Basaltic mantle reservoirs from seismic inversion of reflection data 

2021 ◽  
Author(s):  
Benoit Tauzin ◽  
Lauren Waszek ◽  
Jun Yan ◽  
Maxim Ballmer ◽  
Nick Schmerr ◽  
...  
Keyword(s):  
Seismic Waves ◽  
Thermal State ◽  
Global Scale ◽  
Seismic Inversion ◽  
Planetary Science ◽  
Compressional Waves ◽  
Reflection Data ◽  
Chemical Segregation ◽  
Geodynamic Models ◽  
Plate Subduction

<p>Convective stirring of chemical heterogeneities introduced through oceanic plate subduction results in the marble cake model of mantle composition. A convenient description invokes a chemically unequilibrated mixture of oceanic basaltic crust and harzburgitic lithosphere. Such a composition is required to explain joint observations of shear and compressional waves reflected underneath transition zone (TZ) discontinuities<sup>1</sup>. The formation of basaltic reservoirs at TZ depth results from complex interaction between phase-change induced chemical segregation, subducted slab downward entrainment, and plume upward advection. However, the dominant mechanism to create and maintain the reservoirs is debated, because both present-day reservoir location and the amount of basalt in these reservoirs are unconstrained. Here, Bayesian inversion of SS- and PP-precursors reflection data indicates that the TZ comprises a global average basalt fraction f = 0.32 ± 0.11. We find the most enriched basaltic reservoirs (f = 0.5-0.6) are associated with recent subduction in the circum-Pacific region. We investigate the efficiency of plate subduction to maintain such reservoirs using global-scale thermochemical  convection models<sup>2</sup>.</p><p>[1] Waszek, L., Tauzin, B., Schmerr, N.C., Ballmer, M., & Afonso, J.C. (in review). A poorly mixed mantle and its thermal state inferred from seismic waves.</p><p>[2] Yan, J., Ballmer, M. D., & Tackley, P. J. (2020). The evolution and distribution of recycled oceanic crust in the Earth's mantle: Insight from geodynamic models. <em>Earth and Planetary Science Letters</em>, <em>537</em>, 116171.</p>

An improved method to estimate Q based on the logarithmic spectrum of moving peak points

2019 ◽  
Vol 7 (2) ◽  
pp. T255-T263 ◽  
Author(s):  
Yanli Liu ◽  
Zhenchun Li ◽  
Guoquan Yang ◽  
Qiang Liu
Keyword(s):  
Seismic Waves ◽  
Wave Attenuation ◽  
Real Data ◽  
Peak Frequency ◽  
Seismic Reflection Data ◽  
The Real ◽  
Time Frequency ◽  
Reflection Data ◽  
Text Filtering ◽  
The Impact

The quality factor ([Formula: see text]) is an important parameter for measuring the attenuation of seismic waves. Reliable [Formula: see text] estimation and stable inverse [Formula: see text] filtering are expected to improve the resolution of seismic data and deep-layer energy. Many methods of estimating [Formula: see text] are based on an individual wavelet. However, it is difficult to extract the individual wavelet precisely from seismic reflection data. To avoid this problem, we have developed a method of directly estimating [Formula: see text] from reflection data. The core of the methodology is selecting the peak-frequency points to linear fit their logarithmic spectrum and time-frequency product. Then, we calculated [Formula: see text] according to the relationship between [Formula: see text] and the optimized slope. First, to get the peak frequency points at different times, we use the generalized S transform to produce the 2D high-precision time-frequency spectrum. According to the seismic wave attenuation mechanism, the logarithmic spectrum attenuates linearly with the product of frequency and time. Thus, the second step of the method is transforming a 2D spectrum into 1D by variable substitution. In the process of transformation, we only selected the peak frequency points to participate in the fitting process, which can reduce the impact of the interference on the spectrum. Third, we obtain the optimized slope by least-squares fitting. To demonstrate the reliability of our method, we applied it to a constant [Formula: see text] model and the real data of a work area. For the real data, we calculated the [Formula: see text] curve of the seismic trace near a well and we get the high-resolution section by using stable inverse [Formula: see text] filtering. The model and real data indicate that our method is effective and reliable for estimating the [Formula: see text] value.

Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study

2021 ◽  
Author(s):  
Siddharth Garia ◽  
Arnab Kumar Pal ◽  
Karangat Ravi ◽  
Archana M Nair
Keyword(s):  
Neural Network ◽  
Bulk Density ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Petrophysical Properties ◽  
Seismic Section ◽  
Frequency Model ◽  
Seismic Reflection Data ◽  
Reflection Data

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

scholarly journals Vertical to Horizontal Spectral Ratio (VHSR) Response of Seismic Wave Propagation in a Homogeneous Elastic – Poroelastic Medium Using The Spectral Finite Element Method

2021 ◽  
Vol 11 (1) ◽  
pp. 95
Author(s):  
Sudarmaji Saroji ◽  
Budi Eka Nurcahya ◽  
Nivan Ramadhan Sugiantoro
Keyword(s):  
Finite Element Method ◽  
Elastic Medium ◽  
Seismic Waves ◽  
Low Frequency ◽  
Seismic Wave Propagation ◽  
P Wave ◽  
Poroelastic Medium ◽  
Compressional Waves ◽  
Spectral Finite Element Method ◽  
Spectral Finite Element

<p>Numerical modeling of 2D seismic wave propagation using spectral finite element method to estimate the response of seismic waves passing through the poroelastic medium from a hydrocarbon reservoir has been carried out. A hybrid simple model of the elastic - poroelastic - elastic with a mesoscopic scale element size of about 50cm was created. Seismic waves which was in the form of the ricker function are generated on the first elastic medium, propagated into the poroelastic medium and then transmitted to the second elastic medium. Pororoelastic medium is bearing hydrocarbon fluid in the form of gas, oil or water. Vertical and horizontal component of velocity seismograms are recorded on all mediums. Seismograms which are recorded in the poroelastic and second elastic medium show the existence of slow P compressional waves following fast P compressional waves that do not appear on the seismogram of the first elastic medium. The slow P wave is generated when the fast P wave enters the interface of the elastic - poroelastic boundary, propagated in the poroelastic medium and is transmited to the second elastic medium. The curves of Vertical to horizontal spectrum ratio (VHSR) which are observed from seismograms recorded in the poroelastic and the second elastic medium show that the peak of VHSR values at low frequency correlated with the fluid of poroelastic reservoir. The highest VHSR value at the low frequency which is recorded on the seismogram is above the 2.5 Hz frequency for reservoirs containing gas and oil in the second elastic medium, while for the medium containing water is the highest VHSR value is below the 2.5 Hz frequency.</p>

Fault whispers: Transmission distortions on prestack seismic reflection data

Geophysics ◽  
2000 ◽  
Vol 65 (2) ◽  
pp. 377-389 ◽  
Author(s):  
Paul J. Hatchell
Keyword(s):  
Seismic Data ◽  
Seismic Waves ◽  
Shallow Gas ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Amplitude Versus Offset ◽  
Velocity Changes ◽  
Transmission Distortions ◽  
Amplitude Versus ◽  
Buried Faults

Transmission distortions are observed on prestack seismic data at two locations in the Gulf of Mexico. These distortions produce anomalous amplitude versus offset (AVO) signatures. The locations of the distortion zones are determined using acquisition geometry and ray tracing. No obvious reflection events, such as shallow gas zones, are observed at the predicted locations of the distortion zones. Instead, the distortion zones correlate with buried faults and unconformities. It is postulated that the distortions are produced by velocity changes across buried faults and unconformities. The distortions result from an interference pattern resulting from seismic waves arriving from different sides of the faults. A simple model is developed to explain many of the characteristics of the distortion pattern.

