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80th EAGE Conference & Exhibition 2018 Workshop Programme
- Conference date: 10 Jun 2018 - 15 Jul 2018
- Location: Copenhagen , Denmark
- ISBN: 978-94-6282-257-3
- Published: 10 June 2018
21 - 40 of 99 results
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Remote sensing from space for oil prospecting
By F. RoccaRemote sensing from space implies the use of sensors of gravity or of electromagnetic radiations. I will first recall gravity surveys, as that carried out with the satellite GOCE by the European Space Agency but also, on the oceans, the results obtained by using high resolution altimetry. Then, lower frequency microwave radar imaging methodologies will be presented like ESA Biomass, and of the Argentinean and Italian Space Agencies, SAOCOM, in the bands P and L respectively (435 and 1275MHz). The penetration in the vegetation and the upper layers of the terrain (when dry) will allow the study of the Digital Terrain Model under vegetation and even the layout of the water table in desert areas. The use of microwave imaging radars at higher frequencies (band C, 5 GHz with satellites like Sentinel 1 A/B always of ESA, and band X, 10 GHz, with the satellites of the Italian Space Agency (CosmoSkyMed first and second generation), Terrasar X and Tandem X of the German DLR, and finally the Spanish Paz, allow to evaluate ground morphology and soil rugosity, to detect oil spills on sea, and finally to measure accurately millimeter ground motions.
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Remote Sensing Cloud Tools in the Geological Workflow
Authors L. Turon, J. Dupuy, D. Dhont and E. BlancartWith the increase of the amount and variety of satellite sensors and free data, the access, processing and management of satellite images becomes a real issue. These barriers are being overcome with the increase in storage space and the emergence of deported storage systems (servers, clouds, etc.). Online satellite image processing platforms now enable the processing and visualizations of large volumes of data over wide areas (Chi et al., 2016)
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Video from Space - a new dimension in Earth Observation
More LessA new dimension in Earth Observation is being opened: Color Video from Space. Very High Resolution satellite images recorded in a sequence of 25 frames per second for up to 100 seconds, and locked on to the target area of interest, will change the way we can observe our dynamic world. This will enable new depths of analysis and much improved situational awareness, as well as a deeper understanding what is happening ‘on location’. Such high revisit times become important for high value assets in a dynamic environment, for example Oil & Gas installations, mining infrastructure, ports or transportation hubs. Daily very high-resolution monitoring, with video capability, at multiple times of day, will create new exciting opportunities in the geo-located world and benefit customers in high value / high risk environments.
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Asset Digitalization and Integrity Monitoring - A UAS based approach
By S. LiuOver the last 5 years Nederlandse Aardolie Maatschappij (NAM), a joint venture between Exxon Mobil and Royal Dutch Shell, has completed a campaign of surveying 250 onshore plants and 3 offshore platforms in the Netherlands using unmanned aerial systems (UAS) [1] which are also known as drones. The UAS based survey platform has proven to be not only cost efficient but also adding significant business values in the areas of asset inspection and maintenance, HSE management, engineering basis for design [2]. In this presentation we will begin with key survey equipment and procedure followed by current rules and regulations in NL imposed by Dutch authorities, then we will present two case studies of the UAS surveys, one for an offshore platform in the southern North Sea and the other on an onshore gas processing terminal in UK.
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Subtile Interseismic Strain Rate Distribution Detected from a Spatial Frequency Analysis of InSAR
Authors T. Maurin, J. Berthelon, D. Dhont, F. Koudogbo and A. UrdirozInSAR is a convenient method to uniformly map long term strain rate and was successfully used that way in fast deforming regions. However, because the radar signal might be subject to atmospheric perturbations and could equally register anthropic or hydrologic related deformations, the monitoring of slowly deforming areas remains challenging. The surface displacement may actually be described in the spatial frequency domain as a mix of these various components: the atmospheric term, the ground term and the tectonic term. Each of these terms have a specific bandwidth that can be identified and extracted from the velocity signal. This paper present an approach based on such a spatial frequency analysis that aims at extracting the specific tectonic wavelength signal in order to capture strain rate variability in slow deforming regions.
