Search

A gas field, China, the target reservoirs are mainly delta front depositions, which are tight, thin, and below the tuning thickness. In order to charaterize the thin effective reservoir and porosity distribution in this field, a geostatistical inversion and co-simulation were performed. Using geostatistical inversion technology we can integrate the information of well logs, geological constraints, geostatistical parameters and seismic data to create multiple high resolution realizations, resulting in highly detailed elastic volumes of P-Impedence, Vp/Vs, density, as well as lithofacies distribution and the co-simulated prosity. The combination analysis of the results can be used to propose new drilling locations. New well data shows the validity of this technology, maximum solve the problem of the low porosity, low permeability and thin reservoir.

In this paper, we described some of the key challenges faced and their solutions during the reservoir characterization study. Shear log prediction can be done quickly by methods, such as multi linear regression and also by building a robust petro-elastic model, if pore geometry and rock moduli are known. Both methods provide comparitive results and helps to rectify inaccurate data or fill missing gap. Rock physics modeling also helps to gain confidence on the quality of petrophysical curve by comparing the difference between original (measured) vs. predicted (modelled) curves. Reservoir and non reservoir rocks can be distinguished on elastic properties cross plots. Some thoughts are shared on the methods to calculate petrophysical input, such as shale volume, porosity, water saturation with field wide approach. Once consistent set of logs are available, further analyses, such as well to seismic tie, AVO and time-lapse study can be accomplished.

Seismic Reservoir Characterization is a well-known technique to obtain a better understanding of hydrocarbon-bearing reservoirs by careful analysis and integration of petrophysical and seismic data. The petrophysical properties are normally obtained through the evaluation of well logs and core data. A rock physics model provides the link between these reservoir properties and the surface seismic data. Well log measurements are often subjected to various sources of errors, like borehole rugosity due to washouts and mud filtrate invasion. The invasion effect can be significant for permeable rocks and is the focus of this study.

The economics of unconventional plays can be improved if horizontal wellbores target facies with favorable reservoir and geomechanical properties. An integrated, multi-disciplinary approach has been developed in order to reduce economic risk, facilitate improved and faster decision making and enable more efficient and effective well placement.

Gaussian Beam depth migration (GBM) overcomes the single-wavefront limitation of the most implementations of Kirchhoff migration, and constitutes a cost-effective alternative to full-wavefield imaging methods such as reverse time migration. Common-offset beam migration was originally derived to exploit symmetries available in marine towed-streamer acquisition. However, sparse acquisition geometries, such as cross-spread and ocean bottom, do not easily accommodate the requirements for common-offset, common-azimuth (or common offset-vector, COV) migration. Seismic data interpolation or regularization can be used to mitigate this problem by forming well-populated COV volumes. This procedure is computationally intensive and can, in the case of converted-wave imaging with sparse receivers, compromise the final image resolution. As an alternative, we introduce a common-shot (or common-receiver) beam migration implementation which allows the migration of datasets rich in azimuth, without any regularization pre-processing required. Using analytic, synthetic, and real data examples, we demonstrate that converted-wave-imaging of ocean-bottom-node (OBN) data benefits from this formulation, particularly in the shallow subsurface where regularization is both most necessary and most challenging.

Amplitude versus Offset and Azimuth (AVOAz) Analysis can be separated into two separate parts; Amplitude versus Offset (AVO) analysis and Amplitude versus Azimuth (AVAz) analysis. Useful information about fractures and the anisotropy is obtained by just examining the AVAz. The AVAz can be described as a sum of sinusoids of different periodicities, each characterized by its magnitude and phase. This sum is mathematically equivalent to a Fourier series and hence the coefficients describing the AVAz response are azimuthal Fourier coefficients (azimuthal FCs). This FC parameterization is purely descriptive. The aim of this paper is to help the interpreter understand what these coefficients mean in terms of anisotropic and fracture parameters for the case of P-wave reflectivity using a linearized approximation.

If the migration frequency is high (e.g., 50 Hz), reverse time migration (RTM) can be computationally very expensive and hardware demanding for large 3D data sets with large apertures. For this reason, while the vertical wave-propagation grid is chosen to be dense enough to hold the highest frequency content, a sparser than needed horizontal wave-propagation grid is often used to make high-frequency RTM affordable. As a result, the theoretically alias-free RTM operator suffers from aliasing issues when applied to high-frequency data with steep surface angles. To solve this aliasing issue, we propose first decomposing the input shot gathers of the common-shot RTM into the plane-wave domain using sparse inversion and then applying surface-angle-dependent anti-aliasing filters to individual plane-wave coefficients before transforming them back to the spatial domain. Using 2D synthetic and 3D field data examples, we demonstrate that our method allows RTM to migrate data with a frequency higher than the Nyquist frequency imposed by the horizontal wave-propagation grid without much suffering from aliasing problems.

Variable-depth streamer acquisition has become a popular solution for marine seismic acquisition to obtain data with both low and high frequencies: the curved cable profile produces notch diversity that minimizes the residual ghosts in stacking and the deep towing nature provides high S/N at low frequencies. However, there have been discussions in the industry as to the fidelity of AVO response from the variable-depth streamer data due to the obvious frequency and wavelet changes with offsets. To answer the question as to the AVO fidelity, we need to focus on the following key questions: can we remove the receiver ghost successfully? Can we compensate the earth absorption effect thus balance the spectrum from near to far offset? As a standard practice for deghosting or broadband processing, we are using a suite of deghosting algorithms termed Ghost Wavefield Elimination (GWE) based on inversion in the tau-p domain (Wang et al. 2013, Poole 2013). To analysing the influence of GWE on the AVO response, we start with synthetic datasets which were modelled by visco-elastic wave equation (Kjstansson, 1979), with streamer setting at both conventional flat tow (8m) and depth-variable way (7~50m). Both datasets went through the deghosting processing and quantitatively analysis including XF plot, wavelet check, auto-correlation and AVO response were conducted. These QC demonstrate the GWE deghosting method is very effective in terms of ghost energy attenuation and the AVO responses between conventional flat tow and depth-variable streamer data become almost identical after GWE. However, both AVO curves still decay strongly at longer offsets until we incorporate the Q model from the modelling in the QPSDM migration to reach a match with the Aki Richards synthetic curve. We then check the AVO inversion result with the well data from one for the recent BroadSeis surveys offshore Vietnam. This Broadband dataset was processing with full source/receiver deghosting, complete shallow water de-multiple flow, high end depth imaging and proper Q compensation. The high resolution CIG gathers was then compared with the well synthetic gathers: AVO trends were picked along the top of sand layers and a good match was found when we used the frequency band of 5~60 Hz. The near offsets contains more high frequency signals however the key in the AVO inversion for Broadband data is to use the common frequency band which has best S/N ratio from near to far offsets. We have demonstrated that the AVO fidelity is preserved for the depth-variable streamer data through both synthetic and real data examples with GWE pre-migration deghosting technology and proper pre-stack absorption compensation. We would like thank Chevron for the synthetic test datasets and Rosneft for the permission of the real data example. We would also like to thank CGG for permission to publish this paper.

In this paper we analyze the impact of water bottom reflection on the near field hydrophones in shallow water survey, and the error on the reconstruction source signature it introduced. Then we present a de-blending based approach to attenuate such error.