Quantitative reservoir characterization through simultaneous inversion: A case study from the Burgan field, Kuwait

Al-Zuabi, Yousef; Al-Khaled, Osman; Rabu, Karam Abd; Sulistiono, Denny; Celma, Rafael

doi:10.1190/1.3627537

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The 3D seismic data provides detailed subsurface information, therefore its interpretation using geophysical techniques like inversion is critical for reservoir characterization and modeling Li et al, 2020). The elastic properties of seismic inversion (p-impedance, s-impedance, Vp/Vs ratio, Poisson ratio, and so on) are extremely useful in distinguishing between lithologies, impermeable versus porous facies, and potentially significant reservoirs (Hampson et al, 2005;Al-Zuabi et al, 2011;Fennita et al, 2018;Moghanloo et al, 2018). The elastic characteristics retrieved from seismic data sets are hindered due to a variety of factors such as acquisition geometries, band-limited frequencies, and heterogeneous thin layers on a vertical resolution (Su et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

et al. 2022

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A challenging situation in reservoir characterization arises when reservoirs occur below the vertical resolution of seismic, such as the 6 m gas-prone sands of the heterogenous Khadro Formation. This formation is the Zamzama gas field's secondary reservoir and needs detailed evaluation for optimum production of the field as the deeper primary reservoir of Pab sandstone is facing the problem of early water encroachment in its producing wells. The characterization of thin heterogeneous gas-bearing facies is hampered by the insensitivity of well-bore recording tools in precisely capturing elastic properties. Consequently, petro-elastic models (PEMs) of identified facies are generated that produce consistent elastic responses and discriminate facies in their true elastic domains. The resolution of seismic elastic properties is enhanced to illuminate the thin gas-bearing sands by adopting a Bayesian stochastic inversion process in collaboration with PEMs modeled elastic responses on a fine-scale stratigraphic grid. In addition, the Bayesian framework is used to estimate the litho-facies probability cubes to mitigate the drilling risks by integrating highly sampled posterior elastic volumes, modeled elastic logs, and geologic information of identified litho-facies. The outcomes included prospect identification at the drilled well locations that need to be perforated, while new potential zones are present for additional wells.

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Section: Introductionmentioning

confidence: 99%

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

et al. 2022

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“…It has a greater resolution when compared to the input of real seismic data. This higher resolution is attained by the elimination of band-limited wavelet effects from actual seismic data (Pendrel and Van Riel, 1997;Savic et al, 2006;Latimer et al, 2000;Pendrel, 2001;Veeken and Da Silva, 2004;Russell, 2005;Al-Zuabi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Improved reservoir characterisation using fuzzy logic platform: an integrated petrophysical, seismic structural and poststack inversion study

Jafri

Lashin

Ibrahim

et al. 2017

Exploration Geophysics

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There is a tendency for applying different integrated geophysical approaches for better hydrocarbon reservoir characterisation and interpretation. In this study, petrophysical properties, seismic structural and poststack seismic inversion results are integrated using the fuzzy logic AND operator to characterise the Tensleep Sandstone Formation (TSF) at Powder River Basin (PRB), Wyoming, USA. TSF is deposited in a coastal plain setting during the Pennsylvanian era, and contains cross-bedded sandstone of Aeolian origin as a major lithology with alternative sabkha dolomite/ carbonates. Wireline logging datasets from 17 wells are used for the detailed petrophysical evaluation. Three units of the TSF (A-sandstone, B-dolomite and B-sandstone) are targeted and their major rock properties estimated (i.e. shale/clay volume, V sh ; porosity, f Eff ; permeability, K; fluid saturations, S w and S H ; and bulk volume water, BVW). The B-sandstone zone, with its petrophysical properties of 5-20% effective porosity, 0.10-250 mD permeability and hydrocarbon potential up to 72%, is considered the best reservoir zone among the three studied units. Distributions of the most important petrophysical parameters of the B-sandstone reservoir (V sh , f Eff , K, S w ) are generated as GIS thematic layers.The two-dimensional (2D) and three-dimensional (3D) seismic structural interpretations revealed that the hydrocarbons are entrapped in an anticlinal structure bounded with fault closures at the west of the study area. Poststack acoustic impedance (PSAI) inversion is performed on 3D seismic data to extract the inverted acoustic impedance (AI) cube. Two attribute slices (inverted AI and seismic amplitude) were extracted at the top of the B-sandstone unit as GIS thematic layers. The reservoir properties and inverted seismic attributes were then integrated using fuzzy AND operator. Finally, a fuzzy reservoir quality map was produced, and a prospective reservoir area with best reservoir characteristics is proposed for future exploration. The current study showed that integration of petrophysical, seismic structural and poststack inversion under a fuzzy logic platform can be used as an effective tool for interpreting multiple reservoir zones.

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Establishing the precision sandstone reservoir model using geostatistical inversion: A case study in the South China Sea

Tao¹,

Li²,

Wang

et al. 2016

SEG Technical Program Expanded Abstracts 2016

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Quantitative reservoir characterization through simultaneous inversion: A case study from the Burgan field, Kuwait

Cited by 3 publications

References 4 publications

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

Improved reservoir characterisation using fuzzy logic platform: an integrated petrophysical, seismic structural and poststack inversion study

Establishing the precision sandstone reservoir model using geostatistical inversion: A case study in the South China Sea

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