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Abstract Only

Predicting the lateral continuity, vertical connectivity, and compartmentalization of deep-water reservoirs is critical to both their understanding and producibility. The Aspen Field, located in Green Canyon block 243 at a water-depth of 2,723 feet (Fig. 1), consists of a series of upper Miocene (Rob E) stacked hydrocarbon-bearing sands that are currently under production. Conventional core from the L Sand (main producing interval) displays two primary depositional facies: (1) thickly bedded to massive, weakly consolidated, fine-grained sands that represent amalgamated sheets deposited along the axis of flow and (2) finer grained, medium bedded, layered sheet sands deposited along flow margins. These facies reflect deposition in a distributive deep-water system (Fig. 2).

Figure 1.

Aspen locator map and subsea completion schematic.

Figure 1.

Aspen locator map and subsea completion schematic.

Figure 2.

Schematic diagram from the Pennsylvanian Jackfork Group in Arkansas illustrating the interpreted position of the Aspen Field within the distributive portion of a submarine fan system (from Bouma, 2000).

Figure 2.

Schematic diagram from the Pennsylvanian Jackfork Group in Arkansas illustrating the interpreted position of the Aspen Field within the distributive portion of a submarine fan system (from Bouma, 2000).

The Aspen Field data set provides an opportunity to address three key challenges to our understanding of deep-water reservoir systems in the Miocene of the Gulf of Mexico: (1) what is the lateral continuity of an important reservoir sand, (2) what types of vertical compartmentalization exist in a distributive, sheet sand system, and (3) how does the alteration of volcanic rock fragments (mainly volcanic glass) and the precipitation of clinoptilolite as a cement affect reservoir quality. Through the combination of multiple rock data sets (conventional cores, sidewall cores and well cuttings), high-resolution biostratigraphic analysis, and production data an integrated geologic model has been constructed to characterize the detailed reservoir complexity encountered at Aspen and relate that complexity to well performance.

High-resolution biostratigraphic analysis was applied to all wells in the Aspen Field to help subdivide reservoir packages and delineate sand-body architecture and stratigraphic pinch-outs. This methodology utilizes standard biostratigraphic information (i.e. regional extinction events, paleoecology, and abundance/diversity profiles) coupled with coiling direction reversals (i.e., G. menardii, G. humerosa), size changes (i.e., G. menardii), individual abundance increases (C. mexicana), and local fossil increases in order to define local biomarkers instrumental for field-scale correlation. Four regional biostratigraphic markers were initially defined around the Aspen pay interval providing a general biostratigraphic framework for the field (Fig. 3). The application of high-resolution biostratigraphic analysis not only confirmed regional and semiregional flooding surfaces (marker horizons) in a lower bathyal setting but identified additional local field-scale events between the regional and semi-regional surfaces (Fig. 4). To-date, the long-term production and reservoir simulation have validated this reservoir architecture.

Figure 3.

High-resolution biostratigraphic chart illustrating the detailed subdivision of the Upper Miocene into 8 distinct reservoir seal pairs based on both local and regional biostratigraphic event intervals.

Figure 3.

High-resolution biostratigraphic chart illustrating the detailed subdivision of the Upper Miocene into 8 distinct reservoir seal pairs based on both local and regional biostratigraphic event intervals.

Figure 4.

Reservoir cross-section of the Aspen Field detailing the application of high-resolution biostratigraphic analysis. This methodology resulted in the delineation of several local biomarkers and allowed for more detailed correlations in the L Sand sequence. In addition, the pinchout of the M2 Sand was identified by convergence of Cris-K and CMC paleo-markers and lack of CK-1 marker. GR on left, resistivity on right.

Figure 4.

Reservoir cross-section of the Aspen Field detailing the application of high-resolution biostratigraphic analysis. This methodology resulted in the delineation of several local biomarkers and allowed for more detailed correlations in the L Sand sequence. In addition, the pinchout of the M2 Sand was identified by convergence of Cris-K and CMC paleo-markers and lack of CK-1 marker. GR on left, resistivity on right.

Interpretation of MDT pressure, well log, seismic, seal and lithologic data used in conjunction with high-resolution paleontological data have established multiple reservoir levels that occupy separate pressure regimes. MDT pressure data clearly demonstrate significant reservoir compartmentalization between the various sands at Aspen caused by the distribution of claystone packages occurring at the top of third-order depositional sequences (Fig. 5). Mercury displacement entry pressure data ranges from 1,900 to 3,000 psi and indicates that the capping claystones have effective seal capacity. This equates to a maximum calculated hydrocarbon column of 886 feet for the K Sand, 792 feet for the L Sand, and 670 feet for the M Sand. MDT pressure data also show pressure compartmentalization within the L Sand interval, suggesting that some of the thinner (higher-order) claystones are also effective seals (Fig. 5). An excellent analog for these hierarchical scales of reservoir compartmentalization can be found in the Pennsylvanian Jackfork Group of Arkansas, where similar claystone facies are observed in both inter- and intrasheet sand positions (Fig. 6).

