scholarly journals Linear Noise Removal Using Tau-P Transformation on 3D Seismic Data of Al-Samawah Area - South West of Iraq

2019 ◽  
pp. 2664-2671
Author(s):  
Ahmed Hussein Ali ◽  
Ali M. Al-Rahim
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Noise Attenuation ◽  
3D Seismic ◽  
South West ◽  
3D Data ◽  
P Transformation ◽  
3D Seismic Data ◽  
Different Sources

Tau-P linear noise attenuation filter (TPLNA) was applied on the 3D seismic data of Al-Samawah area south west of Iraq with the aim of attenuating linear noise. TPLNA transforms the data from time domain to tau-p domain in order to increase signal to noise ratio. Applying TPLNA produced very good results considering the 3D data that usually have a large amount of linear noise from different sources and in different azimuths and directions. This processing is very important in later interpretation due to the fact that the signal was covered by different kinds of noise in which the linear noise take a large part.

Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. V385-V396 ◽  
Author(s):  
Mohammad Amir Nazari Siahsar ◽  
Saman Gholtashi ◽  
Amin Roshandel Kahoo ◽  
Wei Chen ◽  
Yangkang Chen
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Random Noise ◽  
Data Driven ◽  
Superior Performance ◽  
Noise Attenuation ◽  
3D Seismic ◽  
Random Noise Attenuation ◽  
3D Data ◽  
3D Seismic Data

Representation of a signal in a sparse way is a useful and popular methodology in signal-processing applications. Among several widely used sparse transforms, dictionary learning (DL) algorithms achieve most attention due to their ability in making data-driven nonanalytical (nonfixed) atoms. Various DL methods are well-established in seismic data processing due to the inherent low-rank property of this kind of data. We have introduced a novel data-driven 3D DL algorithm that is extended from the 2D nonnegative DL scheme via the multitasking strategy for random noise attenuation of seismic data. In addition to providing parts-based learning, we exploit nonnegativity constraint to induce sparsity on the data transformation and reduce the space of the solution and, consequently, the computational cost. In 3D data, we consider each slice as a task. Whereas 3D seismic data exhibit high correlation between slices, a multitask learning approach is used to enhance the performance of the method by sharing a common sparse coefficient matrix for the whole related tasks of the data. Basically, in the learning process, each task can help other tasks to learn better and thus a sparser representation is obtained. Furthermore, different from other DL methods that use a limited random number of patches to learn a dictionary, the proposed algorithm can take the whole data information into account with a reasonable time cost and thus can obtain an efficient and effective denoising performance. We have applied the method on synthetic and real 3D data, which demonstrated superior performance in random noise attenuation when compared with state-of-the-art denoising methods such as MSSA, BM4D, and FXY predictive filtering, especially in amplitude and continuity preservation in low signal-to-noise ratio cases and fault zones.

Deep learning for multitrace sparse-spike deconvolution

Geophysics ◽  
2021 ◽  
pp. 1-64
Author(s):  
Xintao Chai ◽  
Genyang Tang ◽  
Kai Lin ◽  
Zhe Yan ◽  
Hanming Gu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Resolution Enhancement ◽  
Parameter Tuning ◽  
Seismic Exploration ◽  
3D Seismic ◽  
Spatial Continuity ◽  
Additional Information ◽  
3D Data ◽  
3D Seismic Data

Sparse-spike deconvolution (SSD) is an important method for seismic resolution enhancement. With the wavelet given, many trace-by-trace SSD methods have been proposed for extracting an estimate of the reflection-coefficient series from stacked traces. The main drawbacks of the trace-by-trace methods are that they neither use the information from the adjacent seismograms and nor take full advantage of the inherent spatial continuity of the seismic data. Although several multitrace methods have been consequently proposed, these methods generally rely on different assumptions and theories and require different parameter settings for different data applications. Therefore, the traditional methods demand intensive human-computer interaction. This requirement undoubtedly does not fit the current dominant trend of intelligent seismic exploration. Therefore, we have developed a deep learning (DL)-based multitrace SSD approach. The approach transforms the input 2D/3D seismic data into the corresponding SSD result by training end-to-end encoder-decoder-style 2D/3D convolutional neural networks (CNNs). Our key motivations are that DL is effective for mining complicated relations from data, the 2D/3D CNNs can take multitrace information into account naturally, the additional information contributes to the SSD result with better spatial continuity, and parameter tuning is not necessary for CNN predictions. We report the significance of the learning rate for the training process's convergence. Benchmarking tests on the field 2D/3D seismic data confirm that the approach yields accurate high-resolution results that are mostly in agreement with the well logs; the DL-based multitrace SSD results generated by the 2D/3D CNNs are better than the trace-by-trace SSD results; and the 3D CNN outperforms the 2D CNN for 3D data application.

