Recording Technologies for High-Rate Marine Seismic Data

The application of the seismic reflection method to detect stratigraphic changes away from well locations is beginning to gain acceptance among Reservoir Engineers. Unfortunately one of the limitations of this method is the inherent low resolution of seismic data as compared to well log data. Several approaches have been proposed to produce well log-like or pseudo log traces from seismic reflection data. Although there is no improvement in the resolution, the pseudo log traces provide a more convenient way of visually corelating well data to seismic data. A new stratigraphic extrapolation method designed to overcome some of the drawbacks of low resolution is presented. This method consists of relating a model trace (initially derived from well information) to an actual seismic trace by varying the parameters of the model trace within well defined constraints until best match is achieved between them. Extrapolation of the stratigraphy away from the well is performed from trace to trace, the starting model for each trace being determined either from the final model for the previous trace or from an independently postulated model. An interactive computer program is employed for this purpose which allows the incorporation of additional information to the initial model. In this way the final stratigraphic model of the reservoir is the one which best matches the seismic data. Several examples showing the application of the method will be discussed.

A mean-based filter to remove power line harmonic noise from seismic reflection data

Seismic data acquisition is essential for discovering new hydrocarbon targets, where a high-resolution regular-spaced acquisition is critical to obtain high-quality seismic images. However, the high acquisition costs and environmental impacts have motivated designing seismic surveys with fewer sources and receivers than regular-spaced sensing approaches. After the undersampled measurements are acquired, an algorithm reconstructs the missing information necessary for the subsequent processing and interpretation analysis. The removed data is currently selected using random, jittered, and uniform sensing schemes leading to suboptimal seismic image recovery. Therefore, a guided design of undersampled seismic surveys is important as it determines the quality of the reconstructed information. This work proposes an end-to-end (E2E) optimization to design an undersampled seismic acquisition pattern that preserves the high quality of the reconstructed data. The sensing pattern is modeled as a deep binary layer to learn the location of receivers and sources for a particular seismic survey. Simultaneously, a deep neural network recovers the underlying removed data. Once the sensing pattern is designed, it can be used as a seismic acquisition geometry in an area that exhibits a similar geological setting to the training dataset of the E2E model. Extensive experiments were conducted on synthetic and real seismic data from different geological settings. The proposed design was compared with the traditional random, jittered, and uniform sensing schemes. The results validate that a guided design improves the quality of the reconstructed data by up to 4 and 2 dB in peak-signal-to-noise ratio for trace and shot gather reconstruction, respectively.

The University of the West Indies Seismic Research Centre, Trinidad and Tobago, operates a network of over 50 stations for earthquake and volcanic monitoring in the Eastern Caribbean islands. These stations form a seismic network consisting of various types of instrumentation, and communication systems. Over a period of 11 years, the Centre has embarked on an initiative of upgrading and expanding the current network with combinations of broadband and/or strong motion sensors, high dynamic range digitizers and networking equipment to link each station to centralized observatories via high speed digital data transmission medium. To realize such an upgrade and expansion, the Centre has developed a seismic data acquisition system prototype built using open-source hardware and software tools. The prototype is intended to be low-cost using off the shelf hardware components and open-source seismic related software handling data acquisition and data processing in two separate modules. The prototype uses a three-channel accelerometer sensor and can process data into standard MiniSEED format for easy data archiving and seismic data analysis. A global position module provides network time protocol time synchronization within 1 millisecond for accurate timestamping of data. Data can be stored locally on the prototype in twenty-minute data files or securely transferred to a central location via internet with the use of virtual private network capabilities. The prototype is modular in design allowing for components to be replaced easily and the system software can be updated remotely thus reducing maintenance cost.

An approach to model the detailed 3-D distribution of lithofacies and porosity constrained to seismic data is presented. The simulated annealing-based approach explicitly honors the relatively coarse vertical resolution, from a reservoir modeling perspective, and the less than perfect correlation of seismic with lithofacies proportions and effective porosity. Conventional geostatistical procedures such as co-located cokriging or the Markov-Bayes model assume that the seismic attribute has the same volumetric support as the geological modeling cells. The conventional techniques are reviewed, details of the proposed methodology are presented, and a reservoir case study is shown. Introduction Due to geological complications and inherent limitations in seismic data acquisition, seismic inexactly measures lithofacies proportions and average porosity. Typically, the seismic-derived lithofacies proportion and porosity may be correlated with the true values with a correlation of 0.5 to 0.7. The specific seismic attribute and degree of correlation must be calibrated for each reservoir. Geostatistical techniques to integrate seismic data must account for this precision. Another consideration, when using seismic data, is that seismic-derived proportions and porosity represent a volume significantly larger than the typical geological modeling cell. The areal resolution is often comparable. The vertical resolution; however, is 10 to 100 times the resolution of the geological modeling cells. Current geostatistical models are built at a vertical resolution of 1-3 feet and current seismic data informs a 30-100 foot vertical average. The detailed resolution of geostatistical models is considered necessary to transfer the effect of heterogeneities into flow simulators. There is a need for geostatistical modeling tools to explicitly account for the precision and scale of seismic data. Most conventional geostatistical techniques account for the precision of the seismic data by treating it as soft or secondary data. At times, the seismic data is considered to represent an arithmetic volume average of the lithofacies indicator or the porosity. A review of conventional geostatistical techniques will show that they do not simultaneously handle the precision and scale of seismic data. An approach will be proposed based on an extension of the simulated-annealing approach to geostatistical model construction. An objective function is constructed that constrains the model to the seismic data accounting for both the predefined precision and scale of the seismic data. An example from a West Texas Permian Basin reservoir is developed to demonstrate the practical applicability of the proposed approach. The 3-D distribution of porosity is constrained to a seismic attribute representing an imprecise (= 0.5) 50-foot vertical average. In an increasing number of cases, seismic data or hand-drawn geological trend maps are available and we want to build them into detailed 3-D geostatistical models. The methodology proposed in this paper is suited to those situations. The importance of simultaneously accounting for the precision and scale of seismic data could only be assessed by a number of comparative reservoir case studies. These comparative studies are considered important but are not included in this paper. For the time being we must argue that better reservoir models are obtained when they are constrained to the maximum amount of data properly accounting for the precision and scale of each data source. P. 9

