Publications

The reservoir in ‘TS’ Gas field consist of sandstone and siltstone of Middle Arang Formation which is heterogeneous both in vertical and lateral. A better understanding of the reservoir may be achieved through facies gas reservoir modeling by applying geostatistical methods. The reservoir interval divided into two part: MA-3 and MA-5 which are part of Middle Arang Formation with total thickness of 250 feet deposited on littoral to shallow inner sublittoral. 3D seismic data quality is strongly influenced by the presence of gas at reservoir and the quantity of well log data only scattered at the crest of the structure. This causes uncertainty regarding the final results of the modeling. Therefore, Inverse-Q (absorption) is applied to increase Signal to Noise (S/N) ratio and to reduce the effect of gas on the seismic data which produces improved quality of 3D seismic data. To model the spatial distribution of gas reservoir facies in this field AI property is used where AI value of the gas reservoir facies is lower than the non-reservoir facies. Gas reservoir facies modeling was performed using geostatistical methods to integrate geological, geophysical and petrophysical data therefore reducing the level of uncertainty of modeling gas reservoir facies results. The interpretation of facies in each well is used to create a variogram model in which variogram exponential model is the best model will be applied in this field. Based on the variogram modeling that the gas reservoir facies is anisotropy with a NE-SW trend direction. Validation of the model result is performed by removing one of the wells prior to modeling and comparing the modeling results with the well data interpretation. Gas reservoir facies modeling results in an image that the gas reservoir facies develops to the south and that is expected to help development of the ‘TS’ Gas field in the future. Keywords: geostatistics, modeling, facies, clastic, variogram