PETROPHYSICAL ANALYSIS AND FACIES MODELING OF LOWER GORU FORMATION OF KADANWARI FIELD

Abstract

Exploration geosciences are greatly influenced by computational technology. Machine learning and other computational technologies are used for analyzing and arranging large data sets with minimum efforts. This technology can provide information about subsurface that is not approachable by other means and it is useful for hydrocarbon exploration. Since, data of subsurface is vast and quite difficult to handle, machine learning effectively manages the data and provides predictions by using techniques like self-organizing map (SOM). Middle Indus basin is known for heterogeneity and vast hydrocarbon reserves. Lower Goru Formation of Middle Indus Basin acts as a reservoir while Sembar Formation acts as source rock and Upper Goru Formation acts a seal rock. Since Lower Goru Formation has alternating layers of lithology, this formation is known for its lithological heterogeneity. Alternating layers of Lower Goru Formation are divided into further sections by OGDCL and OMV. OGDCL divided formation into Upper sand, shale and marl sequence, Basal sands, Talhar shale and Massive sandstone.OMV divided the respective formation into intervals as A, B, C, D and E interval. Petrophysical analysis for efficient identification of lithology and curves is done, followed by electrofacies analysis using Self Organizing Map technique. Study was for KADANWARI-01, KADANWARI-03, KADANWARI-10 and KADANWARI-11. By petrophysical analysis it is estimated that E and B sand zones are main reservoirs. For E zone shale volume is 15-20 %, effective porosity 10-12 % and water saturation 25-30 %. For B zone shale volume is 8-10%, effective porosity6-8% and water saturation is 30-35%. Self organizing map technique (SOM) is used to determine electrofacies by dividing the data into twenty clusters and later on four clusters were developed by hierarchical clustering. There clusters are used for classification. SOM maps displayed 2D facies map. Results of SOM technique are analyzed by comparison with petrophysical analysis and accuracy is found. Hence this technique can be used for reservoir estimation. Lithological identification by SOM can reduce the risk associated with hydrocarbon exploration.