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The low recovery factor associated with carbonate reservoir and the availability of affordable gas has drawn new attention to a Water Alternative Gas (WAG) strategy to enhance hydrocarbon recovery for reservoir at early stage of production. The use of WAG allows to improve sweep efficiency limiting fingering and hydrocarbon trapping at macroscopic (Water injection - WI) and microscopic (pore) (Gas Injection - GI) level.The purpose of this work is twofold: present a new field development plan optimization formulation generalized for continuous and/or WAG injection and its suggested application to a real reservoir test case. Optimum field development plans for different strategies are presented and the advantages of a WAG strategy as opposite to a continuous water injection are further discussed. The proposed optimization problem covers well placement, control, schedule and gas lift under uncertainty. This problem is formulated as Mixed Integer Nonlinear Programing (MINLP) with non-linear constrains in order to take into account operational restrictions.A carbonate deep water reservoir at the early stages of its exploitation has been used to evaluate this new workflow. An in-house Particle Swarm Optimization (PSO) algorithm has been used to solve this nonlinear optimization problem. The use of a real field as test bench poses additional strength on the robustness of the formulation in presence of a large number of decision variables and constrains. Special care has been taken in the field development plan selection, taking into account the optimal solution as well as additional objectives and sentiments added by the decision makers which increase the realism of the final solution.The optima selected were compared to a WAG base case adjusted using standard commercial tools according to best practice. The results obtained for the current reservoir highlight key performances of the optimization algorithm applied to the WAG strategy. WAG increases the recovery factor and enhance an early production (paramount to improve the NPV according to the economic model) with respect to a continuous WI. The optimization of WAG cycles allows to find the best hydrocarbon production under the constrain imposed by the facilities and the gas availability. The optimum inter-well-distance and schedule results from the compromise between a strong sweep yet avoiding an early water breakthrough and reducing interference among producers. The well layout is therefore an output of the NPV optimization, the carbonate geological characterization and the operation risk. The optimal field development plan has shown a 35% of increment in NPV compared to the base case.This workflow presented and discussed presents an innovative and general formulation for the optimization of a WAG and/or continuous injection for real reservoir under uncertainty. The optimization algorithm allows fast convergence and accurate results. The extension to multiple objective functions and the inclusion of the human decision in the optimization process is also considered.

A proper quantification and propagation of geological and geophysical uncertainty during phase of technical and economic appraisal of a reservoir is of primary importance to evaluate the potential and the risks associated with a new prospect. In this paper we propose an innovative methodology to define a conceptual model for different levels of available information particularly appealing for cases with limited data – such as that of a green field. The proposed methodology aims to construct multiple reservoir realizations which are geologically consistent with the the available data and constrain, and capable to propagate the uncertainty inherent to a given target reservoir specifications. The uncertainty is characterized using analogous reservoirs which are, in the present case, evaluated from an in-house database. The methodology can be resumed in a sequence of well defined steps. First, based on the quality and availability of the information, an optimization problem is formulated and solved to ensure that the generated realizations obey the petro-physical property statistics of the target reservoir. Outputs of the optimization process are the facies proportions yielding to the conditional probability distributions of properties. The physical distribution of the petrophysical properties are then generated based on these inputs using multipoint geostatistical techniques. To correctly propagate the uncertainty in the reservoir model the sensitive controlling variables input to the geostatistical algorithm are defined within a variability range. The selection of the relevant input parameters can be selected based on sensitivity analysis techniques. The stochasticity proper of the geostatistical algorithms used for the property distribution requires special care for the correct application and interpretation of the problem reduction techniques. Static and dynamic objective functions have been used to evaluate the statistical distribution of the generated realizations. From a static point of view the reservoir volumetric has been ranked using the field original-oil-in-place. The dynamic response of the generated realizations upon a simple pattern has been implemented as measure of connectivity. As proof of concept the field-oil-production-rate for an inverted-five-spot pattern under voidage replacement was chosen as dynamic ranking. The methodology has been applied to the Brugge field benchmark which presents 104 geological realizations (Peters et al (2010), Peters et al (2009)). The benchmark was modified in order to simulate a green field, therefore only mean values for the petro-physical properties were extracted and used as input to the methodology whils the complete geological realizations results of the full-field-model (FFM) have been used for validation. A list of equiprobable analogous reservoirs has been identified from the database. Thereof, a number of conceptual models have been created respecting the key static parameters statistics defined. To validate the workflow first the predicted original-oil-in-place from the analogues and from the generated realizations respectively has been positively compared with the benchmark realizations. Due to the absence of structural uncertainty in the benchmark realizations the brute volume and the mean original-oil-in-place compute from the analogues have been set and a large number of geological realizations have been simulated. Results indicate that the trends obtained by the conceptual model for a given production strategy is in agreement with the FFM and the FFM results are located within the produced realizations. The computational time associated with the conceptual model, due to its simplicity, was also very attractive compared to the full model. Noteworthy, the methodology provides important insight on the sensitivity of the geological model to several uncertain static and dynamic parameter and configurations which can be the base for a risk analysis or to quantify the value of additional information.