An Effective Thin Reservoir Prediction Method Which Combines Spectral Inversion and Wide-Band Ricker Wavelet Filtering

Abstract

In recent years, as the top and base of thin reservoir cannot be mapped distinctly, tasks of inferring thin reservoir thickness have become a puzzle for exploration and development geophysicists. Therefore, how to identify the main seismic responses of thin reservoirs effectively has become a hot topic for oil exploration and development. Spectral inversion is exactly a fast-developing seismic inversion method aiming to solve the issue mentioned above. On the basis of spectral decomposition, this method computes reflection coefficients through inversion method under the constraint of objective function in frequency domain. The final result is a wide-band sparse-reflectivity cube. After filtering processing on the reflectivity cube, we can study the thin reservoir by imaging the data with a -900 phase rotation. Generally speaking, band-pass filter operator or Ricker wavelet is usually applied for filtering the reflectivity cube data. However, studies show that a type of wide-band Ricker wavelet composed of Ricker wavelets with different frequency parameters can better approximates the impulse function. This wavelet is characterized by narrow main-lobe, small side-lobe and simple waveform, and has preferable fidelity as well as signal-to-noise ratio (Yu, 1996). Based on the previous studies, an effective thin reservoir prediction method which combines spectral inversion and wide-band Ricker wavelet filtering has been proposed. This method has three features: (1) In order to avoid extracting wavelets within the whole field area, we pay attention mainly on the potential reservoirs which usually locate in the high parts of the structure trap. The typical wells which hold representative feature of interesting structures will be taken for time-varying wavelets extraction. (2) Signal-to-noise ratio is used in the process of wavelet extraction, making the determination of the wavelength much easier. (3) Before the structural constrained filtering, detailed comparative analysis of the spectrum characteristics of the original seismic data, the reflectivity cube data and the wide-band Ricker wavelet has been done. Then, we take LD-A structure within Bohai Bay Basin as an example to show the implement of our method. Several sets of thin sand layers which are hardly to recognize originally have been finally identified. That is to say the method can provide with credible data for well deployment in the following exploration and development.

AAPG Datapages/Search and Discovery Article #90291 ©2017 AAPG Annual Convention and Exhibition, Houston, Texas, April 2-5, 2017