Editorial: Advances and new methods in reservoirs quantitative characterization using seismic data

Editorial on the Research Topic
Editorial: Advances and new methods in reservoirs quantitative characterization using seismic data

Seismic-driven reservoir characterization has always been a practical craft: we work with band-limited measurements, imperfect illumination, and sparse calibration, yet we are expected to produce models that can guide real decisions. In recent years that craft has become more demanding. Targets are thinner and more heterogeneous; unconventional plays require simultaneous attention to fluids and rock mechanics; fractured reservoirs challenge our ability to quantify directionality; and development timelines leave less room for “interpretation by iteration.” At the same time, the technical toolbox has expanded quickly—sparse reconstruction and compressed sensing for resolution, Bayesian and stochastic methods for uncertainty, physics-based nonlinear inversion when approximations break down, and deep learning for pattern extraction across multi-attribute volumes. The meaningful question, however, is not whether methods are new, but whether they make results more usable: clearer assumptions, stronger links to geology and wells, and more honest handling of uncertainty.

This Research Topic, Advances and New Methods in Reservoirs Quantitative Characterization Using Seismic Data, collects sixteen papers that reflect how the community is tackling those demands. The contributions do not point to a single “best” approach; instead, they illustrate that progress often comes from strengthening several links in the chain—geometry, signal description, inversion strategy, and validation—rather than optimizing one module in isolation.

For decades, linearized AVO relationships have enabled efficient workflows, but their limitations are well known—especially under strong contrasts, wide angles, and complex lithologies. Two papers in this collection choose to return to the exact Zoeppritz equations. A nonlinear inversion method for Young’s modulus and shear modulus based on the exact Zoeppritz equations proposes nonlinear inversion for mechanical properties using the exact formulation, and Nonlinear amplitude versus angle inversion using hybrid quantum ant colony optimization and the exact Zoeppritz equation pairs the exact equation with a hybrid optimization strategy. These works share a straightforward message: if the underlying relationship is nonlinear, then either the inversion must embrace that nonlinearity, or the simplification must be justified carefully. The computational and stability challenges are real, which is why choices of parameterization, regularization, and optimization strategy are not secondary details—they are part of the method. A recurring lesson in practice is that many “inversion problems” are actually “data description problems.” If bandwidth is insufficient, wavelets vary with depth, or noise is frequency dependent, inversion will faithfully fit the wrong picture. Several papers in this Research Topic address these foundations head-on.

The paper Fault surface construction method based on point cloud surface reconstruction treats fault surfaces as objects that can be reconstructed from point-cloud constraints. That framing matters. Faults are frequently drawn as crisp boundaries, but their geometry is scale dependent and sensitive to interpretation density. Turning fault surface building into an explicit reconstruction problem is a step toward reproducible structural inputs, which then supports more defensible attribute extraction and inversion constraints. The paper Fourier coefficients-based stepwise Bayesian inversion for elastic and fracture parameters using azimuthal seismic data combines Fourier-based parameterization with a stepwise Bayesian updating strategy to estimate elastic and fracture-related parameters from azimuthal data. Compact parameterization and staged updating are both pragmatic choices for underdetermined problems where data quality, acquisition geometry, and processing artifacts can easily overwhelm the signal. Deep-learning-based natural fracture identification method through seismic multi-attribute data: a case study from the Bozi-Dabei area of the Kuqa Basin, China demonstrates fracture identification using multiple seismic attributes in a real case setting, while Post-stack multi-scale fracture prediction and characterization methods for granite buried hill reservoirs: a case study in the Pearl River Mouth Basin, South China Sea focuses on multi-scale fracture prediction and characterization in a granite buried-hill reservoir using post-stack methods. These papers are useful “reality checks” because they sit where fracture work often struggles: aligning scale, attribute choice, and interpretation objectives in a way that remains interpretable to reservoir teams.

