Practical aspects of an elastic migration/inversion of crosshole data for reservoir characterization: A Paris basin example

Geophysics ◽  
1989 ◽  
Vol 54 (12) ◽  
pp. 1587-1595 ◽  
Author(s):  
W. B. Beydoun ◽  
J. Delvaux ◽  
M. Mendes ◽  
G. Noual ◽  
A. Tarantola
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Reservoir Characterization ◽  
Residual Energy ◽  
Paris Basin ◽  
S Wave ◽  
Depth Level ◽  
Surface Reflection ◽  
Depth Images ◽  
And Migration

The main obstacle for a detailed study of a reservoir is the lack of geophysical and petrophysical information between producing wells. Crosshole seismic data can aid reservoir geologists and engineers in (1) estimating the volume of oil in place, (2) mapping permeability/porosity barriers, and ultimately (3) monitoring and designing enhanced off‐recovery experiments. Due to their high‐frequency content, crosshole seismic data offer an ideal information link between conventional seismic data (surface reflection, VSPs, etc.) and full‐waveform sonic acoustic logs. Using a weightdrop downhole source located at one depth level, acoustic and multicomponent data at two different wells were collected in the Paris basin with interwell distances of about 100 m. The target zone includes three sand reservoir levels between depths of 575 and 600 m. A 2‐D elastic migration/inversion (M/I) of the scattered S‐S and S‐P crosshole field data produced high‐resolution S‐wave velocity and density depth images of the subsurface, extending information away from wells and identifying reservoirs. The residual energy reduction between synthetic seismograms derived from M/I images and field data is 18 percent, confirming that images contain elastic information. Structural dips obtained are very reasonable, the observed vertical spatial resolution being of the order of 3 m. We believe that this is the first time that such techniques have been applied to crosshole data. Elastic M/I images are generally better than elastic VSP‐CDP and migration images and have the advantage of producing global quality measures of images. Such a technique uses as input a background velocity, e.g., a tomogram obtained by traveltime tomography, and complements the background by recovering subsurface discontinuities and changes in elastic parameters within the signal bandwidth.

Twelve years of vertical birefringence in nine‐component VSP data

Geophysics ◽  
2001 ◽  
Vol 66 (2) ◽  
pp. 582-597 ◽  
Author(s):  
Donald F. Winterstein ◽  
Gopa S. De ◽  
Mark A. Meadows
Keyword(s):  
Seismic Data ◽  
Azimuthal Anisotropy ◽  
Vertical Shear ◽  
S Wave ◽  
Seismic Profiles ◽  
Surface Reflection ◽  
Vertical Seismic Profiles ◽  
Reflection Seismic ◽  
San Andreas ◽  
Vsp Data

Since 1986, when industry scientists first publicly showed data supporting the presence of azimuthal anisotropy in sedimentary rock, we have studied vertical shear‐wave (S-wave) birefringence in 23 different wells in western North America. The data were from nine‐component vertical seismic profiles (VSPs) supplemented in recent years with data from wireline crossed‐dipole logs. This paper summarizes our results, including birefringence results in tabular form for 54 depth intervals in 19 of those 23 wells. In the Appendix we present our conclusions about how to record VSP data optimally for study of vertical birefringence. We arrived at four principal conclusions about vertical S-wave birefringence. First, birefringence was common but not universal. Second, birefringence ranged from 0–21%, but values larger than 4% occurred only in shallow formations (<1200 m) within 40 km of California’s San Andreas fault. Third, at large scales birefringence tended to be blocky. That is, both the birefringence magnitude and the S-wave polarization azimuth were often consistent over depth intervals of several tens to hundreds of meters but then changed abruptly, sometimes by large amounts. Birefringence in some instances diminished with depth and in others increased with depth, but in almost every case a layer near the surface was more birefringent than the layer immediately below it. Fourth, observed birefringence patterns generally do not encourage use of multicomponent surface reflection seismic data for finding fractured hydrocarbon reservoirs, but they do encourage use of crossed‐dipole logs to examine them. That is, most reservoirs were birefringent, but none we studied showed increased birefringence confined to the reservoir.