High Resolution, Simultaneous VSP and Land Seismic Acquisition

Seismic swell noise, often observed in marine seismic data, is characterized by high amplitude and low frequencies. This noise significantly hides useful signals, underscoring the importance of attenuating it in the processing pipeline for marine seismic data. To date, most deep learning methods proposed for swell noise suppression have focused on supervised paradigms, which require a large dataset of paired noisy and clean data to gain insights into signal features and swell noise statistics at the training phase. In field data processing, however, obtaining generalizable training data often proves to be challenging. The inherent complexity and variability of the real world often make it difficult to acquire unbiased, noise-free datasets in the context of marine seismic data processing. To address this problem, we present a self-supervised deep learning method for suppressing swell noise even in the absence of access to clean seismic training data. First, we propose a strategy for the synthetic swell noise generation based on the amplitude, frequency, and coherence features of noisy traces. We implement the noisy-as-clean (NAC) strategy, wherein either the original noisy seismic data or its reorganized variant acts as the network’s target. Simultaneously, the network receives the observed noisy seismic data combined with simulated swell noise as its input. By leveraging these “noisy-noisy” pairs, we train a DnCNN network. Experimental evaluations conducted on both synthetic and field data demonstrate that, through the integration of swell noise simulation and the NAC strategy, the trained network consistently achieves superior denoising performance.

The seismic data quality can be improved by evaluating two aspects: seismic data acquisition and seismic imaging. In recent years, a number of innovative acquisition layouts and methods have been introduced that produced better subsurface images compare to previous dataset. However, the seismic technology in the industry has been largely on an ad-hoc basis without any qualitative analysis of the subsurface geology and complexity and its influence on data quality. In this chapter, the poor images beneath shallow gas cloud were analysed by using two types of illumination methods, namely wave propagation and ray interpolation techniques. Meanwhile the data enhancement methodology was conducted through optimization of receiver positions on surface level. This will address the issue of subsurface illumination below complex overburden and shallow anomaly that cause wave field distortion. The outcome from this work aims at developing the new technique of getting a good and reliable seismic data underneath the near surface anomaly by integrating the existing illumination techniques and optimizing the source and receiver positions. The proposed integrated method of illumination analysis, leads to a new attribute known as Illumination Factor (IF), was formulated based on information from two forward modelling methods; (i) Amplitude distribution in spatial domain and (ii) Ray hit count at the target subsurface. Evaluation of the proposed Illumination Factor will set a basis for improving seismic acquisition design, by enhancing seismic data through better illumination analysis while providing an insight for accurate reservoir characterization.KeywordsSeismic illuminationComplex overburdenIllumination factor

Key technologies for complex surface seismic acquisition in the Sichuan Basin and their application effect

Seismic method is one of the emerging geophysical methods in Indonesia. The Indonesian geophysicists have developed many methods of seismic data processing, but very few have developed the seismic data acquisition system. So the geophysicist has a dependency on the other parties to acquire seismic data. Big companies and famous universities have seismic equipment, so they have more opportunities to conduct research in the seismic field. Hence the development of seismic equipment is needed to be able to support the needs of geophysicists in Indonesia. System developed is custom device. System consists of sensor, signal amplification, ADC, and data recording. Sensors can use a geophone or MEMS (micro electromechanical system) accelerometer. Signal amplification can reach 10000 times and converting digital data using 24-bits ADC. Each sensor channel contained module of seismic recording data, the data are sent via the serial communication on a laptop and recorded by PuTTY program. System developed can be used to capture both active and passive seismic data, to seismic exploration or monitoring. Testing in the labscale system, geophone gives velocity response, whereas MEMS gives acceleration response. First order integration of MEMS data can produce velocity data and the first order derivative of data geophone can generate acceleration data.

