Appraising structural interpretations using seismic data — Theoretical elements

Structural interpretation of seismic images can be highly subjective, especially in complex geologic settings. A single seismic image will often support multiple geologically valid interpretations. However, it is usually difficult to determine which of those interpretations are more likely than others. We have referred to this problem as structural model appraisal. We have developed the use of misfit functions to rank and appraise multiple interpretations of a given seismic image. Given a set of possible interpretations, we compute synthetic data for each structural interpretation, and then we compare these synthetic data against observed seismic data; this allows us to assign a data-misfit value to each structural interpretation. Our aim is to find data-misfit functions that enable a ranking of interpretations. To do so, we formalize the problem of appraising structural interpretations using seismic data and we derive a set of conditions to be satisfied by the data-misfit function for a successful appraisal. We investigate vertical seismic profiling (VSP) and surface seismic configurations. An application of the proposed method to a realistic synthetic model shows promising results for appraising structural interpretations using VSP data, provided that the target region is well-illuminated. However, we find appraising structural interpretations using surface seismic data to be more challenging, mainly due to the difficulty of computing phase-shift data misfits.

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Pitfall experiences when interpreting complex structure with low-quality seismic images

Interpretation ◽

10.1190/int-2014-0118.1 ◽

2015 ◽

Vol 3 (1) ◽

pp. SB29-SB37 ◽

Cited By ~ 1

Author(s):

Bob A. Hardage

Keyword(s):

Seismic Data ◽

Structural Model ◽

Signal To Noise Ratio ◽

Complex Structure ◽

Lessons Learned ◽

Structural Deformation ◽

Structural Interpretation ◽

Seismic Image ◽

Data Volume ◽

Seismic Images

Structural interpretation of seismic data presents numerous opportunities for encountering interpretational pitfalls, particularly when a seismic image does not have an appropriate signal-to-noise ratio (S/N), or when a subsurface structure is unexpectedly complex. When both conditions exist — low S/N data and severe structural deformation — interpretation pitfalls are almost guaranteed. We analyzed an interpretation done 20 years ago that had to deal with poor seismic data quality and extreme distortion of strata. The lessons learned still apply today. Two things helped the interpretation team develop a viable structural model of the prospect. First, existing industry-accepted formation tops assigned to regional wells were rejected and new log interpretations were done to detect evidence of repeated sections and overturned strata. Second, the frequency content of the 3D seismic data volume was restricted to only the first octave of its seismic spectrum to create better evidence of fault geometries. A logical and workable structural interpretation resulted when these two action steps were taken. To the knowledge of our interpretation team, neither of these approaches had been attempted in the area at the time of this work (early 1990s). We found two pitfalls that may be encountered by other interpreters. The first pitfall was the hazard of accepting long-standing, industry-accepted definitions of the positions of formation tops on well logs. This nonquestioning acceptance of certain log signatures as indications of targeted formation tops led to a serious misinterpretation in our study. The second pitfall was the prevailing passion by geophysicists to create seismic data volumes that have the widest possible frequency spectrum. This interpretation effort showed that the opposite strategy was better at this site and for our data conditions; i.e., it was better to filter seismic images so that they contained only the lowest octave of frequencies in the seismic spectrum.

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Edge-aware filtering with Siamese neural networks

The Leading Edge ◽

10.1190/tle39100711.1 ◽

2020 ◽

Vol 39 (10) ◽

pp. 711-717

Author(s):

Mehdi Aharchaou ◽

Michael Matheney ◽

Joe Molyneux ◽

Erik Neumann

Keyword(s):

