Image processing of seismic attributes for automatic fault extraction

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. O25-O37 ◽  
Author(s):  
Jie Qi ◽  
Bin Lyu ◽  
Abdulmohsen AlAli ◽  
Gabriel Machado ◽  
Ying Hu ◽  
...  
Keyword(s):  
Image Processing ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Data Interpretation ◽  
Machine Learning Techniques ◽  
High Signal ◽  
Good Resolution ◽  
Low Coherence ◽  
Amplitude Data

Along with horizon picking, fault identification and interpretation is one of the key components for successful seismic data interpretation. Significant effort has been invested in accelerating seismic fault interpretation over the past three decades. Seismic amplitude data exhibiting good resolution and a high signal-to-noise ratio are key to identifying structural discontinuities using coherence or other edge-detection attributes, which in turn serve as inputs for automatic fault extraction using image processing or machine learning techniques. Because seismic data exhibit not only structural reflectors but also seismic noise, we have developed a fault attribute workflow that contains footprint suppression, structure-oriented filtering, attribute computation, “unconformity” suppression, and our new iterative energy-weighted directional Laplacian of a Gaussian (LoG) operator. In general, tracking faults that exhibit a finite offset through a suite of conformal reflectors is relatively easy. Instead, we evaluate the effectiveness of this workflow by tracking faults through an incoherent mass-transport deposit, where the low-frequency contribution of multispectral coherence provides a good fault image. Multispectral coherence also reduces the “stair-step” fault artifacts seen on broadband data. Application of statistical filtering can preserve the discontinuity’s boundaries and reject incoherent backgrounds. Finally, iterative application of an energy-weighted directional LoG operator provides improved fault image by sharpening low-coherence anomalies perpendicular and smoothing low-coherence anomalies parallel to fault surfaces, while at the same time attenuating locally nonplanar anomalies.

Sparse spectral attributes to detect hydrocarbons: A case study from deep dolomite reservoirs

2019 ◽  
Vol 7 (3) ◽  
pp. T701-T711
Author(s):  
Jianhu Gao ◽  
Bingyang Liu ◽  
Shengjun Li ◽  
Hongqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Spectral Amplitude ◽  
Data Set ◽  
Frequency Spectra ◽  
Hydrocarbon Production ◽  
Hydrocarbon Detection ◽  
Time Frequency ◽  
Gas Bearing

Hydrocarbon detection is always one of the most critical sections in geophysical exploration, which plays an important role in subsequent hydrocarbon production. However, due to the low signal-to-noise ratio and weak reflection amplitude of deep seismic data, some conventional methods do not always provide favorable hydrocarbon prediction results. The interesting dolomite reservoirs in Central Sichuan are buried over an average depth of 4500 m, and the dolomite rocks have a low porosity below approximately 4%, which is measured by well-logging data. Furthermore, the dominant system of pores and fractures as well as strong heterogeneity along the lateral and vertical directions lead to some difficulties in describing the reservoir distribution. Spectral decomposition (SD) has become successful in illuminating subsurface features and can also be used to identify potential hydrocarbon reservoirs by detecting low-frequency shadows. However, the current applications for hydrocarbon detection always suffer from low resolution for thin reservoirs, probably due to the influence of the window function and without a prior constraint. To address this issue, we developed sparse inverse SD (SISD) based on the wavelet transform, which involves a sparse constraint of time-frequency spectra. We focus on investigating the applications of sparse spectral attributes derived from SISD to deep marine dolomite hydrocarbon detection from a 3D real seismic data set with an area of approximately [Formula: see text]. We predict and evaluate gas-bearing zones in two target reservoir segments by analyzing and comparing the spectral amplitude responses of relatively high- and low-frequency components. The predicted results indicate that most favorable gas-bearing areas are located near the northeast fault zone in the upper reservoir segment and at the relatively high structural positions in the lower reservoir segment, which are in good agreement with the gas-testing results of three wells in the study area.

scholarly journals Detection of Small Magnetic Fields Using Serial Magnetic Tunnel Junctions with Various Geometrical Characteristics

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5704
Author(s):  
Zhenhu Jin ◽  
Yupeng Wang ◽  
Kosuke Fujiwara ◽  
Mikihiko Oogane ◽  
Yasuo Ando
Keyword(s):  
Magnetic Fields ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
High Signal ◽  
Free Layer ◽  
Signal To Noise ◽  
Noise Spectral Density ◽  
Junction Area ◽  
Geometrical Characteristics ◽  
Noise Ratio

