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Definition
of a 3D Integrated Geological Model in a Complex and Extensive Heavy Oil Field,
Oficina Formation, Faja de Orinoco, Venezuela*
By
Jean-Paul Bellorini, Johnny Casas, Patrick Gilly, Philippe Jannes, Paul
Matthews, David Soubeyrand, and Juan-Carlos Ustariz
Sincor OPCO, Caracas,
Venezuela
Search and Discovery Article #40102 (2003)
*Adapted from “extended abstract” for presentation at the AAPG Annual Meeting, Salt Lake City, Utah, May 11-14, 2003.
Introduction
SINCOR is a giant and fully integrated project combining upstream and downstream. The overall project is being implemented by an Operating Company (OPCO) with Totalfinaelf (47%), Venezuela’s national oil company PDVSA (38%) and Statoil (15%) as shareholders. These companies have brought together teams from both the Exploration & Production and Refining & Marketing segments to extract ‘‘by cold production’’ 200,000 b/d of extra-heavy crude (8-8.5oAPI) from Venezuela’s Orinoco Belt. The SINCOR lease is located in the Zuata block, one of the most prolific areas of the Maturin Sub-Basin (Figure 1). Heavy oil is produced from thick sequences of lower Miocene sands (Oficina Formation) ranging from fluvial to upper delta plain in environment. The fluvial succession is basically divided in four stratigraphic units composed of stacked sand bodies where 80% of the more than 300 horizontal drains have been drilled during the last three years.
uStructural & stratigraphic model
uStructural & stratigraphic model
uStructural & stratigraphic model
uStructural & stratigraphic model
uStructural & stratigraphic model
uStructural & stratigraphic model
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Integrated Geological ModelTo build a coherent 3D model of a reservoir dominated by fluvial deposits is a tremendous task that requires a multi-disciplinary approach and the effective management of a large amount of data (i.e., outcrops, cores, logs, 3D seismic, well tests and production data). To ease this process, the 500 sq km of the SINCOR lease have been divided in three geographical assets regrouping all the E&P disciplines: geophysics, geology and reservoir engineering (Figure 2). These integrated teams share a common project database where all the relevant static inputs required for the 3D geomodel are officially stored. To get the best picture of the reservoir and its associated uncertainties, stochastic modeling conditioned to well data is performed in order to generate several realizations of the geological parameters on which to base reservoir production simulations. Principal steps and direct inputs (static and dynamic) required to build the 3D reservoir model are given here below.
Structural and Stratigraphic ModelGeologists and geophysicists of each asset provide top and bottom structure maps of the fluvial interval based on 3D seismic and well data (vertical, deviated, and horizontal wells included). Because of the low acoustic impedance contrast between sand and shale, it is not possible to image the internal structure of the fluvial reservoir directly on the existing 3D seismic. Therefore, a most-likely framework of the fluvial interval is inferred from detailed well correlation scheme based on sequence stratigraphy. The application of this concept provides a detailed framework that may reduce the risk of miscorrelations between different genetic units. Sequence boundaries were defined in the vertical and the slant wells at the base of the main channel belts (i.e., at the top of the underlying shale where it has not been fully eroded) and correlated at the full field scale. So far, four stratigraphic units, that form the main reservoir architecture, have been identified in the fluvial succession. The thin shale layers at the top of the units may act locally as pressure barriers between overlying and underlying sandstones and may impact the vertical communication within the reservoir. Pressure data from wireline tester tools (SFTT) help in validating the chronostratigraphic correlation scheme and detecting the major shale baffles that are so far the major heterogeneity identified in the fluvial reservoir of SINCOR. This important issue has conditioned the choice of the different modeling techniques applied for building of the 3D reservoir grid.
Modeling of FaciesOnce the geometric framework of the reservoir section has been validated, it is populated with the lithological characteristics of the reservoir rock according to their spatial distribution. The facies model is built integrating a fluvial depositional model that has been clearly identified in core material. The present interpretation of the sedimentological setting varies from meandering to braided according to base sea level changes (changes in accommodation/supply ratio). Only two facies (i.e.
shale and sand) have been defined from the logs and distributed
throughout the field. This simple facies classification was the only way
to incorporate into the modeling process the huge amount of Logging
While Drilling (LWD) data provided by more than 300 horizontal wells.
The pay and the non-pay facies are identified on all the wells
(vertical, slant and horizontal wells) by applying log cut-offs on the
Shaliness (Vsh) and the Porosity (PHIE) curves. Normalized Gamma Ray
logs have been Once all the wells
have been interpreted with a lithofacies log, a 3D distribution of
facies can be performed for the whole reservoir. This distribution of
sands and shale is obtained through stochastic modeling including not
only the well data but also a set of 2D Net to Gross maps as major
constraints. These Net to Gross maps are generated for each
stratigraphic unit (4) by using the well data, the information brought
by an analog outcrop study (Salt Wash Member, Morrison Formation of
Utah) and the geological knowledge of the area (mainly source and
direction of the sand supply). These maps provide also an average Net to
Gross value (facies percentages) for each stratigraphic unit that is
Petrophysical ModelingThe aim of a
geological reservoir model is to provide a complete set of continuous
reservoir parameters (i.e. porosity, permeability and water saturation)
for each cell of the 3D grid. Many different techniques can be The porosity is
determined stochastically within each lithological facies (backbone for
calculating petrophysical parameters). As porosity modeling is
concerned, no seismic attributes that may be Permeability is
considered to be a function of porosity. A set of Water Saturation is calculated based on distributions related to porosity ranges. The full range of porosity has been divided in five arbitrary classes (e.g., between 20% and 25%) in which associated Water Saturation values from logs were statistically analyzed. It turned out that within each porosity class, related Water Saturation data fit a log normal distribution. These distributions condition perfectly the modeling of the Water Saturation for each porosity class. This fast and simple method allows the generation of a consistent water saturation distribution that respects a realistic degree of correlation between porosity and saturation. Besides, the method is perfectly suited for the unconsolidated sands of SINCOR where there is no transition zone (nil capillary pressures). With this simple technique, the distribution of the saturation is performed in one step for all the reservoir cells that are located above the Oil-Water Contact (OWC). The definition of this hydrocarbon contact is complicated due to the small density contrast between the oil (0.965) and the water, the occurrence of a flushed zone, the heterogeneous character of the reservoir, and poor well borehole conditions that negatively impact on the acquisition of reliable geological data. The definition of the OWC is therefore possible by a complete and simultaneous analysis of different types of data: conventional logs, resonance magnetic logs, wireline tester pretests, drill stem tests, and cores.
ConclusionBuilding a coherent 3D geological model of a complex heavy oil field, with 2 years of production is a tremendous task that requires a multi-disciplinary organization and approach, by an integrated team, at each stage of the process. To improve the description of the reservoir heterogeneity, all the available static and dynamic inputs must be introduced in both detailed lithological grid and petrophysical grids that are intimately related. Any modifications to match the dynamic constraints are discussed by the concerned team and introduced into the fine 3D geological grid as deterministic inputs. The integration of such inputs enhances considerably not only the static reservoir description but also the consistency of the future reservoir model which will better reproduce the observed field performance and facilitate at a latter stage the production history matching process. |