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**Concept Tag**

- Advancement (1)
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- amplitude (4)
- analysis (2)
- anisotropic diffusion (1)
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- application (1)
- approach (2)
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**gradient (19)**- Gradiometer (1)
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- inversion (9)
- iteration (4)
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- Pore pressure profile (1)
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- reflection (2)
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- Reservoir Characterization (18)
- reservoir description and dynamics (19)
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- seismic processing and interpretation (16)
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- traveltime (2)
- Upstream Oil & Gas (17)
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- Waveform Inversion (2)
- well logging (2)

**File Type**

Fluvio-deltaic systems have long been an exploration target, but in the shelf of the Gulf of Mexico, preceded the introduction of high-quality 3D seismic and modern 3D geometric attributes. Given the abundance of seismic data, well control, and basin understanding of the depositional history, the Gulf of Mexico provides an excellent natural laboratory to calibrate interpretational tools and workflows that can be applied to other less well-understood basins. To this end we compute a full suite of attributes over modern survey acquired over a salt-controlled minibasin in the Gulf of Mexico to better illuminate channel systems. We found the most-curvature and the valley-shape attributes were particularly effective in delineating continuous channels extending to the edge of the shelf. Other attributes including amplitude, coherence and amplitude gradients indicate the presence of gas-charge, pockmarks, and debris flows. Most-positive curvature best delineates the shelf edge. Together these attributes allow us to interpret subtle channel features in the appropriate structural and depositional framework.

amplitude, application, channel, coherence, curvature, edge, gradient, horizon, Marfurt, minibasin, most-positive curvature, presence, Reservoir Characterization, reservoir description and dynamics, seg las vegas, seismic processing and interpretation, seismic visualization, shelf, slope channel, subtle channel feature, system, Upstream Oil & Gas

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

A new integrated basement study of the Fort Worth basin (FWB) that includes a high-resolution aeromagnetic data (HRAM), its derivatives, 3-D seismic data and well data reveals a highly segmented and complex basement. The preliminary new result of the structural mapping of the basement using HRAM derivatives reveals correlations with features seen on seismic attribute images. This correlation enables us to establish a relationship between basement lineaments and intra-sedimentary faults. Also, new depth estimates from Euler deconvolution provide a basis for comparison with depth-converted seismic data.

basement, basement feature, deconvolution, derivative, edge, estimate, Euler, euler depth estimate, feature, Fort Worth Basin, gradient, highresolution aeromagnetic, HRAM, magnetic source, map, Reservoir Characterization, reservoir description and dynamics, seismic processing and interpretation, structure, tilt derivative, Upstream Oil & Gas

Oilfield Places:

- North America > United States > Texas > Fort Worth Basin (0.99)
- North America > United States > Gulf of Mexico > Ellenburger Formation (0.99)

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Seismic imaging in depth is limited by the accuracy of velocity model estimation. Slope tomography uses the slowness components and traveltimes of picked reflection or diffraction events for velocity model building. The unavoidable data incompleteness requires additional information to assure stability to inversion. One natural constraint for ray based tomography is a smooth velocity model. We propose a new, reflectionangle- based kind of smoothness constraint as regularization in slope tomography and compare its effects to three other, more conventional constraints. The effect of these constraints are evaluated through comparison of the inverted velocity models as well as the corresponding migrated images. We find the smoothness constraints to have a distinct effect on the velocity model but a weaker effect on the migrated data. In numerical tests on synthetic data, the new constraint leads to geologically more consistent models.

Slope tomography is one of the many methods that try to determine a macrovelocity model for time or depth imaging. It uses slowness vector components to improve and stabilize the traveltime inversion. Slope tomography was initially proposed by Billette and Lambaré (1998) as a robust tomographic method for estimating velocity macro models from seismic reflection data. They had recognized the potential efficiency of traveltime tomography (Bishop et al., 1985; Farra and Madariaga, 1988) but also the difficulties associated with a highly interpretative picking. The selected events have to be tracked over a large extent of the pre-stack data cube, which is quite difficult for noisy or complex data. The idea is to use locally coherent events characterized by their slopes in the pre-stack data volume. Such events can be interpreted as pairs of ray segments and provide independent information about the velocity model.

However, the data for slope tomography are incomplete (Bishop et al., 1985). This causes depth and velocity ambiguities that depend strongly on the size of the acquisition aperture (Bube et al., 2005). Therefore, stability and convergence can only be achieved if additional information is prescribed. This additional information contains desirable properties for the solution, reducing ambiguity (Menke, 1989). It can be shown that stability is obtained only if we try to determine a smooth model of the subsurface (Delprat-Jannaud and Lailly, 1992, 1993). Moreover, for ray based inversion, smoothness is a requirement, because rough models cause the forward problem to break down during linear iterations. The use of combined smoothness constraints enables an interpretation-oriented inversion while keeping solutions consistent with the data.

We investigate the effect of different kinds of smoothness constraints in slope tomography, prescribing lateral, vertical and isotropic smoothing constraints in different combinations. Moreover, we propose a structurally motivated smoothing constraint in the direction of a potential reflector. This regularization is based on information that is contained in the data, in contrast to standard regularizations that impose global smoothness constraints. We test the different regularizations on the Marmousoft data set (Billette et al., 2003).

