Seismic Detection Technology Research Articles

Review of Methods for Prediction and Identification of Small Faults

Fault system is the oil and gas reservoir is one of the main control factors of characteristic changes especially, for dense reservoirs, so it is very important for quality reservoir prediction. However, due to the complex relationship and contradiction between the genesis and scale problems of small faults and the characteristics and resolution of seismic response, the seismic detection technology of small fault development zones is one of the current research hotspots. Seismic attribute analysis is one of the effective means for small fault identification and prediction. Various types of seismic attributes can be obtained by doing different mathematical and physical transformations on seismic data, and various different kinds of seismic attributes can be obtained based on seismic data. Different seismic attributes can describe different geological features, and a single seismic attribute has good ability to identify faults, but a single seismic attribute to predict reservoir The single seismic attribute has good ability to identify faults, but the single seismic attribute may have multiple solutions when predicting reservoir parameters. Therefore, by preferring multiple attributes that can reflect the reservoir characteristics in the work area for analysis, multiple solutions can be reduced and the prediction accuracy can be improved, which is very practical in fault prediction.

Distributed acoustic sensing for shallow structure imaging using mechanical noise: A case study in Guangzhou, China

Distributed Acoustic Sensing (DAS) is a novel seismic detection technology that transforms fiber-optic cables into densely sampled seismic stations, showing immense potential in both urban seismology and the oil and gas industry. We deployed a ~ 182-m-long single-core fiber-optic cable in a campus under construction to record human activity and demonstrated the application for seismic imaging. The spectrogram analysis revealed significant differences in ambient noise between the daytime and nighttime at the construction site. During the daytime, the noise energy exhibited dominant frequencies ranging from 2 to 11 Hz, approximately one order of magnitude higher than that at night, which had frequencies ranging from 2 to 5 Hz. Beamforming analysis demonstrated that noise sources exhibited distinct directional characteristics and propagation velocities during daytime and nighttime periods. We extracted the cross-correlation functions between DAS channels from the mechanical noise generated by a large excavator and enhanced signal-to-noise ratio by using the phase-weighted stacking method. The multichannel analysis of surface waves (MASW) method was then employed to obtain 2D near-surface shear-wave velocity. Based on five 60-m-deep boreholes drilled along the DAS array, we established the correlation between shear-wave velocity information and shallow structure. We obtained the unbiased cross-correlation function in the presence of continuous mechanical noise along the DAS array direction, demonstrating the importance of site noise source distribution analysis in ambient noise tomography. Our research is informative for research related to the construction of accurate, high-resolution near-surface shear wave velocity models.

Feature Extraction and Analysis of Microseismic Signal Based on Convolutional Neural Network

Seismic detection technology has been widely used in safety detection of engineering construction abroad. Although it has just started in the field of engineering in our country, its role is becoming more and more important. Through computer technology, micro-seismic detection can provide accurate data for the construction safety detection of large-scale projects, which has important practical significance for the rapid and effective identification of micro-seismic signals. Based on this, the purpose of this article is to study the feature extraction and classification of microseismic signals based on neural games. This article first summarizes the development status of microseismic monitoring technology. Using traditional convolutional neural networks for analysis, a multi-scale feature fusion network is proposed on the basis of convolutional neural networks and big data, the multi-scale feature fusion network is used to research and analyze microseismic feature extraction and classification. This article systematically explains The principle of microseismic signal acquisition and the construction of multi-scale feature fusion network. And use big data, comparative analysis method, observation method and other research methods to study the theme of this article. Experimental research shows that the db7 wavelet base has little effect on the Megatron signal.

The application on the towed-type shallow high-accuracy seismic detection for coastal tidal flats

Restricted by the geological exploration equipment and tidal time, the deep seismic detection technology with large offset for exploration of oil and gas resources is often used in the seismic survey of the coastal tidal flats. Furthermore, the coastal tidal flats are only treated as the transition belt of the paralic zone, resulting in the low degree of geological exploration and low detection accuracy, which cannot meet the requirement for high-accuracy exploration in the coastal tidal flats. Conventional shallow seismic exploration technology can detect effectively the bottom interface and internal horizon of the shallow Quaternary system in the coastal tidal flats, but the equipment for data acquisition is complex, and the acquisition efficiency is low, restricting directly the seismic exploration in the coastal tidal flats. Therefore, a shallow high-accuracy seismic detection technology has been developed in this study, which is suitable for the coastal tidal flats. By making full use of the characteristics of the flat terrain in the coastal tidal flats, the method for towed data acquisition has been used, and the seismic detection equipment has been simplified in the tidal flats. With this method, the exploration cost has been reduced, and the data acquisition efficiency has been improved. The detection accuracy can reach the level of meters, and the detection depth is more than 800m with this method. With this technology, the shallow stratigraphic structure and interface in the coastal tidal flats can be detected effectively, and the location of paleochannels, concealed faults and shallow gas can be determined, which support effectively the geological survey of the coastal zone and offshore engineering, being of great practical significance for scientific disaster prevention and mitigation.

