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

Abstract

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.

Multiple-point geostatistical modeling based on the cross-correlation functions

“…By using a search tree, the TI is scanned once, and all the replicas of the patterns are stored. Therefore, it is possible to apply SNESIM to 3D reservoirs [1,10,32,33,49,69] and hydrogeological modeling [13,25,37,60,61]. However, SNESIM also suffers from intensive memory and CPU requirements, when used in the simulation of large 3D reservoirs.…”

Hierarchical geocellular facies modelling of a turbidite reservoir analogue from the Eocene of the Ainsa basin, NE Spain

“…Smaller heterogeneity scales are incorporated stochastically in the facies models. This is usually achieved by means of object-based (Purvis et al, 2002;Stephen et al, 2002;Smith and Moller, 2003;Hauge et al, 2003;Conybeare et al, 2004;Larue and Friedmann, 2005;Satur et al, 2005) or pixel-based methods (Wonham et al, 2000;Felletti, 2004;Strebelle et al, 2002). More detailed variability caused by the presence of laminae, clasts, etc.…”

Reconstruction of Incomplete Data Sets or Images Using Direct Sampling

“…Consequently, there is no need to store the probabilities associated with all pixels configurations found in the TI. Other algorithms have to store them either in a tree structure (Strebelle 2002) or in lists (Straubhaar et al 2008). Instead, at each simulated node x, the TI is directly and randomly sampled until the appropriate pattern is found.…”

“…Moreover, this definition of neighborhoods is very convenient as it allows the mean radius of the data events to decrease linearly as the density of informed grid nodes becomes higher. This natural variation of the data events' size has the same effect as multiple-grids (Strebelle 2002), and makes their use unnecessary.…”

“…The statistical properties of natural phenomena are often best reproduced by nonparametric approaches (Mariethoz et al 2009b). Since they use non-parametric highorder statistics, multiple-points (MP) simulation methods (Guardiano and Srivastava 1993;Strebelle 2002;Zhang et al 2006;Arpat and Caers 2007) have a high potential for reproducing complex spatial structures present in the data. Reconstructions of geological structures using multiple-points simulations have been performed by Wu et al (2008) using the FILTERSIM algorithm ), Hurley and Zhang (2009) for borehole imagery, and Okabe and Blunt (2004) to generate small scale 3D images of porous media.…”