Slope tomography differs from conventional reflection tomography by the data that are used for the inversion (Billette et al., 2003).

Billette, constraint, coverage, difference, direction, gradient, image, information, inversion, model, reflection, reflector, regularization, Reservoir Characterization, reservoir description and dynamics, seg las vegas, seismic processing and interpretation, slope tomography, smoothness constraint, tomography, traveltime, Upstream Oil & Gas

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Since many geophysical inverse problems are ill-posed, implementation of constraints is effective in reducing solution ambiguity. This paper presents a simple transform method for enforcing lower and upper bounding constraints about the solution to restrict model parameter updates during the inversion process such that non-realistic results are suppressed. The lower and upper bounding constraints are realized by a logarithmic or an inverse hyperbolic tangent transformation of model parameters. The width of the two bounds reflects the reliability of

Commer, conductivity, constraint, equation, formation evaluation, function, gradient, hyperbolic tangent transformation, Imaging, inverse hyperbolic, inversion, log analysis, log conductivity, logarithmic transformation, model, Newman, Reservoir Characterization, reservoir description and dynamics, seg las vegas, seismic processing and interpretation, solution, solution ambiguity, transformation, Upstream Oil & Gas, well logging

An inversion methodology for marine controlled-source electromagnetic (MCSEM) data with approximate Hessianbased optimization and a fast finite-difference time-domain forward operator is presented. Using data from a synthetic hydrocarbon reservoir, we demonstrate that models are reproduced with a spatial resolution determined by the skin depth of the frequencies included in the inversion. Both single and multiple resistive bodies can be resolved in the subsurface. Using reciprocal treatment and multiple frequencies at each receiver position, the comprehensive inversion sequence of a typical MCSEM survey, which should match the acquired data to within the measurement error, executes within ~100 iterations, with about 30 iterations per day, requiring at most a few hundred nodes on a parallel cluster.

approximate hessian-based optimization, Artificial Intelligence, fast finite-difference time-domain, finite-difference time-domain forward code, formation evaluation, forward, geophysics, gradient, gradient calculation, inversion, iteration, marine csem, misfit, model, position, receiver, Reservoir Characterization, reservoir description and dynamics, shallow subsurface, source, structural geology, survey, th annual international, Upstream Oil & Gas

SPE Disciplines:

This paper aims at clarifying the true nature of AVO gradient and the root of its ability as a DHI. It is demonstrated that coefficient B in R(θ)≈A+Bsin2θ may not always be an acceptable approximation to the gradient of amplitude variation with offset (or, in otherwords, with incidence angles). With large offset data being included in AVO inversion, the so-called AVO gradient estimated on basis of the three-term approximation cannot be the coefficient B defined by Shuey in equation (3). Nor can it be the gradient of amplitude versus sin2θ. It is a mixture of coefficient B and the term C(2tg2θ+tg4θ).

The root of ability of the AVO gradient as a DHI might be buried in AVO inversion practice where large offset data are included into the inversion. Such ability and the success of AVO technique might mainly depend upon the degree of approximation of the estimated AVO gradient to the Poisson reflectivity defined by Verm and Hilterman (1995).

amplitude, approximation, coefficient, contribution, equation, gradient, incidence angle, intercept, inversion, Poisson, poisson reflectivity, reflectivity, Reservoir Characterization, reservoir description and dynamics, seismic processing and interpretation, Shuey, three-term approximation, Upstream Oil & Gas, vo gradient, vo inversion, vo technique

Nasyrov, Denis (Saint-Petersburg State University) | Kiyashchenko, Denis (Shell International E&P) | Kiselev, Yurii (Saint-Petersburg State University) | Kashtan, Boris (Saint-Petersburg State University) | Troyan, Vladimir (Saint-Petersburg State University)

The quality of the subsurface images obtained using VSP data strongly depends on the velocity model used for migration. The velocity model derived from surface seismic is often not accurate enough for VSP imaging and there is a need for its improvement. We propose the method for updating of velocities using VSP data. The main idea is to use the images of the subsurface obtained using different types of waves: primary reflections and surface-related multiples. If the background velocity is correct, then these images will be similar, and they will not coincide, if the velocity model is erroneous. We develop the algorithm of velocity updating based on this criterion. The proposed method allows us to retrieve the velocity below the borehole receivers. This is complementary to first break VSP travel time tomography, which helps to retrieve velocity only above the receivers.

analysis, equation, field, gradient, image, layer, method, migration, model, optimization, receiver, reflection, reflector, Reservoir Characterization, reservoir description and dynamics, respect, seg las vegas, seismic processing and interpretation, source, subsurface, update, Upstream Oil & Gas, VSP

