AVO QC During Processing

Coléou, T.; Coulon, Jean-Philippe; Carotti, D.; Dépré, P.; Robinson, Gary; Hudgens, E.

doi:10.3997/2214-4609.20130875

Cited by 9 publications

(6 citation statements)

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“…• Global QC via statistical parameters, calculated at the survey scale, to capture the global behavior of the signal: signal-to-noise ratio (S/N), quality and anomaly attributes for AVO QC (Coleou et al, 2013), and enhanced bandwidth at every preconditioning step. These QCs can be viewed globally or on selected horizons.…”

Section: Seismic Preconditioning and Inversion Workflow Seismic Data mentioning

confidence: 99%

“…Figure 9 shows how the AVO response at the well A location was improved at each step in the preconditioning sequence (including spectral shaping) as shown by the decreasing dispersion of the points around the regression (which is preserved). Coleou et al (2013) introduce an efficient statistical tool that helps to quantify and monitor the AVO compliance of large amounts of prestack data, as well as identifying areas where further improvement in the data quality are required. Repeatability attributes called anomaly and quality, an orthogonalization of crosscorrelation and normalized rms amplitude (Nrms), are calculated between real traces and AVO model traces.…”

Section: Seismic Preconditioning and Inversion Workflow Seismic Data mentioning

confidence: 99%

“…Properly estimated amplitude and phase spectra of the operators that match seismic and log data Figure 10. Quality attribute (Coleou et al, 2013) maps calculated in a 400 ms window at the reservoir level for each angle stack. (a) Raw angle stacks.…”

Section: Wavelet Estimationmentioning

confidence: 99%

“…As for any type of seismic data, a fundamental principle remains true for broadband data: Reservoir characterization will only fully benefit from the seismic data if it is properly processed and imaged. This is achieved through new methods and algorithms that are better suited to the wider bandwidth, and, in particular, the low-frequency content of the data (Mesdag and Schakel, 2015;Soubaras, 2016) as well as the judicious choice of processing parameters coupled with quality control (QC) that makes it possible to assess signal preservation versus noise attenuation (Coleou et al, 2013). In this work, we propose to revisit some of the main steps in the seismic signal treatment and seismic inversion of broadband seismic data, using the Campos Basin variable-depth streamer seismic data set.…”

Section: Introductionmentioning

confidence: 99%

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Integration of broadband seismic data into reservoir characterization workflows: A case study from the Campos Basin, Brazil

Kneller¹,

Peiro²

2018

Interpretation

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Towed-streamer marine broadband data have been key contributors to recent petroleum exploration history, in new frontiers and in mature basins around the world. They have improved the characterization of reservoirs by reducing the uncertainty in structural and stratigraphic interpretation and by providing more quantitative estimates of reservoir properties. Dedicated acquisition, processing, and quality control (QC) methods have been developed to capitalize on the broad bandwidth of the data and allow their rapid integration into reservoir models. Using a variable-depth steamer data set acquired in the Campos Basin, Brazil, we determine that particular care that should be taken when processing and inverting broadband data to realize their full potential for reservoir interpretation and uncertainty management in the reservoir model. In particular, we determine the QC implemented and interpretative processing approach used to monitor data improvements during processing and preconditioning for elastic inversion. In addition, we evaluate the importance of properly modeling the low frequencies during wavelet estimation. We find the benefits of carefully processed broadband data for structural interpretation and describe the application of acoustic and elastic inversions cascaded with Bayesian lithofacies classification, to provide clear interpretative products with which we were able to demonstrate a reduction in the uncertainty of the prediction and characterization of Santonian oil sandstones in the Campos Basin.

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Section: Seismic Preconditioning and Inversion Workflow Seismic Data mentioning

confidence: 99%

Section: Seismic Preconditioning and Inversion Workflow Seismic Data mentioning

confidence: 99%

Section: Wavelet Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integration of broadband seismic data into reservoir characterization workflows: A case study from the Campos Basin, Brazil

Kneller¹,

Peiro²

2018

Interpretation

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“…In Figure 2, the same velocity sections after constrained seismic inversion are shown. The Quality Indicator (QI) (Coléou al., 2013) is a repeatability attribute derived from Correlation and NRMS. It is used to quantify the match between seismic and synthetic traces after seismic inversion.…”

Section: Figure 1: Velocity Models With Sharp Contrasts Obtained By Mmentioning

confidence: 99%

Tomographic Velocities - Challenges and Applications

Vigée¹,

Bader²,

Bénédini³

et al. 2014

Proceedings

Self Cite

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SUMMARYThe resolution of seismic imaging has long been characterized by a mid-frequency gap between the long vertical wavelength components that can be inferred from travel-time tomography and the short vertical wavelength components that can be inferred from seismic migration. The progress in tomography and in broadband acquisition now allows this mid-wavelength gap to be filled and even results in the overlapping between the resolution obtained from velocity model building and from seismic migration. Recent progress in tomographic approaches now provides vertical resolution up to 6 Hz, with in addition, a precise localization of the velocity contrasts. With a case study we discuss their benefits for reservoir characterization, where the missing frequencies are traditionally coming from a model interpolated/ extrapolated from well log information. If the seismic inversion for reservoir characterization is not sensitive to the low-to-mid frequencies, this low-to-mid frequency modelling is critical for the estimation of absolute rock properties, and replacing a model extrapolated from sparse well information by actual measurements is of paramount importance.

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Characterization and probabilistic estimation of tight carbonate reservoir properties using quantitative geophysical approach: a case study from a mature gas field in the Middle Indus Basin of Pakistan

Durrani

Talib

Ali

et al. 2020

J Petrol Explor Prod Technol

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In this study a tight carbonate gas reservoir of early Eocene (S1 formation) is studied for litho-facies estimation and probabilistic estimation of reservoir properties prediction using quantitative geophysical approach from a mature gas field in the Middle Indus Basin, onshore Pakistan. Quantitative seismic reservoir characterization approach relied on well based litho-facies re-classification, Amplitude Variation with Offset (AVO) attributes analysis and Pre-Stack simultaneous inversion attributes constrained with customized well-log and seismic data (gathers) conditioning. Three main litho-facies (hydrocarbon bearing limestone, tight limestone and shale) are classified estimated based on the precise analysis of well data using petrophysical properties. AVO attributes (intercept and gradient) conveniently inspection for amplitude behavior (reflection coefficients) of the possible AVO (class I), fluids and lithology characteristics. Probable litho-facies (tight limestone and shale) are estimated using well based litho-facies classification and inverted seismic attributes (p-impedance and density) from pre-stack simultaneous inversion in a Bayesian framework. Additionally, petrophysical properties (clay volume and porosity) are derived from probabilistic neural network approach using well logs and pre-stack inverted attributes (pimpedance and density) constrained with sample-based seismic attributes (instantaneous, windowed frequency, filters, derivatives, integrated and time).

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AVO QC During Processing

Cited by 9 publications

References 0 publications

Integration of broadband seismic data into reservoir characterization workflows: A case study from the Campos Basin, Brazil

Integration of broadband seismic data into reservoir characterization workflows: A case study from the Campos Basin, Brazil

Tomographic Velocities - Challenges and Applications

Characterization and probabilistic estimation of tight carbonate reservoir properties using quantitative geophysical approach: a case study from a mature gas field in the Middle Indus Basin of Pakistan

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