Integration of feature extraction, attribute combination and image segmentation for object delineation on seismic images

Automatic geological interpretation, specifically modeling salt dome and fault detection, is controversial task on seismic images from complex geological media. In advanced techniques of seismic interpretation and modeling, various strategies are utilized for combination and integration different information layers to obtain an image adequate for automatic extraction of the object from seismic data. Efficiency of the selected feature extraction, data integration and image segmentation methods are the most important parameters that affect accuracy of the final model. Moreover, quality of the seismic data also affects confidence of the selected seismic attributes for integration. The present study proposed a new strategy for efficient delineation and modeling of geological objects on the seismic image. The proposed method consists of extraction specific features by the histogram of oriented gradients (HOG) method, statistical analysis of the HOG features, integration of features through hybrid attribute analysis and image classification or segmentation. The final result is a binary model of the target under investigation. The HOG method here modified accordingly for extraction of the related features for delineation of salt dome and fault zones from seismic data. The extracted HOG parameter then is statically analyzed to define the best state of information integration. The integrated image, which is the hybrid attribute, then is used for image classification, or image segmentation by the image segmentation method. The seismic image labeling procedure performs on the related seismic attributes, evaluated by the extracted HOG feature. Number of HOG feature and the analyzing parameters are also accordingly optimized. The final image classification then is performed on an image which contains all the embedded information on all the related textural conventional and statistical attributes and features. The proposed methods here apply on four seismic data examples, synthetic model of salt dome and faults and two real data that contain salt dome and fault. Results have shown that the proposed method can more accurately model the targets under investigation, compared to advanced extracted attributes and manual interpretations

Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation

Random noise attenuation in seismic data requires employing leading-edge methods to attain reliable denoised data. Efficient noise removal, effective signal preservation and recovery, reasonable processing time with a minimum signal distortion and seismic event deterioration are properties of a desired noise suppression algorithm. There are various noise attenuation methods available that more or less have these properties. We aim to obtain more effective denoised seismic data by assuming 3-D seismic data as a tensor in order three and increasing its dimension to 4-D seismic data by employing continuous wavelet transform (CWT). First, we map 3-D block seismic data to smaller blocks to estimate the low-rank component. The CWT of the tensor is calculated along the third dimension to extract the singular values and their related left/right vectors in the wavelet domain. Afterward, the effective low-rank component is extracted using optimized coefficients for each singular value. Thresholding is applied in the wavelet domain along the third dimension to calculate effective coefficients. Two synthetic and field data examples are considered for performance evaluation of the proposed method, and the results were compared with the competitive random noise suppression methods, such as the tensor optimum shrinkage singular value decomposition, the iterative block tensor singular value thresholding, and the block matching 4-D algorithms. Qualitative and quantitative comparison of the proposed method with other methods indicates that the proposed method efficiently eliminates random noise from seismic data

Geological modelling of petroleum reservoir through multi-scale analysis of faults in complex media

Highlights • Accurate fault model can be built even when sparse drilling wells are available. • The multiresolution fault model provides information of faults with different sizes. • Fault model provides possibility of tectonic and fluid flow analysis simultaneously. • Modelling of faults in different scales, enable more accurate well path design. • The ANN provides optimized parameters for fault detection by ant tracking algorithm. Modelling faults plays a crucial step in the chain of studies through the first phase of the hydrocarbon exploration and its following studies in reservoir engineering, simulation and field development. This study introduces an innovative and automatic integrated approach that combines seismic multi-attributes and well data for faults modelling. The proposed strategy begins with extracting fault-related seismic attributes commonly used for seismic reservoir characterization. Chaos, variance and curvature attributes, typically highlight large-scale faults that shape the structural framework of the study field. In contrast, small-scale faults, influencing subsurface fluid flow in the fractured reservoir, are modeled using the ant-tracking algorithm applied to seismic data. Small-scale and large-scale fault models, then integrated with the conventional fault model to create an integrated discrete fracture network (DFN). This DFN model incorporates information on both large-scale and small-scale faults. The proposed strategy was applied on a geologically complex petroleum field in Iran. The results, validated using Formation Micro Imager (FMI) data, demonstrate accuracy of the integrated DFN model in comparison to conventional approaches on the studied filed, particularly in capturing small-scale faults. Consequently, it can be concluded that the proposed strategy provides a viable alternative for generating accurate DFN model

New insights into the geometry of gas chimneys in the Gorgan plain through seismic attribute integration