Thanks to their high magnetoresistance and integration capability, magnetic tunnel junction-based magnetoresistive sensors are widely utilized to detect weak, low-frequency magnetic fields in a variety of applications. The low detectivity of MTJs is necessary to obtain a high signal-to-noise ratio when detecting small variations in magnetic fields. We fabricated serial MTJ-based sensors with various junction area and free-layer electrode aspect ratios. Our investigation showed that their sensitivity and noise power are affected by the MTJ geometry due to the variation in the magnetic shape anisotropy. Their MR curves demonstrated a decrease in sensitivity with an increase in the aspect ratio of the free-layer electrode, and their noise properties showed that MTJs with larger junction areas exhibit lower noise spectral density in the low-frequency region. All of the sensors were able detect a small AC magnetic field (Hrms = 0.3 Oe at 23 Hz). Among the MTJ sensors we examined, the sensor with a square-free layer and large junction area exhibited a high signal-to-noise ratio (4792 ± 646). These results suggest that MTJ geometrical characteristics play a critical role in enhancing the detectivity of MTJ-based sensors.

scholarly journals Deep learning for low-frequency extrapolation from multioffset seismic data

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. R989-R1001 ◽  
Author(s):  
Oleg Ovcharenko ◽  
Vladimir Kazei ◽  
Mahesh Kalita ◽  
Daniel Peter ◽  
Tariq Alkhalifah
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Low Frequencies ◽  
Seismic Surveys ◽  
Full Waveform ◽  
Frequency Components ◽  
Marine Seismic

Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropriate signal-to-noise ratio in the low-frequency part of the spectrum. We have extrapolated low-frequency data from the respective higher frequency components of the seismic wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. We numerically simulate marine seismic surveys for random subsurface models and train a deep convolutional neural network to derive a mapping between high and low frequencies. The trained network is then tested on sections from the BP and SEAM Phase I benchmark models. Our results indicate that we are able to recover 0.25 Hz data from the 2 to 4.5 Hz frequencies. We also determine that the extrapolated data are accurate enough for FWI application.

Triangle Ranch Headquarters field development using shallow core holes and high‐resolution seismic data

Geophysics ◽  
1989 ◽  
Vol 54 (11) ◽  
pp. 1384-1396
Author(s):  
Howard Renick ◽  
R. D. Gunn
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Processing Parameters ◽  
Mapping Method ◽  
High Signal ◽  
Signal To Noise ◽  
Near Surface ◽  
Field Development ◽  
Reef Structure ◽  
Noise Ratio

The Triangle Ranch Headquarters Canyon Reef field is long and narrow and in an area where near‐surface evaporites and associated collapse features degrade seismic data quality and interpretational reliability. Below this disturbed section, the structure of rocks is similar to the deeper Canyon Reef structure. The shallow structure exhibits very gentle relief and can be mapped by drilling shallow holes on a broad grid. The shallow structural interpretation provides a valuable reference datum for mapping, as well as providing a basis for planning a seismic program. By computing an isopach between the variable seismic datum and the Canyon Reef reflection and subtracting the isopach map from the datum map, we map Canyon Reef structure. The datum map is extrapolated from the shallow core holes. In the area, near‐surface complexities produce seismic noise and severe static variations. The crux of the exploration problem is to balance seismic signal‐to‐noise ratio and geologic resolution. Adequate geologic resolution is impossible without understanding the exploration target. As we understood the target better, we modified our seismic acquisition parameters. Studying examples of data with high signal‐to‐noise ratio and poor resolution and examples of better defined structure on apparently noisier data led us to design an acquisition program for resolution and to reduce noise with arithmetic processes that do not reduce structural resolution. Combining acquisition and processing parameters for optimum structural resolution with the isopach mapping method has improved wildcat success from about 1 in 20 to better than 1 in 2. It has also enabled an 80 percent development drilling success ratio as opposed to slightly over 50 percent in all previous drilling.

High-signal-to-noise-ratio image processing in low-signal-to-bias-ratio environments

1997 ◽  
Author(s):  
Hakan Urey ◽  
William T. Rhodes ◽  
H. John Caulfield ◽  
Zafer Urey
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Signal To Noise ◽  
Ratio Image ◽  
Bias Ratio ◽  
Noise Ratio

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

2018 ◽  
Vol 37 (6) ◽  
pp. 451-461 ◽  
Author(s):  
Zhen Wang ◽  
Haibin Di ◽  
Muhammad Amir Shafiq ◽  
Yazeed Alaudah ◽  
Ghassan AlRegib
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Seismic Data ◽  
Domain Knowledge ◽  
Machine Learning Algorithms ◽  
Hydrocarbon Exploration ◽  
Machine Learning Techniques ◽  
Structural Interpretation ◽  
Salt Domes

