The main objective of this study is to enhance a Predicted Permeability (K_Pred) by integrating Permeability resulted from the interpretation of more than 100 P.T.A. Initially the predicted permeability was generated using neural network method (Combining core and log data) over all 254 wells penetrating the reservoir. To achieve this task a number of workflows have been discussed and tested and finally two methods were implemented which resulted in two permeability models. The first model, consist of generating enhanced permeability maps for each porous zone using Permeability Predicted (K_pred), core and well test data. These maps were used as multiplier in Upscaled model to generate the total permeability then exported to reservoir engineer for simulation. The second model, consist of generating the enhanced permeability by integrating Permeability Predicted (K_pred), core and well test (KH) under each well (Log scale) in order to capture the dynamic changes of the property. This enhanced permeability was populated in geological model using stochastic methods conditioned to Rock Type and porosity.

Defining the range of uncertainty is a crucial part in the oil field development particularly for carbonate reservoirs that have limited well data and with the absence of dynamic data. It is very important to develop an in-depth understanding of the range of uncertainty of all reservoirs parameters such as: - Structure uncertainty - Lithofacies and reservoir rock types - Static reservoir attributes population technique (Porosity, Permeability, & Water Saturation) Although outcrops and analogs are often employed to define reservoirs model parameters, it is still challenging to define and agree on the relationship between modeling parameters and their distribution ranges. This paper addresses the impact of uncertainty of different modeling parameters on the volumetric calculations and full field development scenarios starting with structure model. Various areal and vertical uncertainties were investigated to set the structure uncertainty ranges. Then, the identified depositional environment models were used as guides to set the uncertainty ranges for each lithofacies association. The reservoir rock types were directly affected by both structure and lithofacies association models. Different ranges of variations were used for each rock type within each reservoir layer to ensure capturing the lateral and vertical reservoir heterogeneity and to propose multi distribution scenarios for each reservoir tock type within non-cored intervals/areas. The petrophysical parameters were conditioned to the reservoir rock types model. So, they were directly affected by multi scenarios of RRT models. In conclusion, 20 volumetric estimates were calculated and evaluated to define the probabilistic scenarios P10, P50, and P90 that will be used to investigate the full field development scenarios.

No abstract

No abstract