Niranjan Banik

Velocity anisotropy of shales and depth estimation in the North Sea basin

It is known that in the North Sea basin the depths to major reflectors as determined from surface seismic data are often larger than the well‐log depths. From a study of data sets which tie 21 wells, I found a strong correlation between the occurrence of the depth error and the presence of shales in the subsurface. Assuming that the error is caused by elliptical velocity anisotropy in shales, I measured the anisotropy from a comparison of the well‐log sonic data and the interval velocity profile obtained from the surface seismic data and also from a comparison of the seismic depth and the well‐log depth. It was found that the two methods of measurements agree with each other and also agree qualitatively with the previous laboratory measurements of anisotropy in shale samples. The results strongly suggest that the depth anomaly in the North Sea basin is caused by the velocity anisotropy of shales. A simple method to correct the seismic depth is given.

From pore-pressure prediction to reservoir characterization: A combined geomechanics-seismic inversion workflow using trend-kriging techniques in a deepwater basin

To optimize drilling decisions and well planning in overpressured areas, it is essential to carry out pore-pressure predictions before drilling. Knowledge of pore pressure implies knowledge of the effective stress, which is a key input for several geomechanics applications, such as fault slip and fault seal analysis and reservoir compaction studies. It is also a required input for 3D and 4D seismic reservoir characterization. Because the seismic response of shales and sand depends on their compaction history, the effective stress will govern the sedimentary seismic response. This is in contrast to normally pressured regimes, where the depth below mudline (or overburden stress) is typically used to characterize the compaction effect.

Seismic Reservoir Characterization In Marcellus Shale

The Middle Devonian Marcellus shale that extends from Ohio and West Virginia, northeast into Maryland, Pennsylvania and New York, is believed to hold in excess of a thousand trillion ft of natural gas. High-quality surface seismic data and top-of-the-line processing are essential to characterize these reservoirs and the overburden formations for safe and cost-effective drilling. A workflow comprising data acquisition and processing to prestack seismic inversion and lithofacies classification for characterizing the shale reservoirs is presented. The key elements in this workflow are dense point-receiver data acquisition and processing in the point-receiver domain. A small data set acquired with a proprietary point-receiver system was available to demonstrate the benefits of this methodology. The data were in an area in New York, where the Marcellus formation is known to exist.

Regional And High-resolution 3D Pore-pressure Prediction In Deep-water Offshore West Africa

Using both a regional and a high-resolution (HR), automated 3D velocity analysis method and the appropriate rock models, we predict a regressive pore-pressure system in a deep-water basin, offshore West Africa. The onset of overpressure takes place at the seafloor. The pressure gradient increases slowly but almost continuously, then rapidly to a maximum value, and then gradually reverses back to a lower value. The pressure profile follows certain geologic time horizons and structures very closely. Radial canyons on the seafloor significantly affect the porepressure profile. The high-resolution study provides velocity and pore pressure in more detail than the regional study.

A global acoustic impedance inversion for porosity and lithology prediction in northern Gulf of Mexico

Summary Poststack seismic inversion based on a multitrace global optimization method was conducted as part of a large study that covered about 10,200 km 2 , consisting of 502 Outer Continental Shelf blocks, located primarily in the northern Gulf of Mexico, offshore Louisiana. Considering that seismic inversion was carried out over such a huge area with a long time window (7 s two-way time), lateral and vertical variation of the seismic data were the main concerns. Seismic data conditioning to provide consistent, zero-phase wavelets over the whole volume was done iteratively with wavelet extraction and analysis at well locations. The vertical variability of wavelets due to frequency attenuation and amplitude decays with depth were also incorporated in the inversion process. The principle objective of the seismic inversion was to transform seismic reflection data into quantitative petrophysical properties. Acoustic impedance is commonly used for porosity prediction. Over the study area, the correlation between acoustic impedance and porosity is very poor because the impedance varies substantially vertically and spatially due to varying sedimentation rates and sediment compaction. An alternative transformation was applied to use relative acoustic impedance to predict effective porosity, ϕe, and volume fraction of shale, Vc. The results show that the inverted relative acoustic impedance and predicted ϕe and Vc are in reasonable match with wells over the entire study area. The use of relative acoustic impedance efficiently produced reliable ϕe and Vc. Aside from its limitations, such as disregarding the effects of fluid variations and complex lithological variations on the porosity/impedance relation, the method provides a reliable screening tool for seismic exploration. More quantitative details of the petrophysical properties can be obtained through a more sophisticated inversion method in the prestack domain.

