scholarly journals Simultaneous multiple well-seismic ties using flattened synthetic and real seismograms

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. IM13-IM20 ◽  
Author(s):  
Xinming Wu ◽  
Guillaume Caumon
Keyword(s):  
Seismic Data ◽  
Correlation Method ◽  
Seismic Interpretation ◽  
Synthetic Seismograms ◽  
Rock Properties ◽  
Correlation Problem ◽  
Seismic Image ◽  
Seismic Reflectors

Well-seismic ties allow rock properties measured at well locations to be compared with seismic data and are therefore useful for seismic interpretation. Numerous methods have been proposed to compute well-seismic ties by correlating real seismograms with synthetic seismograms computed from velocity and density logs. However, most methods tie multiple wells to seismic data one by one; hence, they do not guarantee lateral consistency among multiple well ties. We therefore propose a method to simultaneously tie multiple wells to seismic data. In this method, we first flatten synthetic and corresponding real seismograms so that all seismic reflectors are horizontally aligned. By doing this, we turn multiple well-seismic tying into a 1D correlation problem. We then compute only vertically variant but laterally constant shifts to correlate these horizontally aligned (flattened) synthetic and real seismograms. This two-step correlation method maintains lateral consistency among multiple well ties by computing a laterally and vertically optimized correlation of all synthetic and real seismograms. We applied our method to a 3D real seismic image with multiple wells and obtained laterally consistent well-seismic ties.

Creating detailed subsurface models using predictive image-guided well-log interpolation

2017 ◽  
Vol 5 (3) ◽  
pp. T279-T285 ◽  
Author(s):  
Parvaneh Karimi ◽  
Sergey Fomel ◽  
Rui Zhang
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Structural Information ◽  
Rock Properties ◽  
Well Log ◽  
Initial Model ◽  
The North ◽  
Structural Framework ◽  
Seismic Image ◽  
Seismic Reflectors

Integration of well-log data and seismic data to predict rock properties is an essential but challenging task in reservoir characterization. The standard methods commonly used to create subsurface model do not fully honor the importance of seismic reflectors and detailed structural information in guiding the spatial distribution of rock properties in the presence of complex structures, which can make these methods inaccurate. To overcome initial model accuracy limitations in structurally complex regimes, we have developed a method that uses the seismic image structures to accurately constrain the interpolation of well properties between well locations. A geologically consistent framework provides a more robust initial model that, when inverted with seismic data, delivers a highly detailed yet accurate subsurface model. An application to field data from the North Sea demonstrates the effectiveness of our method, which proves that incorporating the seismic structural framework when interpolating rock properties between wells culminates in the increased accuracy of the final inverted result compared with the standard inversion workflows.

Moving faults while unfaulting 3D seismic images

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. IM25-IM33 ◽  
Author(s):  
Xinming Wu ◽  
Simon Luo ◽  
Dave Hale
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Extraction Methods ◽  
Seismic Interpretation ◽  
3D Seismic ◽  
Partial Differential ◽  
Multiple Faults ◽  
Seismic Image ◽  
Seismic Reflectors ◽  
Seismic Images

Unfaulting seismic images to correlate seismic reflectors across faults is helpful in seismic interpretation and is useful for seismic horizon extraction. Methods for unfaulting typically assume that fault geometries need not change during unfaulting. However, for seismic images containing multiple faults and, especially, intersecting faults, this assumption often results in unnecessary distortions in unfaulted images. We have developed two methods to compute vector shifts that simultaneously move fault blocks and the faults themselves to obtain an unfaulted image with minimal distortions. For both methods, we have used estimated fault positions and slip vectors to construct unfaulting equations for image samples alongside faults, and we have constructed simple partial differential equations for samples away from faults. We have solved these two different kinds of equations simultaneously to compute unfaulting vector shifts that are continuous everywhere except at faults. We have tested both methods on a synthetic seismic image containing normal, reverse, and intersecting faults. We also have applied one of the methods to a real 3D seismic image complicated by numerous intersecting faults.

scholarly journals Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodelling and machine learning

2019 ◽  
Author(s):  
Alexander Schaaf ◽  
Clare E. Bond
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Seismic Data ◽  
Fault Plane ◽  
Seismic Interpretation ◽  
Spatial Uncertainty ◽  
Seismic Image ◽  
Quantitative Understanding ◽  
Northern North Sea ◽  
Fault Network