In shale gas settings, for example, fluid presence and brittleness are both central. Simultaneous seismic inversion for fluid indicator and brittleness index in the gas-bearing shale reservoir addresses that need directly by targeting a fluid indicator and brittleness index in a unified inversion framework. Whether joint inversion is superior depends on the data and assumptions, but the intent is clear: reduce inconsistencies that can accumulate when properties are estimated in loosely connected steps. Porosity remains one of the most requested reservoir properties, and also one of the most non-unique from seismic alone. Porosity identification using residual PPTransformer network applies a deep learning architecture to porosity identification, emphasizing feature extraction and representation. In a similar vein of “data-driven, but task-specific,” the pore pressure problem appears as Spatial distribution prediction of pore pressure based on Mamba model, where the target is explicitly the spatial distribution—an important distinction, because pore pressure is rarely used as isolated points; it is used as a coherent field that must align with structure and stratigraphy.

Frequency-broadening method of seismic data based on sparse reconstruction inversion strategy develops a frequency-broadening approach leveraging sparse reconstruction ideas, aiming to enhance effective resolution. Stochastic inversion method based on compressed sensing frequency division waveform indication prior blends stochastic inversion with compressed-sensing-inspired priors in a frequency-division framework, making explicit use of frequency-domain constraints. In parallel, Estimation of the depth-variant seismic wavelet based on the modified unscaled S-transform targets depth-variant wavelet estimation using a modified unscaled S-transform. Depth-variant wavelets are a common reality in field data; treating wavelet behavior as something to estimate rather than assume is often the difference between a clean-looking inversion and a trustworthy interpretation.

Some of the most consequential uncertainties occur at scales smaller than seismic resolution—yet they dominate engineering outcomes. Reducing the uncertainty in the distribution of cm-scale rock properties in the near well-bore region addresses this directly by focusing on centimeter-scale property distribution near the wellbore. The point is not that seismic “resolves centimeters,” but that careful integration and modeling can reduce uncertainty where completion and near-wellbore decisions are sensitive.

At the reservoir scale, uncertainty-aware frameworks appear as well. SmoGSI: smoothed multiscale iterative geostatistical seismic inversion presents a smoothed multiscale iterative geostatistical inversion approach, where the emphasis on multiscale iteration suggests a workflow designed for stability and scale consistency. Geological constraints also feature explicitly in Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method, where facies information is used to constrain reflectivity inversion and reduce non-uniqueness. These contributions reinforce an important practical shift: uncertainty is increasingly treated as an output to manage, rather than a nuisance to hide. Thin reservoirs, in particular, are unforgiving: tuning, interference, and limited resolution can erase the very signal we seek. Seismic prediction technology for thin reservoirs of tight gas in coal measure strata: a case study of Block L in the eastern margin of the Ordos Basin is valuable precisely because it is grounded in such a setting. Thin-bed characterization is rarely about a single “magic attribute”; it is about assembling a workflow that respects the limits of the data while extracting what is still recoverable.

The collection also highlights specialized inversion and modeling directions that broaden the methodological landscape. Stochastic inversion method based on compressed sensing frequency division waveform indication prior and SmoGSI represent different ways of incorporating priors and uncertainty into inversion. Fourier coefficients-based stepwise Bayesian inversion … brings Bayesian updating into azimuthal inversion. Together with the nonlinear Zoeppritz-based works, these papers show a trend toward making assumptions explicit—about priors, about physics, about what the data can and cannot support.

Editing this Research Topic reinforced a view I have held for some time: the most durable advances in quantitative characterization are rarely the ones with the most dramatic claims. They are the ones that clarify what is being assumed, show how the method behaves under realistic conditions, and make it easier for others to reproduce or adapt the workflow. That is why I appreciate the range in this collection—from surface reconstruction for faults, to wavelet estimation, to frequency-domain priors and bandwidth extension, to nonlinear inversion based on exact physics, to learning-based prediction for porosity, fractures, and pore pressure. Each speaks to a different bottleneck that practitioners repeatedly encounter.

We would like to thank all authors for contributing their work and for engaging constructively through review and revision. We are also grateful to the reviewers whose detailed comments strengthened not only the technical presentation, but also the practical framing—often the difference between a method that reads well and one that can be applied. Finally, We thank the Frontiers in Earth Science editorial team for their support throughout the process.

We hope readers will use this collection as a working reference: not a catalogue of fashionable terms, but a set of concrete methods, inversion strategies, signal-description tools, and field-based demonstrations that can be tested, combined, and refined for their own reservoirs and operational constraints.

Author contributions

XL: Writing – original draft. QL: Writing – review and editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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