A technique for identifying microseismic multiplets and application to the Valhall field, North Sea

A fast, fully automatic technique to identify microseismic multiplets in borehole seismic data is developed. The technique may be applied in real time to either continuous data or detected-event data for a number of three-component receivers and does not require prior information such as P- or S-wave time picks. Peak crosscorrelation coefficients, evaluated in the frequency domain, are used as the basis for identifying microseismic doublets. The peak crosscorrelation coefficient at each receiver is evaluated with a weighted arithmetic average of the normalized correlation coefficients of each component. Each component is weighted by the maximum amplitude of the signal for that component to reduce the effect of noise on the calculations. The weighted average correlations are averaged over all receivers in a time window centered on a fixed lag time. The size of the time window is determined from the dominant period in the signal, and the lag time is the time that maximizes the average correlation coefficient. The technique is applied to a three-component passive seismic data set recorded at the Valhall field, North Sea. A large number of microseismic doublets are identified that can be grouped into multiplets, reducing the total number of absolute event locations by a factor of two. Seven large multiplets reflect the repeated multiple rerupturing (up to 30 times on a single fault) and significant stress release. Two major faults dominate the seismic activity, causing at least one-fourth of the observed events.

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Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea

Geophysics ◽

10.1190/1.1444833 ◽

2000 ◽

Vol 65 (5) ◽

pp. 1446-1454 ◽

Cited By ~ 33

Author(s):

Side Jin ◽

G. Cambois ◽

C. Vuillermoz

Keyword(s):

Wave Velocity ◽

North Sea ◽

Random Noise ◽

P Wave ◽

S Wave ◽

Data Set ◽

Avo Analysis ◽

The North ◽

Avo Inversion ◽

The North Sea

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

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The domain of applicability of acoustic full-waveform inversion for marine seismic data

Geophysics ◽

10.1190/1.3250269 ◽

2009 ◽

Vol 74 (6) ◽

pp. WCC91-WCC103 ◽

Cited By ~ 45

Author(s):

Christophe Barnes ◽

Marwan Charara

Keyword(s):

Seismic Data ◽

Seismic Inversion ◽

Three Dimensions ◽

Acoustic Medium ◽

Physical Parameters ◽

S Wave ◽

Data Set ◽

Reflection Seismic ◽

Wave Inversion ◽

Full Wave

Marine reflection seismic data inversion is a compute-intensive process, especially in three dimensions. Approximations often are made to limit the number of physical parameters we invert for, or to speed up the forward modeling. Because the data often are dominated by unconverted P-waves, one popular approximation is to consider the earth as purely acoustic, i.e., no shear modulus. The material density sometimes is taken as a constant. Nonlinear waveform seismic inversion consists of iteratively minimizing the misfit between the amplitudes of the measured and the modeled data. Approximations, such as assuming an acoustic medium, lead to incorrect modeling of the amplitudes of the seismic waves, especially with respect to amplitude variation with offset (AVO), and therefore have a direct impact on the inversion results. For evaluation purposes, we have performed a series of inversions with different approximations and different constraints whereby the synthetic data set to recover is computed for a 1D elastic medium. A series of numerical experiments, although simple, help to define the applicability domain of the acoustic assumption. Acoustic full-wave inversion is applicable only when the S-wave velocity and the density fields are smooth enough to reduce the AVO effect, or when the near-offset seismograms are inverted with a good starting model. However, in many realistic cases, acoustic approximation penalizes the full-wave inversion of marine reflection seismic data in retrieving the acoustic parameters.

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Simultaneous inversion of PP and PS seismic data

Geophysics ◽

10.1190/1.2194533 ◽

2006 ◽

Vol 71 (3) ◽

pp. R1-R10 ◽

Cited By ~ 51

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.

