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

2015 ◽  
Vol 3 (1) ◽  
pp. SB29-SB37 ◽  
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.

scholarly journals Appraising structural interpretations using seismic data — Theoretical elements

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. N29-N40
Author(s):  
Modeste Irakarama ◽  
Paul Cupillard ◽  
Guillaume Caumon ◽  
Paul Sava ◽  
Jonathan Edwards
Keyword(s):  
Seismic Data ◽  
Structural Model ◽  
Synthetic Data ◽  
Target Region ◽  
Structural Interpretation ◽  
Vertical Seismic Profiling ◽  
Seismic Image ◽  
Vsp Data ◽  
Phase Shift Data ◽  
Seismic Images

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.

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

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.

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

Geophysics ◽  
1999 ◽  
Vol 64 (6) ◽  
pp. 1760-1773 ◽  
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.

Depth migration as a valuable tool for prospect derisking in the Colombian Foothills

2017 ◽  
Vol 5 (4) ◽  
pp. SR13-SR21
Author(s):  
Sergio Ibanez Poveda ◽  
Mario Patino ◽  
John Mathewson
Keyword(s):  
Signal To Noise Ratio ◽  
Lessons Learned ◽  
Initial Assessment ◽  
Borehole Data ◽  
Depth Migration ◽  
Time Migration ◽  
Seismic Image ◽  
Prestack Time Migration ◽  
Definition Of ◽  
Seismic Images

Interpreting land seismic data in the Colombian Foothills poses many challenges. Very often, the data have a low signal-to-noise ratio and the subsurface prospective structures are significantly complex. The level of uncertainty can be so high that even experienced interpreters struggle to reconcile the seismic image with their geologic models. Based on previous knowledge and “fast-track” interpretation of old 2D prestack time migration data (no 3D seismic in the area), we identified two interesting plays for further analysis: a triangular zone (play 1) and subthrust anticlines beneath the frontal fault (play 2). Derisking the different prospects associated with plays 1 and 2 required the application of prestack depth migration (PSDM), which reduce uncertainty regarding the position of the structures, their depth, and even their existence. The seismic image in play 1 structures was improved significantly with better definition of the flanks of the anticlines and the frontal closure of the structures, more coherent events, and sharper definition in fault cut-offs. Some apparent play 2 prospects, that were actually “velocity pull-up” anticlines, were corrected by the depth-migration workflow, whereas other structures experience important modifications in their geometry. In both types of plays, depth migration dramatically changed the initial assessment of prospectivity. Based on the better agreement between seismic and borehole data, significant reduction in residual moveout on final PSDM gathers and more coherent seismic images, we believe that the use of depth migration has allowed us to obtain a more accurate representation of the subsurface, and consequently a more rigorous reserves estimation. The use of PSDM was essential to understand the complexities in the prospects evaluated and the risk associated with their exploration. We consider the lessons learned in this study applicable to similar geologic environments worldwide.

Edge-aware filtering with Siamese neural networks

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.

A Seismic Image Denoising Method Based on Kernel-prediction CNN Architecture

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040012
Author(s):  
Li Lou ◽  
Yong Li
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Denoising Method ◽  
Spatial Feature ◽  
Residual Noise ◽  
Seismic Image ◽  
Feature Extractor ◽  
Seismic Images ◽  
Asymmetric Loss Function ◽  
Image Details

To filter noises and preserve the details of seismic images, a denoising method based on kernel prediction convolution neural network (CNN) architecture is proposed. The method consists of two convolution layers and a residual connection, containing a source sensing encoder, a spatial feature extractor and a kernel predictor. The scalar kernel was normalized by the softmax function to obtain the denoised images. In addition, to avoid excessive blur at the expense of image details, the authors put forward the concept of asymmetric loss function, which would enable users to control the level of residual noise and make a trade-off between variance and deviation. The experimental results show the proposed method achieved good denoising effect. Compared with some other excellent methods, the proposed method increased the peak signal-to-noise ratio (PSNR) by about 1.0–3.2 dB for seismic images without discontinuity, and about 1.8–3.9 dB for seismic images with discontinuity.

