Geophysical Prospecting最新文献

The imaging condition is a crucial component of the reverse time migration. In its conventional form, it involves cross-correlating the extrapolated source- and receiver-side wavefields. Effective imaging conditions are usually developed to suppress imaging artefacts (e.g. low-wavenumber artefacts) and enhance the image quality. For acoustic reverse time migration, not only the scalar pressure but also their spatial and/or time derivatives are used in the imaging condition, similar to the gradient terms of adjoint tomography. These operations implicitly introduce additional angle-domain weighting factors to the image results. In this study, based on an analysis of angle-dependent properties of the existing imaging conditions, we propose a new imaging condition tailored for acoustic reverse time migration. It can be implemented efficiently using the variables within the finite-difference solver. Without explicitly measuring wave propagation directions, the proposed imaging condition can naturally suppress the low-wavenumber artefacts while maintaining a relatively wider imaging aperture, thereby corresponding to a broader wavenumber sampling range. Additionally, the evolved imaging conditions for imaging elastic P–P and S–S scattering and reflections are also formulated. In the angle domain, we conduct a comparative analysis between existing imaging conditions and the newly proposed ones. Various numerical examples are provided to demonstrate the advantages of the new imaging conditions. A comprehensive understanding of their angle-domain properties may be further beneficial to constructing reasonable inversion strategies for full waveform inversion.

The construction of an accurate and high-resolution reservoir parameter model is crucial for reservoir characterization. However, due to the band-limited characteristics of seismic data, the inversion results heavily rely on the accuracy of the initial model. Most existing techniques for constructing an initial model interpolate well logging data within the stratigraphic framework, neglecting the effect of the stratigraphic sequence, which compromises the reliability of the initial model. The stratigraphic sequence is essential for dividing stratigraphic evolution stages and defining a geological relationship between reservoirs within the stratigraphic framework. Therefore, an initial model construction method constrained by stratigraphic sequence representation is proposed for pre-stack seismic inversion. The process begins with establishing the stratigraphic framework using horizon and fault data. Subsequently, the collaborative sparse representation algorithm is used to learn a joint dictionary that captures the relationship of structural features between seismic data and stratigraphic sequence from the well logging data. In the process of seismic data representation, the stratigraphic sequence is accurately represented in three-dimensional space by sharing sparse coefficients in the joint dictionary. Finally, the elastic parameter model is constructed by integrating the stratigraphic framework, stratigraphic sequence and well logging data, providing a reliable initial model for pre-stack seismic inversion. The main innovation of the proposed method is the three-dimensional representation of the stratigraphic sequence. A synthetic example demonstrates that the proposed method produces a more accurate initial model than conventional interpolation methods. Additionally, when applied to field data, it yields satisfactory results even without complete S-wave velocity well logging data.

A new method for estimating petrophysical properties from elastic well log or seismic data is evaluated on data from the Carnarvon Basin, Northwest Australia. The study has utilized well and seismic inversion data covering part of the Triassic-aged fluvio-deltaic Mungaroo Formation on the Exmouth Plateau. The method applied is based on several recently published papers to use acoustic impedance, velocity ratio V_p/V_s and estimated constants to calculate clay volume (V_CL), effective porosity and water saturation (S_W). The case study showed exceptional results on well data. A strong match is observed between petrophysically derived V_CL, effective porosity and S_W and the estimates derived from elastic logs. When applied to seismic inversion volumes, pay in the wells is predicted from seismic, and porosity of the sands can be estimated with confidence. Petrophysical properties for nearby direct hydrocarbon indicator–supported prospects could also be evaluated, although an imprint of the direct hydrocarbon indicator was observed on the V_CL prediction, and overall predictions are less than expected based on regional well results. Using these results, a minor modification is proposed to the equations used, and a workflow is derived to enable easy application to other projects. The modified approach was validated on the well data from the original publication. The results also indicate that the approach can be used to help identify erroneous synthetic V_s estimates in limited data settings.

