Seismic characterization of thin bedded reservoirs: The application of elastic geostatistical inversion in the Bongkot Field, Gulf of Thailand

Boonyasatphan, Prat; Uttareun, Ratchadaporn; Sognnes, H. I.; Anansatitporn, Natthanicha; Iamboon, Jakkarin; Seusutthiya, Krongpol; Bancelin, J.P.

doi:10.1190/segam2019-3214301.1

Cited by 1 publication

(3 citation statements)

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“…The reservoirs' variability through PEMs is assessed by the amalgamation of key reservoir properties, i.e., pores configuration, grain-radii, litho-static and pore pressure variations, geochemistry, and the diagenesis effect (Ukaonu et al, 2017;Shakir et al, 2021). Hence, the integration of the reservoir information into the enhanced elastic attributes differentiates gas-filled sands in the petro-elastic domain and the relationship is extended to the multi-dimensional joint distribution of the litho-prob-ability cube (Boonyasatphan et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…The posterior elastic properties result from multiple realizations of the Bayesian inference technique that efficiently handles petro-elastic inversion problems within the constraints of 3D seismic, well logs, and stochastic parameterization (Grana, 2018). The stochastic parameters employed in the inversion procedures for the optimization of workflow include the lateral and vertical variograms, prior models, and PDFs (Boonyasatphan et al, 2019). The stochastic parameters are simulated to obtain posterior elastic attributes and are considered as a possible solution of the selected heterogeneous realization after a good match of input seismic angle gathers and synthetic responses of posterior properties (Liu and Grana, 2018;Babasafari et al, 2020).…”

Section: Low Frequency Models In Stratigraphic Gridmentioning

confidence: 99%

“…7a), allowing variograms to quantify the variables for joint distribution. The horizontal variogram is fitted using the mean-value attribute maps obtained from the deterministic inversion findings, whereas , , , ( ) the vertical variogram parameters are obtained by using the high-frequency content of logs (Boonyasatphan et al, 2019) (Fig. 7b).…”

Section: Bayesian Stochastic Inversionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%