Fracture detection by Gaussian beam imaging of seismic data and image spectrum analysis

Abstract

A B S T R A C TLocalization of fractured areas is of primary interest in the study of oil and gas geology in carbonate environments. Hydrocarbon reservoirs in these environments are embedded within an impenetrable rock matrix but possess a rich system of various microheterogeneities, i.e., cavities, cracks, and fractures. Cavities accumulate oil, but its flow is governed by a system of fractures. A distinctive feature of wave propagation in such media is the excitation of the scattered/diffracted waves by the microheterogeneities. This scattering could be a reliable attribute for characterization of the fine structure of reservoirs, but it has extremely low energy and any standard data processing renders them practically invisible in comparison with images produced by specular reflections. Therefore, any attempts to use these waves for image congestion of microheterogeneities should first have a preliminary separation of the scattering and specular reflections. In this paper, the approach to performing this separation is based on the asymmetric summation. It is implemented by double focusing of Gaussian beams. To do this, the special weights are computed by propagating Gaussian beams from the target area towards the acquisition system separately for sources and receivers. The different mutual positioning of beams in each pair introduces a variety of selective images that are destined to represent some selected singular primitives of the target objects such as fractures, cavities, and edges. In this way, one can construct various wave images of a target reservoir, particularly in scattered/diffracted waves. Additional removal of remnants of specular reflections is done by means of spectral analysis of the scattered/diffracted waves' images to recognize and cancel extended lineaments. Numerical experiments with Sigsbee 2A synthetic seismic data and some typical structures of the Yurubcheno-Tokhomskoye oil field in East Siberia are presented and discussed.

3D diffraction imaging of fault and fracture zones via image spectral decomposition of partial images

“…This provides us with the workflow that is the 3D generalization of the 2D diffraction imaging method (Protasov et al . ). This method works for rather complex models with steep dip layers etc.…”

“…The entire survey area is more than 1000 km 2 , and for the part of these data, the depth image of 115 km 2 is built via Gaussian beam migration (Protasov et al . , , ). As one can observe on the stacked depth image (Fig.…”

“…The paper presents 3D diffraction imaging workflows for fracture detection in 3D that are the generalization of the 2D diffraction imaging approach (Protasov, Reshetova and Tcheverda ). The workflows contain two main procedures: the pre‐stack asymmetric migration procedure, which is weighted data summation and image spectral decomposition (filtering).…”

Two-Stages Techniwue of to Full Waveform Inversion: From Near Surface to Deep Target Geological Structures

“…При известной макроскоростной составляющей, рефлектор корректно определяется в результате выполнения миграционного преобразования зарегистрированных данных. Более того, существует ряд методов, позволяющих отображать рефлектор в «истинных» амплитудах, когда контрастность границ пропорциональна отражающей способности соответствующих локальных геологических объектов [5,7]. Этот факт позволяет представлять искомую полную скоростную модель в виде суперпозиции:…”

Effect of CTimage scale on macro-scale properties estimation

“…Современные методы обработки сейсмических данных позволяют строить сейсмические изображения среды с чрезвычайно высокой детализацией, вплоть до локализации скоплений субсейсмических объектов, таких как трещины и каверны, путем выделения и об-работки рассеянной компоненты волнового поля [Ли-сица и др., 2013;Protasov et al, 2016]. Наряду с этим существуют и современные методики построения изо-бражений в "истинных" амплитудах, т. e. с сохранени-ем импедансов.…”