Multiple scenario modelling methodology has been used to study gas injectionin a fractured carbonate reservoir to facilitate an increase in productivity and ultimate field recovery as a result of reservoir repressurisation. The complex physical processes involved in the production mechanism, and are latively sparse data set, highlighted the underlying problem of making predictions based on reservoir models for which a large degree of uncertainty exists. These uncertainties have a significant impact, both in the understanding of the reservoir fracture geology and in the data used for describing the fluid flow processes. A novel approach was developed to represent uncertainty ranges in model forecasts in which multiple history-matched simulation models were constructed to represent the impact of arange in each of the uncertainty parameters on the forecast. The methodology builds on an approach previously reported in Bentley and Woodhead (Ref.1). The results were then combined statistically into probability distributions of recovery profiles. The results show clearly the large impact of data uncertainty on incremental recovery arising from the gas injection scheme. The treatment of uncertainty presented here is a step forward in the modelling methodology for mature assets in which multiple scenario modelling has to beconstrained by the necessity to history match all model realisations against production data.
Predictive models for a sub-surface asset invariably suffer from large sub-surface uncertainties. Such uncertainties can remain significant throughout the asset life cycle and can easily impact the economic viability of the underlying field development scenarios. This paper builds on a methodology presented by Bentley and Woodhead (Ref. 1), in which it was shown how sub-surface uncertainty can be properly managed through the use of carefully designed multiple realisations of the sub-surface model.
The multiple scenario method is applied here to a mature fractured carbonate field. The primary objective of this study was to evaluate the feasibility of injecting gas to repressurise the reservoir back to the initial saturation pressure, thereby increasing the productivity and ultimate field recovery.
In view of the short time period allowed for the study (6 months) asimplified approach was taken to deriving 3D static models for the reservoir in which the static modelling procedure was streamlined. Within this approach the correlation and model building procedure was considerably shortened by carefully selecting a sub-set of the wells for use as the basis of the model construction and adopting a pragmatic approach to property modelling, concentrating on the large scale reservoir features. Starting with a thorough uncertainty analysis, simple reservoir simulation models were then built and these were used to evaluate and quantify the impact of uncertainty on model predictions. These predictions were then further validated and tuned by building more detailed models. Finally a statistical analysis of simulation results was made to allow future recovery and production profile predictions tobe made for any combination of the uncertainty parameters. Both the likely outcome of a gas injection scheme and the associated risks could then be assessed. Furthermore, the relative impact of each of the uncertainty parameters on predicted recoveries gave an indication of where future data gathering activities should be directed to best narrow the range of uncertainty.
The field being considered consists of a densely fractured carbonatereservoir. The structure is slightly asymmetric with steeply-dipping flanks. Seismic coverage of the producing area is poor and the top structure maps are based exclusively on well control. Volumetric uncertainties are therefore large.