Fault Detection From 3-D Seismic Data and Distribution of Conjugate Faults in the Bakken Formation

Abstract Fault detection is an important, but time-consuming task in seismic data interpretation. Traditionally, seismic attributes, such as coherency (Marfurt et al., 1998) and curvature (Al-Dossary et al., 2006) are used to detect faults. Recently, machine learning methods, such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data (Wu et al., 2019). The most used algorithm is U-Net (Ronneberger et al., 2015), which can accurately and efficiently provide probability maps of faults. However, probabilities of faults generated by semantic segmentation algorithms are not sufficient for direct recognition of fault types and reconstruction of fault surfaces. To address this problem, we propose, for the first time, a workflow to use instance segmentation algorithm to detect different fault lines. Specifically, a modified CNN (LaneNet; Neven et al., 2018) is trained using automatically generated synthetic seismic images and corresponding labels. We then test the trained CNN using both synthetic and field collected seismic data. Results indicate that the proposed workflow is accurate and effective at detecting faults.

Download Full-text

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

Interpretation ◽

10.1190/int-2021-0137.1 ◽

2021 ◽

pp. 1-67

Author(s):

Stewart Smith ◽

Olesya Zimina ◽

Surender Manral ◽

Michael Nickel

Keyword(s):

Machine Learning ◽

Fault Detection ◽

Seismic Data ◽

Fault Prediction ◽

Machine Learning Techniques ◽

Gas Field ◽

Seismic Fault ◽

Learning Techniques ◽

Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

Download Full-text

Fault detection from 3D seismic data using Artificial Intelligence

10.3997/2214-4609.202132013 ◽

2021 ◽

Author(s):

J. Lowell ◽

P. Szafian

Keyword(s):

Artificial Intelligence ◽

Fault Detection ◽

Seismic Data ◽

3D Seismic ◽

3D Seismic Data

Download Full-text

Automatic Fault Detection Based on Seismic Data Correlation Analysis

72nd EAGE Conference and Exhibition incorporating SPE EUROPEC 2010 ◽

10.3997/2214-4609.201400720 ◽

2010 ◽

Author(s):

N. L. Ivanova JSC ◽

A. G. Averbukh JSC ◽

E. R. Ahmetova JSC

Keyword(s):

Fault Detection ◽

Correlation Analysis ◽

Seismic Data ◽

Data Correlation

Download Full-text

Fault Detection Based on Fully Convolutional Networks (FCN)

Journal of Marine Science and Engineering ◽

10.3390/jmse9030259 ◽

2021 ◽

Vol 9 (3) ◽

pp. 259

Author(s):

Jizhong Wu ◽

Bo Liu ◽

Hao Zhang ◽

Shumei He ◽

Qianqian Yang

Keyword(s):

Fault Detection ◽

Seismic Data ◽

Binary Image ◽

Training Data ◽

Real Field ◽

Convolutional Network ◽

Efficient Manner ◽

Data Set ◽

Seismic Image ◽

End To End

It is of great significance to detect faults correctly in continental sandstone reservoirs in the east of China to understand the distribution of remaining structural reservoirs for more efficient development operation. However, the majority of the faults is characterized by small displacements and unclear components, which makes it hard to recognize them in seismic data via traditional methods. We consider fault detection as an end-to-end binary image-segmentation problem of labeling a 3D seismic image with ones as faults and zeros elsewhere. Thus, we developed a fully convolutional network (FCN) based method to fault segmentation and used the synthetic seismic data to generate an accurate and sufficient training data set. The architecture of FCN is a modified version of the VGGNet (A convolutional neural network was named by Visual Geometry Group). Transforming fully connected layers into convolution layers enables a classification net to create a heatmap. Adding the deconvolution layers produces an efficient network for end-to-end dense learning. Herein, we took advantage of the fact that a fault binary image is highly biased with mostly zeros but only very limited ones on the faults. A balanced crossentropy loss function was defined to adjust the imbalance for optimizing the parameters of our FCN model. Ultimately, the FCN model was applied on real field data to propose that our FCN model can outperform conventional methods in fault predictions from seismic images in a more accurate and efficient manner.

