Most of the old oil fields in China have reached high water cut stage, in order to meet the booming energy demanding, oil production rate must be kept in the near future with corresponding IOR (Improving Oil Recovery) methods. Z106 oilfield lies in Shengli Oilfields Area at the Yellow River delta. It was put into development in 1988. Since the oil belongs to heavy oil, the oil-water mobility ratio is so unfavourable that water cut increases very quickly. Especially for reservoir Ng21, the sand rock is sediment from river channel, the permeability heterogeneity and heavy oil properties together lead to extremely poor water flooding efficiency. In order to improve the oil recovery, IOR methods are needed urgently. Considering all practical situations for this reservoir and present technique level, polymer flooding method has been selected as an IOR test with numerical simulation. For polymer flooding, since polymer resolution has the capability of enlarging water viscosity, it controls the mobility of water phase and at the same time improves the driving efficiency. During polymer flooding simulation, many factors must be taken into account for the construction of mathematical model, such as inaccessible pore volume, polymer shear thinning effect, polymer adsorption, relative permeability reduction factors, etc. All simulations were done with black oil model with polymer option in ECLIPSE. Simulation results for a theoretical river channel reservoir with serious permeability heterogeneity and heavy oil, and simulation results for practical reservoir Ng21, both have shown that polymer flooding is a feasible method for IOR. For reservoir Ng21, with polymer slug size of 0.235 PV, polymer concentration at 1.5 kg/m3, the final oil recovery after polymer flooding could reach 12.8%, the enhanced oil recovery is about 5%. If only the developable oil reserve being taken into account, the final oil recovery is about 34%, and enhanced oil recovery from polymer flooding is more than 12%. For such heavy oil river channel reservoir to reach such a final oil recovery, it could be concluded as a great success. Since there are still many such oil reservoirs in Shengli Oilfields Area, polymer flooding will be of great importance for improving oil recovery in this area in the near future.

Data marts storing pre-aggregated data, prepared for further roll-ups, play an essential role in data warehouse environments and lead to significant performance gains in the query evaluation. However, in order to ensure the completeness of query results on the data mart without to access the underlying data warehouse, null values need to be stored explicitly; this process is denoted as negative caching. Such null values typically occur in multidimensional data sets, which are naturally very sparse. To our knowledge, there is no work on shrinking the null tuples in a multi-dimensional data set within ROLAP. For these tuples, we propose a lossless compression technique, leading to a dramatic reduction in size of the data mart. Queries depending on null value information can be answered with 100% precision by partially inflating the shrunken data mart. We complement our analytical approach with an experimental evaluation using real and synthetic data sets, and demonstrate our results.

Data marts and caching are two closely related concepts in the domain of multi-dimensional data. Both store pre-computed data to provide fast response times for complex OLAP queries, and for both it must be guaranteed that every query can be completely processed. However, they differ extremely in their update behaviour which we utilise to build a specific data mart extended by cache semantics. In this paper, we introduce a novel cache exploitation concept for data marts - coarse-grained caching - in which the containedness check for a multi-dimensional query is done through the comparison of the expected and the actual cardinalities. Therefore, we subdivide the multi-dimensional data into coarse partitions, the so called cubletets, which allow to specify the completeness criteria for incoming queries. We show that during query processing, the completeness check is done with no additional costs.

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