A shipboard system for the digital acquisition and processing of marine seismic profiling data has been developed and extensively used during the past two years. Deployed at sea for twelve months, the system supported studies of the Eastern Atlantic Continental Margin. The system consists of two computer subsystems of which one is dedicated to data acquisition and storage while the second performs processing operations. Data processing is carried out in a post real-time manner with results generally available writhin 24 hours. The system has shown high reliability and cost effective operation.

As a kind of supergene geological phenomenon, ground fissure has brought great inconvenience to human life. In addition, it also has a close relationship with earthquake. However, it is very difficult to ascertain the extension depth of ground fissure since its concealment and uncertainty. In this paper, 3D seismic exploration is used to detect ground fissure in Shanxi Province of China. Specific parameters for seismic data acquisition, processing and interpretation are analysed. Firstly, seismic data acquisition method and its corresponding parameters are discussed. Small dose explosive sources and high frequency geophones are used. Small trace interval and appropriate fold are also adopted. Secondly, seismic data processing is processed from shot record to seismic profile. Multi-domain loop iteration de-noising is used to get high signal-to-noise ratio data. Accurate near surface model, interactive iteration and residual static correction are used to eliminate the impact of low velocity zone and the static correction problem. Large common middle point bin and small velocity analysis interval are used for high accuracy velocity spectrum analysis. The mute parameter of stretching distortion and the migration aperture are researched for shallow ground fissure detection. Thirdly, seismic data interpretation is processed to get ground fissure distribution. Fault enhanced filter is used to improve the signal-to-noise ratio effectively and the chimney cube is used to identify ground fissure automatically. Thus, the specific 3D seismic exploration method used in this paper is suitable for ground fissure detection. Cited as : Shi, S., Liu, Z., Feng, J., Feng, G., Li, M. Using 3D seismic exploration to detect ground fissure. Advances in Geo-Energy Research, 2020, 4(1): 13-19, doi: 10.26804/ager.2020.01.02

It is well known that CSEM data are sensitive to the presence of hydrocarbons, but they lack the sensitivity to fine scale structural detail that can be attained from seismic data. In contrast, seismic data can provide detailed structural information and resolve some rock properties, notably porosity, but cannot unambiguously map out fluid properties. In this regard, joint seismic and CSEM data interpretation is becoming popular. In this paper, we present a global, Genetic Algorithms (GA), joint inversion algorithm for estimating reservoir parameters from both seismic AVA and CSEM data. Through an example, based on the Luva gas field, we demonstrate the power of the two datasets in the retrieval of reservoir parameters when jointly interpreted.

Road traffic noise is a special kind of high amplitude noise in seismic or acoustic data acquisition around a road network. It is a mixture of several surface waves with different dispersion and harmonic waves. Road traffic noise is mainly generated by passing vehicles on a road. The geophones near the road will record the noise while receiving the seismic signal. The amplitude of the traffic noise is much larger than the signal, which masks the effective information and degrades the quality of acquired data. At the same time, the traffic noise is coupled with the effective signal, which makes it difficult to separate them. Therefore, attenuating traffic noise is the key to improving the quality of the final processing results. In recent years, denoising methods based on convolution neural networks (CNN) have shown good performance in noise attenuation. These denoising methods can learn the potential characteristics of acquired data, thus establishing the mapping relationship between the original data and the effective signal or noise. Here, we introduce a method combining UNet networks with asymmetric convolution blocks (ACBs) for traffic noise attenuation, and the network is called the ACB-UNet. The ACB-UNet is a supervised deep learning method, which can obtain the distribution characteristics of noise and effective signal through learning the training data and then effectively separate the two to achieve noise removal. To validate the performance of the proposed method, we apply it to synthetic and real data. The data tests show that the ACB-UNet can obtain good results for high amplitude noise attenuation and is practical and efficient.

This study is aimed at identifying the ultradeep gas reservoir structure and gas‐rich areas in the middle section of the southern margin of the Junggar Basin. Based on high‐precision microgravity data collected from the complex terrain areas of Huxi and Tudong in the middle section of the southern margin of the Junggar Basin, residual gravity anomalies of deep reservoirs were extracted after numerical conversion and various corrections. Combined with comprehensive geological interpretation, favorable areas for natural gas exploration in the Lower Cretaceous and Upper Jurassic were delineated. The results demonstrate that high‐precision microgravity detection offers significant advantages including cost‐effectiveness, environmental friendliness, rapid implementation, and the ability to reflect overall planar effects in optimizing gas reservoir targets. The microgravity results show strong consistency with geological features seismic profile characteristics and gas enrichment patterns observed in drilling wells. Continuous and stable low residual gravity anomalies effectively characterize gas traps and enrichment zones. As a geophysical method for oil and gas exploration, microgravity technology can be integrated with seismic methods or applied independently in areas where seismic data acquisition is challenging. This study significantly expands the application of microgravity detection in natural gas exploration marking its first use in the exploration of deep sandstone gas reservoirs at burial depths exceeding 7300 m. Moreover, in complex mountainous terrain where seismic acquisition and processing are difficult, high‐precision microgravity detection proves to be an effective technique for gas reservoir exploration.