Geophysical Prospecting最新文献

Missing seismic traces from data acquisition limits often significantly degrade data quality. This study presents an unsupervised method using implicit neural representation (INR), specifically sinusoidal representation network (SIREN), to enhance seismic data quality from a single shot gather. Notably, the unsupervised framework trains the SIREN by optimizing it on the observed traces in the single-gather data. The network learns a continuous function, enabling the reconstruction of missing data at any spatio-temporal coordinate. This algorithm directly addresses both missing trace interpolation and the enhancement of sparsely sampled data resolution. Key network design choices, such as exponential frequency scaling and dense skip connections, are shown to enhance reconstruction accuracy by mitigating spectral bias and incorporating multi-scale features. Furthermore, our analysis of different coordinate handling strategies identifies a key trade-off on the geometry setting. Reframing interpolation as a super-resolution task enables the successful reconstruction of up to 75% regularly missing traces and can maintain continuity across large gaps of up to 10 traces. However, this method proves geometrically inaccurate for irregular missing data, as it discards true physical coordinates, leading to incorrect solutions. In contrast, strategies that maintain physical coordinates show significantly degraded performance when faced with such large-scale data gap. The proposed framework successfully interpolated multichannel seismic data and enhanced sparse ocean bottom cable (OBC) data resolution. Although challenges remain for large irregular gaps and computational efficiency, this work establishes SIREN as a promising unsupervised tool for single-gather seismic interpolation and sparse data resolution enhancement without requiring external training data.

Stochastic reservoir characterisation relies on the careful integration of geological modelling and geophysical data, enabling prediction and uncertainty quantification for reservoir decision-making. In this paper, we address some of the computational challenges associated with Bayesian reservoir characterisation, focusing on key obstacles: demanding geophysical forward modelling, high dimensionality of spatial variables and effective posterior sampling of reservoir variables given geophysical data and well information. Leveraging a pseudo-Bayesian approach, we replace the intricate forward model for seismic amplitude-versus-offset data with a computationally efficient multivariate adaptive regression splines method, resulting in a 34-times acceleration in computations. For handling high-dimensional variables modelled by Gaussian random fields, we employ a fast Fourier transform technique. We use a preconditioned Crank–Nicolson method for efficient Markov chain Monte Carlo sampling from the posterior of the reservoir variables. The approach is motivated by challenging reservoir conditions at the Alvheim field in the North Sea, where we demonstrate our approach for Bayesian posterior sampling of oil and gas saturation and clay content conditional on seismic amplitude data and well information. We compare our results with those obtained from an approximate ensemble-based Kalman method for posterior sampling.

Methane is a primary component of natural gas that is the cleanest fossil fuel to support the sustainable development of our society. Electrical survey methods are frequently employed for the exploration of methane that is majorly existing in hydrocarbon systems in the subsurface earth. However, although the quantitative interpretation of electrical survey data relies on the knowledge about the electrical properties of methane bearing rocks, it remains poorly understood about how dissolved methane affects the electrical resistivity of brine and brine-saturated sandstones. We bridge this knowledge gap by measuring the electrical resistivity of brine and brine-saturated artificial sandstone samples with varying brine salinity, pore pressure and temperature, as a function of dissolved methane. We find that the dissolution of methane improves the electrical resistivity of brine, and the improvement can be best fitted by a regression equation comprising both a constant and an exponential part. We also find that the improvement in the brine resistivity increases with reducing brine salinity, pore pressure and temperature, where reducing brine salinity, pore pressure and temperature also improve the brine resistivity with no dissolve methane. Experiment on the rock samples shows that the resistivity of the artificial sandstones with dissolved methane behaves in a similar way to the brine resistivity, in terms of its dependence on the brine salinity, pore pressure and temperature. Further analyses demonstrate that the dissolution of methane in brine does not affect the cementation exponent of the rocks, and therefore the rock resistivity with dissolved methane can be predicted on basis of its constant cementation exponent and the brine resistivity with dissolved methane. The results not only reveal the effects of dissolved methane on the electrical resistivity of brine and sandstones at varying conditions but also pave the way to the interpretation of electrical survey data for the better quantification of dissolved methane in the hydrocarbon systems.

Pre- and post-stack seismic inversion is the primary approach for converting collected seismic data into geophysical property models particularly velocity for subsurface interpretation and reservoir characterization. The traditional workflows often start from an initial property model and iteratively revise it by minimizing the misfit between the acquired real seismic dataset and the synthetic one derived from the updated property models, here denoted as the soft constraint in seismic space. However, it heavily relies on human supervision in building a good initial model and monitoring the misfit optimization process. On the contrary, most of the recent deep learning-based workflows target at non-linearly mapping seismic patterns to properties measured at available well only, here denoted as the hard constraint in property space. Correspondingly, its accuracy greatly depends on the availability of sufficient training wells; otherwise, overfitting occurs causing the machine prediction not meeting the soft constraint throughout the target seismic survey. To resolve these limitations, this study presents a practical workflow that enables rock property inversion from partial-stack seismic via two physics-guided convolutional neural networks (CNNs), with the first one embedding the approximated AVO gradient to build initial property models that satisfy the hard constraint and the second one embedding the reflection coefficients to refine the models by enforcing the soft constraint. Between both CNNs is the use of well-established relevant physics to generate pseudo property–reflectivity–seismic pairs for training the second CNN. Its added values are validated through applications to the Volve survey in North Sea and the Exmouth survey in Western Australia. The produced property volumes not only are observed of high lateral consistency and vertical resolution but also derive synthetic seismic data that are closely correlated with actual seismic data.

Accurately and effectively handling undulating interfaces, including both free surfaces and internal interfaces, remains a key challenge in seismic exploration. Traditional scalar wave equations typically neglect the influence of internal interfaces on wave propagation. To address this, the wave equation in layered media (WEILM) is established by introducing the Dirac delta function. For undulating interfaces, existing methods are mostly grid-based, and often require additional grid processing to achieve accurate description, which increases computational cost. Therefore, by introducing the meshless method combined with free surfaces boundary conditions, this article proposes the method for dealing with undulating surfaces on the basis of the radial-basis-function-generated finite difference (RBF-FD) method. Theoretical analysis indicates that the stability of the proposed method is affected by the sum of the stencil node weights. Numerical experiments show that, compared with the scalar wave equation, the interfaces term introduced in WEILM effectively adjusts waveform amplitudes. Moreover, relative to the classical Lax–Wendroff correction (LWC) method, our approach can avoid spurious diffraction waves caused by grid discretization when handling undulating surfaces. By applying RBF-FD to solve WEILM in conjunction with the method for dealing with undulating surfaces, complex seismic wavefield, including undulating surfaces, can be simulated with higher accuracy.