Marjorie Levy

Using multiple-point statistics to build geologically realistic reservoir models: the MPS/FDM workflow

Abstract Building geologically realistic reservoir models that honour well data and seismic-derived information remains a major challenge. Conventional variogram-based modelling techniques typically fail to capture complex geological structures while object-based techniques are limited by the amount of conditioning data. This paper presents new reservoir facies modelling tools that improve both model quality and efficiency relative to traditional geostatistical techniques. Geostatistical simulation using Multiple-Point Statistics (MPS) is an innovative depositional facies modelling technique that uses conceptual geological models as training images to integrate geological information into reservoir models. Replacing the two-point statistic variogram with multiple-point statistics extracted from a training image enables to model non-linear facies geobody shapes such as sinuous channels, and to capture complex spatial relationships between multiple facies. In addition, because the MPS algorithm is pixel-based, it can handle a large amount of conditioning data, including many wells, seismic data, facies proportion maps and curves, variable azimuth maps, and interpreted geobodies, thus reducing the uncertainty in facies spatial distribution. Facies Distribution Modelling (FDM) is a new technique to generate facies probability cubes from user-digitized depositional maps and cross-sections, well data, and vertical facies proportion curves. Facies probability cubes generated by FDM are used as soft constraints in MPS geostatistical modelling. They are critical, especially with sparse well data, to ensure that the spatial distribution of the simulated facies is consistent with the depositional facies interpretation of the field. A workflow combining MPS and FDM has been successfully used in Chevron to model important oilfield assets in both shallow- and deep-water depositional environments. Sedimentary environments can be characterized by a succession of deposition of elements, or rock bodies, through time. These elements are traditionally grouped into classes, commonly named ‘depositional facies’, based on their lithology, petrophysical properties, and biological structures. For example, the typical depositional facies encountered in fluvial environments are high permeability sand channels, with levées and crevasse splays, having a more variable range of permeability and net-to-gross ratio, within a background of low permeability shaley facies.

Tengiz Field (Republic of Kazakhstan) Unit 1 Platform Static Model: Using a Hybrid Depositional - Diagenetic Approach

Traditional rock typing combined with an inferred depositional linkage for variogram-based simulation is a standard approach in carbonates. In the Tengiz Unit 1 platform, reservoir properties of carbonates as defined by Petrophysical Rock Types (PRTs) are the product of primary depositional facies and diagenetic modification that have separate spatial trends and interactions. Careful, multidisciplinary and targeted analysis is required to unravel such trends from the usually complex hard data sets, but is critical as an understanding of the trends forms the basis for reservoir modeling of the Tengiz Unit 1 platform. Depositional cycles in the Unit 1 Tengiz platform (Late Visean to Bashkirian) are made up of a succession of generally shoaling lithofacies overlying a sharp base with evidence for subaerial exposure and/or flooding. Systematic study of the diagenetic products of several sequences across the platform using petrography, stable isotopes and CL revealed that the diagenetic modification includes early meteoric dissolution and subsequent cementation, late burial dissolution and late burial bitumen cementation.. PRTs are designed to include spatial attributes of the combined stratigraphic, facies and diagenetic framework form the basis for the Multiple Point Statistics and Facies Distribution Modeling (MPS/FDM) simulation of the SIM08T Unit 1 static platform reservoir model. Of the six PRTs, one is linked to volcanic ash (PRT 1), one associated with bitumen (PRT 2) and four with increasing porosity (PRTs 3-6) where PRT 3 tight and PRTs 4-6 represent increasing reservoir quality with PRT 6 the highest quality. PRT maps and a vertical proportion curve were used to generate the facies probability cube and convolved with training images, specifying the spatial interrelationship, to generate a PRT realization. The revised sequence stratigraphic framework and integration of novel concepts in modeling the diagenetic overprint addressed the need for a refined understanding of the platform in preparation for the FGP miscible gas injection project. In addition, the extensive use of MPS/FDM modeling approaches in Unit 1 has resulted in a more realistic integration of both depositional and diagenetic trends in the Unit 1 platform.

Pattern Waterflood Development in a Giant, Mature Oil Field: Minas NW Segment Reservoir Characterization, Scale-Up, and Flow Modeling

Reservoir characterization involves the quantification, integration, reduction, and analysis of geological, petrophysical, seismic, and engineering data. The principal goal of reservoir characterization is to derive a spatial understanding of interval heterogeneity. The result of this analysis can be improved dramatically using 3-D interpretation and analysis techniques. The product of 3D reservoir characterization is a 3D reservoir model which is largely a function of the stratigraphic framework. Sequence stratigraphy concepts are used to identify and analyze five major depositional sequences in the early Miocene to middle Miocene Sihapas Group in the Northwest Segment of the Minas Field. The purpose of this paper is to discuss the process and results of a combined, 3-D deterministic and stochastic reservoir modeling procedure. Three-dimensional reservoir modeling results in an improved geologic interpretation while providing an integrated approach and allowing for immediate model updates as a new data is acquired. The geologic modeling phase of the Minas Northwest Segment project successfully integrated all available interpreted and measured data into a high-resolution, full-field model. Statistical and geostatistical data revealed strong relationships between geologic trends and petrophysical patterns of reservoir heterogeneity. Facies regions combined with the powerful geostatistical methods in G2/GOCAD ++ effectively captured both geologic trends and petrophysical relationships in the final model. A scaled-up version of the geologic model was used to build streamtube and finite difference simulation models to predict recovery performance for various waterflooding and horizontal well scenarios. An inverted 7-spot pattern waterflood over a high-graded portion of the Northwest Segment was selected as the optimum development approach. The results and recommendations for this pattern waterflood project will be implemented beginning in 1998. The results derived from 3D scale-up and subsequent flow simulations showed that the geologic model used in this study adequately represents subsurface flow in Minas Northwest Segment.