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Quantifying the uncertainty in an AVO interpretation

Geophysics ◽

10.1190/1.1451395 ◽

2002 ◽

Vol 67 (1) ◽

pp. 117-125 ◽

Cited By ~ 22

Author(s):

Richard T. Houck

Keyword(s):

North Sea ◽

Seismic Data ◽

Reflection Coefficients ◽

Amplitude Variation ◽

Data Set ◽

Amplitude Variation With Offset ◽

Full Characterization ◽

The North ◽

The North Sea

Lithologic interpretations of amplitude variation with offset (AVO) information are ambiguous both because different lithologies occupy overlapping ranges of elastic properties, and because angle‐dependent reflection coefficients estimated from seismic data are uncertain. This paper presents a method for quantifying and combining these two components of uncertainty to get a full characterization of the uncertainty associated with an AVO‐based lithologic interpretation. The result of this approach is a compilation of all the lithologies that are consistent with the observed AVO behavior, along with a probability of occurrence for each lithology. A 2‐D line from the North Sea illustrates how the method might be applied in practice. For any data set, the interaction between the geologic and measurement components of uncertainty may significantly affect the overall uncertainty in a lithologic interpretation.

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Data-driven adaptive decomposition of multicomponent seabed seismic recordings: Application to shallow-water data from the North Sea

Geophysics ◽

10.1190/1.2778766 ◽

2007 ◽

Vol 72 (6) ◽

pp. V133-V142 ◽

Cited By ~ 7

Author(s):

Remco Muijs ◽

Johan O. A. Robertsson ◽

Klaus Holliger

Keyword(s):

North Sea ◽

Decomposition Method ◽

Data Driven ◽

Full Potential ◽

S Wave ◽

Data Set ◽

The North ◽

Decomposition Procedure ◽

The North Sea

Exploiting the full potential of multicomponent seabed seismic recordings requires the decomposition of the recorded data into their upgoing and downgoing P- and S-wave constituents. We present a case study from the North Sea, where a novel adaptive wave-equation-based decomposition method is applied to a 2D data set shot inline with a cable-based seabed seismic acquisition system. The data were recorded in relatively shallow [Formula: see text] water, such that severe interference exists between primary reflections and water-layer multiples. Such conditions represent a challenge for many decomposition methods, because these often require a significant amount of interpretive, user-defined input. Conversely, the adaptive algorithm demonstrated in this study is fully data-driven, requiring as sole input a rough estimate of the water depth. The importance of careful mutual calibration of the sensors is demonstrated by critically assessing the properties of the derived calibration filters and the resulting estimates of the elastic properties of the seabed. To assess the effectiveness of the decomposition procedure, we compare a number of key events identified in the unprocessed data with their equivalents in the decomposed wavefields. The results of this case study show that the noninteractive decomposition method, which was demonstrated on seabed seismic data acquired in deep [Formula: see text] water, can be applied successfully in shallower conditions without further modification.

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AVO inversion and poroelasticity with P- and S-wave moduli

Geophysics ◽

10.1190/geo2011-0214.1 ◽

2012 ◽

Vol 77 (6) ◽

pp. N17-N24 ◽

Cited By ~ 76

Author(s):

Zhaoyun Zong ◽

Xingyao Yin ◽

Guochen Wu

Keyword(s):

Seismic Data ◽

Real Data ◽

P Wave ◽

Model Parameters ◽

Initial Model ◽

S Wave ◽

Data Set ◽

Statistical Probability ◽

Avo Inversion ◽

Poroelasticity Theory

The fluid term in the Biot-Gassmann equation plays an important role in reservoir fluid discrimination. The density term imbedded in the fluid term, however, is difficult to estimate because it is less sensitive to seismic amplitude variations. We combined poroelasticity theory, amplitude variation with offset (AVO) inversion, and identification of P- and S-wave moduli to present a stable and physically meaningful method to estimate the fluid term, with no need for density information from prestack seismic data. We used poroelasticity theory to express the fluid term as a function of P- and S-wave moduli. The use of P- and S-wave moduli made the derivation physically meaningful and natural. Then we derived an AVO approximation in terms of these moduli, which can then be directly inverted from seismic data. Furthermore, this practical and robust AVO-inversion technique was developed in a Bayesian framework. The objective was to obtain the maximum a posteriori solution for the P-wave modulus, S-wave modulus, and density. Gaussian and Cauchy distributions were used for the likelihood and a priori probability distributions, respectively. The introduction of a low-frequency constraint and statistical probability information to the objective function rendered the inversion more stable and less sensitive to the initial model. Tests on synthetic data showed that all the parameters can be estimated well when no noise is present and the estimated P- and S-wave moduli were still reasonable with moderate noise and rather smooth initial model parameters. A test on a real data set showed that the estimated fluid term was in good agreement with the results of drilling.

