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

Abstract

These 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-of-the-pudding lies only in the model's ability to accurately pre-dict field performance. Finally, we also ask - should the mode-ling 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 matching methods that use sensitivity coefficients. The question being addressed was: does constraining the geological models to the same dynamic data always create an imprint over the underlying geological variation and result in similar predictions? Preliminary results indicate that the history-matching overprint tends to mask some of the dramatic geological variations. This can have significant ramifications in modeling strategies, especially when assessing uncertainty in presence of substantial history. Introduction Reservoir characterization and modeling has evolved significantly over the past decade or so, with an ever growing emphasis around the "shared earth model" concept. This shared earth model needs to honor all log and seismic data, while maintaining a consistent picture of the depositional and the diagenetic character of the reservoir. On the other hand, where flow is of paramount importance, identification of appropriate flow paths that may or may not be purely dependent of the geological character, takes precedence. It is obvious that these two cannot be mutually exclusive, and yet with advances in reservoir characterization and geostatistics identification of unique flow paths still remains elusive.

Why Is Reservoir Architecture an Insignificant Uncertainty in Many Appraisal and Development Studies of Clastic Channelized Reservoirs?

“…Unpublished and in-house studies indicate that reservoir architecture is rarely one of the key uncertainties identified in assessments of volumetric or dynamic uncertainty in appraisal and development studies (publications supporting this statement include Jones et al, 1999;Friedmann et al, 2001;Jian et al, 2002;Castellini et al, 2003;Meddaugh, 2006). The only cases known in which reservoir architecture rises in relative importance are when there is a dependency (sometimes not obvious) between it and volumetric or dynamic parameters.…”

A study of conceptual model uncertainty in large‐scale CO2 storage simulation

“…The issue of optimal heterogeneity resolution is being explored by petroleum researchers concerned with reservoir production in extremely data‐poor environments [e.g., Castellini et al , 2003; Friedmann et al , 2003]. Using a set of characterization data for a deepwater reservoir, Milliken et al [2007] built two families of geologic models at different complexities.…”

The Effect of Geologic Parameters and Uncertainties on Subsurface Flow: Deepwater Depositional Systems

“…The Mahakam dataset affords an excellent opportunity to look at the subsurface flow in channel systems. Previous studies using synthetically generated channels 1,2,3 have found that the effect of channel width/thickness ratio, sinuosity, and stacking pattern have a weak effect on subsurface flow especially sweep efficiency. Our results in Group #3 corroborate this result.…”

“…The link between geology and subsurface flow has been examined extensively in the literature. For example, Larue and coworkers [1][2][3][4] have looked at fluvial systems using both conceptual models and the Meren reservoir. They examined a large number of models in an effort to investigate the largest possible variety of geologic interpretations and found a very wide range of resulting behavior (200% variation in breakthrough time and a factor of two variation in oil recovery).…”