In this project, low-salinity water flooding has been modeled on ECLIPSE black oil simulator in three cases for a total field production life of twenty-five years. In the first case, low-salinity water flooding starts fifteen years after secondary water flooding. For the second case, low-salinity water flooding starts five years after secondary water flooding and runs till the end of the field production life. For the third case, low-salinity water flooding starts five years after secondary water flooding, but low-salinity water flooding is injected in measured pore volumes for a short period of time; then, high-salinity water flooding was resumed till the end of the field production life. This was done to measure the effect of low-salinity water flooding as slug injection. From the three cases presented, oil recovery efficiency, field oil production rate, and field water cut were observed. Increased percentages of 22.66%, 35.12%, and 26.77% were observed in the three cases, respectively.

Following on from the work by Tang and Morrow in 1998, enormous progress has been made in the development of low salinity water flooding for improved oil recovery, (IOR). This paper has assessed the applicability of modified salinity as an IOR scheme in the Niger Delta. A laboratory approach which entailed the use of crude oil, brine and core plugs from fields X and Z located in the Niger Delta was adopted. Several options were assessed: progressive dilution of the injected brine, variation of concentration of divalent cations in the injection fluid, high salinity flooding followed by low salinity flooding (HSF, LSF) and variation of low salinity slug size. The results obtained are very promising as additional recoveries in the order of +5% to 21% were obtained from this work. Based on the promising results of this on-going research, recommendations on future improvements were also outlined.

Prior to embarking on a laboratory and subsequently pilot test for a potential improved oil recovery scheme in a green or brown field, it is important to have a sense of potential gains from the available options. This is usually done using correlations. Whereas there had been existing models for use in making these approximations, this work has developed a robust correlation for use in estimating the potential reduction in residual oil saturation post Optimized Salinity Water flooding (OPTSWF) (and consequently additional recovery) as a function of change in Interfacial tension (IFT), change in salinity, porosity, permeability, start residual oil saturation, and API gravity of the crude oil. This was done for a field in the Niger Delta. The model was tested against available data and showed good correlation with a correlation coefficient ranging from 99.36% to 99.89%. Also, the performance of the model was tested alongside that proposed by Tripathy et. al and in all cases, the model developed by this work performed better with lower RMS errors.

In petroleum reservoir management, the essence of well placement is to develop and maintain reservoir pressure in order to achieve maximum production for economic benefits. Large production can be achieved with the placement of multiple wells but this approach is capital intensive and inefficient for the development of a reservoir. A preferable option is the optimal placement of production and injection wells so as to fully capitalize on the imbedded hydrocarbons at a relatively decreased capital investment. The aim of this study is to use developed algorithm and a black oil simulator to place wells in the zones for optimal recovery in the reservoir. Optimal production was determined out of eight scenarios created from well placement in a hypothetical reservoir (finch reservoir) using a black oil simulator, alongside an algorithm developed with java for determining the best possible locations for well placement, taking into consideration the reservoir permeability, fluid saturation, and pay zone thickness. The results of this study reveal that well placement using the engineering judgment coupled with the application of the algorithm using a black oil simulator results in better production compared to other scenarios which consider the combined effect of algorithm and black oil simulator alone.

This project focuses on building a reservoir sub-sea network model for a condensate field in the gulf of Guinea, the Duke Field. It integrates the five developed Duke reservoirs, development wells and subsea network using the Petroleum Experts' Integrated Production Model suite of software, (IPM) which is widely used in the E&P industry especially for integrated forecasting, surveillance and production system optimization that require integration of surface and subsurface models.Following the acquisition and quality control of data from other teams working on the Duke Field, a network model which integrates the five Duke reservoirs, their associated wells and subsea network up to the production separator was built. The model was initialized and used to predict full field performance under different scenarios.Finally, a water injection allocation sensitivity study was performed and the results were analyzed both technically and economically. From the technical point of view, the option to reallocate 10 kbwpd from reservoir U to reservoir P-upper North and another 10 kbwpd from Reservoir ST to reservoir Q-Lower brought about the optimum recovery. This was also supported by a simple economic analysis. It was then recommended that additional water injectors be drilled in P-Upper North and Q-Lower to unlock an additional 8.4 MMSTB of reserves resulting from higher sweep efficiencies and better pressure maintenance.