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Inversion and interpretation of a 3D seismic data set from the Ouachita Mountains, Oklahoma

Geophysics ◽

10.1190/1.3073005 ◽

2009 ◽

Vol 74 (2) ◽

pp. B37-B45 ◽

Cited By ~ 3

Author(s):

Abuduwali Aibaidula ◽

George McMechan

Keyword(s):

Seismic Data ◽

Acoustic Impedance ◽

3D Visualization ◽

Wave Impedance ◽

P Wave ◽

3D Seismic ◽

S Wave ◽

Ouachita Mountains ◽

Data Set ◽

3D Seismic Data

Acoustic impedance inversion (AI) and simultaneous angle-dependent inversion (SADI) of a 3D seismic data set characterize reservoirs of Mississippian Morrowan age in the triangle zone of the frontal Ouachita Mountains, Oklahoma. Acoustic impedance of the near-angle seismic data images the 3D spatial distributions of Wapanucka limestone and Cromwell sandstone. Lamé [Formula: see text] ([Formula: see text] and [Formula: see text]) and [Formula: see text] sections are derived from the P-wave and S-wave impedance ([Formula: see text] and [Formula: see text]) sections produced by the SADI. Lithology is identified from the gamma logs and [Formula: see text]. The [Formula: see text], [Formula: see text], and [Formula: see text] are interpreted in terms of a hydrocarbon distribution pattern. The [Formula: see text] is used to identify high [Formula: see text] regions that are consistent with high sand/shale ratio. The estimated impedances and derived Lamé parameter sections are consistent with the interpretation that parts of the Wapanucka limestone and Cromwell sandstone contain potential gas reservoirs in fault-bounded compartments. The Cromwell sandstone contains the main inferred reservoirs; the two largest of these are each [Formula: see text] in pore volume. The inversion results also explain the observed low production in previous wells because those did not sample the best compartments. We propose a single new well location that would penetrate both reservoirs; 3D visualization facilitates this recommendation.

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A parameterization study for elastic VTI full-waveform inversion of hydrophone components: Synthetic and North Sea field data examples

Geophysics ◽

10.1190/geo2017-0073.1 ◽

2017 ◽

Vol 82 (6) ◽

pp. R299-R308 ◽

Cited By ~ 11

Author(s):

Antoine Guitton ◽

Tariq Alkhalifah

Keyword(s):

North Sea ◽

Waveform Inversion ◽

Transversely Isotropic ◽

Horizontal Velocity ◽

Full Waveform Inversion ◽

Ocean Bottom ◽

S Wave ◽

Data Set ◽

Full Waveform ◽

Streamer Data

Choosing the right parameterization to describe a transversely isotropic medium with a vertical symmetry axis (VTI) allows us to match the scattering potential of these parameters to the available data in a way that avoids a potential tradeoff and focuses on the parameters to which the data are sensitive. For 2D elastic full-waveform inversion in VTI media of pressure components and for data with a reasonable range of offsets (as with those found in conventional streamer data acquisition systems), assuming that we have a kinematically accurate normal moveout velocity ([Formula: see text]) and anellipticity parameter [Formula: see text] (or horizontal velocity [Formula: see text]) obtained from tomographic methods, a parameterization in terms of horizontal velocity [Formula: see text], [Formula: see text], and [Formula: see text] is preferred to the more conventional parameterization in terms of [Formula: see text], [Formula: see text], and [Formula: see text]. In the [Formula: see text], [Formula: see text], and [Formula: see text] parameterization and for reasonable scattering angles (<[Formula: see text]), [Formula: see text] acts as a “garbage collector” and absorbs most of the amplitude discrepancies between the modeled and observed data, more so when density [Formula: see text] and S-wave velocity [Formula: see text] are not inverted for (a standard practice with streamer data). On the contrary, in the [Formula: see text], [Formula: see text], and [Formula: see text] parameterization, [Formula: see text] is mostly sensitive to large scattering angles, leaving [Formula: see text] exposed to strong leakages from [Formula: see text] mainly. These assertions will be demonstrated on the synthetic Marmousi II as well as a North Sea ocean bottom cable data set, in which inverting for the horizontal velocity rather than the vertical velocity yields more accurate models and migrated images.