High‐frequency reflections in granite? Delineation of the weathering front in granodiorite at Piñon Flat, California

Geophysics ◽  
1999 ◽  
Vol 64 (6) ◽  
pp. 1828-1835 ◽  
Author(s):  
Stanley J. Radzevicius ◽  
Gary L. Pavlis
Keyword(s):  
High Frequency ◽  
Seismic Waves ◽  
Lower Layer ◽  
Specular Reflection ◽  
Earth Model ◽  
Remarkable Feature ◽  
Reflection Data ◽  
Weathered Layer ◽  
High Frequency Seismic Waves ◽  
Seismic Lines

We analyze data from two orthogonal seismic lines 336 m in length collected at Piñon Flat, California, over weathered granodiorite bedrock. Each line was made up of 10 reversed segments 84 m in length. We analyzed the first arrivals from these data and found dramatic variations in velocity along the profiles. An upper layer (approximately 2-m thick) known from trenching to be composed of soil and sandy grus had measured velocities ranging from 400 to 700 m/s. Velocities inferred from refraction analysis of first arrivals of the reversed lines revealed a heterogeneous lower layer below the soil with measured velocities of 1600–2700 m/s by a depth of 15 m. We interpret these data to be measuring velocities of a deeply weathered unit characterized by granodiorite corestones embedded in a matrix of saprolite. The most remarkable feature of these data emerged from attempting to process the same data as reflection data. Simple bandpass filtering in the 250–400 Hz band revealed a bright, impulsive arrival with three characteristic properties: (1) irregular velocity moveout that is inconsistent with that expected from a layered earth model, (2) the arrival is at a nearly constant time‐depth on all data, and (3) the arrival tends to be followed by a ringing coda whose frequency varies from trace to trace. This arrival ties exactly with a velocity discontinuity measured in a borehole located on one of the profiles that we interpret as the base of the weathered layer. We suggest this arrival is a specular reflection from a weathering front that occurs along horizontal sheeting joints at a fixed depth below the surface. This surface acts as an effective mirror for high‐frequency seismic waves which are then channeled upward through an intact, high-Q path of unaltered blocks of granodiorite to define the observed signals at the surface.

scholarly journals Application of model-based inversion technique in a field in the coastal swamp depobelt, Niger delta

2018 ◽  
Vol 6 (1) ◽  
pp. 122
Author(s):  
Okoli Austin ◽  
Onyekuru Samuel I. ◽  
Okechukwu Agbasi ◽  
Zaidoon Taha Abdulrazzaq
Keyword(s):  
Niger Delta ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Seismic Reflection Data ◽  
Model Based ◽  
Reflection Data ◽  
X 32 ◽  
Niger Delta Basin ◽  
Bypassed Oil

Considering the heterogeneity of the reservoir sands in the Niger Delta basin which are primary causes of low hydrocarbon recovery efficiency, poor sweep, early breakthrough and pockets of bypassed oil there arises a need for in-depth quantitative interpretation and more analysis to be done on seismic data to achieve a reliable reservoir characterization to improve recovery, plan future development wells within field and achieve deeper prospecting for depths not penetrated by the wells and areas far away from well locations. An effective tool towards de-risking prospects is seismic inversion which transforms a seismic reflection data to a quantitative rock-property description of a reservoir. The choice of model-based inversion in this study was due to well control, again considering the heterogeneity of the sands in the field. X-26, X-30, and X-32 were used to generate an initial impedance log which is used to update the estimated reflectivity from which we would obtain our inverted volumes. Acoustic impedance volumes were generated and observations made were consistent with depth trends established for the Niger Delta basin, inverted slices of Poisson impedances validated the expected responses considering the effect of compaction. This justifies the use of inversion method in further characterizing the plays identified in the region.

scholarly journals A seismically induced onshore surge deposit at the KPg boundary, North Dakota

2019 ◽  
Vol 116 (17) ◽  
pp. 8190-8199 ◽  
Author(s):  
Robert A. DePalma ◽  
Jan Smit ◽  
David A. Burnham ◽  
Klaudia Kuiper ◽  
Phillip L. Manning ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Waves ◽  
Complex Dynamics ◽  
High Energy ◽  
Global Scale ◽  
River Channel ◽  
Impact Event ◽  
Fine Grained ◽  
Hell Creek Formation ◽  
Chicxulub Impact