Figure 5.

Aspen pressure plot showing original (lower right) and post-production (upper left) reservoir pressures demonstrating compartmentalization within the primary producing L Sand interval.

Figure 5.

Aspen pressure plot showing original (lower right) and post-production (upper left) reservoir pressures demonstrating compartmentalization within the primary producing L Sand interval.

Figure 6.

Outcrop photographs of the Pennsylvanian Jackfork Group from Baumgartner's Quarry near Kirby, Arkansas. Sandstones shown are interpreted as basin-floor sheet deposits and demonstrate reservoir partitioning on at least two scales. The upper photo shows two distinct sheet sand complexes separated by an interval of thick mudstone while the lower photo documents a higher order of partitioning within a sheet sand complex.

Figure 6.

Outcrop photographs of the Pennsylvanian Jackfork Group from Baumgartner's Quarry near Kirby, Arkansas. Sandstones shown are interpreted as basin-floor sheet deposits and demonstrate reservoir partitioning on at least two scales. The upper photo shows two distinct sheet sand complexes separated by an interval of thick mudstone while the lower photo documents a higher order of partitioning within a sheet sand complex.

Diagenesis is an additional complication in reservoir characterization at the Aspen Field. To determine its effects on the reservoir, core was collected in zones thought to be impacted by diagenesis. An analysis of thin-sections created from core showed that tuffaceous, volcanic rock fragments and glass shards are common constituents in the Aspen reservoirs (Fig. 7). An additional component was the zeolite clinoptilolite [(Na,K,Ca)2-3Al3(Al,Si)2Si13O36-12H2O], which precipitated as a result of devitrification of volcanic constituents. Clinoptilolite crystals ranging from 3 to 30 microns in size were dispersed in varying degrees throughout the pore system and significantly reduced inter-granular pore volume (Fig. 8). Clinoptilolite abundance ranges from 4 to 19 wt. % by X-ray diffraction techniques with an average of 9% (n = 44) and is observed in all samples of the Aspen L Sand.

Figure 7.

Core photographs, x-radiographs, associated thin sections and SEM images showing clinoptilolite partially occluding intergranular pores. Clinoptilolite a diagenetic alteration product of volcanic glass occurs as tabular-to coffin-shaped crystals that loosely adhere to grain surfaces and range from approximately 3-30 microns in diameter.

Figure 7.

Core photographs, x-radiographs, associated thin sections and SEM images showing clinoptilolite partially occluding intergranular pores. Clinoptilolite a diagenetic alteration product of volcanic glass occurs as tabular-to coffin-shaped crystals that loosely adhere to grain surfaces and range from approximately 3-30 microns in diameter.

Figure 8.

Thin section microphotographs of clinoptilolite in Aspen reservoirs showing its association with volcanic rock fragments, volcanic glass shards and dissolved glass shards.

Figure 8.

Thin section microphotographs of clinoptilolite in Aspen reservoirs showing its association with volcanic rock fragments, volcanic glass shards and dissolved glass shards.

The presence of clinoptilolite cements can have a number of effects on formation evaluation including: (1) a lowered resistivity tool response due to elevated capillary water, high cation exchange, and the presence of water in the mineral matrix; (2) an optimistic density porosity evaluation that fails to account for clinoptilolite's low (2.1–2.2 gm/cc) matrix density; and (3) a significant decrease in permeability (Fig. 9). In addition, a major production problem can occur due to the migration of clinoptilolite and other fines into pore throats. This problem may be significantly exacerbated by producing at high flow rates or by significant perturbations to the production rate (as may occur during unexpected shut-ins and restarts).

Figure 9.

Permeability vs. % clinoptilolite plot showing reservoir quality in the Aspen L Sand compared to GOM reservoirs of similar facies and age.

Figure 9.

Permeability vs. % clinoptilolite plot showing reservoir quality in the Aspen L Sand compared to GOM reservoirs of similar facies and age.

In summary, an understanding of the lateral continuity, vertical connectivity, distribution of sealing facies and diagenetic overprint of deep-water reservoirs is critical for an accurate assessment of their producibility. Sheet-sand reservoirs are often thought of as relatively uniform in their stratigraphic correlation and flow characteristics; however, this example gives evidence to the contrary. The Aspen Field dataset provides a robust example of the complexity of sheet-sand reservoirs from pore to bed-set scale and underscores the importance of data integration from multiple sources to accurately characterize reservoir architecture and quality.

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