Horizon extraction using ordered clustering on a directed and colored graph

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.

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

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA27-WA39 ◽  
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.

Confirming Reservoir Geometry and Exploration Opportunity through 5D Interpolation PSDM in The Sub-ophiolite Zone, Banggai Sula Basin, Central Sulawesi

2021 ◽  
Author(s):  
M. Ahmad
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Complex Structure ◽  
Geological Model ◽  
Processing Efficiency ◽  
Data Control ◽  
Velocity Information ◽  
Reservoir Geometry ◽  
New Interpretation ◽  
Central Sulawesi

In 2020, Pertamina EP made another gas discovery in Banggai-Sula Basin. However, the prediction missed the top of formation by several hundred meters and therefore increased the uncertainty in the actual amount of the discovered gas resources. The previous geological model was based on 3D Pre-Stack Depth Migrated seismic data from 2018. With only one well as data control, plus complex structure and the proximity to the Batui thrust, the velocity has not been modeled correctly. To reduce the uncertainty, a reprocessed seismic data is required to help building an updated geological model. The newly drilled well, WOL-002, provides new velocity information for PSDM reprocessing. It is however located below an ophiolite layer when signals are very weak. To improve the signal to noise ratio, a powerful yet time-consuming 5D Interpolation is employed. This step significantly improves image quality especially below the thrust. Since this project is tightly time constrained, a careful parameterization has been done for improving processing efficiency. As a result, the imaging can be finished ahead of schedule. Reflector’s depth is confirmed using not only velocity data from well W-20 but also from the neighboring field. New interpretation based on the 2020 reprocessing suggests a larger structure in the subsurface compared to the previous model. This newly processed 3D seismic is also used for identifying new prospect closer toward the thrust zones.

2.5D multifocusing imaging of crooked-line seismic surveys

Geophysics ◽  
2021 ◽  
pp. 1-67
Author(s):  
Hossein Jodeiri Akbari Fam ◽  
Mostafa Naghizadeh ◽  
Oz Yilmaz
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Line Geometry ◽  
Data Set ◽  
Seismic Surveys ◽  
Seismic Image ◽  
New Formulation ◽  
Optimization Search

Two-dimensional seismic surveys often are conducted along crooked line traverses due to the inaccessibility of rugged terrains, logistical and environmental restrictions, and budget limitations. The crookedness of line traverses, irregular topography, and complex subsurface geology with steeply dipping and curved interfaces could adversely affect the signal-to-noise ratio of the data. The crooked-line geometry violates the assumption of a straight-line survey that is a basic principle behind the 2D multifocusing (MF) method and leads to crossline spread of midpoints. Additionally, the crooked-line geometry can give rise to potential pitfalls and artifacts, thus, leads to difficulties in imaging and velocity-depth model estimation. We develop a novel multifocusing algorithm for crooked-line seismic data and revise the traveltime equation accordingly to achieve better signal alignment before stacking. Specifically, we present a 2.5D multifocusing reflection traveltime equation, which explicitly takes into account the midpoint dispersion and cross-dip effects. The new formulation corrects for normal, inline, and crossline dip moveouts simultaneously, which is significantly more accurate than removing these effects sequentially. Applying NMO, DMO, and CDMO separately tends to result in significant errors, especially for large offsets. The 2.5D multifocusing method can perform automatically with a coherence-based global optimization search on data. We investigated the accuracy of the new formulation by testing it on different synthetic models and a real seismic data set. Applying the proposed approach to the real data led to a high-resolution seismic image with a significant quality improvement compared to the conventional method. Numerical tests show that the new formula can accurately focus the primary reflections at their correct location, remove anomalous dip-dependent velocities, and extract true dips from seismic data for structural interpretation. The proposed method efficiently projects and extracts valuable 3D structural information when applied to crooked-line seismic surveys.

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