Generation of an Accurate Conceptual Model for Green Fields
Authors: De Paola G. , Torrado R. Rodriguez , Bhattacharjya D. , Mello U. , Embid S. 
Published: June 2014

Abstract
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.