Band-limited scattered wavefield reconstruction beneath complex overburdens using the Marchenko method

2021 ◽  
Author(s):  
David Vargas ◽  
Ivan Vasconcelos ◽  
Matteo Ravasi
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Accurate Estimation ◽  
Target Area ◽  
Complex Media ◽  
Depth Level ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Band Limited ◽  
Internal Multiples

&lt;p&gt;Structural imaging beneath complex overburdens, such as sub-salt or sub-basalt, typically characterized by high-impedance contrasts represents a major challenge for state-of-the-art seismic methods. Reconstructing complex geological structures in the vicinity of and below salt bodies is challenging not only due to uneven, single-sided illumination of the target area but also because of the imperfect removal of surface and internal multiples from the recorded data, as required by traditional migration algorithms. In such tectonic setups, most of the downgoing seismic wavefield is reflected toward the surface when interacting with the overburden's top layer. Similarly, the sub-salt upcoming energy is backscattered at the salt's base. Consequently, the actual energy illuminating the sub-salt reflectors, recorded at the surface, is around the noise level. In diapiric trap systems, conventional seismic extrapolation techniques do not guarantee sufficient quality to reduce exploration and production risks; likewise, seismic-based reservoir characterization and monitoring are also compromised. In this regard, accurate wavefield extrapolation techniques based on the Marchenko method may open up new ways to exploit seismic data.&lt;/p&gt;&lt;p&gt;The Marchenko redatuming technique retrieves reliable full-wavefield information in the presence of geologic intrusions, which can be subsequently used to produce artefact-free images by naturally including all orders of multiples present in seismic reflection data. To achieve such a goal, the method relies on the estimation of focusing operators allowing the synthesis of virtual surveys at a given depth level. Still, current Marchenko implementations do not fully incorporate available subsurface models with sharp contrasts, due to the requirements regarding the initialization of the focusing functions. Most importantly, in complex media, even a fairly accurate estimation of a direct wave as a proxy for the required initial focusing functions may not be enough to guarantee sufficiently accurate wavefield reconstruction.&lt;/p&gt;&lt;p&gt;In this talk, we will discuss a scattering-based Marchenko redatuming framework which improves the redatuming of seismic surface data in highly complex media when compared to other Marchenko-based schemes. This extended version is designed to accommodate for band-limited, multi-component, and possibly unevenly sampled seismic data, which contain both free-surface and internal multiples, whilst requiring minimum pre-processing steps. The performance of our scattering Marchenko method will be evaluated using a comprehensive set of numerical tests on a complex 2D subsalt model.&lt;/p&gt;

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.

Real data results of Kirchhoff elastic wave migration

Geophysics ◽  
1986 ◽  
Vol 51 (4) ◽  
pp. 1006-1011 ◽  
Author(s):  
Ting‐Fan Dai ◽  
John T. Kuo
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Horizontal Layer ◽  
S Wave ◽  
P Waves ◽  
S Waves ◽  
Surface Data ◽  
And Migration ◽  
Wave Migration ◽  
Kirchhoff Integral

Although Kirchhoff integral migration has attracted considerable attention for seismic data processing since the early 1970s, it, like all other seismic migration methods, is only applicable to compressional (P) waves. Because of a recent surge of interest in shear (S) waves, Kuo and Dai (1984) developed the Kirchhoff elastic (P and S) wave migration (KEWM) formulation and migration principle for the case of source and receiver noncoincidence. They obtained encouraging results using two‐dimensional (2-D) synthetic surface data from various geometric elastic models, including a dipping layer, a composite dipping and horizontal layer, and two layers over a half‐space.