Gas chimneys and gas clouds in the subsurface media are known as one of the indications of possible petroleum reservoirs. Investigations of their properties are mostly initiated by seismic attribute interpretation on reflection seismic data. However, due to the complexity of their behavior and their difficult interpretation of seismic attributes, state-of-the-art methods are mostly required to be applied on the seismic data to prevent any misinterpretation. This is mostly done through attribute integration and multi-attribute analysis. This research presents a study on seismic attributes and integration on several 2D seismic reflection lines from the Gorgan Plain. It is located in Northeast Iran, on the western border of the region’s well-known Kopeh-Dagh fold and thrust belt, and southeastern border of the South Caspian Basin. Hydrocarbon systems of the Gorgan Plain are poorly known and have not been widely studied, but according to preliminary investigations, this region has the potential for hydrocarbon occurrences. The aim of this study is to investigate presence and then delaminate the affected area of possible gas chimneys that are related to possible hydrocarbon reservoirs. Gas chimneys are assumed to be created due to the routes, mostly made by faults, that provoke light hydrocarbons components to migrate toward the surface. Preliminary interpretations of seismic reflection data in this study revealed that at least two gas chimneys occurred within the Gorgan Plain. As it was mentioned, since they are mostly due to the faulting above the hydrocarbon reservoir, gas chimney and heavy faulting might exhibit the same effects on the seismic data and then on its attributes, which are amplitude reduction and high damping on energies, distortion of the waveshape and seismic velocity reduction. Thus, care should be taken in separation of these two different geologic phenomena on seismic attributes. This also was done in this study through utilized integration of the most relevant seismic attributes such as Instantaneous-phase, Chaos, Variance and Remove-bias attributes. Based on the result of interpretations and according to the evolution of the basin and its structural reconstruction on other studies, gas chimneys of the Gorgan Plain, are in relation to the operation of fault zones in Cenozoic erathem in the region. These fault zones which cut the entire Cenozoic erathem, create the pathway for vertical migration of hydrocarbons through Cheleken formation (reservoir rock) and its overburden sedimentary sequences. In other words, operation of fault zones within Cenozoic sedimentary sequence, is the main reason for gas seepage in the Gorgan Plain, which is also shown in seismic data

Automatic mud diapir detection using ANFIS expert systems algorithm; a case study in the Gorgan plain, Iran

Automatic seismic data interpretation is a significant method in the exploration of geophysics. Complexities of the subsurface structures and the subsurface wave propagation media, make the decision-making process difficult in seismic data interpretation. Nevertheless, the extent of related knowledge and using the expert system method in seismic data interpretation can mitigate this problem. An expert system is a knowledge-based system that applies its knowledge in a complex and specific area and acts as an expert end-user consultant. This study investigates the design of an ANFIS expert system for mud diapirs detection with seismic data analysis in Gorgan plain. This method was applied to seismic attributes from a complex geological mud diapir bearing structure from south of the Caspian Sea. The south of the Caspian Sea is one of the richest area as petroleum reserves, and the Gorgan plain has various mud diapirs, which act as indicators of hydrocarbon reservoirs. The expert system design process to identify mud diapirs on seismic sections was modeled in two approaches including manual and automatic seismic data interpretation. In the first approach, the experience of the expert was collected by manual interpretation of training data and used to create a knowledge base and inference of the expert system in the second approach. The validation verified the accuracy of this method with an average accuracy of 90.1% according to using minimum knowledge to develop a knowledge base of the designed ANFIS expert system

Random noise attenuation in seismic data using an adaptive thresholding and the second-order variant time-reassigned synchrosqueezing transform

Seismic data analysis often faces the challenge of random noise contamination from various sources. To overcome this, innovative noise attenuation methods utilizing seismic signal properties are needed. This study focuses on efficiently suppressing random noise in the domain of time and frequency by accurately estimating instantaneous frequency using the single-valued group delay characteristic of seismic signals. The time-reassigned synchrosqueezing transform (TSST) and its second-order variant (TSST2) offer high-resolution time-frequency representations (TFRs) for noise suppression. Expanding on these advancements, we propose an efficient noise suppression method that integrates the adaptive thresholding model into the TSST2 framework and employs sparse representation of the TFR through low-rank estimation. This method effectively attenuates noise while preserving essential signal information. The proposed approach operates trace by trace on recorded data, initially transforming it into a sparse subspace using TSST2. The adaptive thresholding model then decomposes the resulting TFR into sparse and semi-low-rank components, achieving a high-resolution and sparse TFR for efficient separation of noise and signal. After noise suppression, the seismic data can be fully reconstructed by inversely transforming the semi-low-rank component data into the time domain. This method addresses previous limitations in noise attenuation techniques and provides a practical solution for enhancing seismic data quality

An unsplit perfectly matched layer for second-order heterogeneous time-domain elastic wave equation

Many geophysical inversion methods rely on simulating wave propagation. With the advent of advanced computing systems and the need for precise forward models for Full Waveform Inversion (FWI) and Reverse Time Migration (RTM) applications, elastic wave modeling has attracted more attention. In order to solve the wave equation, which is a Partial-Differential-Equation (PDE), using numerical methods such as Finite Difference (FD), an absorbing layer is defined at the boundaries of the model to avoid unwanted reflections. In wave simulations, a perfectly matched layer (PML) is a highly effective absorbing layer. In many simulations, applying a second-order equation system is simpler and more practical. Despite its usefulness, extending PML to second-order systems generates some difficulties because the method was originally designed for first-order systems. This paper proposes an unsplit PML implementation for the second-order heterogeneous elastic wave equation by making use of the auxiliary differential equations. This method has a lower computing cost compared to earlier studies and can be easily incorporated into existing codes. Numerical examples indicate the method’s satisfactory performance