Connectivity represents one of the fundamental properties of a reservoir that directly affects recovery. If a portion of the reservoir is not connected to a well, it cannot be drained. Geobody or sandbody connectivity is defined as the percentage of the reservoir that is connected, and reservoir connectivity is defined as the percentage of the reservoir that is connected to wells. Previous studies have mostly considered mathematical, physical and engineering aspects of connectivity. In the current study, the stratigraphy of connectivity is characterized using simple, 3D geostatistical models. Based on these modelling studies, stratigraphic connectivity is good, usually greater than 90%, if the net: gross ratio, or sand fraction, is greater than about 30%. At net: gross values less than 30%, there is a rapid diminishment of connectivity as a function of net: gross. This behaviour between net: gross and connectivity defines a characteristic ‘S-curve’, in which the connectivity is high for net: gross values above 30%, then diminishes rapidly and approaches 0. Well configuration factors that can influence reservoir connectivity are well density, well orientation (vertical or horizontal; horizontal parallel to channels or perpendicular) and length of completion zones. Reservoir connectivity as a function of net: gross can be improved by several factors: presence of overbank sandy facies, deposition of channels in a channel belt, deposition of channels with high width/thickness ratios, and deposition of channels during variable floodplain aggradation rates. Connectivity can be reduced substantially in two-dimensional reservoirs, in map view or in cross-section, by volume support effects and by stratigraphic heterogeneities. It is well known that in two dimensions, the cascade zone for the ‘S-curve’ of net: gross plotted against connectivity occurs at about 60% net: gross. Generalizing this knowledge, any time that a reservoir can be regarded as ‘two-dimensional’, connectivity should follow the 2D ‘S-curve’. For channelized reservoirs in map view, this occurs with straight, parallel channels. This 2D effect can also occur in layered reservoirs, where thin channelized sheets are separated vertically by sealing mudstone horizons. Evidence of transitional 2D to 3D behaviour is presented in this study. As the gross rock volume of a reservoir is reduced (for example, by fault compartmentalization) relative to the size of the depositional element (for example, the channel body), there are fewer potential connecting pathways. Lack of support volume creates additional uncertainty in connectivity and may substantially reduce connectivity. Connectivity can also be reduced by continuous mudstone drapes along the base of channel surfaces, by mudstone beds that are continuous within channel deposits, or muddy inclined heterolithic stratification. Finally, connectivity can be reduced by ‘compensational’ stacking of channel deposits, in which channels avoid amalgamating with other channel deposits. Other factors have been studied to address impact on connectivity, including modelling program type, presence of shale-filled channels and nested hierarchical modelling. Most of the stratigraphic factors that affect reservoir connectivity can be addressed by careful geological studies of available core, well log and seismic data. Remaining uncertainty can be addressed by constructing 3D geological models.

Static descriptive measures can be used to quantify characteristics of a 3D reservoir model. These static measures may have implications for the prediction or interpretation of dynamic performance and can draw attention to geological uncertainties that may impact flow behaviours. This study reviews, modifies and introduces techniques to characterize the spatial distribution of permeability in reservoir models, with emphasis placed on connectivity and continuity analysis. Topics include: the relationship between connectivity and percolation theory; definition of types of reservoir connectivity; methods of measuring connectivity; connectivity as a function of distance; connectivity maps; categorical classifications of connectivity; types of reservoir path lengths; and continuity lines. The key factors controlling reservoir connectivity are identified. Static measures can be used to locate regions of higher sweep efficiency and lower tortuosity that are connected to the wells.

It is well established that uncertainty exists in simulated recovery forecasts due to the ambiguity in the measurement and representation of the reservoir and geologic parameters. This is especially true for immature projects, such as deep-water reservoirs, where the high cost of data limits the information that is available to build reservoir models. We present two strategies, based on Experimental Design, to quantitatively assess this uncertainty in recovery predictions for primary and waterflood processes. We apply the Experimental Design methodology to channelized sandstone systems because of their relevance to many deep-water projects. We choose to study synthetic geological analogs of channelized systems that are built from panoply of relevant parameters while taking into account the uncertainty that exists in the estimation of their ranges. We use the results of this study to generate type curves with neural networks. The trained neural networks can be used to rapidly predict reservoir performance where field data is very limited. We discuss applications of this methodology on field cases from western Africa. Introduction An irony of contemporary petroleum exploration and development is that although technological advances have been significant, risk has not been reduced in all cases. In fact, risk may actually be greater in many frontier reservoir development projects. For example, in deepwater projects the large initial capital investments due to costs associated with platform design, and well construction, are made with limited knowledge of reservoir architecture and geology. The high cost of drilling, completing and coring wells limits the availability of geological, petrophysical and engineering data, which are needed to build reliable reservoir simulation models to help in the decision process. A method that can identify the key parameters governing uncertainty in production and economic forecast in the early phases of the study will significantly ameliorate the data acquisition program. Simulation is often the tool of choice in the planning and evaluation of sequential reservoir development phases. Typically, earth scientists build the most representative geological model applying expert knowledge using well logs and other geological data. A few geostatistical realizations are generated to sample the uncertainty in geological parameters. A representative combination of geology, fluid and flow parameters, along with well locations constitutes the base case model. This model is then simulated to obtain production profiles and recovery factor for a chosen recovery process. Finally, economic performance indicators (ROI, NPV) are computed for the project. Estimating recovery uncertainty is complicated because it requires an understanding of both the reservoir's static architecture and dynamic behavior during production. Recovery depends on structural, stratigraphic, and per-meability architecture, fluid and engineering properties, drive mechanisms, and spacing/orientation of producing and injecting wells. The uncertainty, which is associated with the measurement and estimation of these parameters, will result in uncertainty in reservoir performance estimates.

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