Nonlinear pairwise alignment of seismic traces

Geophysics ◽  
2004 ◽  
Vol 69 (6) ◽  
pp. 1552-1559 ◽  
Author(s):  
Christopher L. Liner ◽  
Robert G. Clapp
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Pairwise Alignment ◽  
Optimization Method ◽  
Synthetic Seismograms ◽  
Amino Acid Sequences ◽  
Global Alignment ◽  
Global Optimization Method ◽  
S Wave ◽  
Alignment Problem

Seismic trace alignment is a recurring need in seismic processing and interpretation. For global alignment via static shift, there are robust tools available, including crosscorrelation. However, another kind of alignment problem arises in applications as diverse as associating synthetic seismograms to field data, harmonizing P‐ and S‐wave data, residual NMO, and final multilevel flattening of common image gathers. These cases require combinations of trace compression, extension, and shift—all of which are time variant. The difficulty is to find a mapping between the traces that is in some senseoptimum. This problem is solved here using a modified form of the Needleman‐Wunsch algorithm, a global optimization method originally developed for aligning amino acid sequences in proteins. Applied to seismic traces, this algorithm provides a nonlinear mapping of one seismic trace onto another. The method extends to multitrace alignment since that problem can be broken down into a cascade of pairwise alignments. Seismic implementation of the Needleman‐Wunsch algorithm is a promising new tool for nonlinear alignment and flattening of seismic data.

Source-side spatial wavefield gradients in land seismic exploration

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. P73-P85 ◽  
Author(s):  
Cédéric Van Renterghem ◽  
Cédric Schmelzbach ◽  
David Sollberger ◽  
Mauro Häusler ◽  
Johan Olof Anders Robertsson
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Seismic Exploration ◽  
Gradient Estimates ◽  
Spatial Gradient ◽  
Receiver Side ◽  
S Wave ◽  
Wave Source ◽  
Rotational Components ◽  
Gradient Based

The recording of seismic data using arrays of densely spaced receivers enables the estimation of the spatial gradient components of the wavefield, in addition to the acquisition of conventional translational motion. We have extended the concept of array-based receiver-side gradiometry to the source-side and investigated the potential of combining source- and receiver-side gradient estimates for land seismic exploration. The robustness of array-based gradient source formation is demonstrated with a field data reciprocity experiment. We apply a gradient-based elastic wavefield decomposition technique to small arrays of densely spaced vertically and horizontally oriented force sources and determine with synthetic and field data examples that the processing of data obtained from multicomponent source arrays allows us to simulate a composite source that theoretically only emits S-waves at all emergence angles. A promising application of the gradient-based S-wave source is downhole S-wave imaging. Finally, by combining source- and receiver-side gradient estimates, 49C seismic data can be obtained comprising three translational components, three rotational components, and one divergence component on the source and receiver side. This concept could have a significant potential to enhance the acquisition and processing of data from locally dense arrays in land seismic exploration.

Bayesian facies inversion on a partially dolomitized isolated carbonate platform. A case study from Central Luconia province, Malaysia.

Geophysics ◽  
2020 ◽  
pp. 1-47
Author(s):  
George Ghon ◽  
Dario Grana ◽  
Eugene C. Rankey ◽  
Gregor T. Baechle ◽  
Florian Bleibinhaus ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Carbonate Platform ◽  
Gaussian Mixture ◽  
Model Parameters ◽  
Elastic Model ◽  
Probabilistic Prediction ◽  
S Wave ◽  
Porosity Distribution

We present a case study of geophysical reservoir characterization where we use elastic inversion and probabilistic prediction to predict 9 carbonate lithofacies and the associated porosity distribution. The study focuses on an isolated carbonate platform of middle Miocene age, offshore Sarawak in Malaysia, which has been partly dolomitized — a process that increased porosity and permeability of the prolific gas reservoir. The 9 lithofacies are defined from one reference core and include a range of lithologies and pore types, covering limestone and dolomitized limestone, each with vuggy varieties, as well as sucrosic and crystalline dolomites with intercrystalline porosity, and also argillaceous limestones, and shales. To predict lithofacies and porosity from geophysical data, we adopt a probabilistic algorithm that employs Bayesian theory with an analytical solution for conditional means and covariances of posterior probabilities, assuming a Gaussian mixture model. The inversion is a 2-step process, first solving for elastic model parameters P- and S-wave velocities and density from 2 partial seismic stacks. Subsequently, lithofacies and porosity are predicted from the elastic parameters in the borehole and across a 2-D inline. The final result is a model that consists of the pointwise posterior distributions of facies and porosity at each location where seismic data are available. The facies posterior distribution represents the facies proportions estimated from seismic data, whereas the porosity distribution represents the the probability density function at each location. These distributions provide the most likely model and its associated uncertainty for geological interpretations.

