Avon, France
Avon, France

The name of SeisQuaRe means Seismic Qualification Reservoir Wikipedia.

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Shtuka A.,Seisquare | Sandjivy L.,Seisquare | Mari J.L.,French Institute of Petroleum | Dutzer J.F.,GDF SUEZ | And 2 more authors.
73rd European Association of Geoscientists and Engineers Conference and Exhibition 2011: Unconventional Resources and the Role of Technology. Incorporating SPE EUROPEC 2011 | Year: 2011

Geostatistical "filters" using Factorial Kriging are increasingly used for cleaning geophysical data sets from organized spatial noises that are difficult to get rid of by standard geophysical filtering. The understanding and handling of such kind of spatial processing is not easy for geophysicists who are neither used nor trained to handle stochastic models. In this paper we demonstrate the formal equivalence between Factorial Kriging models and usual geophysical filters (Wiener, (F,k), Median) and the added value of stochastic modelling that is the quantification of the quality of the filtering process. Cases studies on pre stack gathers and VSP wave separation illustrate the fact that theses stochastic techniques are generic and apply to all filtering contexts.

Piriac F.,Seisquare | Sandjivy L.,Seisquare | Shtuka A.,Seisquare | Mari J.L.,French Institute of Petroleum | Haquet C.,Royal Dutch Shell
72nd European Association of Geoscientists and Engineers Conference and Exhibition 2010: A New Spring for Geoscience. Incorporating SPE EUROPEC 2010 | Year: 2010

Stochastic modelling and processing (sigma processing) has been applied to a real PSDM data set (pre and post stack) in order to evaluate its contribution to better imaging and AVO processing. Sigma processing may be considered as a step in a conventional processing sequence. It can be applied on CMP gathers after NMO correction or on pre stack migration common image gathers (CIG). It consists in modelling the experimental spatial autocorrelation function of the data into its "signal" and "noise" components and then look for the best estimate of the "signal" using factorial kriging estimator. The technology breakthrough is its ability to: Easily assess the spatial content of a data set and to check the underlying assumptions of the geophysical processing such as stationarity (no trend), flattening, non correlation of the noise from trace to trace. Optimize the signal to noise ratio within a consistent theoretical framework (kriging) and quantify the quality of the result (kriging variance). When applying it to PSDM data on a real case study, the first objective was to make "familiar" with this stochastic way of processing amplitude data, and to control the consistency of the results with more standard geophysical processes. © 2010, European Association of Geoscientists and Engineers.

Sandjivy L.S.,Seisquare | Shtuka A.S.,Seisquare
2nd EAGE Integrated Reservoir Modelling Conference - Uncertainty Management: Are we Doing it Right? | Year: 2014

The poster illustrates an operational concept for uncertainty management in E&P, to maximize the value of oil/gas reservoirs (i.e. the number of producible barrels per dollar spent at each and every step of the exploration/production cycle). Managing uncertainty in E&P means the ability to quantify and propagate uncertainty throughout successive geoscience workflows, always using a consistent and objective geostatistics model. Two recent case studies illustrate uncertainty management in E&P. The first case study shows how to successfully implement a new exploration well. The second shows how to optimize safety in storing high intensity nuclear waste in a shale environment.

Sandjivy L.D.,Seisquare | Shtuka A.,Seisquare | Merer F.A.,Seisquare
76th European Association of Geoscientists and Engineers Conference and Exhibition 2014: Experience the Energy - Incorporating SPE EUROPEC 2014 | Year: 2014

Despite tremendous progress in acquisition and processing techniques, geophysical data sets continue to carry uncertainty impacting the performance of processing, interpretation and subsurface property modeling workflows. This paper is a plea for consistent uncertainty quantification and propagation throughout successive geophysical workflows ("Uncertainty Management"), in view of maximizing the performance of geophysical workflows and the reliability of static/dynamic reservoir models. The authors run through the mathematical framework applied to Uncertainty Management. They then explain how to build stochastic geophysical processes and how to implement them into standard geophysical workflows. An example is provided with focus on Gross Rock Volume (and P10, P50, P90 structural depth cases) computations in support of efficient prospect ranking and discovery appraisal. The achievement is to empower geophysicists, maximize the efficiency of E&P decision-making and contribute to sustainable and profitable Earth resource management.

Sandjivy L.D.,Seisquare | Shtuka A.,Seisquare | Mari J.L.,IFP School | Yven B.,Andra Inc
76th European Association of Geoscientists and Engineers Conference and Exhibition 2014: Experience the Energy - Incorporating SPE EUROPEC 2014 | Year: 2014

At the feasibility stage of a high level radioactive waste facility in the of the eastern Paris Basin, the French National Radioactive Waste Management Agency (Andra) is conducting innovative and extensive characterization of the Callovo-Oxfordian argillaceous rock (Cox) and neighbouring Oxfordian and Dogger limestones. High resolution 3D seismic data are used to model the distribution of mechanical and hydrogeological properties, of the geological formations. Assessing the reliability of the modeling is crucial for making decision on the design and implementation of the radioactive waste facility. The cornerstone of the model reliability assessment is to first address the reliability of seismic stacked amplitudes that are input to the geophysical inversion. Stochastic processing of the pre-stack amplitude gathers enables to compute a Spatial Quality Index (SQI) attached to stacked amplitude data. SQI can be considered as a reliable indicator of the preservation of true amplitude by the processing sequence. In the field example of the storage of radioactive waste in a safe geological shale environment, SQI helps validating the identification of lateral variations of shale content inside the target layer and of a high porous layer in the limestone just below.

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