In a new field development, enormous time and energy are put in to identify and quantify both static and dynamic reservoir uncertainties. This could be very challenging when information is limited and not readily available. These data uncertainties vary from project to project depending on the quantity of reliable reservoir data acquired during the exploration and appraisal stage. The first step towards managing reservoir uncertainty in field development is to identify and quantify the key reservoir parameters that affect hydrocarbon in place and recovery. These parameters or factors are so many that their combinational effects result in multiple cases of development options, which will require many simulation runs. Stochastic and statistical data analysis coupled with Experimental Design(ED) have been used in recent times to reduce time and energy expended in managing key reservoir uncertainty parameters. The case study presented in this paper gives an illustration of a successful ED application in a new gas field development. A detailed workflow was created to identify and quantify the uncertainties and risks in the dynamic model using probabilistic approach. This resulted in Probabilistic field forecasts that were further used to test surface development options in order to determine the optimal field development strategy that is robust to multiple realisations of reservoir uncertainties

Recent advances in computing speed and software call for a better practical approach to subsurfacesurface network modelling. Looking at it from the angle of Integrated Asset management, which requires the subsurface and surface teams to work as one in providing input and solution to field development plan challenges, this comes as an important tool in long term planning of a project. With this approach, team members from various disciplines, will be able to work directly with others, sharing ideas and making real time decisions on a regular basis.The case study in this paper shows how such integration resulted in time and cost savings when good and quick decisions are made on a project. this shows a clear departure from the traditional subsurface/surface engineering relation in field development studies, where the subsurface reservoir simulation work is done independent of surface considerations. This paper highlights the benefits of the new approach to the project.

In producing assets, the drive to reduce production deferments is increasing the interests in injection fall-off (IFO), as against pressure build-up, tests for reservoir characterisation. The IFO, which entails the measurement and analysis of pressure transients due to the shut-in of an injector after a period of continuous injection, provides estimates of important subsurface parameters such as static pressure, transmissibility and, where applicable, induced-fracture characteristics. This paper documents the application of IFO in selected cases from an oil-producing deepwater field. The pressure fall-off curves obtained for two water injectors in the subject deepwater field are analysed. Particular attention is paid to the analysis of the fall-off curves and the identification of the various flow regimes. The estimation of key subsurface parameters such as skin factor, transmissibility, and boundary characteristics is detailed.To reduce interpretation uncertainties, an integrated application of geoscience data has been employed to constrain the possible range of IFO interpretations. In addition, the IFO results have been used to rationalise the declining injectivity indices observed in the subject injectors, while providing clues for these observations at the pore scale. In essence, this work reechoes the credentials of IFO, as a credible and cheaper alternative to pressure build-up tests, for the characterisation of subsurface systems.