As a process that identifies geologic structures of interest such as faults, salt domes, or elements of petroleum systems in general, seismic structural interpretation depends heavily on the domain knowledge and experience of interpreters as well as visual cues of geologic structures, such as texture and geometry. With the dramatic increase in size of seismic data acquired for hydrocarbon exploration, structural interpretation has become more time consuming and labor intensive. By treating seismic data as images rather than signal traces, researchers have been able to utilize advanced image-processing and machine-learning techniques to assist interpretation directly. In this paper, we mainly focus on the interpretation of two important geologic structures, faults and salt domes, and summarize interpretation workflows based on typical or advanced image-processing and machine-learning algorithms. In recent years, increasing computational power and the massive amount of available data have led to the rise of deep learning. Deep-learning models that simulate the human brain's biological neural networks can achieve state-of-the-art accuracy and even exceed human-level performance on numerous applications. The convolutional neural network — a form of deep-learning model that is effective in analyzing visual imagery — has been applied in fault and salt dome interpretation. At the end of this review, we provide insight and discussion on the future of structural interpretation.

scholarly journals Machine Learning:A Review

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Isonkobong Christopher Udousoro
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Data Interpretation ◽  
Relevant Information ◽  
Machine Learning Techniques ◽  
Predictive Analysis ◽  
Learning Approaches ◽  
Processing Data ◽  
Learning Techniques ◽  
Machine Learning Applications

Due to the complexity of data, interpretation of pattern or extraction of information becomes difficult; therefore application of machine learning is used to teach machines how to handle data more efficiently. With the increase of datasets, various organizations now apply machine learning applications and algorithms. Many industries apply machine learning to extract relevant information for analysis purposes. Many scholars, mathematicians and programmers have carried out research and applied several machine learning approaches in order to find solution to problems. In this paper, we focus on general review of machine learning including various machine learning techniques. These techniques can be applied to different fields like image processing, data mining, predictive analysis and so on. The paper aims at reviewing machine learning techniques and algorithms. The research methodology is based on qualitative analysis where various literatures is being reviewed based  on machine learning.

Seismic Data Interpretation using Digital Image Processing

2017 ◽  
Author(s):  
Abdullatif A. Al-Shuhail ◽  
Saleh A. Al-Dossary ◽  
Wail A. Mousa
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Seismic Data ◽  
Data Interpretation

Interpretation of full-azimuth broadband land data from Saudi Arabia and implications for improved inversion, reservoir characterization, and exploration

2013 ◽  
Vol 1 (2) ◽  
pp. T167-T176 ◽  
Author(s):  
Brian P. Wallick ◽  
Luis Giroldi
Keyword(s):  
Saudi Arabia ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Gas Field ◽  
Frequency Model ◽  
Low Frequencies ◽  
Limited Bandwidth

Interpretation of conventional land seismic data over a Permian-age gas field in Eastern Saudi Arabia has proven difficult over time due to low signal-to-noise ratio and limited bandwidth in the seismic volume. In an effort to improve the signal and broaden the bandwidth, newly acquired seismic data over this field have employed point receiver technology, dense wavefield sampling, a full azimuth geometry, and a specially designed sweep with useful frequencies as low as three hertz. The resulting data display enhanced reflection continuity and improved resolution. With the extension of low frequencies and improved interpretability, acoustic impedance inversion results are more robust and allow greater flexibility in reservoir characterization and prediction. In addition, because inversion to acoustic impedance is no longer completely tied to a wells-only low-frequency model, there are positive implications for exploration.

Extended stable factor method for the inverse Q-filter

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. T155-T163
Author(s):  
Yong Li ◽  
Gulan Zhang ◽  
Jing Duan ◽  
Chengjie He ◽  
Hao Du ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Time Frequency ◽  
Stable Factor ◽  
Factor Method ◽  
Data Application ◽  
Two Parameters ◽  
Limit Assumption

The commonly used stable factor methods for the inverse [Formula: see text]-filter achieve good performance in seismic data processing; however, the constant gain-limit assumption in these methods is not associated with the effective frequency band of seismic data and cannot obtain desirable results with high resolution and high signal-to-noise ratio (S/N). Our extended stable factor method for the inverse [Formula: see text]-filter extends these methods by introducing two parameters and constant or self-adaptive gain limit to achieve the desirable high-resolution and high-S/N result. The extended stable factor method for the inverse [Formula: see text]-filter can be implemented in the frequency or time-frequency domain; the latter implementation achieves a higher S/N. Analysis of synthetic signals and field seismic data application illustrate that our method can produce a desirable high-resolution and high-S/N result.

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