◽  
S Wave ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Seismic Amplitude ◽  
Text Prediction ◽  
A Site

We have posed a question whether the differences between various [Formula: see text] predictors affect one of the ultimate goals of [Formula: see text] prediction, generating synthetic amplitude variation with offset (AVO) gathers to serve as a calibration tool for interpreting the seismic amplitude for rock properties and conditions. We address this question by evaluating examples in which we test several such predictors at an interface between two elastic layers, at pseudowells, and at a real well with poor-quality S-wave velocity data. The answer based on the examples presented is that no matter which [Formula: see text] predictor is used, the seismic responses at a reservoir are qualitatively identical. The choice of a [Formula: see text] predictor does not affect our ability (or inability) to forecast the presence of hydrocarbons from seismic data. We also find that the amplitude versus angle responses due to different predictors consistently vary along the same pattern, no matter which predictor is used. Because our analysis uses a “by-example” approach, the conclusions are not entirely general. However, the method of comparing the AVO responses due to different [Formula: see text] predictors discussed here is. Hence, in a site-specific situation, we recommend using several relevant predictors to ascertain whether the choice significantly affects the synthetic AVO response and if this response is consistent with veritable seismic data.

Improving the quality of prestack inversion by prestack data conditioning

2015 ◽  
Vol 3 (1) ◽  
pp. T5-T12 ◽  
Author(s):  
Bo Zhang ◽  
Deshuang Chang ◽  
Tengfei Lin ◽  
Kurt J. Marfurt
Keyword(s):  
Reservoir Characterization ◽  
Incident Angle ◽  
Correlation Coefficients ◽  
Seismic Inversion ◽  
P Wave ◽  
Rock Properties ◽  
Prestack Inversion ◽  
Data Conditioning ◽  
Prestack Seismic Inversion ◽  
Hockey Sticks

Prestack seismic inversion techniques provide valuable information of rock properties, lithology, and fluid content for reservoir characterization. The confidence of inverted results increases with increasing incident angle of seismic gathers. The most accurate result of simultaneous prestack inversion of P-wave seismic data is P-impedance. S-impedance estimation becomes reliable with incident angles approaching 30°, whereas density evaluation becomes reliable with incident angles approaching 45°. As the offset increases, we often encounter “hockey sticks” and severe stretch at large offsets. Hockey sticks and stretch not only lower the seismic resolution but also hinder long offset prestack seismic inversion analysis. The inverted results are also affected by the random noises present in the prestack gathers. We developed a three-step workflow to perform data conditioning prior to simultaneous prestack inversion. First, we mitigated the hockey sticks by using an automatic nonhyperbolic velocity analysis. Then, we minimized the stretch at the far offset by using an antistretch workflow. Last, we improved the signal-to-noise ratio by applying prestack structure-oriented filtering. We evaluated our workflow by applying it to a prestack seismic volume acquired over the Fort Worth Basin, Texas, USA. The results inverted from the conditioned prestack gathers have higher resolution and better correlation coefficients with well logs when compared to those inverted from conventional time-migrated gathers.

Interpretation of AVO anomalies

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. 75A3-75A13 ◽  
Author(s):  
Douglas J. Foster ◽  
Robert G. Keys ◽  
F. David Lane
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
P Wave ◽  
Rock Properties ◽  
Light Hydrocarbons ◽  
S Wave ◽  
Amplitude Variation With Offset ◽  
P Wave Velocity ◽  
Fluid Properties ◽  
Porosity Changes

We investigate the effects of changes in rock and fluid properties on amplitude-variation-with-offset (AVO) responses. In the slope-intercept domain, reflections from wet sands and shales fall on or near a trend that we call the fluid line. Reflections from the top of sands containing gas or light hydrocarbons fall on a trend approximately parallel to the fluid line; reflections from the base of gas sands fall on a parallel trend on the opposing side of the fluid line. The polarity standard of the seismic data dictates whether these reflections from the top of hydrocarbon-bearing sands are below or above the fluid line. Typically, rock properties of sands and shales differ, and therefore reflections from sand/shale interfaces are also displaced from the fluid line. The distance of these trends from the fluid line depends upon the contrast of the ratio of P-wave velocity [Formula: see text] and S-wave velocity [Formula: see text]. This ratio is a function of pore-fluid compressibility and implies that distance from the fluid line increases with increasing compressibility. Reflections from wet sands are closer to the fluid line than hydrocarbon-related reflections. Porosity changes affect acoustic impedance but do not significantly impact the [Formula: see text] contrast. As a result, porosity changes move the AVO response along trends approximately parallel to the fluid line. These observations are useful for interpreting AVO anomalies in terms of fluids, lithology, and porosity.

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