A case study of azimuthal AVO analysis with anisotropic spreading correction
Geomechanical properties of tight, low-porosity reservoirs are largely governed by natural fracture networks. Hence, reliable estimation of fracture density and orientation is extremely important for cost-effective hydraulic completion and hydrocarbon production. Direct information about fracturing can be obtained using borehole methods, such as image log analysis, which provide estimates of fracture counts and orientations on various scales. The main shortcoming of borehole measurements is that they are sensitive only to formation properties in the immediate vicinity of the well. In some cases, the spatial distribution of fractures can be inferred from fault maps obtained by coherence analysis of surface seismic data. However, the correlation between areas of high fracture density and fault locations is not always straightforward.
Moveout-based geometrical-spreading correction for converted waves
Geometrical-spreading correction is an important component of amplitudevariation-with-offset (AVO) analysis, which provides high-resolution information for anisotropic parameter estimation and fracture characterization. Here, we extend the algorithm of moveout-based anisotropic spreading correction (MASC) to mode-converted PSV-waves in VTI (transversely isotropic with a vertical symmetry axis) media and symmetry planes of orthorhombic media. While the geometrical-spreading equation in terms of reflection traveltime has the same form for all wave modes in laterally homogeneous media, reflection moveout of PS-waves is more complicated than that of P-waves (e.g., it can become asymmetric in common-midpoint geometry). Still, for models with a horizontal symmetry plane, long-spread reflection traveltimes of PS-waves can be well-approximated by the Tsvankin-Thomsen and Alkhalifah-Tsvankin moveout equations, which are widely used for P-waves. Although the accuracy of the Alkhalifah-Tsvankin equation is somewhat lower, it includes less moveout parameters and helps to maintain the uniformity of the MASC algorithm for Pand PS-waves. The parameters of both moveout equations are obtained by leastsquares traveltime fitting or semblance analysis and are different from those for P-waves. Testing on full-wavefield synthetic data generated by the reflectivity method for layered VTI media confirms that MASC accurately reconstructs the plane-wave conversion coefficient from conventional-spread PS data. Errors in the estimated conversion coefficient, which become noticeable at moderate and large offsets, are mostly caused by offset-dependent transmission loss of PS-waves.
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention
Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.