Geophysical Prospecting最新文献

Fractures represent a critical structural feature in unconventional reservoirs, as they create essential pathways for the migration and accumulation of oil and gas. Therefore, fracture characterization is a fundamental task in the exploration of unconventional hydrocarbon resources. Conventional fracture characterization methods typically do not account for the inherent anisotropy of the formation, which arises from the sedimentary environment and fluid distribution, often leading to inaccurate fracture predictions. To address this challenge, we propose a petrophysical model that incorporates inherent anisotropy, employing rock physics modelling to accurately characterize fracture distribution. Furthermore, to reduce the substantial workload involved in manually calibrating the petrophysical model, we introduce a one-dimensional convolutional neural network combined with an attention mechanism. By leveraging the advanced nonlinear learning capabilities of the convolutional neural network, we aim to fit the petrophysical model and extend its application across all exploration wells and the entire field. The effectiveness and feasibility of the proposed method are demonstrated through experiments using actual borehole data from a fracture-dominated reservoir.

Deep-strata high-pressure reservoirs are a key research area in subsurface resource exploration. The complex mix of in situ pressure, anisotropy and fluid saturation in rocks leads to unclear seismic responses and uncertainties in wave propagation. Using acoustoelasticity theory and assuming weak anisotropy, we derived equations for the elastic parameters of stressed orthotropic media. These equations use anisotropic parameters to describe the unstressed elastic properties of orthotropic media. Then, using the Gassmann equation and low-frequency poro-elasticity, we found elastic parameters for single fluid-saturated orthotropic media. Non-welded interfaces serve as a reasonable approximation for tiny fractures and are ubiquitous in subsurface formations, and the viscous fluid present within these interfaces contributes to the observable attenuation of seismic waves. Using elastic parameters of stressed, fluid-saturated orthotropic media, we formulated reflection and transmission coefficient equations for these interfaces based on linear-slip theory. Using these equations, we analysed how stress, fluid saturation and interface changes affect seismic response and wave propagation. We then analysed how frequency, porosity, viscosity, fracture weakness and other physical properties affect seismic behaviour within and at the medium's interface. By constructing exact equations, we have achieved a more realistic simulation of subsurface seismic response. This enhancement in simulation accuracy facilitates a deeper understanding of the seismic response patterns observed in deep and complex subsurface reservoirs. Furthermore, it provides a solid theoretical foundation for fluid identification and reservoir prediction in actual subsurface reservoir scenarios.

The transformation of elastic impedance (EI) from partial-angle-stacked seismic data is a crucial technique in the domains of reservoir modelling. Conventionally, EI inversion is performed on a per-angle basis, leading to significant discrepancies in EI values across different angles, which may not accurately represent actual conditions. When the signal-to-noise ratio (SNR) of seismic data is low, the inverted EI tends to be unstable, resulting in poor-quality inversion outcomes. This research proposes a novel method that allows for enabling the derivation of EI for various angles simultaneously inverted from multiple partial angle-stack seismic datasets in one process. The aim of simultaneous inversion is to potentially ensure consistent EI results. To obtain this aim, we utilize an advanced regularization method called the Gramian constraint. Consequently, the objective function for the simultaneous inversion of multiple EIs is developed. Results from both synthetic and field data demonstrate improved stability in EI inversion, especially for the case of low SNR.

The transient electromagnetic method (TEM) is a prominent geophysical technique, and the TEM inversion for resistivity models is a crucial aspect of physical exploration. However, TEM inversion faces challenges such as nonlinearity, multiple solutions and ill-conditioning, which can lead to inaccurate results. In response to these challenges, metaheuristic algorithms have been extensively studied for their innovative approaches to solving inverse problems. Despite this, many existing metaheuristic inversion algorithms exhibit limitations, including premature convergence, slow convergence speed and inadequate computational accuracy. To address these issues, an improved bat algorithm (IBA) that incorporates logistic chaotic mapping and a spiral flight strategy (Logistic Chaotic Mapping and Spiral Flight Strategy-Based Bat Algorithm, LSBA) has been proposed. The logistic chaotic mapping strategy is utilized to initialize the population of the bat algorithm to enhance the initial convergence rate. Moreover, the spiral flight strategy facilitates the bats’ escape from local optima, thereby improving the algorithm's local exploration capabilities and solution accuracy. Numerical simulations, synthetic models and field experiments have demonstrated that the LSBA significantly enhances solution precision (the degree of closeness between the algorithm's inverted parameters and the true values), convergence speed and anti-noise performance. The LSBA effectively retrieves the stratigraphic parameters of the true model and accurately represents the geological information of actual mining areas, thereby validating the efficacy and feasibility of the proposed approach in TEM inversion.

Advanced seismicity monitoring is needed for CO₂ sequestration monitoring. Current regulator practices (so-called traffic light systems—TLS) are limited to mitigate public hazards and associated risks caused by induced seismicity. Such seismicity is often associated with slip on larger faults below the reservoir. We propose an advanced seismic monitoring strategy that not only accounts for felt seismicity but also targets seismicity in the seal and reservoir. This novel concept of tiered seismicity criteria for an advanced seismic monitoring strategy is governed by a storage site's specific geological properties (underburden, reservoir and seal). These observed seismicity criteria can be set by the regulator or operator to develop a corresponding and fit for purpose system that further manages induced seismicity to ensure seal integrity and storage longevity.

Efficient noise removal in seismic data is crucial for accurately analysing subsurface structures because noise generated during field acquisition can considerably degrade data quality. Traditional single-domain denoising methods often struggle to preserve weak signals in prestack seismic data, potentially leading to the loss of critical information. To address this issue, we propose a novel dual-domain (DD) denoising approach called non-local means via patch ordering in DD (DD–PONLM). This method leverages the strengths of both time–space and transform domains to minimize the leakage of weak events. By employing non-local self-similarity and iterative processing in the time–space domain and discrete cosine transform domain, the proposed method effectively reduces noise while preserving weak signals. We validate the effectiveness of our method through extensive testing on both asynthetic and a field example. The results are compared with several traditional single-domain methods, demonstrating that DD–PONLM considerably improves the preservation of weak signals and reduces artefacts, such as the Gibbs phenomenon, associated with transform domain processing. This DD strategy not only enhances the signal-to-noise ratio but also preserves structural fidelity, making it a robust solution for seismic data denoising.

The brittleness index is a crucial parameter for evaluating the brittleness of subsurface reservoirs. Accurate brittleness determination optimizes fracture design and guides oil and gas extraction, especially in shale formations. Traditionally, the brittleness index assumes isotropy, which fails to capture the anisotropic nature of shale reservoirs and often leads to prediction errors. To mitigate this challenge, this study introduces a stiffness coefficient matrix specifically designed for anisotropic media and proposes a brittleness index equation tailored for transverse isotropic (VTI) media. Experimental results show that the proposed anisotropic brittleness index provides a more accurate assessment of shale reservoir brittleness than the conventional isotropic brittleness index. Ultimately, the anisotropic brittleness index is applied to field logging data, thereby validating the effectiveness of the method in distinguishing between reservoirs of high and low brittleness.