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Identification of hydrocarbons in chalk reservoirs from surface seismic data: South Arne field, North Sea

Geological Survey of Denmark and Greenland Bulletin ◽

10.34194/geusb.v7.4823 ◽

2005 ◽

Vol 7 ◽

pp. 13-16

Author(s):

Peter Japsen ◽

Anders Bruun ◽

Ida L. Fabricius ◽

Gary Mavko

Keyword(s):

Wave Velocity ◽

North Sea ◽

Seismic Data ◽

Pore Space ◽

Sedimentary Rocks ◽

Rock Physics ◽

Acoustic Properties ◽

P Wave ◽

S Wave ◽

Reservoir Rocks

Seismic data are mainly used to map out structures in the subsurface, but are also increasingly used to detect differences in porosity and in the fluids that occupy the pore space in sedimentary rocks. Hydrocarbons are generally lighter than brine, and the bulk density and sonic velocity (speed of pressure waves or P-wave velocity) of hydrocarbon-bearing sedimentary rocks are therefore reduced compared to non-reservoir rocks. However, sound is transmitted in different wave forms through the rock, and the shear velocity (speed of shear waves or S-wave velocity) is hardly affected by the density of the pore fluid. In order to detect the presence of hydrocarbons from seismic data, it is thus necessary to investigate how porosity and pore fluids affect the acoustic properties of a sedimentary rock. Much previous research has focused on describing such effects in sandstone (see Mavko et al. 1998), and only in recent years have corresponding studies on the rock physics of chalk appeared (e.g. Walls et al. 1998; Røgen 2002; Fabricius 2003; Gommesen 2003; Japsen et al. 2004). In the North Sea, chalk of the Danian Ekofisk Formation and the Maastrichtian Tor Formation are important reservoir rocks. More information could no doubt be extracted from seismic data if the fundamental physical properties of chalk were better understood. The presence of gas in chalk is known to cause a phase reversal in the seismic signal (Megson 1992), but the presence of oil in chalk has only recently been demonstrated to have an effect on surface seismic data (Japsen et al. 2004). The need for a better link between chalk reservoir parameters and geophysical observations has, however, strongly increased since the discovery of the Halfdan field proved major reserves outside four-way dip closures (Jacobsen et al. 1999; Vejbæk & Kristensen 2000).

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Exploration of the data space via trans-dimensional sampling: the case study of seismic double difference data

10.5194/se-2021-79 ◽

2021 ◽

Author(s):

Nicola Piana Agostinetti ◽

Giulia Sgattoni

Keyword(s):

Seismic Data ◽

Time Window ◽

Time Windows ◽

Correlation Coefficients ◽

Double Difference ◽

Seismicity Rate ◽

Data Space ◽

Double Differences ◽

Time Invariant

Abstract. Double differences (DD) seismic data are widely used to define elasticity distribution in the Earth's interior, and its variation in time. DD data are often pre-processed from earthquakes recordings through expert-opinion, where couples of earthquakes are selected based on some user-defined criteria, and DD data are computed from the selected couples. We develop a novel methodology for preparing DD seismic data based on a trans-dimensional algorithm, without imposing pre-defined criteria on the selection of couples of events. We apply it to a seismic database recorded on the flank of Katla volcano (Iceland), where elasticity variations in time has been indicated. Our approach quantitatively defines the presence of changepoints that separate the seismic events in time-windows. Within each time-window, the DD data are consistent with the hypothesis of time-invariant elasticity in the subsurface, and DD data can be safely used in subsequent analysis. Due to the parsimonious behavior of the trans-dimensional algorithm, only changepoints supported by the data are retrieved. Our results indicate that: (a) retrieved changepoints are consistent with first-order variations in the data (i.e. most striking changes in the DD data are correctly reproduced in the changepoint distribution in time); (b) changepoint locations in time do correlate neither with changes in seismicity rate, nor with changes in waveforms similarity (measured through the cross-correlation coefficients); and (c) noteworthy, the changepoint distribution in time seems to be insensitive to variations in the seismic network geometry during the experiment. Our results proofs that trans-dimensional algorithms can be positively applied to pre-processing of geophysical data before the application of standard routines (i.e. before using them to solve standard geophysical inverse problems) in the so called exploration of the data space.

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