Abstract. In recent years uncertainty has been widely recognized in geosciences, leading to an increased need for its quantification. Predicting the subsurface is an especially uncertain effort, as our information either comes from spatially highly limited direct (1-D boreholes) or indirect 2-D and 3-D sources (e.g. seismic). And while uncertainty in seismic interpretation has been explored in 2-D, we currently lack both qualitatitive and quantitative understanding of how interpretational uncertainties of 3-D datasets are distributed. In this work we analyze 78 seismic interpretations done by final year undergraduate (BSc) students of a 3-D seismic dataset from the Gullfaks field located in the northern North Sea. The students used Petrel to interpret multiple (interlinked) faults and to pick the Base Cretaceous Unconformity and Top Ness horizon (part of the Mid-Jurassic Brent Group). We have developed open-source Python tools to explore and visualize the spatial uncertainty of the students fault stick interpretations, the subsequent variation in fault plane orientation and the uncertainty in fault network topology. The Top Ness horizon picks were used to analyze fault offset variations across the dataset and interpretations, with implications for fault throw. We investigate how this interpretational uncertainty interlinks with seismic data quality and the possible use of seismic data quality attributes as a proxy for interpretational uncertainty. Our work provides a first quantification of fault and horizon uncertainties in 3-D seismic interpretation, providing valuable insights into the influence of seismic image quality on 3-D interpretation, with implications for deterministic and stochastic geomodelling and machine learning.

scholarly journals Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodeling and machine learning

Solid Earth ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 1049-1061 ◽  
Author(s):  
Alexander Schaaf ◽  
Clare E. Bond
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Seismic Data ◽  
Seismic Interpretation ◽  
Spatial Uncertainty ◽  
Qualitative And Quantitative ◽  
Seismic Image ◽  
Quantitative Understanding ◽  
Northern North Sea ◽  
Fault Network

Abstract. In recent years, uncertainty has been widely recognized in geosciences, leading to an increased need for its quantification. Predicting the subsurface is an especially uncertain effort, as our information either comes from spatially highly limited direct (1-D boreholes) or indirect 2-D and 3-D sources (e.g., seismic). And while uncertainty in seismic interpretation has been explored in 2-D, we currently lack both qualitative and quantitative understanding of how interpretational uncertainties of 3-D datasets are distributed. In this work, we analyze 78 seismic interpretations done by final-year undergraduate (BSc) students of a 3-D seismic dataset from the Gullfaks field located in the northern North Sea. The students used Petrel to interpret multiple (interlinked) faults and to pick the Base Cretaceous Unconformity and Top Ness horizon (part of the Middle Jurassic Brent Group). We have developed open-source Python tools to explore and visualize the spatial uncertainty of the students' fault stick interpretations, the subsequent variation in fault plane orientation and the uncertainty in fault network topology. The Top Ness horizon picks were used to analyze fault offset variations across the dataset and interpretations, with implications for fault throw. We investigate how this interpretational uncertainty interlinks with seismic data quality and the possible use of seismic data quality attributes as a proxy for interpretational uncertainty. Our work provides a first quantification of fault and horizon uncertainties in 3-D seismic interpretation, providing valuable insights into the influence of seismic image quality on 3-D interpretation, with implications for deterministic and stochastic geomodeling and machine learning.

Noise suppression of time-migrated gathers using prestack structure-oriented filtering

2016 ◽  
Vol 4 (2) ◽  
pp. SG19-SG29 ◽  
Author(s):  
Bo Zhang ◽  
Tengfei Lin ◽  
Shiguang Guo ◽  
Oswaldo E. Davogustto ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Seismic Analysis ◽  
Rock Properties ◽  
Fort Worth ◽  
Edge Preserving ◽  
Fort Worth Basin ◽  
Seismic Image ◽  
Data Conditioning

Prestack seismic analysis provides information on rock properties, lithology, fluid content, and the orientation and intensity of anisotropy. However, such analysis demands high-quality seismic data. Unfortunately, noise is always present in seismic data even after careful processing. Noise in the prestack gathers may not only contaminate the seismic image, thereby lowering the quality of seismic interpretation, but it may also bias the seismic prestack inversion for rock properties, such as acoustic- and shear-impedance estimation. Common postmigration data conditioning includes running window median and Radon filters that are applied to the flattened common reflection point gathers. We have combined filters across the offset and azimuth with edge-preserving filters along the structure to construct a true “5D” filter that preserves amplitude, thereby preconditioning the data for subsequent quantitative analysis. We have evaluated our workflow by applying it to a prestack seismic volume acquired over the Fort Worth Basin, TX. The inverted results from the noise-suppressed prestack gathers are more laterally continuous and have higher correlation with well logs when compared with those inverted from conventional time-migrated gathers.

