What is Relevant to Flow? A Comprehensive Study Using a Shallow Marine Reservoir

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThese days "estimating uncertainty" is the mantra. As we do this, we ask ourselves which is better: an array of geologically simple rapidly history-matched models, or a single geologically comprehensive, carefully history-matched model. After all, uncertainty, which is normally characterized by a range of forecasts from techniques such as Experimental Design, is difficult to quantify using just one model however comprehensive it may be. Yet if forecasts are obtained from a series of simple models, how good are they? Choosing one over the other also has significant implications in the time required for modeling, and also reservoir management.Specific questions, that directly affect the cost of modeling, come to mind. These are: What is the optimal level of geological detail, especially if the uncertainty management plan includes history-matching and simulating a series of models? Can the oil trapped behind the flood front be estimated by a series of simple lateral (shales) barriers/baffles or do we always need an extensive sequence stratigraphy framework? A detailed model may not be as amenable to uncertainty estimation by the virtue of its size. Is field-scale history matching adequate or is well-by-well history matching a must? Perhaps the brute force approach of probabilistic forward modeling provides the panacea. After all the proof-ofthe-pudding lies only in the model's ability to accurately predict field performance. Finally, we also ask -should the modeling strategy, i.e., comprehensive vs. simple, be dependent on the response variable of interest, e.g. ultimate recovery factor vs. infill drill locations. As such, if ultimate recovery is the objective, a simple model may suffice. To answer questions like these, we re-visit current reservoir modeling paradigms.As a datum, we use a comprehensively modeled waterflood from Western Africa. This reservoir was modeled using extensive sequence stratigraphic techniques. The model was scaled-up from about 14 million cells to about 280,000 cells using a flow-based scale-up algorithm, carefully preserving all the mappable mudstones above flooding surfaces. History-matching for a 30-year period was systematically conducted with a team of field engineers and simulation specialists. The whole process took about a year to complete. Against this datum, we compare a series of rapidly built geological models that still honor all the data and the overall depositional architecture, and yet are significantly different from the datum geological model by the virtue of the modeling strategies implemented. The different modeling strategies vary in complexity from changes in variogram lengths and direction to simple tank models with stochastic sandstones and mudstones conditioned by well data. Various geostatistical algorithms were also investigated for facies modeling and petrophysical properties population within facies.The new models were history-matched using the conventional manual method and two separate assisted-history match...

Modelling facies belt distribution in fan deltas coupling sequence stratigraphy and geostatistics: The Eocene Sant Llorenç del Munt example (Ebro foreland basin, NE Spain)

“…Recently, geocellular models comparable to those built from subsurface reservoirs (MacDonald et al, 1992;Jian et al, 2002;Smith and Møller, 2003;Castellini et al, 2003;Svanes et al, 2004;Ainsworth, 2005;Larue and Friedmann, 2005) have been built from outcrops (Novakovic et al, 2002;Ravenne, 2002;Falivene et al, 2006c). This has proved to be an extremely useful technique to improve subsurface facies modelling approaches.…”

“…Shoreface and deltaic shallow-marine depositional systems make important reservoirs throughout the World, and consequently they have been the topic of numerous modelling studies (MacDonald et al, 1992;Hodgetts et al, 2001;Jian et al, 2002;Castellini et al, 2003;Li and White, 2003;Noad, 2004;Larue and Legarre, 2004;Ainsworth, 2005;Howell et al, 2008a). From a modelling perspective, fan deltas are a class of shallow-marine system that is less well studied, although locally they constitute important reservoirs.…”

“…Typical petrophysical values were taken from other similar subsurface settings (Table 3). Stochastic petrophysical modelling, which is typically applied in subsurface modelling studies (Jian et al, 2002;Castellini et al, 2003;Larue and Legarre, 2004), was not considered because this would introduce additional variability and could mask the effects related to sedimentary architecture.…”

“…Vertical exaggeration is 3Â. evaluated the impact of increasing complexity in the modelling strategy for a shoreface reservoir; whereas Castellini et al (2003) assessed the implication of the modelling scales.…”

Reservoir Modeling Methods and Characterization Parameters for a Shoreface Reservoir: What Is Important for Fluid-Flow Performance?

“…Field production data were used in a parallel engineering study 14 and are not discussed here. The only constraints for fluid-flow modeling were: (1) a minimum bottomhole pressure of 300 psi, (2) a maximum oil-production rate of 3,000 B/D for the production wells, (3) a maximum bottomhole pressure of 4,000 psi, and (4) a maximum water-injection rate of 25,000 B/D for the injection wells.…”