Seismic Exploration Data Research Articles

Transition to the use of digital assistants in the kinematic interpretation of the data of seismic exploration by the example of the problem of improving the quality of seismic data after summation and reliability of the tectonic model forecast

Modern seismic exploration still faces the challenges of automating processes and increasing the reliability of work results, especially in regions with complex geological conditions. An important place in the cycle of seismic surveys is occupied by the stage of kinematic interpretation, the main purpose of which is a detailed understanding of the structural features of the geological section and obtaining a reasonable geological model of a particular region of study.
 The cost of an error at this stage of the work is quite high, but the interpretation processes require significant labor costs, and the results often contain errors. Standard algorithms and methodological approaches do not fully provide solutions to the full range of tasks, which necessitates the search for new approaches to the interpretation of seismic data.
 In recent years, there has been increasing interest in attracting the capabilities of artificial intelligence to solve production problems. New approaches to solving the problems of the stage of kinematic interpretation of seismic data based on the use of artificial intelligence through machine learning and deep neural networks are proposed:
 technology of elimination of irregular noises of the total seismic data to improve the quality of the initial seismic material and simplify the stage of structural interpretation;
 technology of probabilistic forecast of disturbance systems and obtaining a detailed tectonic model.
 Theoretical foundations are presented and the results of applying technologies on a series of real production projects are demonstrated, which confirm the advantages of using neural networks in interpretation to eliminate subjectivity and significantly reduce time costs at the stage of structural constructions in various geological conditions.

Optimizing Source Wavelets Extracted from the Chirp Sub-Bottom Profiler Using an Adaptive Filter with Machine Learning

In this study, we extracted three source wavelets of a Chirp sub-bottom profiler (SBP), which is widely used for high-resolution marine seismic exploration, using a MATLAB-based graphical user interface tool for computational processing. To extract the source wavelet for general seismic exploration data processing techniques, including that for Chirp SBPs, we first evaluated source wavelet extraction techniques using an adaptive machine learning filter. Subsequently, we performed deterministic deconvolution by extracting the optimal source wavelet from the raw data of Chirp SBP. This source wavelet was generated by applying an adaptive filter. Various methods have been studied to solve the multivariate optimization problem of minimizing the error; in this study, a least-mean-square algorithm was selected owing to its suitability for application to geophysical time-series data. Extracting the source wavelet is a crucial part of high-resolution marine seismic wave exploration data processing. Our results highlight the effectiveness of performing deterministic deconvolution by extracting source wavelets using adaptive filters, and we believe that our method is useful for marine seismic exploration data processing.

Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation

Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process. To solve this problem, this paper proposes a deep convolutional neural network denoising model based on noise estimation (MCD-DCNN). This model is primarily composed of two modules, the noise estimation module and the denoising module. The noise estimation module uses a multiscale convolutional neural network to better extract the characteristics of random noise in the seismic data. To make full use of the extracted features, a dense connection method is adopted between the multiscale convolutions in the noise estimation module. In the denoising module, we use multiscale convolutions and dense connections to replace the original convolutional neural network and use the residual structure (ResNet) and batch normalization (BN) to improve the denoising effect and running speed of the model. In this experiment, single trace and simple and complex profile data are used as input to simulate the real data processing environment. Finally, we compare the denoising effects of the MCD-DCNN model proposed in this paper with the current mainstream feed-forward denoising convolutional neural network (DnCNN) and a fast and flexible denoising convolutional neural network (FFDNet) models. The comprehensive results show that under the condition of a given prior noise level, the denoising performance of the FFDNet and MCD-DCNN models are comparable. In the absence of a priori noise level, the denoising performance of the FFDNet model drops sharply, while the denoising performance of MCD-DCNN is not affected; therefore, MCD-DCNN is more in line with actual seismic denoising.

Integrated approach to the typification and localization of deposits of the anomalous section of the Bazhenov formation of the Frolov oil and gas region

Introduction. The subject of the study of this work is the deposits of the anomalous section of the Bazhenov formation (ASB) of Western Siberia, the disturbed occurrence of which was recorded by 2D, 3D seismic exploration and borehole data at many fields of the Frolov oil and gas region. The research area unites the company’s assets in the KhMAO and the Tyumen region, which are part of the large hydrocarbon cluster “ZIMA”. 
 
 Aim. In order to typify various complexes of rocks of the Bazhenov formation and further localization of deposits, a comprehensive core analysis, GIS and seismic studies were performed. 
 