Two approaches for Q estimation and its application to seismic inversion

Summary In this paper, we present two complimentary approaches for interval Q estimation from surface seismic data. A layer-based approach and smooth solutions algorithms are presented and evaluated by comparing estimated Q from surface seismic data with VSP data. We also show how, by using a 3D attenuation estimation algorithm together with a time- and space-varying compensation algorithm (inverse Q filtering), we were able to derive an attenuation field an d to improve the wavelet stability as well as the P-impedance inversion results. The choice of attenuation estimation algorithm is related to the statistical nature of the attenuation field in the subsurface. Theory We consider the problem of plane-wave propagation in attenuating media characterized by a velocity, V, and quality factor, Q, as given by equation 1. ) ( / ) ( ) ( 1 ) , ( kx t i V x fQ o kx t i x o e e u e e u t x u = = ω ω π ω α . (1) We further assume that the relation between velocity, frequency, and quality factor can be characterized using the causal constant Q model of Futterman (1962),

Trend-kriged seismic velocities to predict pore pressure and to model effective stress for reservoir characterization in a deepwater basin

Summary To optimize drilling decisions and well planning in overpressured areas, predrill pore pressure predictions are essential. Knowledge of pore pressure implies knowledge of the effective stress, which is one of the key inputs for 3D and 4D seismic reservoir characterization. This case study focuses on how high-resolution seismic velocities (Banik et al., 2003) were used to predict pore pressure and effective stress in a deepwater environment. The velocity to pore pressure transform was calibrated using existing well log data. To allow for consistency between well and seismic data and stratigraphic layers, geostatistical mapping (trendkriging) techniques were applied using several key horizons. Thus, the final trend-kriged model is constrained by the structural framework and the geology of the basin.

Effective -stress-based reservoir characterization in an offshore basin

Summary Effective stress is a key attribute that enables the prediction of subsurface lithology units in overpressured basins. Because the seismic response of shales and sand depends on their compaction history, the effective stress will govern the sedimentary seismic response. This is in contrast to normally pressured regimes, where the depth below mudline (or overburden stress) is typically used to characterize the compaction effect. Effective stress enables one to map nonstationary sedimentary compaction in space. We use seismically derived effective stress as an additional attribute in the Bayesian Lithofacies classification (Bachrach et al., 2004; Mukerji et al., 2001). The other attributes in the process are elastic parameters such as acoustic and shear impedances and density obtained from the multiattribute seismic inversion (Roberts et al., 2005). The modified reservoir characterization method has been applied to a deepwater basin that contains reservoir units of the Pleistocene to the mid-Miocene age extending over a large area and is known to contain overpressure zones.

Quantifying Reservoir Properties of the East Texas Woodbine Through Rock Physics And Multiattribute Seismic Inversion

Multiattribute seismic inversion (MASI) is a newly developed prestack inversion technique for inverting seismic data into elastic parameter attributes. The method integrates several major technologies (Roberts, et al., 2005), including AVO processing and analysis, well-log editing and calibration, prestack waveform inversion (PSWI) (Mallick, et al., 2000), wavelet processing, and poststack inversion. The outputs are high-resolution absolute acoustic and shear impedance and density volumes consistent with the seismic data and the well-log data. The inverted elastic parameter volumes are used for detailed interpretation of lithofacies and pore-fluid content in the subsurface. Combined with rock physics modeling and rock property mapping through LithoCube (Bachrach, et al., 2004) and joint porosity-saturation inversion (Bachrach and Dutta, 2004), the method provides a powerful tool for quantitative reservoir description and characterization. The results are the most-probable litho-class, porosity, and saturation with uncertainties of prediction at every sample point in the 3-D volume. Recently we applied this method to identify and characterize Woodbine sandstone reservoirs in an onshore East Texas field.