New Exploration Discovery Aided by Automatic Facies Classification in Deepwater Mixed Volcanic-Carbonate-Igneous Rock, Offshore South China

Abstract High-quality hydrocarbon sources, reservoir, and secondary dissolution pores and natural fractures are the key elements for the successful exploration in the B deepwater area of South China Sea. Traditional lithofacies classification especially for volcanic-carbonate-igneous lithofacies is time-consuming and can't satisfy efficient decision making. Natural fractures development is highly controlled by lithofacies, and vertical reservoir connectivity is determined by fractures and dissolution pores (vugs). An efficient and automatic facies classification based on integrated data is critical to identifying hydrocarbon reservoirs and securing new hydrocarbon discovery. Both electrical and ultrasonic image were used to identify fractures and calculate fracture parameters. Based on the amplitude and travel time image, five types of fractures are identified: closed fractures filled with clay, closed fractures cemented by calcite/quartz, fully open fractures, partially open fractures, and vuggy open fractures. Triple combo logging, masterlog, core data and dual borehole imaging logs were utilized to manually identify lithofacies. A total of thirteen lithofacies within four categories were identified, namely (1) volcanic lithofacies: tuff/volcanic breccia; (2) igneous lithofacies: granite porphyry and basalt/altered basalt; (3) limestone lithofacies: sandy/shaly/bioclastic limestone; and (4) dolomitic lithofacies: altered/dolomite. Then, two automatic geological facies analysis, heterogenous rock analysis (HRA) and 2D Indexed Probabilized Self-Organizing Map (IPSOM) were applied using fracture density, fracture length, and secondary porosity, effective porosity, and permeability index from Stoneley inversion. Five facies were classified as facies-A, facies-B, and facies-C, facies-D and facies E. Facies-A and facies-B are considered as top two best facies for hydrocarbon accumulation. Two gas zones (Zone 1 and Zone 4) were identified by the integration of automatic geological facies, mud gas and petrophysical interpretation. The lithofacies of Zone 1 shows bioclastic limestone at the top, altered massive basalt and dolomite in the middle and laminated altered basalt interbedded with limestone at the bottom. 48 effective fractures are developed for Zone 1 and the main HRA and IPSOM facies are facies-A and facies-B. The lithofacies of Zone 4 is mainly altered basalt with 5 effective fractures, and the main HRA and IPSOM facies for Zone 4 are facies-B and facies-C. Time delay and frequency shift from dipole shear wave data are relatively higher within the two gas zones which further confirmed the fracture and dissolution development. Furthermore, vertical reservoir connectivity between two gas zones was analyzed based on fractures, vugs and the angle between the fracture strike and the in-situ stress orientation. The overall vertical reservoir connectivity is concluded good which is confirmed by formation testing data. The automatic facies classification based on fracture characterization and petrophysical interpretation aided efficient hydrocarbon identification. The results are reliable inputs for advanced geological and geophysical modelling and new well location deployment. The new discovery in the mixed volcanic-carbonate-igneous reservoirs also provides a new exploration direction and potential reserves replacement.