Nonlinear beamforming for enhancement of 3D prestack land seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V283-V296 ◽  
Author(s):  
Andrey Bakulin ◽  
Ilya Silvestrov ◽  
Maxim Dmitriev ◽  
Dmitry Neklyudov ◽  
Maxim Protasov ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Sensor Data ◽  
Reservoir Properties ◽  
Near Surface ◽  
3D Data ◽  
Common Reflection Surface ◽  
Single Sensor ◽  
The Common

We have developed nonlinear beamforming (NLBF), a method for enhancing modern 3D prestack seismic data acquired onshore with small field arrays or single sensors in which weak reflected signals are buried beneath the strong scattered noise induced by a complex near surface. The method is based on the ideas of multidimensional stacking techniques, such as the common-reflection-surface stack and multifocusing, but it is designed specifically to improve the prestack signal-to-noise ratio of modern 3D land seismic data. Essentially, NLBF searches for coherent local events in the prestack data and then performs beamforming along the estimated surfaces. Comparing different gathers that can be extracted from modern 3D data acquired with orthogonal acquisition geometries, we determine that the cross-spread domain (CSD) is typically the most convenient and efficient. Conventional noise removal applied to modern data from small arrays or single sensors does not adequately reveal the underlying reflection signal. Instead, NLBF supplements these conventional tools and performs final aggregation of weak and still broken reflection signals, where the strength is controlled by the summation aperture. We have developed the details of the NLBF algorithm in CSD and determined the capabilities of the method on real 3D land data with the focus on enhancing reflections and early arrivals. We expect NLBF to help streamline seismic processing of modern high-channel-count and single-sensor data, leading to improved images as well as better prestack data for estimation of reservoir properties.

ACQUISITION TECHNOLOGY OPENS UP DIFFICULT DATA AREAS FOR 3D EXPLORATION

2002 ◽  
Vol 42 (1) ◽  
pp. 607
Author(s):  
C.R.T Ramsden ◽  
A.S Long
Keyword(s):  
Data Quality ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Cost Savings ◽  
3D Seismic ◽  
Single Source ◽  
North West ◽  
3D Data ◽  
Long Offset ◽  
Dual Source

3D seismic technologies have advanced rapidly during the 1990s. The new generation of seismic vessels such as the Ramform design with their massive towing capacities has changed the way in which modern seismic data is acquired. This has resulted in a large increase worldwide in the use of 3D seismic data during the exploration phase because of the reduction in the cost of 3D data. A statistical database has emerged showing that drilling on 3D data will double the commercial success rate compared to drilling on 2D data.Historically, dual-source acquisition has dominated exploration (by comparison to single-source acquisition) due to cost savings associated with the fact that singlesource acquisition implies a geophysical requirement to tow the streamers at half the separation of dual-source acquisition. Data quality associated with single-source acquisition, however, is typically much superior to dualsource data. The ability now to tow 12–16 streamers has reduced costs so that single-source acquisition is now cost effective. The surveys using single-source acquisition allow 3D data to be acquired with significantly higher trace densities and crew efficiencies than industry standard, and are called High Density 3D or HD3D. These surveys have benefits of increased fold, improved spatial resolution and improved imaging quality, and can now be routinely conducted, especially in difficult data areas.The North West Shelf of Australia is a difficult data area because of the presence of strong multiple noise trains that often mask or interfere with the primary reflections (Ramdsen et al, 1988). Standard multiple attenuation techniques have had only limited success. HD3D with its higher trace density and 40% improvement in signal-to-noise ratio has resulted in improved data quality in difficult data areas, and should result in data improvements on the North West Shelf as well.Furthermore, the Continuous Long Offset (CLO) recording technique using Ramform technology is a dualvessel operation that has demonstrated significant operational efficiency improvements in long offset (typically deep water/targets) 3D seismic acquisition. Survey turnaround times can be reduced by as much as half of those using conventional techniques. The CLO technique is particularly well suited for deepwater recording.

scholarly journals Enhancing trap and fault seal analyses by integrating observations from HR3D seismic data with well logs and conventional 3D seismic data, Texas inner shelf

2019 ◽  
Vol 496 (1) ◽  
pp. 253-279 ◽  
Author(s):  
Johnathon L. Osmond ◽  
Timothy A. Meckel
Keyword(s):  
Seismic Data ◽  
Analytical Techniques ◽  
3D Seismic ◽  
Inner Shelf ◽  
Geological Interpretation ◽  
Industry Standard ◽  
3D Data ◽  
Well Data ◽  
Gas Chimneys ◽  
3D Seismic Data

AbstractAn understanding of trap and fault seal quality is critical for assessing hydrocarbon prospectivity. To achieve this, modern analytical techniques leverage well data and conventional industry-standard 3D seismic data to evaluate the trap, and any faults displacing the reservoir and top seal intervals. Above all, geological interpretation provides the framework of trap and fault seal analyses, but can be hindered by the data resolution, quality and acquisition style of the conventional seismic data. Furthermore, limiting the analysis to only the petroleum system at depth may lead to erroneous perceptions because interpreting overburden features, such as shallow faults or gas chimneys, can provide valuable observations with respect to container performance, and can to help validate trap and fault seal predictions. A supplement to conventional 3D data are high-resolution 3D seismic (HR3D) data, which provide detailed images of the overburden geology. This study utilizes an HR3D seismic volume in the San Luis Pass area of the Texas inner shelf, where shallow fault tips and a sizeable gas chimney are interpreted over an unsuccessful hydrocarbon prospect. Static post-drill fault seal and trap analyses suggest that the primary fault displacing the structural closure could have withheld columns of gas c. 100 m high, but disagree with our HR3D seismic interpretations and dry-well analyses. From our results, we hypothesize that tertiary gas migration through fault conduits reduced the hydrocarbon column in the prospective Early Miocene reservoir, and may have resulted from continued movement along the intersecting faults. Overall, this study reinforces the importance of understanding the overburden geology and geohistory of faulted prospects, and demonstrates the utility of pre-drill HR3D acquisition when conducting trap and fault seal analyses.

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