 Materials and methods. According to the results of lithological study of the core and petrophysical interpretation of logging diagrams, have been identified various types of rocks in the interval of the Bazhenov formation. According to the results of the interpretation of the seismic survey materials, contoured zones that differ in the wave pattern by different coherence of the axes of common phase. The revealed differences in seismic sections compared with borehole data and geological bodies mapped based on the obtained patterns. 
 
 Results. Based on a comprehensive interpretation of the core, GIS and seismic studies, established the zonality of the distribution of various types of deposits of the Bazhenov formation, the relationship of the development of ASB zones with the introduction of Early Cretaceous sedimentary bodies and showed the introduction of detrital material from the overlying rocks. 
 
 Conclusions. The authors of the article conclude that the development of anomalous sections of the Bazhenov formation involves several stages of the introduction of landslide bodies of overlying rocks, according to the gradation of Neocomian clinocyclites in the north-west direction. Within the study area, mapped three large landslide bodies in the Bazhenov formation interval, each of which was formed an internal zonality and because of the introduction of rocks from the overlying interval.

Lithofacial analysis and possibilities for prediction of properties on geophysical research and seismic exploration data by methods of machine learning

The success of a development strategy for any field depends on the degree of knowledge of the geological structure of its main reservoirs. As the area is drilled out, the concept of the structure of the hydrocarbon accumulation is refined, but in the case of a complex structure of the void space of the reservoirs and the lithological heterogeneity of the section over the area, geological uncertainties and risks during the subsequent placement of wells remain high. For these reasons, one of the main problems in hydrocarbon production is predicting rock types and the distribution of fluids throughout the reservoir away from wells, since the determination of rock properties is a major source of uncertainty in reservoir modeling studies [1, 2]. The proposed project will demonstrate algorithms based on machine learning methods that allow predicting the distribution of lithology and the uncertainty of lithofacies variability in the section.

Application of seismic multiattribute machine learning to determine coal strata thickness

Abstract The coal mining industry is developing automated and intelligent coal mining processes. Accurate determination of the geological conditions of working faces is an important prerequisite for automated mining. The use of machine learning to extract comprehensive attributes from seismic data and the application of that data to determine the coal strata thickness has become an important area of research in recent years. Conventional coal strata thickness interpretation methods do not meet the application requirements of mines. Determining the coal strata thickness with machine learning solves this problem to a large extent, especially for issues of exploration accuracy. In this study, we use seismic exploration data from the Xingdong coal mine, with the 1225 working face as the research object, and we apply seismic multiattribute machine learning to determine the coal strata thickness. First, through optimal selection, we perform seismic multiattribute extraction and optimal multiparameter selection by selecting the seismic attributes with good responses to the coal strata thickness and extracting training samples. Second, we optimise the model through a trial-and-error method and use machine learning for training. Finally, we illustrate the advantages of this method using actual data. We compare the results of the proposed model with results based on a single attribute, The results show that application of seismic multiattribute machine learning to determine coal strata thickness meets the requirements of geological inspection and has a good application performance and practical significance in complex areas.

Noise Reduction Method of Φ-OTDR System Based on EMD-TFPF Algorithm

The phase-sensitive optical time domain reflectometry ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR) system has been gradually applied to vertical seismic profile exploration due to its excellent anti-electromagnetic interference, extremely high resolution and sensitivity, and wide detection range. However, the actual data is often disturbed by random noise, which seriously affects the system’s ability to recognize low-frequency disturbances. In order to suppress the background noise and improve the signal-to-noise ratio (SNR), a fusion noise reduction method based on empirical mode decomposition and time-frequency peak filtering (EMD-TFPF) algorithm is studied and introduced. In terms of seismic exploration data processing, the algorithm has good denoising performance. Using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR system based on optical synchronous reference heterodyne detection as the experimental platform, the EMD-TFPF noise reduction algorithm is applied to the position information and compared with the VSS-NLMS, VMD-NWT and EMD-soft methods. Experimental results show that under the conditions of 0.1 Hz and 5 V vibration interference, the EMD-TFPF method improves the SNR to 37.6 dB, which is much higher than the other three noise reduction algorithms. In addition, this method broadens the low-frequency response range of the system to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> Hz. The improvement of the system’s ability to recognize low-frequency disturbance events will undoubtedly accelerate the practical process of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR system in seismic exploration.