Processing of random roadway source signals based on a cross-correlation algorithm in the deconvolution domain

Advanced seismic detection technology utilising random roadway sources is advantageous method adapting to the development of dynamic intelligent technologies for the detection of hidden and disastrous geological structures. However, roadheaders generate complex signals, that are continuous and random with seismic wavelets that are wider and longer than conventional source wavelets, and these complex wavelets cannot be directly subjected to data processing. To address this problem, based on two basic techniques, namely, deconvolution and cross-correlation, a cross-correlation algorithm in the deconvolution domain is proposed to process continuous random roadheader signals, convert those signals into normal-impulse seismic data, and extract the arrivals of direct and reflected waves and other information. First, the feasibility of the algorithm is explained based on a derivation of theoretical formulas. Then, to verify the applicability of the algorithm to the processing of actual data, a random signal from a tamping source is processed; the continuous random signals are successfully converted into impulse signals, and the extracted in-phase direct waves are clear with good continuity. Moreover, the continuous random signal processing results are compared with an active source signal, and good consistency is found, verifying the effectiveness of the algorithm. Finally, an application analysis of the algorithm is carried out using an actual acquired roadheader source signal. After implementing the algorithm, the random roadheader signal is successfully converted into a normal-impulse seismic signal, and the direct wave arrivals are extracted. Based on the arrival times of the direct waves, the longitudinal wave velocity of the coal seam is calculated as 1918 m/s, which is consistent with the actual velocity. A comprehensive analysis of the test results confirms that the cross-correlation algorithm in the deconvolution domain is both feasible and effective and that the processed seismic signals can be used for subsequent processing and interpretation.

Application of Discrete Fracture Network Model in the Simulation of Massive Fracturing in Tight Oil Reservoir

Massive fracturing technology can form a complex fracture network and a large drainage area by multi-stage/multi-cluster perforation and injection of low viscosity fluids such as silckwater.the traditional bi-wing planar model is not applicable in the simulation of massive fracturing, and a new network model is needed. The pseudo-three-dimension (P3D) Discrete Fracture Network (DFN) model are based on self-similar solution methodology, and the fracture characteristics are then calculated numerically. we performed a comparison between DFN model and Cluster Facture model through the integration of rock mechanics, well logging and micro seismic detection technology. After the mini-fracture analysis and closure analysis, parameters like instantaneous shut-in pressure (ISIP), closure pressure and reservoir permeability were obtained. Based on these parameters, pressure history match and SRV match were performed, the result shows that DFN model works better than the Cluster Fracture model in the matching. The research proves the feasibility of DFN model in the simulation of massive fracturing in tight oil reservoirs.

Numerical simulation for accuracy of velocity analysis in small-scale high-resolution marine multichannel seismic technology

When used with large energy sparkers, marine multichannel small-scale high-resolution seismic detection technology has a high resolution, high-detection precision, a wide applicable range, and is very flexible. Positive results have been achieved in submarine geological research, particularly in the investigation of marine gas hydrates. However, the amount of traveltime difference information is reduced for the velocity analysis under conditions of a shorter spread length, thus leading to poorer focusing of the velocity spectrum energy group and a lower accuracy of the velocity analysis. It is thus currently debatable whether the velocity analysis accuracy of short-arrangement multichannel seismic detection technology is able to meet the requirements of practical application in natural gas hydrate exploration. Therefore, in this study the bottom boundary of gas hydrates (Bottom Simulating Reflector, BSR) is used to conduct numerical simulation to discuss the accuracy of the velocity analysis related to such technology. Results show that a higher dominant frequency and smaller sampling interval are not only able to improve the seismic resolution, but they also compensate for the defects of the short-arrangement, thereby improving the accuracy of the velocity analysis. In conclusion, the accuracy of the velocity analysis in this small-scale, high-resolution, multi-channel seismic detection technology meets the requirements of natural gas hydrate exploration.