Identifying and de-risking near-field opportunities through reliable pre-stack broadband attributes: examples from the Paleocene North Sea (UK–Norway) injectites play

Pernin, Noémie; Feuilleaubois, Laurent; Bird, Tim; Reiser, C.

doi:10.1144/sp494-2019-11

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…\end{equation}$$This convolution model can be used at small angles of less than 25°, but at larger angles the difference from the wave propagation model is considerable (Mallick, 2001). However, there exist several inversion studies where the convolutional model is used for angles up to 30° for large contrast media (Ahmed et al., 2022; Özdemir & Flanagan, 2004; Reseir & Cyrille, 2022). For small contrast media, the validity can be extended to even larger angles.…”

Section: Theorymentioning

confidence: 99%

Facies‐constrained transdimensional amplitude versus angle inversion using machine learning assisted priors

Dhara

Sen

Biswas

2023

Geophysical Prospecting

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We present a methodology for seismic inversion that generates high-resolution models of facies and elastic properties from pre-stack data. Our inversion algorithm uses a transdimensional approach where, in addition to the layer properties, the number of layers is treated as unknown. In other words, the data itself determine the correct model parameterization, that is, the number of layers. The reversible jump Markov Chain Monte Carlo method is an effective tool to solve such transdimensional problems as it generates models of reservoir properties along with uncertainty estimates. However, current implementations of the reversible jump Markov Chain Monte Carlo algorithms do not account for the non-Gaussian and multimodal nature of model parameters. The target elastic reservoir properties generally have multimodal and non-parametric distribution at each location of the model. The number of modes is equal to the number of facies.Taking these factors into account, we extend the reversible jump Markov Chain Monte Carlo algorithm to simultaneously invert for discrete facies and continuous elastic reservoir properties. The proposed extension to the algorithm iteratively samples the facies, by moving from one mode to another, and elastic properties by sampling within the same mode. The integration of facies classification within the inversion reduces nonuniqueness, improves convergence speed and produces geologically consistent results.The workflow uses machine learning to generate probabilistic priors for the model parameters. We validate our approach by applying it to a synthetic dataset generated from a well log with two facies and then to a complex synthetic two-dimensional model involving three facies having overlapping elastic property distribution. Finally, we apply our algorithm to a field dataset acquired over an unconventional reservoir. Our algorithm demonstrates the usefulness of incorporating facies information in seismic inversion and also the feasibility of inverting for facies from seismic data.

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Section: Theorymentioning

confidence: 99%

Facies‐constrained transdimensional amplitude versus angle inversion using machine learning assisted priors

Dhara

Sen

Biswas

2023

Geophysical Prospecting

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Introduction to deepwater sedimentary systems

Rotzien

Woodruff

Adams³

et al. 2022

Deepwater Sedimentary Systems

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Recognition and characterization of small-scale sand injectites in seismic data: implications for reservoir development

Bradaric

Andersen

Lecomte

et al. 2021

JGS

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Small-scale (< 20 m), non-resolvable sand injectites can constitute a large part of the net-to-gross volume and affect fluid flow in the reservoir. However, they may also cause challenges for well placement and reservoir development because they are too small to be reliably constrained by reflection seismic data. It is therefore important to better understand how small-scale injectites influence seismic images and may be recognized and characterized above reservoirs. The Grane Field (North Sea) hosts numerous small-scale sand injectites above the main reservoir unit, causing challenges for well placement, volume estimates and seismic interpretation. Here, we investigate how such small-scale sand injectites influence seismic images and may be characterized by (1) using well-, 3D seismic- and outcrop data to investigate geometries of small-scale sand injectites (0-15 m) and creating conceptual models of injectite geometries, (2) performing seismic convolution modelling to investigate how these would be imaged in seismic data, and (3) compare these synthetic seismic images to actual 3D seismic from the well-investigate Grane Field.Our results show that despite injectites being below seismic resolution, small-scale sand injectites can be detected in seismic data. They are more likely to be detected with high thickness (> 5 m), steep dip (> 30°), densely spaced sand injectites, and homogeneous background stratigraphy. Furthermore, as fraction of sand injectites increases the top reservoir amplitude will decrease. Moreover, comparison of the synthetic seismic images with real seismic data from the Grane Field indicates that the low-amplitude anomalies and irregularities observed above the reservoir may be a result of the overlying sand injectites. Additionally, the comparison strongly suggests that the Grane Field hosts sand injectites that are thicker and located further away from the top reservoir than what is indicated by well observations. These results may be used to improve well planning and develop reservoirs with overlying sand injectites.Supplementary material: A PDF file containing all the seismic modelling results allowing the reader to flip back and forth between the different models is available at https://www.doi.org/10.6084/m9.figshare.14333102 . Well logs from well 25/11-18 T2 are available at https://factpages.npd.no/pbl/wellbore_documents/2358_25_1_18_COMPLETION_REPORT_AND_LOG.pdf

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Identifying and de-risking near-field opportunities through reliable pre-stack broadband attributes: examples from the Paleocene North Sea (UK–Norway) injectites play

Cited by 4 publications

References 29 publications

Facies‐constrained transdimensional amplitude versus angle inversion using machine learning assisted priors

Facies‐constrained transdimensional amplitude versus angle inversion using machine learning assisted priors

Introduction to deepwater sedimentary systems

Recognition and characterization of small-scale sand injectites in seismic data: implications for reservoir development

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