Detecting and locating microseismic events with stacking velocity analysis for surface monitoring

Microseismic surface monitoring may suffer from the signal-to-noise ratio (SNR) in the data and lack of datasets to constrain a realistic velocity model. Therefore, an 1D depth velocity, which is derived from the well log and calibrated with perforation shot, is commonly utilized in microseismic monitoring. In this study, we simultaneously detect and locate microseismic events by applying a stacking method without using a depth velocity model. Since the 1D layered velocity model could be approximately equivalent to a RMS velocity, we borrow the velocity analysis concept in traditional exploration seismic data processing and apply it on mocroseismic surface monitoring. We stack the perforation shot waveform along a hyperbolic curve and pick the maximum stacking energy to obtain a stacking velocity above the target zone. The microseismic events are automatically detected and located by a stacking procedure with the known stacking velocity in time domain. The events are then converted from time to depth using the average velocity calculated from the perforation shot. Synthetic examples show that our approach can successfully recover the true locations of events with a large amount of noises added to the data. The real data results also suggest that this method is applicable for effectively detecting and locating events with low SNR. Although the approach with stacking velocity is limited to the layered media, this restriction is acceptable for many unconventional reservoirs.

Virtual Reality and Computer-Aided 3D Seismic Exploration Data Acquisition Planning and Design System Optimization

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Frequency‐dependent seismic properties in layered and fractured rocks with partial saturation

ABSTRACTSeismic waves propagating in a fractured porous rock with fluids can be greatly attenuated due to the wave‐induced fluid flow, including the macroscopic Biot flow, the mesoscopic interlayer fluid flow and the microscopic squirt flow. However, the comprehensive effects of the above wave‐induced fluid flows on the frequency‐dependent seismic properties in layered and fractured rocks with partial saturation are still poorly understood. To obtain such knowledge, we present a superposition approach accounting for the dispersion and attenuation induced by the multi‐scale wave‐induced fluid flows in the above‐mentioned target rocks, and study the influences of the water saturation, layer thickness ratio and fracture properties (i.e. fracture density and fracture aspect ratio) on the frequency‐dependent vertical P‐wave velocity (VP0). The results demonstrate that the increasing water saturation enhances the dispersion and attenuation of VP0 in the full frequency band, and the varying layer thickness ratio not only affects the dispersion and attenuation induced by the interlayer fluid flow, but also influences those caused by the Biot flow and squirt flow. The fracture properties are also found to significantly impact the seismic properties of the target rock. In addition to its effects on the squirt flow, the increasing fracture density is shown to weaken the dispersion and attenuation induced by the Biot flow, and enhance those caused by the interlayer fluid flow. The obtained results provide new insights into the frequency‐dependent seismic properties in the partially saturated layered rocks with fractures, and the knowledge gained from these results has important practical applications for the successful interpretation of fluid and fracture properties applying the seismic exploration data acquired in fractured reservoirs.

Case study: Improving the quality of the seismic reflection image for a Fujian mineral exploration data set with offset-domain common-image gathers

Seismic reflection is a proven and effective method commonly used during exploration of deep mineral deposits in Fujian, China. In seismic data processing, rugged depth migration based on wave-equation migration can play a key role in handling surface fluctuations and complex underground structures. Because wave-equation migration in the shot domain cannot output offset-domain common-image gathers in a straightforward way, the use of traditional tools for updating the velocity model and improving image quality can be quite challenging. To overcome this problem, we have used the attribute migration method. This works by sorting the migrated stack results for every single-shot gather into the offset gathers. The value of the offset that corresponds to each image point is obtained from the ratio of the original migration results to the offset-modulated shot-data migration results. A Gaussian function is proposed to map every image point to a certain range of offsets. This helps improve the signal-to-noise ratio, which is especially important in handling low-quality seismic data obtained during mineral exploration. Residual velocity analysis is applied to these gathers to update the velocity model and improve the image quality. The offset-domain common-image gathers are also used directly for real mineral exploration seismic data with rugged depth migration. After several iterations of migration and updating the velocity, our procedure achieves an image quality better than the one obtained with the initial velocity model. The results can help with the interpretation of thrust faults and deep deposit exploration.