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.

scholarly journals Application of seismo-stratigraphic interpretation techniques to offshore West Greenland

1988 ◽  
Vol 140 ◽  
pp. 64-66
Author(s):  
J.A Chalmers
Keyword(s):  
Pilot Study ◽  
Seismic Data ◽  
Seismic Interpretation ◽  
Shelf Break ◽  
The West ◽  
Regional Geology ◽  
West Greenland ◽  
Oil Companies ◽  
Greenland Basin ◽  
Basement Ridge

A pilot study is being conducted to determine if the use of seismo-stratigraphic interpretation techniques can increase the understanding af the geology of offshore West Greenland in order to reassess the prospectivity of the area. During the period 1975 to 1979, a number of concessions offshore West Greenland were licensed to various consortia of oil companies to search for petroleum. Some 40 000 km of seismic data were acquired, all of which is now released. Five wells were drilled, all of them dry, and all concessions were relinquished by the industry by 1979. The regional geology of offshore West Greenland has been summarised by Manderscheid (1980) and Henderson et al. (1981). They show the West Greenland Basin to consist of fairly uniformly westward dipping sediments bordered near the shelf break by a basement ridge. These authors used what may be termed 'conventional' techniques of seismic interpretation. However, since that time the techniques of seismo-stratigraphy (Vail et al., 1977; Hubbard et al., 1985) have become established. They are now being applied to study seismic data acquired during the mid-1970s.

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

2021 ◽  
Vol 40 (10) ◽  
pp. 751-758
Author(s):  
Fabien Allo ◽  
Jean-Philippe Coulon ◽  
Jean-Luc Formento ◽  
Romain Reboul ◽  
Laure Capar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Hybrid Techniques ◽  
Geothermal Wells

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

Some Aspects of Seismic Data Reverse Time Migration for Salt Tectonics Geology of the Dnieper-Donets Basin

2021 ◽  
Author(s):  
Pavlo Kuzmenko ◽  
Viktor Buhrii ◽  
Carlo D'Aguanno ◽  
Viktor Maliar ◽  
Hrigorii Kashuba ◽  
...  
Keyword(s):  
Seismic Data ◽  
Full Range ◽  
Donets Basin ◽  
Salt Tectonics ◽  
Imaging Method ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Seismic Image ◽  
Complex Geology

Abstract Processing of the seismic data acquired in areas of complex geology of the Dnieper-Donets basin, characterized by the salt tectonics, requires special attention to the salt dome interpretation. For this purpose, Kirchhoff Depth Imaging and Reverse Time Migration (RTM) were applied and compared. This is the first such experience in the Dnieper-Donets basin. According to international experience, RTM is the most accurate seismic imaging method for steep and vertical geological (acoustic contrast) boundaries. Application of the RTM on 3D WAZ land data is a great challenge in Dnieper-Donets Basin because of the poor quality of the data with a low signal-to-noise ratio and irregular spatial sampling due to seismic acquisition gaps and missing traces. The RTM algorithm requires data, organized to native positions of seismic shots. For KPSDM we used regularized data after 5D interpolation. This affects the result for near salt reflection. The analysis of KPSDM and RTM results for the two areas revealed the same features. RTM seismic data looked more smoothed, but for steeply dipping reflections, lateral continuity of reflections was much improved. The upper part (1000 m) of the RTM has shadow zones caused by low fold. Other differences between Kirchhoff data and RTM are in the spectral content, as the former is characterized by the full range of seismic frequency spectrum. Conversely, beneath the salt, the RTM has reflections with steep dips which are not observed on the KPSDM. It is possible to identify new prospects using the RTM seismic image. Reverse Time Migration of 3D seismic data has shown geologically consistent results and has the potential to identify undiscovered hydrocarbon traps and to improve salt flank delineation in the complex geology of the Dnieper-Donets Basin's salt domes.

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