Changing scale and domain of a petrophysical and elastic properties based log‐ facies classification

Maffioletti, Francesca; Bardin, Silvia; Grana, Dario; Paparozzi, E.; Ruvo, Livio; Sala, Claudio; Tarchiani, C.

doi:10.1190/1.3513335

Cited by 9 publications

(1 citation statement)

References 5 publications

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“…A log-facies characterization based on petrophysical and elastic properties (part 1) is first achieved via a multivariate statistical technique applied to volumetric logs (mineral fractions and effective porosity) obtained from quantitative log interpretation and velocity data (P-wave, S-wave velocity and/or V P /V S ratio) as input (see Maffioletti et al 2010;Pirrone et al 2011). The final result of the log-facies classification is a set of log-facies with a distinguishable petrophysical and elastic signature and a log-facies profile at the well locations.…”

Section: F I E L D a P P L I C A T I O Nmentioning

confidence: 99%

Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

Grana

Paparozzi

Mancini

et al. 2012

Geophysical Prospecting

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In this paper we present a case history of seismic reservoir characterization where we estimate the probability of facies from seismic data and simulate a set of reservoir models honouring seismically‐derived probabilistic information. In appraisal and development phases, seismic data have a key role in reservoir characterization and static reservoir modelling, as in most of the cases seismic data are the only information available far away from the wells. However seismic data do not provide any direct measurements of reservoir properties, which have then to be estimated as a solution of a joint inverse problem. For this reason, we show the application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties to condition reservoir geostatistical simulations. The studied case is a clastic reservoir in the Barents Sea, where a complete data set of well logs from five wells and a set of partial‐stacked seismic data are available. The multi‐property workflow is based on seismic inversion, petrophysics and rock physics modelling. In particular, log‐facies are defined on the basis of sedimentological information, petrophysical properties and also their elastic response. The link between petrophysical and elastic attributes is preserved by introducing a rock‐physics model in the inversion methodology. Finally, the uncertainty in the reservoir model is represented by multiple geostatistical realizations. The main result of this workflow is a set of facies realizations and associated rock properties that honour, within a fixed tolerance, seismic and well log data and assess the uncertainty associated with reservoir modelling.

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Section: F I E L D a P P L I C A T I O Nmentioning

confidence: 99%

Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

Grana

Paparozzi

Mancini

et al. 2012

Geophysical Prospecting

Self Cite

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Extreme learning machine for multivariate reservoir characterization

Liu

Ge²,

Chen³

et al. 2021

Journal of Petroleum Science and Engineering

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Lithologic and geomechanical facies classification for sweet spot identification in gas shale reservoir

et al. 2016

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Unconventional reservoirs require advanced technologies such as horizontal well placement and hydraulic fracturing to be successfully exploited at economic rates. In this context, static and dynamic reservoir quality (RQ) concepts are introduced. Static RQ or standard RQ comprises a set of petrophysical parameters that describe formation tendency for development. Dynamic RQ or completion quality is defined by a set of geomechanical parameters that estimate formation tendency to be fractured. The convergence of static and dynamic RQs allows for evaluating the production potential of a field; particularly, productive sweet spots are located in those intervals in which good static and dynamic RQs are detected. We have developed a workflow to identify producible intervals in unconventional reservoirs by means of lithologic and geomechanical facies classification. Starting from core data, a clustering technique is used to create a set of lithologic facies that are then extended to the logged interval and characterized in terms of static RQ. The same approach is used to classify the logged interval with a set of geomechanical facies in which dynamic RQ is estimated. The integration of lithologic and geomechanical facies leads to sweet spot identification. Workflow application to available data from the Barnett Shale Formation allows us to classify the logged interval with four log facies (LF) and five geomechanical facies (GF) and to identify productive sweet spots in the upper and middle Lower Barnett. Eventually, LF and GF are linked to seismic facies probability volumes and Young’s modulus from elastic inversion of surface seismic. Seismic-driven geostatistical realization of LF and GF provides static and dynamic RQs volumes that are combined into volumes of productive and nonproductive facies.

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Changing scale and domain of a petrophysical and elastic properties based log‐ facies classification

Cited by 9 publications

References 5 publications

Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

Extreme learning machine for multivariate reservoir characterization

Lithologic and geomechanical facies classification for sweet spot identification in gas shale reservoir

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