Seismic Fault Detection is a crucial aspect of oil exploration. While traditional deep learning methods struggle to handle complex seismic data patterns, training a deep learning model solely on synthetic seismic data may not yield satisfactory results. This research paper, involves utilizing a pre-trained vision-based transformer to extract relevant features from seismic data. By leveraging the knowledge learned from a different but related task, the model can capture general fault patterns in field data. In this framework, a portion of the pre-trained model architecture is employed and trained using a Structural Similarity-based loss function to learn fault-related features. This allows the model to adapt to the faulted structures of a geological dataset and improve fault detection performance in field data applications. In comparison to the state-of-the-art method, the proposed method yields improved results on real field dataset.

Subsurface lithology identification from well log signals is a crucial step in geological exploration, providing essential information about rock formation properties and fluid flow. Accurate identification of lithofacies aids in reservoir characterization and hydrocarbon exploration. This letter presents a novel approach for lithofacies identification from well logs using graph-based feature extraction and classification. The existing instance-based methods ignore the sequential information in well log signals, which can provide valuable insights about the local lithology. The proposed approach treats each instance in a temporal sequence as a node in a graph that captures the local geological information by aggregating temporally neighborhood nodes to create an embedded feature space. Two separate aggregating schemes are proposed, one using a spatial kernel approach and the other using an attention-based network layer, to find the nonlinear relationship between the feature vectors and give more weights to the nearest vectors in the feature space. The graph structure allows the network to incorporate spatial and relational information between different well log features into the classification process, leading to improved accuracy of predictions. The experiment is run on real-world data from the oil and gas exploration field at Krishna-Godavari (KG) Basin, India. The proposed method outperforms traditional feature-based classification and provides a unique way to enhance the representation of the well log signals for lithofacies classification tasks.

Geological fault detection is a crucial aspect of oil exploration. With the advancements in deep learning, the challenging task of accurate fault detection has gained popularity. While traditional deep learning methods struggle due to the small sample problem and the labor-intensive fault labeling process, training a deep learning model solely on synthetic seismic data may not yield satisfactory results due to the disparities between synthetic and real seismic data. To mitigate the impact of these differences, we propose employing an instance weighting (IW)-based transfer learning (TL). This approach involves utilizing a pretrained deep-learning model to extract fault-related features from seismic data. By leveraging the knowledge learned from a different but related task, the TL model can capture general fault patterns that can be applicable to real seismic data. In this framework, a portion of the pretrained model is employed to learn fault-related features, which can then be fine-tuned using a smaller amount of labeled real seismic data. This allows the model to adapt to the complexities of the actual geological situation and improve fault detection performance in field data applications. The proposed method has been tested on the Indian Krishna Godavari Basin dataset. The method yields satisfying results in spite of the high imbalance between the fault and nonfault classes.

In recent years, there has been significant progress in utilizing neural networks and deep learning methods for enhancing the delineation of seismic faults. However, the scarcity of labeled data has posed a challenge in training such networks, leading to a reliance on synthetic samples. Consequently, the task of annotation has become a crucial component within machine learning frameworks. Data labeling not only consumes considerable time but also necessitates a high level of precision. To address the above limitations, an Artificial Intelligence-powered interactive annotation tool has been developed. This tool aims to minimize the immense human effort involved in labeling data by offering an efficient and accurate solution. By leveraging the power of artificial intelligence, the tool enables faster and more precise annotation. The effectiveness and reliability of the proposed tool are affirmed through the observed enhancements in segmentation quality and the average speedup achieved in the annotation process.

Seismic sparse layer inversion (SLI) plays a crucial role in improving the resolution of seismic data for accurate subsurface characterization. However, achieving high accuracy and efficient runtime in SLI is of paramount importance for reservoir characterization. In this paper, we address the SLI problem by constructing a dictionary using odd and even reflection coefficients. The conventional Linear Programming (LP) approach suffers from the equal penalization of all model parameters, resulting in ghost layers and high computational costs on Central Processing Units(CPUs). To overcome these limitations, we propose an enhanced formulation by incorporating Tikhonov regularization, dynamically penalizing the model parameters based on the target seismic trace. Moreover, we optimize the computational runtime by leveraging the power of a Graphical Processing Unit (GPU)-based LP solver. Our algorithm is implemented on an NVIDIA RTX 4000 GPU with 8GB dedicated memory and tested on the Indian WADU dataset. Results demonstrate the efficacy of our proposed method in accurately delineating thin seismic layers compared to state-of-the-art techniques. Additionally, we highlight the superior runtime performance of the GPU-based LP formulation compared to its CPU implementation, further enhancing the efficiency of the SLI process.

Seismic fault detection has become an important research topic in geo-science. Semblance-based coherence algorithm is widely used to detect seismic faults, folds, and fractures. However, the algorithm is computationally expensive on Central Processing Unit (CPU) when the seismic datasets are too large. Also, existing commercial geoscience software solutions use serial or batch processing modes using CPU-based computation which leads to a long execution time. In this paper, we present a fast and parallel implementation of semblance algorithm using General Purpose Graphical Processing Unit (GPGPU) powered with Compute Unified Device Architecture (CUDA). This is accomplished with a parallel kernel map of the algorithm through multiple threads. We also adopted a strategy for efficient memory occupancy and CPU-GPU communication with minimal latency. The algorithm is implemented on NVIDIA RTX 4000 GPU model with 8GB dedicated GPU memory and tested with Netherland F3 and Indian Krishna-Godavari (KG) Basin datasets. Our CUDA implementation achieved considerable speedup over its conventional CPU implementation on both datasets. Also, the proposed algorithm achieves faster times speedup are reported on both datasets over the commercial software OpenDtect. For extensive study, optimal runtime of the algorithm with variation of the parallel threads is also reported here.

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.

In this paper, an automatic multi-stage seismic fault detection framework is introduced. Initially, the seismic data is pre-processed using structure oriented anisotropic diffusion filtering. Next, we propose to estimate a target seismic trace using a linear combination of neighborhood traces. For improved estimation, the neighborhood traces are augmented. The squared residual trace between the target trace and the estimated trace contains the required fault information. We refer to this error trace as the augmented collaborative representation based fault extractor. At the fault location, the error magnitude is larger than usual. This helps in localization of seismic faults. Finally, the fault path is estimated using Hough transform. The performance of the proposed framework is evaluated on both complex synthetic and real-time datasets. The superior performance of proposed fault extractor over the existing algorithm is also demonstrated here.