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Probabilistic inversion into lithology and fluid classes in the North Sea – Comparison of one- and two-step approach
Authors E. Aker, H. Kjønsberg, P. Røe and Ø. KrøsnesLithology and fluid prediction from seismic data is traditionally done in two steps; first an inversion of the seismic data to elastic parameters, and subsequently a prediction of lithology and fluid based on a rock physics model linking the elastic parameters to individual lithology and fluid combinations. Recently, a number of inversion algorithms have been developed that, based on Bayesian statistical methodology, estimate the probability of lithology and fluid directly from seismic data. In this paper we compare the performance of two state-of-the-art Bayesian inversion algorithms on a real data set from the Volund field in the North Sea. The first algorithm follows the traditional two-step approach and cannot take into account the stratigraphic ordering of lithology and fluid. The second algorithm, referred to as one-step, evaluates possible lithology and fluid combinations within a vertical window around each inversion point enabling correct stratigraphic ordering. We find that the one-step inversion resolves more details and honours the data more strongly than the two-step approach. The latter is more prone to return the prior model if information in the seismic data is not sufficiently strong. Both models detect hydrocarbon filled sand injectites that are typical for the field.
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Solving for facies in seismic inversion as essential for realistic reservoir models
More LessTraditional seismic inversion approaches solve in two steps, first for the elastic properties of the subsurface using seismic data, followed by facies classification. We perform the inversion of facies and elastic properties simultaneous. The stochastic (or geostatistical) inversion is done on a stratigraphic structural grid in a Bayesian framework using the MCMC Markov Chain Monte Carlo algorithm. Combining all seismic, well and geological constraints simultaneously is required to reduce uncertainty and ensure a consistent and unbiased integration of all data.
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Facies-based Reservoir Characterisation through the Asset lifecycle
More LessSeismic lacks low frequencies, so for absolute seismic inversion a so-called Low Frequency Models (LFM) is required. Starting from an empty LFM, we would like to post Sand values where there is Sand, Shale values where there is Shale, etc. Typically we don’t know precisely where the various facies are located in the subsurface; after all, understanding the facies distribution is one of the main aims of seismic inversion. So populating the LFM as outlined in the previous paragraph is not normally possible. The LFM’s constructed to date are therefore compromised (e.g. well log interpolation leads to averaging, resulting in impedance values unrepresentative of the facies present). Importantly, during the inversion the seismic cannot ‘fix’ a compromised LFM as – we come full circle here – seismic lacks low frequencies! We introduce a facies-based approach that overcomes this issue. For each facies expected (e.g. Shale, Water-Sand, Oil-Sand), a LFM is constructed, and all are input to the inversion (i.e. the low frequency information is over-specified). The inversion can then decide which LFM is used where, based on the facies estimate, which is one of the quantities inverted for. So the LFM ultimately used in the inversion is an output, not an input.
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Fluid Prediction from Time-lapse Seismic AVO Data
Authors O.B. Forberg, Ø. Kjøsnes and H. OmreReliable reservoir characterization of both the porosity/permeability and the fluid distribution is important for reservoir management. Geoscientific experience, seismic data with good coverage and logs along a small number of well traces provide the basis for this characterization. The time-static porosity/permeability distribution is challenging to assess, while the time-dynamic fluid distribution is even more challenging to monitor. Reliable characterization of the dynamic fluid filling is crucial for reservoir engineering management including the design of efficient infill well drilling programs. Our study is focused on prediction of the fluid dynamics based on time-lapse seismic AVO data. We apply spatial Bayesian inversion methodology, necessitating a prior model on the reservoir characteristics. This is challenging because the saturation is bimodal. We present a solution using a selection Gaussian prior model and a Gauss-linear relationship between the reservoir characteristics and the seismic responses. The methodology is tested on seismic data generated from well observations from Kneler in the Alvheim oil and gas field. The results are encouraging, preserving the bimodality of the saturation even in the presence of considerable error.
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Investigating the relationship between prior information and seismic resolution in a Bayesian setting.