Neural Networks ◽

Seismic Data ◽

Learning Ability ◽

Seismic Exploration ◽

Data Sets ◽

3D Seismic ◽

New Class ◽

Seismic Image ◽

Siamese Networks ◽

Seismic Images

Recent demands to reduce turnaround times and expedite investment decisions in seismic exploration have invited new ways to process and interpret seismic data. Among these ways is a more integrated collaboration between seismic processors and geologist interpreters aiming to build preliminary geologic models for early business impact. A key aspect has been quick and streamlined delivery of clean high-fidelity 3D seismic images via postmigration filtering capabilities. We present a machine learning-based example of such a capability built on recent advances in deep learning systems. In particular, we leverage the power of Siamese neural networks, a new class of neural networks that is powerful at learning discriminative features. Our novel adaptation, edge-aware filtering, employs a deep Siamese network that ranks similarity between seismic image patches. Once the network is trained, we capitalize on the learned features and self-similarity property of seismic images to achieve within-image stacking power endowed with edge awareness. The method generalizes well to new data sets due to the few-shot learning ability of Siamese networks. Furthermore, the learning-based framework can be extended to a variety of noise types in 3D seismic data. Using a convolutional architecture, we demonstrate on three field data sets that the learned representations lead to superior filtering performance compared to structure-oriented filtering. We examine both filtering quality and ease of application in our analysis. Then, we discuss the potential of edge-aware filtering as a data conditioning tool for rapid structural interpretation.

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Horizon extraction using ordered clustering on a directed and colored graph

Interpretation ◽

10.1190/int-2018-0161.1 ◽

2020 ◽

Vol 8 (1) ◽

pp. T1-T11

Author(s):

Zhining Liu ◽

Chengyun Song ◽

Kunhong Li ◽

Bin She ◽

Xingmiao Yao ◽

...

Keyword(s):

Seismic Data ◽

Spatial Information ◽

Spatial Distributions ◽

Global Level ◽

Clustering Method ◽

Colored Graph ◽

Seismic Image ◽

Level Information ◽

Novel Method ◽

Seismic Images

Extracting horizons from a seismic image has been playing an important role in seismic interpretation. However, how to fully use global-level information contained in the seismic images such as the order of horizon sequences is not well-studied in existing works. To address this issue, we have developed a novel method based on a directed and colored graph, which encodes effective context information for horizon extraction. Following the commonly used framework, which generates horizon patches and then groups them into horizons, we first build a directed and colored graph by representing horizon patches as vertices. In addition, edges in the graph encode the relative spatial positions of horizon patches. This graph explicitly captures the geologic context, which guides the grouping of the horizon patches. Then, we conduct premerging to group horizon patches through matching some predefined subgraph patterns that are designed to capture some special spatial distributions of horizon patches. Finally, we have developed an ordered clustering method to group the rest of the horizon patches into horizons based on the pairwise similarities of horizon patches while preserving geologic reasonability. Experiments on real seismic data indicate that our method can outperform the autotracking approach solely based on the similarity of local waveforms and can correctly pick the horizons even across the fault without any crossing, which demonstrates the effectiveness of exploring the spatial information, i.e., the order of horizon sequences and special spatial distribution of horizon patches.

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Building realistic structure models to train convolutional neural networks for seismic structural interpretation

Geophysics ◽

10.1190/geo2019-0375.1 ◽

2020 ◽

Vol 85 (4) ◽

pp. WA27-WA39 ◽

Cited By ~ 10

Author(s):

Xinming Wu ◽

Zhicheng Geng ◽

Yunzhi Shi ◽

Nam Pham ◽

Sergey Fomel ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Structural Information ◽

Ground Truth ◽

Structural Features ◽

Image Features ◽

Training Data ◽

Structural Interpretation ◽

Seismic Image ◽

Seismic Images

Seismic structural interpretation involves highlighting and extracting faults and horizons that are apparent as geometric features in a seismic image. Although seismic image processing methods have been proposed to automate fault and horizon interpretation, each of which today still requires significant human effort. We improve automatic structural interpretation in seismic images by using convolutional neural networks (CNNs) that recently have shown excellent performances in detecting and extracting useful image features and objects. The main limitation of applying CNNs in seismic interpretation is the preparation of many training data sets and especially the corresponding geologic labels. Manually labeling geologic features in a seismic image is highly time-consuming and subjective, which often results in incompletely or inaccurately labeled training images. To solve this problem, we have developed a workflow to automatically build diverse structure models with realistic folding and faulting features. In this workflow, with some assumptions about typical folding and faulting patterns, we simulate structural features in a 3D model by using a set of parameters. By randomly choosing the parameters from some predefined ranges, we are able to automatically generate numerous structure models with realistic and diverse structural features. Based on these structure models with known structural information, we further automatically create numerous synthetic seismic images and the corresponding ground truth of structural labels to train CNNs for structural interpretation in field seismic images. Accurate results of structural interpretation in multiple field seismic images indicate that our workflow simulates realistic and generalized structure models from which the CNNs effectively learn to recognize real structures in field images.

