The challenges and impact of compartmentalization in reservoir appraisal and development

Abstract

As an industry we have been poor at identifying and predicting the effect of reservoir compartmentalization on fluid flow throughout field life. In the context of harder-to-find reserves and rising development costs it is vital to have a well-rounded strategy in place to identify and mitigate uncertainties and risks associated with compartmentalization. The key challenge of today is therefore to improve predictive capability.Historically we have relied too heavily on 'single complex' linear modelling approaches to understand the impact of compartmentalization. Lately, we have begun to place a greater emphasis on using a forensic level of reservoir analysis coupled with the use of dynamic signals from production data and constant down hole monitoring of fluid-type, pressures and temperatures. Evidence is mounting that as fields deplete they evolve mechanically over production time-scales leading to changes in fault behaviour, stress configuration, compaction and hence compartmentalization; such factors are commonly not predicted at start-up. Our challenge has been to develop toolkits and workflows which integrate an appropriate range of geological models iteratively coupled with dynamic data. We need to develop analytical approaches that enable real-time updates from the evolving reservoir & fluid system to iteratively modify our models and improve their predictive power. This will allow us to make better-informed decisions at every stage of field life.The upstream exploration and production business is commonly driven by the expectation that we can now get more from our older existing fields, continue with positive reserve replacement ratios (the ratio between: reserves booked from discoveries, field extensions and improved recovery schemes; and production over a given period), manage rising costs, and make better informed decisions when faced with increased geological complexity in new developments. Thus, production teams are pressured to deliver on promises derived from geological models and production rate profiles experienced and/or predicted earlier in a field's Appraisal or Development phase. There are, however, many examples in the literature where compartmentalization has unexpectedly impacted field development (e.g. Gainski et al. 2010;McKie et al. 2010). It follows that successful production optimization is controlled by our ability to characterize and predict reservoir performance and fluid flow behaviour over the life-cycle of a hydrocarbon field. In many cases, this understanding comes through experience, relatively late in field life (e.g. Barkved et al. 2003;Clifford et al. 2005;Moulds et al. 2005;Barr et al. 2007;Gainski et al. 2010). Understanding the impact of compartmentalization on fluid flow and the ability to predict it prior to drilling wells is a long-term intention of many companies, but it is consistently under-evaluated and underestimated. It would appear that we still have some way to go before we consistently predict the impact of faults, fractures, stress and other reservoir heteroge...

EXTREME CAPILLARY HETEROGENEITIES AND IN SITU FLUID COMPARTMENTALIZATION DUE TO CLUSTERS OF DEFORMATION BANDS IN SANDSTONES

“…To develop realistic geological models for implementation in such reservoir models, robust estimations of the petrophysical properties of deformation bands, deformation band clusters and sub-seismic deformation-band dominated faults, as well as their likely geometric configurations and frequencies, are required. These can only be derived from outcrop analogues and detailed structural geological studies (fault and fractures logs, micro-structural analysis) (Fox and Bowman, 2010).…”

Geometry and compartmentalization of fluvial meander-belt reservoirs at the bar-form scale: Quantitative insight from outcrop, modern and subsurface analogues

“…Given the frequency with which this style of stratigraphic compartmentalization is observed, and how it is seen to affect hydrocarbon production, the recognition of meander-belt architectures in subsurface datasets should forewarn of the likely influence of intra-channel-belt compartmentalization. Because it is advocated that the evaluation of styles of compartmentalization should be undertaken in the early appraisal stages of the exploration and production workflow (Smalley & Hale 1996;Fox & Bowman 2010;Jolley et al 2010;Smalley & Muggeridge 2010), there is a need for predictive tools that can be used in this context. Representation of the compartmentalization of the upper part of point bars determined by muddy abandonment deposits, with reference to the architecture represented in parts A and B and to the particular arrangement of injection and production wells represented; it is shown how the development of attic reservoir compartments might result in poor sweep efficiency in a scenario of oil production by waterflooding.…”

“…Smalley & Hale 1996;Calvert 2005;Páez et al 2010). Yet, it is claimed that the diagnosis of compartmentalization in early stages of field development likely improves management throughout a field lifecycle (Smalley & Hale 1996;Fox & Bowman 2010;Smalley & Muggeridge 2010): hence, there is need for empirical knowledge of reservoir architectures that can be used to make pre-drill predictions and to inform development plans. The quantitative tools presented here serve this purpose for meander-belt reservoirs.…”

Reducing uncertainty and risk through field-based studies

“…The data, associated models and interpretations have enabled a deeper understanding of the nature and range of subsurface uncertainties, and how these impacted risks to delivering promised resource assessments, reservoir production profiles, production forecasts and well rates ( Fig. 2) (Fox & Bowman 2010).…”