Iranian Journal of Oil and Gas Science and Technology

Abstract The petrophysical parameters of the Ghar Formation are characterized in this study. A combination of pre-stack seismic data gathers and well-log data is used to estimate water saturation and shale volume in the Ghar reservoir. First, the highest possible correlation between the well logs and the seismic inversion data was established for this purpose. After extracting the optimal wavelet, an accurate relationship between the estimated values and the core data was obtained. Next, using data from another well, the validity of the constructed model was examined. The results showed that the combination of three attributes—instantaneous cosine of phase, , and —can accurately estimate the shale volume of the reservoir, with a correlation coefficient of approximately 70%. Although the two layers in the Ghar section have a shale volume of about 10%, the overall shale volume in the reservoir area is negligible. The logarithm of the ratio of compressional wave velocity to shear wave velocity attribute exhibits the highest correlation, approximately 62%. Finally, validation using previously unutilized well-log data demonstrated an accuracy of about 90% in predicting these properties.

Abstract Seismic well tying is a crucial part of the interpretation phase in exploration seismology. Tying wells usually involves forward modeling a synthetic seismogram from sonic and density logs and then matching the obtained synthetic seismogram to the seismic reflection data. A huge amount of time is required to deal with it, yet the outcome signal may not be satisfying and may be suffering a low cross correlation between the seismic signal and the synthetic one; it also requires a high quality synthetic trace. Another problem with the so-called manual tying is that the tying process is not repeatable, indicating that one can rarely obtain the same stretched and squeezed signal if the tying procedure is repeated. In recent years, some researchers have used the dynamic time warping (DTW) method to address well tying problems. They have obtained good results according to the correlation between the seismic signal and the warped synthetic signal. This research demonstrates that the result will be better if filtering is applied before tying, and then the warped signal is smoothed. We also propose a simpler algorithm for extracting a warped signal from the warping curve and the original synthetic trace, which gives rise to better performance for well tying.

Abstract Random or incoherent noise is an important type of seismic noise, which can seriously affect the quality of the data. Therefore, decreasing the level of this category of noises is necessary for increasing the signal-to-noise ratio (SNR) of seismic records. Random noises and other events overlap each other in time domain, which makes it difficult to attenuate them from seismic records. In this research, a new technique is produced, by joining FX deconvolution (FXD) and a special kind of median filter in order to suppress random noise from seismic records. The technique is operated in some stages; firstly, FXD is tried to eliminate the Gaussian noise, and the median filter is fixed to diminish the spike-like noise. The synthetic dataset and field data examples (from an oil field in the southwest of Iran) have been employed to demonstrate that random noise reduction can be attained, while the signal content will not be destroyed considerably. The final results indicate the authority of the proposed strategy in suppressing random noises, whereas signal information is almost protected during the filtering.

Abstract Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases associated with supervised classification methods. In this study, we follow supervised classification scheme under classifiers, the support vector classifier (SVC), and multilayer perceptrons (MLP) to provide an opportunity for directly assessing the feasibility of different classifiers. Before choosing classifier, we evaluate extracted seismic attributes using forward feature selection (FFS) and backward feature selection (BFS) methods for logical SFA. The analyses are examined with data from an oil field in Iran, and the results are discussed in detail. The numerical relative errors associated with these two classifiers as a proxy for the robustness of SFA confirm reliable interpretations. The higher performance of SVC comparing to MLP classifier for SFA is proved in two validation steps. The results also demonstrate the power and flexibility of SVC compared with MLP for SFA.