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Structure of giant buried mud volcanoes in the South Caspian Basin: Enhanced seismic image and field gravity data by using normalized full gradient method

Interpretation ◽

10.1190/int-2018-0009.1 ◽

2018 ◽

Vol 6 (4) ◽

pp. T861-T872

Author(s):

Mehrdad Soleimani ◽

Hamid Aghajani ◽

Saeed Heydari-Nejad

Keyword(s):

Seismic Data ◽

Root Zone ◽

Gravity Data ◽

Complex Structure ◽

Normal Faults ◽

Mud Volcanoes ◽

South Caspian Basin ◽

Structural Interpretation ◽

Seismic Image ◽

Normalized Full Gradient

Defining the root zone of mud volcanoes (MVs), structural interpretation, and geologic modeling of their body is a problematic task when only seismic data are available. We have developed a strategy for integration of gravity and seismic data for better structural interpretation. Our strategy uses the concept of the normalized full gradient (NFG) for integration of gravity and seismic data to define geometry and the root zone of MVs in the southeast onshore of the Caspian Sea. Our strategy will increase the resolution of the seismic envelope compared with the conventional Hilbert transform. Prior to interpretation, we applied the NFG method on field gravity data. First, we perform a forward-modeling step for accurate NFG parameter definition. Second, we estimate the depth of the target, which is the root zone of the MV here. Interpretation of field gravity data by optimized NFG parameters indicates an accurate depth of the root zone. Subsequently, we apply the NFG method with optimized parameters on a 2D seismic data. Application of our strategy on seismic data will enhance resolution of the seismic image. The depth of the root zone and the geometry of the MV and mud flows was interpreted better on the enhanced image. It also illustrates the complex structure of a giant buried MV, which was not well-interpreted on conventional seismic image. Interpretation of the processed data reveals that the giant MV had lost its connection to its reservoir, whereas the other MV is still connected to the mud reservoir. The giant MV is composed of complex bodies due to pulses in the mud flows. Another MV in the section indicates narrow neck with anticline and listric normal faults on its top. Thus, application of the NFG concept on seismic image could be considered as an alternative to obtain enhanced seismic image for geologic interpretation.

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THE USE OF VSP TECHNIQUES IN FIELD EVALUATION

The APPEA Journal ◽

10.1071/aj85022 ◽

1986 ◽

Vol 26 (1) ◽

pp. 226

Author(s):

R.L. Ireson

Keyword(s):

Seismic Data ◽

Mode Conversion ◽

Field Evaluation ◽

Scalar Wave ◽

Seismic Profiling ◽

Vertical Seismic Profiling ◽

Vsp Data ◽

Reservoir Porosity ◽

Deconvolution Techniques ◽

Inversion Techniques

Vertical Seismic Profiling (VSP) techniques have been developed which have found application in the development and production of hydrocarbons as well as in exploration.A VSP is initiated by outlining the objectives of the survey and, using a model of the geology in the vicinity of the borehole, applying inversion techniques to determine the VSP method best suited to provide the required data. Reflection coverage, critical refractions, and mode conversion data (using 3-component geophones) can be used to obtain a structural, lithologic, and petrophysical interpretation.Structural interpretations using recently developed migration techniques based on the scalar wave equation can now provide images of the sub-surface which are superior to those obtained using previous ray-trace schemes.Acoustic impedance estimates from VSP surveys processed with deterministic deconvolution techniques and optimal amplitude recovery to obtain maximum temporal resolution can be more accurate than those obtained from surface seismic data. Offset VSP data can also give an estimate of reservoir porosity variations away from the borehole.

