A new geostatistical approach, known as multiple-point statistics (MPS) simulation, recently has been proposed to generate 3D depositional facies models that integrate both large-scale information derived from seismic data and fine-scale information derived from well logs, cores, and analog studies. In this paper, the practicality, flexibility, and CPU advantage of this new approach are demonstrated through the modeling of an actual deepwater turbidite reservoir. First, 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 nonconditional object-based simulation algorithm. Then, disconnected sand bodies are interpreted from seismic amplitude data using a principal component cluster analysis technique, while a sand probability cube is generated using a principal component proximity transform of the same seismic. Multiple-point geostatistics allows simulating multiple realizations of channel bodies similar to the training image, constrained to the local sand probabilities, partially interpreted sand bodies, and well-log data. As illustrated in this paper, to account for uncertainty about the geometry of the sand bodies, different training images associated with alternative conceptual models proposed by the geologists can be considered.

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Geological 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. Introduction Geostatistics aims at building multiple alternative reservoir models thereby assessing uncertainty about the reservoir. One major challenge of geostatistical modeling is to integrate information from different sources obtained at different resolutions:well-data which is sparse but of high resolution, on the order of one foot,seismic data which is exhaustive but of much lower resolution, on the order of 10's of feet in thevertical direction,conceptual geological models, which could quantify reservoir heterogeneity from the layer scale to the basin scale. Variogram-based algorithms allow integrating well and seismic data using a pixel-based approach: First, the well data are assigned to the closest simulation grid nodes. Then, all unsampled nodes are simulated conditional to well and seismic data using some form of co-kriging1. Variogram-based geostatistics is inadequate in integrating geological concepts since the variogram is too limited in capturing complex geological heterogeneity. A variogram is a two-point statistics that poorly reflects a geologists' prior conceptual vision of the reservoir architecture.

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.

Limiting Ambiguity: Our Approach. In this study, we try to limit the potential for ambiguous outcomes caused by redundant attribute character. Our approach is designed to derive all meaningful seismic attributes in a single coordinated transformation, which would be followed by a calibration of the most significant attributes using reservoir data from the wells. The objective is to distill the amplitude signal into its most distinctively elemental compo-