Geostatistical Integration Of Rock Physics, Seismic Amplitudes And Geological Models In North-Sea Turbidite Systems

Abstract

Abstract Turbidite systems are complex heterogeneous siliciclastic deposits calling for a new approach to geophysical and geostatistical reservoir characterization. The aim of this paper is to demonstrate the integration of seismic amplitude and rock physics data into realistic geological scenarios using a novel geostatistical approach. Avseth (2000, 2001 with others) presented an integrated rock physics/seismic inversion approach to predict seismic facies within the Glitne Field reservoir, North Sea. The outcome of this work is a seismic derived conditional probability of facies. We provide a geostatistal methodology for integrating this large-scale probability model with smaller scale geological models consisting of channel complexes typical for known turbidite systems around the world. Introduction Turbidite reservoirs currently represent major hydrocarbon targets in several areas in the world. These deep-water clastic systems are often characterized by a complex sand distribution, stretching the limits of current, conventional seismic and geostatistical modeling and analysis tools. Nevertheless, reservoirs of this type must produce at high rate in order to return the large drilling and production cost. Hence the reservoir heterogeneity must be accurately quantified and the associated uncertainty measured in order to determine the capital investment risk involved. We propose a methodology to create fine-scale reservoir models of turbidite systems, constrained by pre-stack seismic and well-log data. We then apply it to a field in the preproduction phase in the North-Sea. In the first stage, a probability model of observing sandy and shaly facies - as originally presented by Avseth et al. (2001, Geophysics) and Avseth (2000, PhD Dissertation) - is determined based on the seismic data only. The original contribution of this paper comes in the second phase, when this coarse-scale seismic derived probability map is integrated with smaller scale variations of submarine channels using a new geostatistical method. The second step is needed because1)the deep seismic (2km) cannot detect individual smaller scale channels which may act as conduits of fluids,2)the seismic model is not locally constrained by small scale well-log data3)although the seismic derived facies probablity model provides uncertainty on the absence or presence of facies, it does not provide uncertainty of future cumulative oil production. The geostatistical approach proceeds first by simulating a reservoir training image using a Boolean simulation algorithm for channels. This training image is not constrained by any reservoir specific data, it is merely conceptual. Next a pixel-based geostatistical simulation method uses this training image to constrain a set of alternative facies models (gridblocks of size 12.5×12.5×1m) to the seismic derived facies probability, the small scale well data and the geometric patterns of channels as depicted by the training image. We show that our methodology is fast, general and integrates consistently all the available data at relevant scales. Conditional distributions of facies from seismic The Glitne Field To showcase our approach, we use data from the Glitne field, a reservoir of Tertiary age, located in the South Viking graben in the North Sea. A comprehensive database is available with well logs from 7 wells, including P-wave velocity (Vp), shear wave velocity (Vs), density, gamma ray and resistivity data. All wells have Vp logs, only two wells have Vs logs. Pre-stack amplitude data from 2D lines and a 3D seismic amplitude cube is available. Facies associations in turbidite systems In deep-water siliciclastic systems one can encounter six different lithofacies at the seismic scale. Table 1 provides a definition for each facies type. However in the Glitne field only 4 facies types, namely type II, III, IV and V are recognized, representing a gradual transition from clean sandstone to pure shale. Moreover, facies II is subdivided into three classes, namely IIa, IIb and IIc, see Table 1.