Seismic fault analysis is one of the most critical aspects of the oil and natural gas industries. Many crucial decisions like borehole drilling and exploration are taken based on the presence of a seismic fault. Manually identifying faults is an old and time taking method. However, many new methods have been developed in the recent past that automate seismic fault detection. Convolutional neural network (CNN) is the most used method among them. In this paper, we propose an approach for training CNNs using seismic attributes and data augmentation. A mixture of synthetic and real seismic data is used to create the training and testing datasets. Additionally, we augment training data to increase diversity. We consider three seismic attributes: gradient structure tensor (GST) based coherence, semblance based coherence, and local discontinuity. Then we extract 2D patches, which act as input to CNN.

Spectral decomposition helps the geophysicists in enhancing the data interpretation as certain geological features may get highlighted at a particular frequency. The transformation of 1-D seismic trace into the corresponding frequency components gives a better analysis of the stratigraphy in the subsurface. We propose the multispectral coherence approach to delineate the stratigraphic features such as faults. First, the data are spectrally decomposed using continuous wavelet transform as well as a recently developed synchrosqueezing wavelet transform. Once all the seismic traces are spectrally decomposed with a band of frequencies, we propose a modified spectral balancing technique that enhances the resolution of seismic data. The spectrally balanced data are then subjected to time–frequency (T-F) analysis, which results in multispectrum data of corresponding frequency of certain bandwidth. Gradient structure tensor-based coherence is applied on spectrally balanced data as well as selected frequency bands of T-F data as the stratigraphic features may get highlighted in a certain frequency band. Finally, all the coherence images are statistically fused using a weighted mean to get finer and sharper fault lines with very little noise. This proposed method helps to better visualize all the possible subtle and minor faults present in the data. Experimental results on field seismic data show that subtle and minor faults are more apparent and discernible using the proposed method.

The goal of lithofacies classification is to predict a facies profile at the well site by using the values of rock parameters obtained or computed in well log analysis. Knowledge of lithofacies profiles is required to map the depositional environment of the subsurfaces, from which hydrocarbon zones may be discovered. Statistical techniques give the most feasible categorization of lithological facies across the borehole by maximizing a model, which can predict the chance of a group of rock samples belonging to a specific class. The majority of existing algorithms categorize each sample in the well log separately and classify the different classes. However, mainly, these techniques failed for the complex environments, where the actual data is very imbalanced. In this work, this issue is addressed by using the multistage support vector machine (SVM) classification algorithm with modified regularization parameters for each class. In our case, minority classes are getting worse accuracy than majority classes, which is improved by applying the higher penalty parameter to the minority classes. The method is applied to the Krishna-Godavari basin dataset where seven lithofacies are identified. The entire statistical methodology allows us to transmit the ambiguity from data collected at the well site to the estimated facies profiles. The method’s benefits are validated by the field data application results. This contribution provides a useful method for lithofacies classification and reservoir projection using SVM.

Lithofacies extraction is an essential step for mapping the earth’s subsurface, which is crucial for effective hydro-carbon extraction. The identification of lithofacies in borehole location is frequently handled as a part of the core data analysis. The exact knowledge of the various lithological units is also provided by the downhole well log data. In our present work, we use the Walsh low pass filter to the wire-line log signal responses and the bed boundary identification algorithm to spot the bed boundaries at their corresponding depth with an automated approach. Initially, we construct a stepped version of the well log data using the Walsh domain filter with a fixed step width. Afterwards, each version of the well log data is given a different weight according to their influence on lithological change. The fluctuation at a particular location is compared with a reference value to generate an automatic set of lithological boundaries. Within a complicated environment of sedimentary strata, the Walsh low pass filtering can correctly locate the thin lithological units. Further, the proposed work is an efficient way of understanding the subsurface inhomogeneity and identification of the lithofacies boundaries. The approach is tested successfully on the well log data obtained from the Krishna-Godavari basin.

In this paper, we propose a fault extraction method using curvature attribute and log -Gabor filter with the help of saliency technique, which is the improvement of the dip, edge, and azimuth attributes. In these attributes, sometimes pieces of information are confusing so interpretation can be diverted from the aim. Different types of curvatures are calculated, but the most positive and most negative curvatures are taken into account because these are more sensitive to the edges. These curvature attributes ascertain the fault lineaments, which are easy to contain within the seismic section. The visual saliency approach is incorporated for probable fault point highlighting on the curvature attribute rather than conventional coherence techniques. Further, the log-Gabor filter is used for the enhancement of the fault edges. The proposed approach results in fault detection in the seismic volume. Experimental results validate the robustness of the algorithm.

In this paper, we propose a fault detection method using a multiscale fusion of dip attribute in seismic dataset with the help of Hough transform and unconstrained optimization. Dip is the geometric attribute which corresponds to the dip of the seismic events. First, the dip is estimated by complex trace analysis and then it is multiscale analyzed using Gaussian or Laplacian pyramid to enhance the geometric dip and reduce noise. Dip attribute is calculated at each level of the pyramid and is fused using statistical properties such as mean, median and weighted mean. Followed by dip estimation, semblance attribute is calculated to accentuate likely fault points which involves local multiscale fused dip information. Discontinuity map is calculated from the semblance attribute and Hough transform is applied after thresholding the discontinuity map to extract faults. Finally with the information of local maximum in the discontinuity map and initial detected fault lines, an optimized version of fault is calculated using unconstrained optimization with brute force algorithm. Illustration of experimental results validates the robustness of the proposed algorithm on various seismic sections.