The most immediate effects of the terminal-Cretaceous Chicxulub impact, essential to understanding the global-scale environmental and biotic collapses that mark the Cretaceous–Paleogene extinction, are poorly resolved despite extensive previous work. Here, we help to resolve this by describing a rapidly emplaced, high-energy onshore surge deposit from the terrestrial Hell Creek Formation in Montana. Associated ejecta and a cap of iridium-rich impactite reveal that its emplacement coincided with the Chicxulub event. Acipenseriform fish, densely packed in the deposit, contain ejecta spherules in their gills and were buried by an inland-directed surge that inundated a deeply incised river channel before accretion of the fine-grained impactite. Although this deposit displays all of the physical characteristics of a tsunami runup, the timing (<1 hour postimpact) is instead consistent with the arrival of strong seismic waves from the magnitude Mw∼10 to 11 earthquake generated by the Chicxulub impact, identifying a seismically coupled seiche inundation as the likely cause. Our findings present high-resolution chronology of the immediate aftereffects of the Chicxulub impact event in the Western Interior, and report an impact-triggered onshore mix of marine and terrestrial sedimentation—potentially a significant advancement for eventually resolving both the complex dynamics of debris ejection and the full nature and extent of biotic disruptions that took place in the first moments postimpact.

Attenuation of seismic waves in dry and saturated rocks: I. Laboratory measurements

Geophysics ◽  
1979 ◽  
Vol 44 (4) ◽  
pp. 681-690 ◽  
Author(s):  
M. N. Toksöz ◽  
D. H. Johnston ◽  
A. Timur
Keyword(s):  
Seismic Waves ◽  
Reference Sample ◽  
Spectral Ratios ◽  
Attenuation Coefficients ◽  
Compressional Waves ◽  
S Waves ◽  
Pulse Transmission ◽  
Water Saturated ◽  
Quality Factor Q ◽  
Q Values

The attenuation of compressional (P) and shear (S) waves in dry, saturated, and frozen rocks is measured in the laboratory at ultrasonic frequencies. A pulse transmission technique and spectral ratios are used to determine attenuation coefficients and quality factor (Q) values relative to a reference sample with very low attenuation. In the frequency range of about 0.1–1.0 MHz, the attenuation coefficient is linearly proportional to frequency (constant Q) both for P‐ and S‐waves. In dry rocks, [Formula: see text] of compressional waves is slightly smaller than [Formula: see text] of shear waves. In brine and water‐saturated rocks, [Formula: see text] is larger than [Formula: see text]. Attenuation decreases substantially (Q values increase) with increasing differential pressure for both P‐ and S‐waves.

scholarly journals Deep learning for fast simulation of seismic waves in complex media

2019 ◽  
Author(s):  
Ben Moseley ◽  
Tarje Nissen-Meyer ◽  
Andrew Markham
Keyword(s):  
Network Design ◽  
Network Architecture ◽  
Seismic Waves ◽  
Waveform Inversion ◽  
Computational Cost ◽  
Seismic Inversion ◽  
Complex Media ◽  
Convolutional Network ◽  
Simulation Accuracy ◽  
The Media

Abstract. The simulation of seismic waves is a core task in many geophysical applications. Numerical methods such as Finite Difference (FD) modelling and Spectral Element Methods (SEM) are the most popular techniques for simulating seismic waves in complex media, but for many tasks their computational cost is prohibitively expensive. In this work we present two types of deep neural networks as fast alternatives for simulating seismic waves in horizontally layered and faulted 2D acoustic media. In contrast to the classical methods both networks are able to simulate the seismic response at multiple locations within the media in a single inference step, without needing to iteratively model the seismic wavefield through time, resulting in an order of magnitude reduction in simulation time. This speed improvement could pave the way to real-time seismic simulation and benefit seismic inversion algorithms based on forward modelling, such as full waveform inversion. Our first network is able to simulate seismic waves in horizontally layered media. We use a WaveNet network architecture and show this is more accurate than a standard convolutional network design. Furthermore we show that seismic inversion can be carried out by retraining the network with its inputs and outputs reversed, offering a fast alternative to existing inversion techniques. Our second network is significantly more general than the first; it is able to simulate seismic waves in faulted media with arbitrary layers, fault properties and an arbitrary location of the seismic source on the surface of the media. It uses a convolutional autoencoder network design and is conditioned on the input source location. We investigate the sensitivity of different network designs and training hyperparameters on its simulation accuracy. We compare and contrast this network to the first network. To train both networks we introduce a time-dependent gain in the loss function which improves convergence. We discuss the relative merits of our approach with FD modelling and how our approach could be generalised to simulate more complex Earth models.

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