Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery

Low-frequency information is important in reducing the nonuniqueness of absolute impedance inversion and for quantitative seismic interpretation. In traditional model-driven impedance inversion methods, low-frequency impedance background is from an initial model and is almost unchanged during the inversion process. Moreover, the inversion results are limited by the quality of the modeled seismic data and the extracted wavelet. To alleviate these issues, we investigate a double-scale supervised impedance inversion method based on the gated recurrent encoder-decoder network (GREDN). We first train the decoder network of GREDN called the forward operator, which can map impedance to seismic data. We then implement the well-trained decoder as a constraint to train the encoder network of GREDN called the inverse operator. Besides matching the output of the encoder with broadband pseudo-well impedance labels, data generated by inputting the encoder output into the known decoder match the observed narrowband seismic data. Both the broadband impedance information and the already-trained decoder largely limit the solution space of the encoder. Finally, after training, only the derived optimal encoder is applied to unseen seismic traces to yield broadband impedance volumes. The proposed approach is fully data-driven and does not involve the initial model, seismic wavelet and model-driven operator. Tests on the Marmousi model illustrate that the proposed double-scale supervised impedance inversion method can effectively recover low-frequency components of the impedance model, and demonstrate that low frequencies of the predicted impedance originate from well logs. Furthermore, we apply the strategy of combining the double-scale supervised impedance inversion method with a model-driven impedance inversion method to process field seismic data. Tests on a field data set show that the predicted impedance results not only reveal a classical tectonic sedimentation history, but also match the corresponding results measured at the locations of two wells.

Kutei Basin oil bearing sand reservoirs mapping using integrated seismic methods

The Lower Kutei Basin which contains several giant oil and gas fields is located on the East Kalimantan, Indonesia. This paper discusses the identification and mapping of oil-filled reservoirs and their depositional facies by integrating seismic stratigraphy, attributes, and AI (Acoustic Impedance) inversion methods. The log data cross-plots show that AI can be used to distinguish oil-sands from wet sands and shale, and to derive the total porosity of the sands. However, AI and amplitude values are greatly affected by the oil, porosity and tuning effects, hence they cannot be used to identify the facies containing the oil-bearing sands. Therefore, to map the facies containing the oil-filled sands, the AI map is combined with the variance and sweetness maps. It can be seen clearly from the variance and sweetness maps that the oil-sands suggested by the AI map are contained in a narrow and elongate meander-like geometry which is typical of channel facies. The variance and sweetness maps suggest that there are two channels in the study area. To determine which channel is thicker, spectral decomposition RGB map was made. The result suggests that the right channel is more prospective as it associates with thicker sand deposits. The combination of variance, sweetness and RGB maps strongly indicate that the channels in the study area are in upper-slope environment, and the thicker oil-sands are located in the eastward of the study area.

Overpressure-generating mechanisms in the Blok F3, North Sea, Netherland

Block F3 North Sea is a block with pore pressure values that vary over time due to complex geological conditions such as burial and various sedimentation zones. Pore pressure is one of the important aspects that need to be analyzed as a basis for the identification of zones and overpressure mechanisms. Overpressure is a greater pore pressure condition than normal pressure and may cause drilling problems, such as kicks, blowouts, etc. This study calculated pore pressure values using the eaton method approach with well data and seismic data. Both data are integrated for generating pore pressure values in 1D and 3D. 1D Modelling uses Interactive Petrophysics 3.5, while 3D modeling uses Petrel software. In 3D modeling, the variables used are interval velocity and inversion velocity obtained by acoustic impedance inversion. The sub-variables used are the inversion density and the regression density obtained from well density acoustic impedance inversion. The existence of a 1D overpressure zone at a depth of 1,100 – 1,800 m with an overpressure value of 3,836 – 18,975 kPa. In addition, the overpressure value based on the 3D model is 8,000 – 18,000 kPa. The overpressure zone is validated using an acoustic impedance inversion model with a high value of 5,200 – 5,380 (m/s)*(gr/cc). Overpressure in Block F3 is predicted to occur from disequilibrium compaction..