Introducing More Geology in Stochastic Reservoir Modelling

Dubrule, Olivier

doi:10.1007/978-94-011-1739-5_29

Cited by 25 publications

(16 citation statements)

References 27 publications

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“…To accurately integrate geological models, object-based algorithms have been developed since they allow modeling realistic geological geometries according to prior geological description 2,3,4,5,6 . A number of important drawbacks have been observed in applying such models to large 3D cases:…”

Section: Spe 77425mentioning

confidence: 99%

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Multiple-Point Geostatistics

Strebelle¹,

Payrazyan²,

Caers³

2002

Proceedings of SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractGeological interpretation and seismic data analysis provide two complementary sources of information to model reservoir architecture. Seismic data affords the opportunity to identify geologic patterns and features at a resolution on the order of 10's of feet, while well logs and conceptual geologic models provide information at a resolution on the order of one foot. Both the large-scale distribution of geologic features and their internal fine-scale architecture influence reservoir performance. Development and application of modeling techniques that incorporate both large-scale information derived from seismic and fine-scale information derived from well logs, cores, and analog studies represents a significant opportunity to improve reservoir performance predictions.In this paper we present a practical new geostatistical approach for solving this difficult data integration problem and apply it to an actual, prominent reservoir. Traditional geostatistics relies upon a variogram to describe geologic continuity. However, a variogram, which is a two-point measure of spatial variability, cannot describe realistic, curvilinear or geometrically complex patterns. Multiple-point geostatistics uses a training image instead of a variogram to account for geological information. The training image provides a conceptual description of the subsurface geological heterogeneity, containing possibly complex multiple-point patterns of geological heterogeneity. Multiple-point statistics simulation then consists of anchoring these patterns to well data and seismic-derived information. This work introduces a novel alternative approach to traditional Bayesian modeling to incorporate seismic.The focus in this paper lies in demonstrating the practicality, flexibility and CPU-advantage of this new approach by applying it to an actual deep-water turbidite reservoir. Based on well log interpretation and a global geological understanding of the reservoir architecture, a training image depicting sinuous sand bodies is generated using a non-conditional object-based simulation algorithm. Disconnected sand bodies are interpreted from seismic amplitude data using a principal component cluster analysis technique. In addition, a map of local sand probabilities obtained from a principal component proximity transform of the same seismic is generated. Multiple-point geostatistics then simulates multiple realizations of channel bodies constrained to the local sand probabilities, partially interpreted sand bodies and well-log data. The CPU-time is comparable to traditional geostatistical methods.

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Section: Spe 77425mentioning

confidence: 99%

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Multiple-Point Geostatistics

Strebelle¹,

Payrazyan²,

Caers³

2002

Proceedings of SPE Annual Technical Conference and Exhibition

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“…The commonly used simulation methods can be classified into pixel-based methods and objectbased methods 18 .…”

Section: Geostatistical Simulation Algorithmsmentioning

confidence: 99%

Reducing Uncertainties in Production Forecasts by Constraining Geological Modeling to Dynamic Data

Hu¹,

Ravalec²,

Blanc³

et al. 1999

Proceedings of SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A. fax 01-972-952-9435. AbstractBuilding reservoir geological models that are consistent with all available information is necessary to reduce uncertainties in production forecasts. The stochastic/geostatistical approach is the only feasible way of integrating all kinds of data ranging from the geological knowledge to the production history. A stochastic model is chosen that accounts for geological knowledge, and geostatistical simulation provides realizations of the stochastic model. These realizations can be conditioned to static data at well locations to reduce the model uncertainty in the area where data are available.All along the different stages of a field life more and more dynamic data are available. This paper addresses the issue of generating model realizations that account for available dynamic data to further reduce uncertainty in the underlying geological model. A general tool has been developed to build geostatistical realizations that match both the available geological and dynamic data. The core of this tool is the Gradual Deformation Method that allows consistent modification of a realization in a continuous way. This method can be combined with a nonlinear optimization method for constraining realizations of a stochastic model to dynamic data. This approach is operational for Gaussian or non-Gaussian pixel based models. Model realizations can be deformed globally or locally while preserving the geostatistical characteristics. Several examples are provided to illustrate different features of the gradual deformation approach.

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“…With the continuous improvement in the accuracy of reservoir prediction, stochastic modeling or combinations of deterministic and stochastic modeling methods have become more common (Falivene et al, 2006). Traditional stochastic modeling is mainly divided into variogram-based and objectbased methods (Dubrule, 1993). The object-based approach establishes a reservoir structure model by directly generating an overall target block, with a Boolean method and an indicator point process (Deutsch and Wang, 1996;Jones, 2001;Li et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Study on facies-controlled model of a reservoir in Xijiang 24-3 oilfield in the Northern Pearl River Mouth Basin

Zheng

Chen

Lü

et al. 2018

Adv. Geo-Energy Res.

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Abstract:To better understand the internal structure of the Xijiang 24-3 Oilfield in the Northern Pearl River Mouth Basin, a reasonable and effective three-dimensional quantitative geological model is needed to characterize the distribution characteristics of the reservoir. Xijiang 24-3 Oilfield is a braided river delta deposition, which contains many sedimentary microfacies in three subfacies zones. The reservoir structure is complex and heterogeneous. The reservoir modeling research was conducted on the Xijiang 24-3 oilfield H3A reservoir. Analysis of the oilfield core, the well-logging, and seismic data, establish a regional structural model. The regional sedimentary microfacies model is simulated by multi-point geostatistics. The reservoir property model is established by using facies-controlled modeling technology with sedimentary microfacies as a constraint condition. A facies-controlled property model and a non-facies-controlled property model are established respectively. The faciescontrolled model corresponds well with the sedimentary microfacies of the reservoir, and the variation trend of the model shows a higher coincidence rate with the distribution characteristics of the well. The facies-controlled model reflects the spatial distribution characteristics of the physical properties of the underground reservoir and is in accord with the actual geological understanding of the reservoir. This model also provides a reliable basis for the next stage of reservoir description and a basis for further exploration and development work.

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Introducing More Geology in Stochastic Reservoir Modelling

Cited by 25 publications

References 27 publications

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Multiple-Point Geostatistics

Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Multiple-Point Geostatistics

Reducing Uncertainties in Production Forecasts by Constraining Geological Modeling to Dynamic Data

Study on facies-controlled model of a reservoir in Xijiang 24-3 oilfield in the Northern Pearl River Mouth Basin

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