By P.E. HarrisModern, one-step inversions build litho-class boundaries into the inversion process by coupling the elastic property inversion with the lithology identification process. This sharpens the result and may permit the identification of very thin units that are not necessarily recognised in a traditional inversion. A further benefit of modern techniques is that the locations of horizons can be updated within the inversion process, again exploiting the interaction between elastic properties and lithology identification in the one-step process. These remarks suggest that it is the interplay between elastic properties and spatial/temporal (geological) information that improves results of modern inversion over the traditional. In effect, resolution is improved by combining separation of litho-classes in elastic properties with separation in a spatio-temporal sense. To a large degree, the geological information is captured in the prior model. In exploration settings, where little is known, the classification process becomes essentially just a partitioning of the elastic property space, and the resolution is similar to that recoverable from a traditional elastic property inversion. However, as more prior information becomes available, it becomes possible to resolve litho-classes that may overlap in elastic properties due to their spatial distribution.
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Probabilistic Seismic Inversion of Facies and Petrophysical Properties Using Gaussian Mixture and Markov Chain prior Models
Authors T. Fjeldstad and P. AvsethPrediction of petrophysical properties and elastic attributes has become an important part of the exploration phase in the oil and gas industry to predict the presence of hydrocarbons subsurface. We focus on prediction of lithology/fluid classes, petrophysical properties and elastic attributes given geophysical observations. State of the art techniques are often based on minimization of the error, with respect to a given loss function, between a synthetic forward model and the observed data, either by probabilistic assessment or numerical optimization. We operate in a Bayesian framework where the objective is to assess the posterior probability density/mass function of the variables of interest subsurface.
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4D seismic coda waves
By D.E. LumleyTime-lapse seismic imaging of the earth's interior, and quantitative estimation of time-varying changes in rock and fluid properties, has produced many spectacular results over the past 30 years; however, we are still making many approximations, and extracting only a small percentage of the information available in the full time-lapse seismic wavefields. I will present advanced concepts in full wavefield imaging and inversion (including 4D RTM and 4D FWI) to enhance 4D seismic reservoir monitoring.
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Target-oriented elastic full-waveform inversion through acoustic extended migration redatuming
Authors E. Biondi, B. Biondi and G. BarnierElastic full-waveform inversion (FWI) has the potential of simultaneously invert all the scales of the elastic subsurface model while accounting for the elastic effects present in the recorded data. However, its application on production field datasets is limited by its high computational cost. In fact, the computational time of the elastic Green’s functions involved during any data inversion is much higher compared to the cost of the acoustic ones.
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Using one-way propagators to build a full wavefield inversion process
Authors E.J. Verschuur, M. Davydenko and A. GargNowadays, there is a strong emphasis on explaining the full seismic wavefield for obtaining detailed subsurface information, which is indeed the way forward to improve the final resolution of images and – after that – reservoir properties. Within the full waveform inversion (FWI) community there is a strong bias towards one type of modelling, which is the finite-difference based solutions of the wave equation. However, one issue associated with FWI is that it does not yet provide a full strategy towards a broadband elastic reservoir inversion process. Traditionally, FWI is used for estimating the velocity model that is used as input for standard migration-inversion approaches, thereby losing the advantages of the full waveform approach, such as including the effect of multiples. As an alternative, the Joint Migration Inversion process, with an operator-based modelling engine, provides an open framework that can include many physical features (such as anisotropy, elastic angle-dependent reflectivity), without having to re-implement a certain wave equation. By using one-way propagators in combination with reflectivity operators, a full two-way response can be built. It provides consistent full wavefield outputs that can lead to accurate elastic parameters in the reservoir, while fully removing the overburden imprint.
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Review of different expressions for the extended Born approximate inverse operator
Authors H.J. Chauris and E. CocherSeveral authors have published different expressions for the pseudo-inverse operator in the case of the subsurface extended Born modelling. We review here the principles to establish such inverse operators and show the close relationships between them. With one of the strategy, we then illustrate how triplicated wave fields can be properly handled and how the inverse operator can be incorporated as a preconditioner for least-squares migration in the extended domain.
