Impact of Structural and Stratigraphic Heterogeneities in Deep Water Development

Onyeagoro, K.; Naruk, Stephen J.; Vlugt, Frans Van der; Barton, Mark D.; Pirmez, Carlos; O’Byrne, Ciaran

doi:10.2118/111909-ms

Cited by 4 publications

(1 citation statement)

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“…Quantification of the effect of stratigraphic and structural uncertainties on the dynamic performance is of principal importance in geologically complex reservoirs [1,2,9,12]. In general, geoscientists and reservoir engineers generate a few geologic realizations such that the simulation outcome will cover a reasonable range to approximate the forecast statistics.…”

Section: Introductionmentioning

confidence: 99%

A flow-based pattern recognition algorithm for rapid quantification of geologic uncertainty

2009

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Geologic uncertainties and limited well data often render recovery forecasting a difficult undertaking in typical appraisal and early development settings. Recent advances in geologic modeling algorithms permit automation of the model generation process via macros and geostatistical tools. This allows rapid construction of multiple alternative geologic realizations. Despite the advances in geologic modeling, computation of the reservoir dynamic response via full-physics reservoir simulation remains a computationally expensive task. Therefore, only a few of the many probable realizations are simulated in practice. Experimental design techniques typically focus on a few discrete geologic realizations as they are inherently more suitable for continuous engineering parameters and can only crudely approximate the impact of geology. A flowbased pattern recognition algorithm (FPRA) has been developed for quantifying the forecast uncertainty as an alternative. The proposed algorithm relies on the rapid characterization of the geologic uncertainty space represented by an ensemble of sufficiently diverse static model realizations. FPRA characterizes the geologic uncertainty space by calculating connectivity distances, which quantify how different each individual realization is from all others in terms of recovery response.Fast streamline simulations are employed in evaluating these distances. By applying pattern recognition techniques to connectivity distances, a few representative realizations are identified within the model ensemble for full-physics simulation. In turn, the recovery factor probability distribution is derived from these intelligently selected simulation runs. Here, FPRA is tested on an example case where the objective is to accurately compute the recovery factor statistics as a function of geologic uncertainty in a channelized turbidite reservoir. Recovery factor cumulative distribution functions computed by FPRA compare well to the one computed via exhaustive full-physics simulations.

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Section: Introductionmentioning

confidence: 99%

A flow-based pattern recognition algorithm for rapid quantification of geologic uncertainty

2009

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Reservoir connectivity in deep-water turbidite deposits over geological and production time scales: an integrated study of the Shwe field, Bay of Bengal

Lim,

Lee

2024

J Petrol Explor Prod Technol

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History-matching surface-based property models using the ensemble Kalman filter

Trani¹,

Graham²

2016

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Hydrocarbon reservoirs often contain thin and irregularly shaped low-permeability heterogeneities at the interface between sedimentary bodies that can act as barriers or baffles where they are aligned perpendicular to the flow. These heterogeneities are often not explicitly captured in simulation models because they occur over lengthscales smaller than a single simulation grid block and, as such, are ubiquitously captured in flow simulations using transmissibility multipliers. This parameterization implies that the properties of the fine-scale heterogeneities are a direct function of the properties in the adjacent grid cells. A new methodology is proposed where fine-scale heterogeneities are modelled using properties attached to surfaces and updated independently from other uncertain reservoir properties present during assisted history matching. This method is validated via a synthetic reservoir simulation model, using the ensemble Kalman filter to assimilate production history. The update of geometrical and petrophysical parameters related to fine-scale heterogeneity improves the history match and production forecast in comparison to traditional implicit techniques that do not honour the characteristics of the heterogeneity. The results indicate that, for history matching, it can be important to capture fine-scale heterogeneities independently from other uncertain reservoir properties even if the fine-scale heterogeneities do not exert a large control on reservoir response.

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Impact of Structural and Stratigraphic Heterogeneities in Deep Water Development

Cited by 4 publications

References 1 publication

A flow-based pattern recognition algorithm for rapid quantification of geologic uncertainty

A flow-based pattern recognition algorithm for rapid quantification of geologic uncertainty

Reservoir connectivity in deep-water turbidite deposits over geological and production time scales: an integrated study of the Shwe field, Bay of Bengal

History-matching surface-based property models using the ensemble Kalman filter

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