Download Full-text

A local binary patterns/variance operator based on guided filtering for seismic fault detection

SN Applied Sciences ◽

10.1007/s42452-021-04866-0 ◽

2021 ◽

Vol 3 (12) ◽

Author(s):

Renfei Tian ◽

Xue Lei ◽

Min Ouyang

Keyword(s):

Fault Detection ◽

Seismic Data ◽

Oil And Gas ◽

Local Binary Patterns ◽

Identification Accuracy ◽

Practical Significance ◽

Noise Interference ◽

Oil And Gas Exploration ◽

Seismic Fault ◽

Guided Filtering

AbstractAiming at suppressing noise interference, improving the fault detection ability of seismic data, fully excavating the effective information in seismic data, and further improving the accuracy of fault detection, this study proposes a seismic fault detection method that combines the local binary pattern/variance (LBP/VAR) operator with guided filtering. The proposed method combines the advantages of LBP/VAR and guided filtering to remove noise from seismic data, and can simultaneously smooth the data and preserve linear features. When compared with several existing methods (coherent operator, LBP/VAR operator, LBP/VAR operator based on median filtering, and Canny operator based on guided filtering), the proposed method exhibits a better SNR, a better ability to identify small faults, and robustness to noise. This novel algorithm can control the balance between noise attenuation and effective signal preservation as well as effectively detect faults in seismic data. Therefore, the proposed method effectively improves the fault identification accuracy, facilitates the gas-bearing analysis of the structure, provides guidance for the actual well location deployment of the project, and has important practical significance for oil and gas exploration and development.

Download Full-text

Fault detection using neural network

Journal of Physics Theories and Applications ◽

10.20961/jphystheor-appl.v1i1.4718 ◽

2017 ◽

Vol 1 (1) ◽

pp. 70

Author(s):

Elistia Liza Namigo

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Fault Detection ◽

Seismic Data ◽

The Other ◽

Detection Technique ◽

Occurrence Probability ◽

Data Volume ◽

Data Result

<p class="Abstract">Fault detection technique using neural networks have been successfully applied to a seismic data volume. This technique is basically creating a volume that highlights faults by combining the information from several fault indicators attributes (i.e. similarity, curvature and energy) into fault occurrence probability. This is performed by training a neural network on two sets of attributes extracted at sample locations picked manually - one set represents the fault class and the other represents the non-fault class. The next step is to apply the trained artificial neural network on the seismic data. Result indicates that faults are more highlighted and have better continuity since the surrounding noise are mostly suppressed.</p>

Download Full-text

Fault detection using neural network

Journal of Theoretical and Applied Physics ◽

10.20961/jotap.v1i1.4718 ◽

2017 ◽

Vol 1 (1) ◽

pp. 70

Author(s):

Elistia Liza Namigo

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Fault Detection ◽

Seismic Data ◽

The Other ◽

Detection Technique ◽

Occurrence Probability ◽

Data Volume ◽

Data Result

<p class="Abstract">Fault detection technique using neural networks have been successfully applied to a seismic data volume. This technique is basically creating a volume that highlights faults by combining the information from several fault indicators attributes (i.e. similarity, curvature and energy) into fault occurrence probability. This is performed by training a neural network on two sets of attributes extracted at sample locations picked manually - one set represents the fault class and the other represents the non-fault class. The next step is to apply the trained artificial neural network on the seismic data. Result indicates that faults are more highlighted and have better continuity since the surrounding noise are mostly suppressed. </p>

Download Full-text

On: “Subsurface fault detection using seismic data for hazardous‐waste‐injection well permitting: An example from St. John the Baptist Parish, Louisiana” by E. V. Zinni (March‐April 1995 GEOPHYSICS, 60, p. 468–475)

Geophysics ◽

10.1190/1.1444142 ◽

1997 ◽

Vol 62 (1) ◽

pp. 381-383

Author(s):

Don L. Warner

Keyword(s):

Water Quality ◽

Structural Geology ◽

Fault Detection ◽

Hazardous Waste ◽

Seismic Data ◽

Injection Well ◽

John The Baptist ◽

Freshwater Aquifers

Zinni (1995) has studied the stratigraphy and structural geology of injection reservoirs and aquifers in the vicinity of the DuPont Ponchartrain Works plant and has concluded that “The injection of liquid hazardous waste into the 2440-, 1430-m, and 1130-m injection reservoirs could jeopardize not only the water quality of the Covington aquifer, but possibly other shallow freshwater aquifers….”

Download Full-text