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Multiple-parameters inversion: enhancing wave-matter interaction
Authors J. Virieux, R. Brossier, L. Métivier, P. Trinh, W. Zhou and P. YangSeismic waves honor wave propagation equation which is described by local model parameters. Full waveform inversion (FWI) attempts to distinguish automatically between reflection and transmission regimes through the implicit analysis of the different phases with variable signals expressed into seismic traces. Therefore, this approach, sensitive to phase and amplitude information, should require adequat description of different model parameters such as velocities, anisotropy coefficients and attenuation quality factors embedded into the spatial heterogeneous description of the model. Based on a single scattering approximation, FWI relies on the amplitude modulation in order to distinguish between model parameters at a point of the medium without considering important effects coming from spatial variations: only illumination (and curvature to lesser extent) is considered. Overcoming this limitation could be achieved by approximate scale separation specifying the wave-matter interaction often expressed through velocity/impedance parameterization or by preconditioning the model update through prior information. These additional strategies complement nicely the highresolution performance of FWI without too drastic restriction in the model building. It does not overcome the intrinsic influence of large-amplitude phases compared to small-amplitude phases which is a characteristic feature of least-squares methods. Alternative strategies could be foreseen essentially based on stricter scale separation.
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Least-squares imaging with multiples
Authors R. Soubaras and B. GratacosWe present an imaging tool derived from an Inversion Velocity Analysis (IVA) framework. Using a second-order Gauss-Newton update (Soubaras and Gratacos (2017)), we jointly invert for the source wavelet and for an extended reflectivity, by minimizing the data misfit between the measured raw shot records and the modeled shots. The second order update results in a reflectivity which is a least-squares migration. The modeling is based on one-way wave-equation propagation and includes the source wavelet, source and receiver ghost and multiples up to a given order. The presence of the multiples makes the wavelet estimation stable as the wavelet-reflectivity ambiguity is solved by fitting the first order modeled multiple to the data. As an unconstrained extended reflectivity is used, amplitude versus angle (AVA) effects are estimated. The input can be raw shots as source wavelet estimation and deconvolution, source and receiver deghosting and multiple attenuation are automatically performed by the joint inversion. An example on a real 2D real dataset is shown.
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Least-squares reverse-time migration with dynamic time warping
More LessLeast-squares reverse-time migration (LSRTM) has been shown to improve image quality over conventional RTM by enhancing the resolution, balancing illumination, and suppressing migration artefacts. However, it is also known to be sensitive to velocity errors. In the presence of velocity errors, predicted data show different moveouts from the observed data, which will hinge LSRTM convergence and yield sub-optimal results. To mitigate velocity errors, we propose to apply dynamic time warping (DTW) to the observed data and shift them towards the predicted data to improve data matching and subsequently images. In this paper, we show 2 synthetic examples and 1 real data example to demonstrate the advantages of dynamic time warping. Our observations show that dynamic time warping helps with event focusing, corrects phase distortion, improves event amplitudes, and thus improves event continuity.
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Q-least-squares reverse time migration with viscoacoustic deblurring filters
By C.Y.Q. ChenAttenuation compensation least-square reverse time migration (Q-LSRTM) linearly inverts for the subsurface reflectivity model from lossy data. It can compensate for the amplitude loss and phase distortion due to the strong subsurface attenuation compared to the conventional migration methods. However, the inverted images from Q-LSRTM with a certain number of iterations are often observed to have lower resolution when compared with the benchmark acoustic LSRTM from acoustic data. This because the adjoint Q propagators used for backpropagating the residual are also attenuative. To increase the resolution and accelerate the convergence of Q-LSRTM, we used viscoacoustic deblurring filters as a preconditioner for Q-LSRTM. Numerical tests on synthetic and field data demonstrate that the Q-LSRTM combined with viscoacoustic deblurring filters can produce images with higher resolution and more balanced amplitudes when there is strong atteunation in the background medium. The proposed preconditioning method is also shown to significantly increase the convergence rate of Q-LSRTM.
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FWI velocity models for quantitative interpretation of a deep water GOM dataset
Authors Y. Cobo, C. Calderon-Macias and S. ChiFull waveform inversion (FWI) has become the method of choice for deriving shallow velocity models that potentially improve interpretation of images in complex geologically settings. Recently, a combination of diving waves and reflections is being used resulting in an increase of depth range for updating the model. In this work, we evaluate the potential for utilizing velocities from FWI as a background model for quantitative interpretation. Our results show that using a velocity model as a low frequency model (LFM) with a higher vertical and lateral resolution obtained from the FWI process results in a higher quality post-stack inversion compared to the traditional approach that uses sparse well velocities extrapolated within a structural framework as a LFM.
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