Advances in surface-wave tomography for near-surface applications

Surface-wave (SW) tomography is a technique that has been widely used in the field of seismology. It can provide higher resolution relative to the classical multichannel SW processing and inversion schemes that are usually adopted for near-surface applications. Nevertheless, the method is rarely used in this context, mainly due to the long processing times needed to pick the dispersion curves as well as the inability of the two-station processing to discriminate between higher SW modes. To make it efficient and to retrieve pseudo-2D/3D S-wave velocity (VS) and P-wave velocity (VP) models in a fast and convenient way, we develop a fully data-driven two-station dispersion curve estimation, which achieves dense spatial coverage without the involvement of an operator. To handle higher SW modes, we apply a dedicated time-windowing algorithm to isolate and pick the different modes. A multimodal tomographic inversion is applied to estimate a VS model. The VS model is then converted to a VP model with the Poisson's ratio estimated through the wavelength-depth method. We apply the method to a 2D seismic exploration data set acquired at a mining site, where strong lateral heterogeneity is expected, and to a 3D pilot data set, recorded with state-of-the-art acquisition technology. We compare the results with the ones retrieved from classical multichannel analysis.

A procedure of automatic evaluation of residual statistic adjustments for increasing information value of exploration seismology data

In present-day seismic exploration the quality of the observed wave field that guarantees obtaining the most exact and complete information on the structure of the studied area plays an important role. Therefore much attention is paid to elaboration of procedures for elimination of different noises and distortions present in the registered data. They include correction of statics or calculation and maintenance of static adjustments with taking into account the influence of small velocities zone (SVZ) and locality relief at each station of reception and explosion along the profile or observation area to the form of hodograph.&#x0D; A procedure of automatic finding of residual statistic corrections based on usage of seismograms of equal distances and first wave arrivals without conducting their correlations that gives a possibility to exclude the effect of operator mistakes to the result has been considered. A theory has been proposed with algorithm consisting of putting in the observed wave fields, assortment of paths, finding temporal shifts by equidistant paths and computation of correcting adjustments for all the sources and receivers and the programming realization of finding the residual statistic corrections as a new interactive program corst3D, aimed at increasing the level of studies of structure of geological medium of different complexity by the data of both 2D and 3D seismic exploration. Effectiveness of the given procedure at the real data of seismic exploration for improvement of their quality and as a result for rising information value of their processing and interpretation has been shown together with colleagues of «GEOUNIT» company.

Application of artificial intelligence algorithm in geological exploration

In order to make seismic exploration have a good geological effect, seismic exploration data need to be divided into different classes or clusters. In this paper, K-means algorithm of artificial intelligence algorithm is used to cluster massive data and cluster geological exploration seismic data. The results show that the selection of clustering number k is the best. Clustering analysis is an important research field in data mining. K-means algorithm is an important branch of clustering algorithm. This algorithm has its inherent shortcomings and needs to be improved to adapt to geological exploration seismic data. In the future, it will be widely used in various fields and stages of geological exploration.

Challenges of carbonate platform identification and characterization from seismic data in frontier exploration – evaluating a possible Miocene example from the SE Caribbean

Abstract The purpose of this study is to demonstrate that the identification of carbonate platforms in frontier exploration using seismic data remains challenging despite the variety of attributes that can be extracted from the data and their integration with other sources of information. A Miocene example from offshore Tobago (Southern Caribbean) is evaluated using 3D seismic data integrated with regional geology, potential fields, analogues and structural restoration. The data are compatible with interpreting the target as shallow-marine carbonates, and a conceptual model is presented. However, far fewer of the seismic characteristics are diagnostic of a carbonate platform, and despite the intensive approach and the use of published criteria for subsurface carbonate interpretation, it was impossible to conclusively identify the target as a shallow-marine carbonate. Alternative explanations such as volcaniclastics, eroded remnants of siliciclastics and basement highs are considered. The study illustrates that it is common to find situations where the origin of a prospective geobody cannot be determined beyond significant levels of uncertainty unless it is drilled. If other elements of the petroleum system are favourable, this irreducible risk has to be accepted to avoid overlooking attractive carbonate reservoirs, provided all available data are used and all possible alternative interpretations considered.

Sedimentation features of reservoir formation and leakage assessment of the Galmaz underground gas storage

The performed complex interpretation of the data of three-dimensional seismic exploration and geophysical study of wells (GIS) using the results obtained from the analysis of core material by laboratory and experimental methods, making it possible to identify paleo conditions for the formation of objects with collector and screening properties on the Galmaz area. The results of the study, which revealed the lithological features of the structure of natural reservoirs, served as the basis for assessing the reservoir properties of the reservoirs and the tightness of the shielding horizons, to which the Galmaz underground gas storage is confined. According to the authors, in the formation, which was in operation for a long time (1960—2006), the Galmaz gas field was mainly played by the eruptive channels of the mud volcano of the same name. At the same time, the maternal strata of the Oligocene—Miocene were the gas-generating source. The results of a comprehensive interpretation of seismic data and well logging showed that confined deposits to reservoirs of the productive stratum (PT-I), Akchagyl and Apsheron are communicated through eruptive channel of a mud volcano. Tire layers, represented by transgressive clays accumulated in shelf conditions, are reliable enough for storage of gas by covers.