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Using petrophysics and cross‐section balancing to interpret complex structure in a limited‐quality 3-D seismic image

Geophysics ◽

10.1190/1.1444682 ◽

1999 ◽

Vol 64 (6) ◽

pp. 1760-1773 ◽

Cited By ~ 5

Author(s):

Bob A. Hardage ◽

Virginia M. Pendleton ◽

R. P. Major ◽

George B. Asquith ◽

Dan Schultz‐Ela ◽

...

Keyword(s):

Cross Section ◽

Seismic Data ◽

Complex Structure ◽

Negative Influence ◽

Near Surface ◽

Structural Interpretation ◽

West Texas ◽

Seismic Image ◽

Variable Surface ◽

Seismic Area

A study was done to characterize deep, prolific Ellenburger gas reservoirs at Lockridge, Waha, West Waha, and Worsham‐Bayer fields in Pecos, Ward, and Reeves counties in West Texas. A major effort of the study was to interpret a 176-mi2 3-D seismic data volume that spanned these fields. Well control defined the depth of the Ellenburger, the principal interpretation target, to be 17 000–21 000 ft (5200–6400 m) over the image area. Ellenburger reflection signals were weak because of these great target depths. Additionally, the top of the Ellenburger had a gentle, ramp‐like increase in acoustic impedance that did not produce a robust reflection event. A further negative influence on seismic data quality was the fact that a large portion of the 3-D seismic area was covered by a variable surface layer of low‐velocity Tertiary fill that was, in turn, underlain by a varying thickness of high‐velocity salt/anhydrite. These complicated near‐surface conditions attenuated seismic reflection signals and made static corrections of the data difficult. The combination of all these factors has caused many explorationists to consider this region of west Texas a no‐record seismic area for deep drilling targets. Although the 3-D seismic data aquired in this study produced good‐quality images throughout the post‐Mississippian section (down to ∼12 000 ft, or 3700 m), the images of the deep Ellenburger targets (∼20 000 ft, or 6100 m) were limited quality. The challenge was to use this limited‐quality 3-D image to interpret the structural configuration of the deep Ellenburger and the fault systems that traverse the area so that genetic relationship could be established between fault attributes and productive Ellenburger facies. Two techniques were used to produce a reliable structural interpretation of the 3-D seismic data. First, log data recorded in 60-plus wells within the 3-D image space were analyzed to determine where there was evidence of overturned and repeated units caused by thrusting and evidence of missing sections caused by normal faulting. These petrophysical analyses allowed reliable fault patterns and structural configurations to be build across 3-D seismic image zones that were difficult to interpret by conventional methods. Second, cross‐section balancing was done across the more complex structural regimes to determine if each interpreted surface that was used to define the postdeformation structure had a length consistent with the length of that same surface before deformation. The petrophysical analyses thus guided the structural interpretation of the 3-D seismic data by inferring the fault patterns that should be imposed on the limited‐quality image zones; the cross‐section balancing verified where this structural interpretation was reliable and where it needed to be adjusted. This interpretation methodology is offered here to benefit others who are confronted with the problem of interpreting complex structure from limited‐quality 3-D seismic images.

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Transforming walk‐away VSP data into reverse VSP data

Geophysics ◽

10.1190/1.1443862 ◽

1995 ◽

Vol 60 (4) ◽

pp. 968-977 ◽

Cited By ~ 13

Author(s):

Rune Mittet ◽

Ketil Hokstad

Keyword(s):

Synthetic Data ◽

Computer Time ◽

Elastic Model ◽

Data Sets ◽

Walk Away ◽

Vertical Seismic Profiling ◽

Experimental Configuration ◽

Vsp Data ◽

Using Data ◽

High Order Finite Difference

Marine walk‐away vertical seismic profiling (VSP) data can be transformed into reverse VSP data using an elastic reciprocity transformation. A reciprocity transform is derived and tested using data generated with a 2-D high‐order, finite‐difference modeling scheme in a complex elastic model. First, 201 shots are generated with a walk‐away VSP experimental configuration. Both the x‐component and the z‐component of the displacement are measured. These data are collected in two common receiver data sets. Then two shots are generated in a reverse VSP configuration. We demonstrate that subtraction of the reverse VSP data from the walk‐away VSP data gives very small residuals. The transformation of walk‐away data into reverse VSP data makes prestack shot‐domain migration feasible for walk‐away data. Synthetic data from a multishot walk‐away experiment can be obtained from one or a few modeling operations with a RVSP experimental configuration. The required computer time is reduced by two orders of magnitude.