This paper presents a new method for segmenting channel features from 3D seismic volumes. Anisotropic diffusion using Gaussian-smoothed first order structure tensors is conducted along the strata of seismic volumes in a way that filters across discontinuous regions from noise or faulting, while preserving channel edges. The eigenstructure of the second order structure tensor is used to generate an estimation of orientation and channel curvature. Gaussian smoothing of second order tensor orientations accounts for noisy vectors from imprecise finite difference calculations and generates a stable tensor across the image. Analysis of the confidence and direction of second order eigenvectors is used to enhance depositional curvature in channel features by generating a confidence and curvature attribute. The tensor-derived attribute forms the terms of a PDE, which is iteratively updated as an implicit surface using the level set process. This technique is tested on two 3D seismic volumes with results that demonstrate the effectiveness of the approach.

anisotropic diffusion, approach, Artificial Intelligence, channel, Channel Segmentation, channelness, confidence, curvature, diffusion, discontinuity, feature, Gaussian, gradient, image, method, orientation, Reservoir Characterization, reservoir description and dynamics, segmentation, seismic processing and interpretation, seismic strata, structure, surface, tensor

Technology:

Introduction Full tensor gravity gradiometry is becoming more commonplace within exploration projects where the benefits of high resolution, multi-component data is proving invaluable for discerning both deep and shallow structures. This paper will demonstrate how the signals measured by gradiometers achieve this by presenting a series of simple examples. Exploiting multi-tensor measurements When a full survey is conducted over an area with adequate sampling then, within the limitations of signal to noise and a few constants of integration, it is possible to predict the gravitational potential and any of its associated derivatives using measurements of only a single field quantity. Common methods of achieving this include Fourier transformations (integration and differentiation in the spatial frequency domain) and equivalent source inversions. For these ideal surveys, measuring multiple components of gravity or gravity gradient serves only to increase the accuracy rather than the ...

advantage, anomaly, geologic modeling, geological modeling, geology, gradient, Gradiometer, gravity, gravity gradient, high frequency, high resolution, high resolution gravity gradient, information, inversion, profile, Reservoir Characterization, reservoir description and dynamics, signal, survey, survey line, terrain

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (0.32)

The growing use of the controlled-source electromagnetic method (CSEM) for exploration applications has been driving the technical development of data acquisition, as well as three-dimensional (3D) modeling and imaging techniques. However, targeting increasingly complex geological environments also further enhances the problems inherent in large-scale inversion, such as nonuniqueness and resolution issues. In this paper, we report on two techniques to mitigate these problems. We use 3D joint CSEM and MT inversion to improve the model resolution. Further, a hybrid model parameterization approach is presented, where traditional cell-based model parameters are used simultaneously within a parametric inversion.

Large-scale inverse problems are usually under-determined, meaning that there are more unknowns, typically in the form of digitized model meshes, than data. This adds to the problem that errors are associated with every geophysical data. The resulting issue is referred to as the problem of non-uniqueness of inverse solutions. To mitigate this problem and to improve the resolution in an inversion, it is common to take advantage of complementary natures of different geophysical datasets. In electromagnetic imaging, magnetotelluric (MT) data, providing conductivity structure information on a gross scale, can be combined with CSEM data. With the latter method responding stronger to thin resistive targets, the joint CSEM and MT inversion has the potential of limiting ambiguities in the EM data interpretation relevant to many exploration scenarios.

However, even with improved resolution capabilities, the solutions of 3D large-scale cell-based (or pixel-based) inversions with finely sampled models usually are still nonunique. Several strategies have been reported to limit the ambiguities for reconstructed targets and its conductivities. For cell-based problems, model-smoothing constraints are usually applied, limiting the solutions to a class of geologically more meaningful ones, i.e. avoiding too high conductivity contrasts. A different approach is to actually address the under-determinacy by casting the problem into a parametric problem. Usually, particular geometric shapes are assumed in parametric solutions, requiring a priori information. A model parameterization can for example be based on interfaces known from seismic reflection data. The 2D sharp-boundary inversion algorithm by Smith et al. (1999) features a parameterization with variable nodebased boundaries and greatly limits the number of unknowns. Parametric inversion algorithms have also been used for the simultaneous reconstruction of both geometry and conductivity of unknown regions (Commer, 2003; Zhang et al., 2007). The obvious drawback of such methods is the necessity of sufficient background information in order to find a suitable model parameterization.

Here, we propose to use a hybrid approach, overlaying a cell-based inversion for a particular area of interest with a parametric inversion. This combines the advantages of cellbased and structure-based model parameters. We present two joint inversion examples using synthetic CSEM andMT data. The first example employs only cell-based model parameters, and simulates a survey in a marine environment. Second, we present an inversion study for a surface survey, using the hybrid parameterization approach.

Our inversion algorithm’s underlying finite-difference (FD) forward modeling algorithm for EM field simulation solves a modified form of the vector Helmholtz equation for scattered or total electric fields.

algorithm, Artificial Intelligence, Commer, conductivity, CSEM, error, formation evaluation, geophysical journal international, gradient, inversion, joint inversion, las vegas, layer, model-based inversion, Newman, optimal conductivity reconstruction, parameterization, parametric inversion, problem, reconstruction, Reservoir Characterization, reservoir description and dynamics, resolution, seismic processing and interpretation, Upstream Oil & Gas

SPE Disciplines:

Technology: Information Technology > Artificial Intelligence > Representation & Reasoning > Model-Based Reasoning (0.42)

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