Seismic imaging of S-wave structures of shallow sediments in the East China Sea using OBN multicomponent Scholte-wave data

The shear-wave (S-wave) structures of shallow marine sediments are important for offshore geotechnical studies, deep crustal S-wave imaging, multicomponent seismic exploration, and underwater acoustics studies. We have applied the multicomponent Scholte-wave analysis technique to an active-source shallow marine seismic profile in the East China Sea. Scholte waves have been excited by shots from a 5450 inch3air-gun array and their recordings have been conducted at the seafloor using ocean bottom nodes (OBNs). First, we extract the common-receiver gathers (CRGs) and correct for the time drift simultaneously using a forward and inverse fast Fourier transform resampling algorithm. Three CRGs of seismic sensors are used for Scholte-wave analysis. Raw sensor CRGs are rotated to the inline, crossline, and vertical coordinate system. The rotated tilt and roll angle are directed using the inner electric compass log value, and the shot inline azimuth is estimated using the particle motion method. Then, the velocity spectra are calculated from the inline and vertical components using the phase-shift method. Higher Scholte-wave modes dominate on the horizontal components, whereas the stronger fundamental mode dominates on the vertical components. The multicomponent velocity spectrum stacking method is adopted to produce the final dispersion energy image. Up to four modes of dispersion curves are retrieved within the 1.1–4.3 Hz frequency band. The multimode dispersion curve inversion is constructed for imaging the shallow sediments. The results suggest a low [Formula: see text] of 180–650 m/s and few lateral variations within the top 0.5 km of shallow marine sediments in the East China Sea. This model can provide an important reference for offshore geotechnical investigations, especially for OBN multicomponent seismic exploration data processing. The use of OBNs has high feasibility in [Formula: see text] imaging for shallow marine sediments when combined with the Scholte-wave dispersion-curve inversion.

To the question of the breaking depressional tectonics Azerbaijan's zone according to seismic exploration data

There are very few tectonic maps of oil and gas areas of the Republic and explanatory notes to them. They contain, in large numbers, factual material and important theoretical and applied conclusions. However, the results of seismic surveys carried out over the past 25 years indicate some shortcomings in these maps. The paper provides specific examples that prove the correctness of this conclusion and recommendations for their elimi nation.

Application of 3D seismic data fine processing technology in District 31, Qinan coal mine

In order to further find out the geological condition of District 31, Qinan coal mine, according to the characteristics of mining area data and geological tasks, the techniques of pre-stack noise, deconvolution and prestack time migration were applied, and the 3d seismic original data collected before the mining area were processed again. The results of two fault interpretations: 152 faults were basically the same, 6 faults were revised, 60 faults were newly discovered, and 6 faults were negated; According to the classification and statistical comparison of fault reliability, there were 115 reliable faults in the first interpretation and 195 in the second interpretation, which increased by 80. The results show that the signal to noise ratio and resolution of 3D seismic data were improved, the breakpoint is clearer and the fault interpretation is more reasonable and reliable. Fine processing can improve the utilization rate of the original data of 3D seismic exploration, save the acquisition cost, improve the economic benefit, and provide reliable basic data for the development and utilization of coal resources.

Seismic exploration device based on time-frequency space algorithm

Among the exploration methods of oil and gas resources, one of the most important exploration methods is seismic exploration. Due to economic development, the previous shallow exploration has been unable to meet the needs, and it is necessary to conduct exploration in deep geological layers. However, as the depth increases, the signal of the geophone gradually weakens and the noise interference increases, which makes the denoising work of seismic exploration more important. Aiming at the above problems, this paper proposes the research of seismic exploration equipment based on time-frequency space algorithm, and uses different window lengths for processing based on the time-frequency peak filtering algorithm. Through simulation experiments on seismic exploration data with different frequencies, the results show that the method proposed in this paper improves the effective signal amplitude maintenance by more than 11%, and the amplitude maintenance is higher than 88%. The signal-to-noise ratio also improved from -8dB before filtering to 12.13dB. This shows that the method proposed in this paper can better meet the needs of seismic exploration signal processing.