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Multitask learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network

Geophysical Journal International ◽

10.1093/gji/ggz418 ◽

2019 ◽

Vol 219 (3) ◽

pp. 2097-2109 ◽

Cited By ~ 3

Author(s):

Xinming Wu ◽

Luming Liang ◽

Yunzhi Shi ◽

Zhicheng Geng ◽

Sergey Fomel

Keyword(s):

Neural Network ◽

Image Processing ◽

Fault Detection ◽

Convolutional Neural Network ◽

Basic Step ◽

Edge Preserving ◽

Structural Interpretation ◽

Seismic Image ◽

Normal Vectors ◽

Seismic Images

Summary Fault detection in a seismic image is a key step of structural interpretation. Structure-oriented smoothing with edge-preserving removes noise while enhancing seismic structures and sharpening structural edges in a seismic image, which, therefore, facilitates and accelerates the seismic structural interpretation. Estimating seismic normal vectors or reflection slopes is a basic step for many other seismic data processing tasks. All the three seismic image processing tasks are related to each other as they all involve the analysis of seismic structural features. In conventional seismic image processing schemes, however, these three tasks are often independently performed by different algorithms and challenges remain in each of them. We propose to simultaneously perform all the three tasks by using a single convolutional neural network (CNN). To train the network, we automatically create thousands of 3-D noisy synthetic seismic images and corresponding ground truth of fault images, clean seismic images and seismic normal vectors. Although trained with only the synthetic data sets, the network automatically learns to accurately perform all the three image processing tasks in a general seismic image. Multiple field examples show that the network is significantly superior to the conventional methods in all the three tasks of computing a more accurate and sharper fault detection, a smoothed seismic volume with better enhanced structures and structural edges, and more accurate seismic normal vectors or reflection slopes. Using a Titan Xp GPU, the training processing takes about 8 hr and the trained model takes only half a second to process a seismic volume with $128\, \times \, 128\, \times \, 128$ image samples.

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Geologic modeling study of foothill area of the Junggar Basin rim

Interpretation ◽

10.1190/int-2018-0003.1 ◽

2018 ◽

Vol 6 (4) ◽

pp. SM19-SM26

Author(s):

Guanlong Zhang ◽

Peng Xiang ◽

Jinduo Wang ◽

Tieliang Lyu ◽

Yulei Qiao ◽

...

Keyword(s):

Seismic Data ◽

Structural Model ◽

Junggar Basin ◽

Velocity Model ◽

Western China ◽

Mountain Area ◽

Tectonic Model ◽

Thrust Belts ◽

Seismic Image ◽

Comprehensive Modeling

The varied terrain and complex subsurface structure in the foothill segment of fold and thrust belts results in low-quality seismic data. Therefore, a structural model built only on the basis of seismic will have low reliability and be nonunique. To solve these problems, we have developed a comprehensive modeling method for foothill zones that combines gravity, magnetic (MT), electric, and seismic data. Information from gravity, MT, electric, and seismic data is fully used in each step of modeling. This reduces the nonuniqueness and guarantees the rationality and reliability of the tectonic model. The core of this procedure is simultaneous joint inversing gravity, MT, electric, and seismic multiparameters, which improves the accuracy of velocity model and results in a higher quality of seismic image. The Hala’alate Mountain area in the Junggar Basin, western China, is chosen as the application place, and the process of modeling is evaluated in detail; the structural model is proven to be correct by drilling data. Our method is more accurate and reliable than methods only using seismic data to build a geologic model in foothill zones.

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