Geostatistical inversion for 3D confidence evaluation of facies prediction: A Gulf of Guinea example

Araman, Alexandre; Cadoret, Thierry; Pernia, L.; Minken, David; Moyen, R.

doi:10.1190/1.3627539

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…It delivers probability cubes related to each lithology occurrence considered during the classification process. This process combines the uncertainties related to the classification approach, represented by the PDFs, and the non-uniqueness of the seismic inversion problem, represented by the multirealizations (Araman et al, 2011). At the end of the process these probability cubes are also used to obtain a pseudo porosity estimation using a linear combination between the acoustic impedance and the lithology probabilities.…”

Section: Lithoseismic Classificationmentioning

confidence: 99%

Predicting Lithology And Porosity Of Carbonate Reservoirs Using Geostatistical Stochastic Pre-stack Inversion For Geomodeling Constraint

et al. 2014

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This paper describes a methodology to characterize lithology variations impacting the porosity distribution in a carbonate reservoir. We performed a stochastic seismic inversion to obtain multiple realizations of elastic properties discriminating between the target lithologies. These multi-realizations were then used in a supervised lithoseismic classification for obtaining cubes of occurrence probability of lithologies. Finally, a pseudo porosity cube is obtained using a linear combination between the acoustic impedance and the lithology probabilities. The entire workflow is performed in a stratigraphic grid layeringconsistent with geomodeling grid. Such capability is important for further use of the results as 3D constraint during the geomodel infilling. Lithology probability cubes show proportions in agreement with vertical facies proportions observed from well data.

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Section: Lithoseismic Classificationmentioning

confidence: 99%

Predicting Lithology And Porosity Of Carbonate Reservoirs Using Geostatistical Stochastic Pre-stack Inversion For Geomodeling Constraint

et al. 2014

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High-resolution water-saturation prediction using geostatistical inversion and neural network methods

Mohamed¹,

Hemdan²,

Hosny³

et al. 2019

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One of the main challenges that we face is the accurate prediction of pore-fluid properties with the highest possible resolution. The seismic resolution is the most limiting factor, especially in our case, in which the main reservoirs are deepwater turbidite channels and their thin beds typically fall below the seismic tuning thickness. Therefore, we designed a new workﬂow that combines the geostatistical inversion and the neural network analysis with the aim of predicting a 3D high-resolution water saturation (sampled every 1 ms), overcoming the limitation of seismic detectability and providing better reservoir characterization. The power of the geostatistical inversion is that it provides multiple model realizations, and each realization honors the well data (statistical information and logs) and the seismic data. These realizations are more reliable and high-resolution versions of the elastic parameters. On the other hand, the main advantage of the neural network is that it establishes a stable nonlinear link between the input seismic and inversion results and the target water saturation. The available data set for this study includes three angle stacks and seven wells from Scarab field, offshore Nile Delta. The resulted high-resolution saturation volume was tested using blind-well analysis and revisit post the drilling of a new well later on. It gave spectacular results in both cases. The normalized correlations between the predicted saturation volume and the real saturation logs are 0.87 and 0.89, respectively. The results prove the validity of the workflow to accurately predict water saturation with a higher resolution than ever before.

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Geostatistical inversion for 3D confidence evaluation of facies prediction: A Gulf of Guinea example

Cited by 2 publications

References 4 publications

Predicting Lithology And Porosity Of Carbonate Reservoirs Using Geostatistical Stochastic Pre-stack Inversion For Geomodeling Constraint

Predicting Lithology And Porosity Of Carbonate Reservoirs Using Geostatistical Stochastic Pre-stack Inversion For Geomodeling Constraint

High-resolution water-saturation prediction using geostatistical inversion and neural network methods

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