Q-compensated acoustic impedance inversion of attenuated seismic data: Numerical and field-data experiments

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R553-R567 ◽  
Author(s):  
Xintao Chai ◽  
Genyang Tang ◽  
Fangfang Wang ◽  
Hanming Gu ◽  
Xinqiang Wang
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Field Data ◽  
Reservoir Characterization ◽  
Rock Properties ◽  
Inversion Method ◽  
Data Sets ◽  
Anelastic Attenuation ◽  
Text Filtering ◽  
Frequency Components

Acoustic impedance (AI) inversion is of great interest because it extracts information regarding rock properties from seismic data and has successful applications in reservoir characterization. During wave propagation, anelastic attenuation and dispersion always occur because the subsurface is not perfectly elastic, thereby diminishing the seismic resolution. AI inversion based on the convolutional model requires that the input data be free of attenuation effects; otherwise, low-resolution results are inevitable. The intrinsic instability that occurs while compensating for the anelastic effects via inverse [Formula: see text] filtering is notorious. The gain-limit inverse [Formula: see text] filtering method cannot compensate for strongly attenuated high-frequency components. A nonstationary sparse reflectivity inversion (NSRI) method can estimate the reflectivity series from attenuated seismic data without the instability issue. Although AI is obtainable from an inverted reflectivity series through recursion, small inaccuracies in the reflectivity series can result in large perturbations in the AI result because of the cumulative effects. To address these issues, we have developed a [Formula: see text]-compensated AI inversion method that directly retrieves high-resolution AI from attenuated seismic data without prior inverse [Formula: see text] filtering based on the theory of NSRI and AI inversion. This approach circumvents the intrinsic instability of inverse [Formula: see text] filtering by integrating the [Formula: see text] filtering operator into the convolutional model and solving the inverse problem iteratively. This approach also avoids the ill-conditioned nature of the recursion scheme for transforming an inverted reflectivity series to AI. Experiments on a benchmark Marmousi2 model validate the feasibility and capabilities of our method. Applications to two field data sets verify that the inversion results generated by our approach are mostly consistent with the well logs.

scholarly journals High-resolution reservoir characterization using deep learning-aided elastic full-waveform inversion: The North Sea field data example

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA137-WA146
Author(s):  
Zhen-dong Zhang ◽  
Tariq Alkhalifah
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
North Sea ◽  
Seismic Data ◽  
Field Data ◽  
Reservoir Characterization ◽  
Waveform Inversion ◽  
Oil Field ◽  
Full Waveform Inversion ◽  
Full Waveform

Reservoir characterization is an essential component of oil and gas production, as well as exploration. Classic reservoir characterization algorithms, deterministic and stochastic, are typically based on stacked images and rely on simplifications and approximations to the subsurface (e.g., assuming linearized reflection coefficients). Elastic full-waveform inversion (FWI), which aims to match the waveforms of prestack seismic data, potentially provides more accurate high-resolution reservoir characterization from seismic data. However, FWI can easily fail to characterize deep-buried reservoirs due to illumination limitations. We have developed a deep learning-aided elastic FWI strategy using observed seismic data and available well logs in the target area. Five facies are extracted from the well and then connected to the inverted P- and S-wave velocities using trained neural networks, which correspond to the subsurface facies distribution. Such a distribution is further converted to the desired reservoir-related parameters such as velocities and anisotropy parameters using a weighted summation. Finally, we update these estimated parameters by matching the resulting simulated wavefields to the observed seismic data, which corresponds to another round of elastic FWI aided by the a priori knowledge gained from the predictions of machine learning. A North Sea field data example, the Volve Oil Field data set, indicates that the use of facies as prior knowledge helps resolve the deep-buried reservoir target better than the use of only seismic data.

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