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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.
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.
In this paper, we present workflows, key relationships and results of multiple stochastic fault seal analyses conducted on geocellular geological or (static) reservoir grids. Ranges of uncertainties are computed from new and published datasets for the different input relationships (e.g. throw, VShale to VClay, fault clay prediction, fault rock clay content to permeability); these are used as input into stochastic modelling processes and the impact of each is assessed. The power of stochastic modelling to focus interpretation and risking effort is reviewed. Reducing the uncertainty distributions from the published data ranges has a massive impact on the range of predicted fault seal properties. Halving the uncertainties associated with the computation of the transmissibility multiplier, for instance, reduces this range from 7 to 1-1.5 orders of magnitude of the base-case value (no uncertainty). Importantly, when combined together, the median predictions from each individual parameter do not lead to the median value for the final prediction; average relationships combined together will not therefore produce the average final prediction. This is a powerful result for two reasons: first, current geological modelling packages use global trends to define fault properties and so are likely to predict spurious results; and secondly, reducing the uncertainty on specific relationships by around 50% is an achievable goal. Locally calibrated datasets and relationships (field-specific) based on carefully characterized samples should allow for this improvement in prediction accuracy. This paper presents a review of fault seal techniques, published data and the potential pitfalls associated with the analyses.Incorporating uncertainty during fault seal analysis via stochastic 3D modelling has the potential to rapidly identify critical high-risk seal or flow zones. The result should be more accurately risked prospects or field geological models. Simple uncertainty incorporation techniques, such as varying throw and clay smear, combined with the computation of the distribution of probable reservoirreservoir cross-fault juxtaposition windows, are very powerful, but are currently unavailable in most commercial reservoir geological modelling packages. Utilizing these techniques has the potential to improve the accuracy of predictions. In this contribution, we outline workflows to allow uncertainty to be incorporated into fault seal analyses conducted directly on geocellular geological or reservoir models (e.g. pillar-based grids).Stochastic multiple realization techniques are widely implemented in reservoir geological and property modelling processes (Handyside et al. 1992) but are currently under-utilized in fault seal predictions (e.g. James et al. 2004). The strength of stochastic approaches is in the analysis and prediction of results where the key relationships have significant natural variability. Fault morphology and fault rock properties certainly fall within this category (e.g.In systems which are known to vary significantly...
In order to properly meet up with the ever-increasing demand for petroleum products worldwide, it has become increasingly necessary to produce oil and gas fields more economically and efficiently. Waterflooding is currently the most widely used secondary recovery method to improve oil recovery after primary depletion. A crucial component required to conduct an efficient waterflooding operation is an optimal production setting, most especially with respect to the amount of water involved. This research work has been carried out to develop a model that can be used to maximize oil recovery and minimize water production with the least amount and number of waterflood variables in order to minimize the secondary recovery investment cost. The gradient-based approach to optimize the production and net present value (NPV) from a waterflood reservoir using the flow rates or bottom hole pressures of the production wells as the controlling factors with the use of smart well technology was applied. In this approach, a variant of the optimal switching time technique was used in the optimization process to equalize the arrival times of the waterfront at multiple producers, thereby increasing the cumulative oil production. The optimization procedure involved maximizing the objective function (NPV) by adjusting a set of manipulated variables (flow rates). The optimal pressure profile of the waterflood scenario that gave the maximum NPV was obtained as the solution to the waterflood problem. The proposed optimization methodology was applied to a waterflood process carried out on a reservoir field developed by a five-spot recovery design in the Niger Delta area of Nigeria, which was used as a case study. The forward run was carried out with a commercial reservoir oil simulator. The results of the waterflood optimization revealed that an increase in the net present value of up to 9.7% and an increase in cumulative production of up to 30% from the base case could be achieved.
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