Although the early-Precambrian crystalline basement is now only sporadically exposed in the northern and south-western parts of the Yangtze Block, it is supposed to have a widespread distribution beneath its Neoproterozoic and Phanerozoic covers. Here we present results of regional aeromagnetic data processing in consideration of remanent magnetization to investigate the spatial distribution of the early-Precambrian basement buried deep under the Upper Yangtze Block and surrounding areas. The direct analytic signal amplitude of the aeromagnetic anomalies, which is less affected by the magnetization direction, reveals a broader basement below the Sichuan Basin, extending far north to the Micang Mountain. A comparison between the direct analytic signal amplitude with the reduction to the pole aeromagnetic anomalies indicates that possible remanent magnetization exists beneath the Micang Mountain near the boundary between the Qinling Orogen and Sichuan Basin. The automatic depth from extreme points transform is then performed on the direct analytic signal amplitude to estimate the depth to the early-Precambrian crystalline basement. A synthetic model of a magnetic interface with remanent magnetization and random noise shows that the depth from extreme points method is able to resolve variable basement depths. Application of the depth from extreme points method to the direct analytic signal amplitude of the Upper Yangtze Block presents meaningful results about the early-Precambrian crystalline basement undulations. It is shallow and uplifted beneath the Sichuan Basin, extending north to the Qinling-Dabie Orogen, probably corresponding to the ancient Chuanzhong palaeo-uplift. Although it gradually deepens to the east, the deepest basement is buried under the Jiangnan Orogen, which is likely associated with the collision-induced crustal thickening between the Yangtze and Cathaysia blocks during the assembly of the Columbia supercontinent. Large gas fields around the Sichuan Basin are found at the slopes or depressions between basement uplifts, indicating that the deep marine carbonate rocks in the south and east of Sichuan Basin, particularly those located at the slopes or depressions between ancient basement uplifts, are favourable targets for further petroleum exploration.

We present a numerical method to simulate seismic wave propagation in coupled fluid and porous media. We developed a numerical finite element–based algorithm to approximate solutions to viscoacoustic and Biot's equations, considering the open pore conditions at the interfaces between both media. The algorithm architecture allows to simulate arbitrary distributions of viscoacoustic and poroelastic regions, facilitating the modelling of heterogeneous systems involving complex geometries. The algorithm includes a double parallelization scheme whose efficiency in terms of computing time and memory requirements was tested for different core distributions and mesh sizes. We validate our proposal by performing a comparison between its results and those obtained with a well-known freely available code. We test its capabilities by studying two different scenarios with geophysical interest: a lake with an irregular bottom and a fractured porous medium.

Shear wave velocity is an essential parameter in reservoir characterization and evaluation, fluid identification and prestack inversion. However, conventional data-driven or model-driven shear wave velocity prediction methods exhibit several limitations, such as lack of training data sets, poor model generalization and weak model robustness. In this study, a model- and data-driven approach is presented to facilitate the solution of these problems. We develop a theoretical rock physics model for fractured limestone reservoirs and then use the model to generate synthetic data that incorporates geological and geophysical knowledge. The synthetic data with random noise is utilized as the training data set for the artificial neural network, and a well-trained shear wave velocity prediction model, random noise shear wave velocity prediction neural network, is established by parameter tuning, which fits the synthetic data with noise well. The neural network is applied directly to the real field area. Compared with conventional shear wave prediction methods, such as empirical formulas and the improved Xu–White model, the prediction results show that the random noise shear wave velocity prediction neural network has better prediction performance and generalization. Furthermore, the prediction results demonstrate the efficacy of the proposed approach, and the approach has the potential to perform shear wave velocity prediction in real areas where training data sets are unavailable.

We adopt extreme value theory to estimate the upper limit of the next record-breaking magnitudes of induced seismic events. The methodology is based on order statistics and does not rely on knowledge of the state of the subsurface reservoir or injection strategy. The estimation depends on the history of record-breaking events produced by the anthropogenic activities. We apply the methodology to three different types of industrial operations: natural gas production, saltwater disposal and hydraulic fracturing. We show that the upper limit estimate provides a reliable and realistic upper bound for magnitudes of the record-breaking events in investigated datasets including 15 publicly available datasets. The predicted magnitudes do not overestimate the observed magnitudes by more than 1.0 magnitude unit and underestimation is rare, probably resulting from insufficient sampling of the statistical distribution of the induced seismicity. The richest dataset, sourced from downhole and surface monitoring of the Preston New Road hydraulic fracturing, provides reliable estimates of the magnitudes over three orders of magnitudes with only slight underprediction of the largest observed event. While the detection of weaker events improves the performance of the method, we show that it can be applied even with a few observed record-breaking events to provide reliable estimates of magnitudes. However, care must be taken to ensure that event catalogues are estimated consistently across a range of magnitudes.