STOCHASTIC INVERSION INTEGRATING SEISMIC DATA, LITHO-FACIES PHYSICAL PROPERTIES, AND MULTIPLE-POINT GEOSTATISTICS FOR RESERVOIR CHARACTERIZATION

Trace inversion for reservoir parameters is affected by angle averaging of seismic data and wavelet distortion on the migration image. In an alternative approach to stochastic trace inversion, the data are inverted prestack before migration using 3D dynamic ray tracing. This choice makes it possible to interweave trace inversion with Kirchhoff migration. The new method, called ray-based stochastic inversion, is a generalization of current amplitude versus offset/amplitude versus angle (AVO/AVA) inversion techniques. The new method outperforms standard stochastic inversion techniques in cases of reservoir parameter estimation in a structurally complex subsurface with substantial lateral velocity variations and significant reflector dips. A simplification of the method inverts the normal-incidence response from reservoirs with approximately planar layering at the subsurface target locations selected for inversion. It operates along raypaths perpendicular to the reflectors, the direction that offers optimal resolution to discern layering in a reservoir. In a test on field data from the Gulf of Mexico, reservoir parameter estimates obtained with the simplified method, the estimates found by conventional stochastic inversion, and the actual values at a well drilled after the inversion are compared. Although the new method uses only 2% of the prestack data, the result indicates it improves accuracy on the dipping part of the reservoir, where conventional stochastic inversion suffers from wavelet stretch caused by migration.

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Stochastic inversion of seismic data by implementing image-quilting to build a lithofacies model for reservoir characterization of Delhi Field, Louisiana

First Break ◽

10.3997/1365-2397.n0119 ◽

2018 ◽

Vol 36 (9) ◽

pp. 47-57

Author(s):

Mitra Azizian ◽

Thomas L. Davis

Keyword(s):

Seismic Data ◽

Reservoir Characterization ◽

Stochastic Inversion

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Geologically Driven Stochastic Inversion of Seismic Data for Reservoir Characterization in Tyumen Formation (Russian)

10.2118/202034-ru ◽

2020 ◽

Author(s):

Anastasia Sergeenva Pirogova ◽

Kirill Alexandrovich Epov

Keyword(s):

Seismic Data ◽

Reservoir Characterization ◽

Stochastic Inversion

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Stochastic inversion of seismic data by implementing image quilting to build a lithofacies model for reservoir characterization of Delhi Field, LA

10.1190/segam2018-2998581.1 ◽

2018 ◽

Author(s):

Mitra Azizian ◽

Thomas L. Davis

Keyword(s):

Seismic Data ◽

Reservoir Characterization ◽

Stochastic Inversion

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Integrated Stochastic Inversion Approach Assessing Thin Reservoirs Full Potential in the Prolific Oil-Prone Periphery of Hassi Messaoud Field, Algeria

10.2118/207417-ms ◽

2021 ◽

Author(s):

Mohamed Samy Tawfik ◽

Medjdouba Nasrine ◽

Sabiha Annou ◽

Aiouna Ahcene ◽

Abderaouf Haddouche ◽

...

Keyword(s):

Seismic Data ◽

Reservoir Characterization ◽

Rock Physics ◽

Full Potential ◽

Reservoir Properties ◽

Stochastic Inversion ◽

Area Of Interest ◽

Characterization Study ◽

Reservoir Potentiality ◽

Saharan Platform

Abstract Nowadays it become harder, risker and more expensive understanding the reservoir potentiality and design the optimum development plan for challenging thin reservoirs. In a geological complex area, integrated seismic reservoir characterization approach was crucial to unlock the potentiality for the study area, which is located at Oued Mya basin, SE Saharan platform Algeria. Seismic data analysis is one of the key procedures for characterizing reservoirs and monitoring subsurface properties. Integration of seismic stochastic inversion and geological model will help to demonstrate the link between seismic and reservoir properties more quantitative. The seismic data of region were challenging, with around 30 wells drilled over 400 Km2. To overcome the challenges, the available geological and geophysical data were integrated to construct the reservoir characterization study and reduce drilling uncertainty. Ensure the reservoir characterization process was constrained by a robust workflow and consistent with the available geophysical, geological, and petrophysical data. Petrophysical interpretation, seismic interpretation, rock physics analysis and Stochastic Inversion processes were carried out. These processes were integrated to characterize the lateral and vertical extent of the lithofacies in five stacked reservoirs across the area of interest to identify the potential delineation of thin reservoirs of nine-meter thickness.

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