Evaluation of low permeability conglomerate reservoirs based on petrophysical facies: A case study from the Triassic Baikouquan Formation, northern Mahu Sag, Junggar Basin, China

Abstract

The determination of high-quality intervals in oil reservoirs has always been challenging work while their petrophysical attributes are relatively low. To date, extensive reservoir evaluation studies have been undertaken to locate favorable zones for oil exploration and exploitation. However, without the geological constraint principles, the selected evaluation parameters depend heavily on experience or pure mathematical methods. Hence, we propose a reservoir evaluation method based on petrophysical facies to undertake the reservoir quality assessment for the conglomerate reservoirs of the Triassic Baikouquan Formation in the northern Mahu sag, Junggar Basin, western China. The lithofacies and diagenetic facies were first identified based on laboratory data. Subsequently, petrophysical facies types were recognized by the combination of lithofacies and diagenetic facies. Afterward, capillary pressure curves of different petrophysical facies were clustered to clarify the reservoir quality variation in distinct geological origins. Consequently, the evaluation standard was established and the reservoir quality distribution was analyzed and drawn out. These results indicate that there exist 4 rock types in the Baikouquan Formation (from RT1 to RT4). The distributional status of the optimal reservoir (RT1) and the worst reservoir (RT4) are primarily determined by grain size, while rock type 2 (RT2) and rock type 3 (RT3) are predominantly controlled by dissolution and compaction. Moreover, an evident positive association exists between the rock type and the single well production capacity, which reveals that the utilization of petrophysical facies to assess reservoir quality is reliable. It is concluded that reservoir evaluation based on petrophysical facies harbors the potential to be a pivotal tool for reservoir property assessment and prediction of favorable porosity and permeability zones.