The study of the diagenetic processes is a fundamental step to achieve a more accurate and reliable prediction of flow performance, as diagenesis alters the original relationships between depositional facies and petrophysical properties. Thence, a quantitative integration of all data and available analysis is a key-factor to obtain a realistic reservoir model.

A new integrated approach for diagenesis characterization and simulation is here presented. An application case was performed on the silici-clastic Egyptian reservoir of Baltim starting from quantitative diagenetic data.

Chlorite content, which is here strictly related to grain size, affects the overall reservoir permeability. Particularly, "pore filling" type chlorite is responsible for the flow performance deterioration.

A relationship between diagenesis and log data was defined using density and spectral gamma ray (thorium) logs as predictors of pore-filling chlorite. A good relationship was also observed between grain size and the previously defined log-facies from multivariate statistical processing (cluster analysis), enabling us to assign a well defined depositional meaning to each of them.

The right evaluation of the petrographic-diagenetic input represents an excellent "starting line" for the following phases.

First, a traditional reservoir workflow (pre-diagenesis characterization) has been carried out testing different geostatistical facies simulations approaches (Sequential Indicator Simulation, Object Based Approach, Truncated Gaussian Simulation, Pluri Gaussian Simulation and Multiple Point Statistics). Then, a new methodological workflow for post-diagenesis reconstruction has been implemented, using a matrix that combines sedimentological information and diagenetic indicator at wells. Finally the petrophysical parameters have been simulated classically according to the characteristics of each post-diagenesis facies.

The results, compared to well test permeability and analysed with the flow simulator, show a good to very good match with the measured data. The match quality is clearly dependent on the mutual consistency of the facies distribution and the conceptual sedimentological model conditioning diagenetic effects.

You can access this article if you purchase or spend a download.