The BZ condensate gas field in Archaeozoic metamorphic formation is one of the biggest discoveries in Bohai Bay Basin. With a burial depth exceeding 4400 meters and formation temperatures over 200°C, the operational environment surpasses the limits of many advanced wireline logging tools. Additionally, reservoir characterization and analysis of controlling factors are highly challenging due to strong reservoir heterogeneity introduced by complex lithologies, fractures, and secondary pores. To acquire high-quality data and provide valuable interpretation products, a novel method integrating advanced Logging While Drilling (LWD) technologies was introduced, achieving excellent results. Firstly, LWD high-definition electrical image, sonic and nuclear magnetic data were employed for data acquisition under these tough logging conditions, which provided a robust basis for reservoir characterization. And then, the electrical image was processed and interpreted for fracture and vug identification and evaluation. Sonic data was processed for the Stoneley reflection and waveform attenuation, which helped to validate the effectiveness of fractures and secondary pores. Nuclear magnetic data was utilized for porosity evaluation and pore structure analysis. Ultimately, the petrophysical parameters, production logging data, and well production data were analyzed by using an artificial intelligence method to classify the reservoirs into three different groups and established a reservoir quality index (RQI) for production estimation. It was found that well production is mainly controlled by open faults, open fractures, open vugs, reservoir types, effective reservoir thickness and the maximum horizontal in-situ stress direction. As a result, RQI was innovatively created by combining fracture parameters, vug parameters, sonic parameters, effective reservoir thickness, and conventional logs, which provided a quick and effective way for production estimation. The wells with high production were found to have 3 characteristics. The first one is to have a small angle difference between the strike of conductive fractures and the maximum horizontal in-situ stress direction, usually less than 30 deg. The second one is to have more fracture-pore reservoir type with better vertical connectivity. Fracture-pore reservoirs not only have high fracture porosity, but also high secondary porosity indicating well-developed fractures and vugs. The third one is to have good reservoir quality, with RQI greater than 1. RQI exhibits a strong correlation with well production, wherein a higher RQI value typically translates into higher well production. Wells with more good production zones classified by supervised Kohonen neural networks method are likely to have high well production. The integration method has been effectively applied in the metamorphic formation of the Bohai Bay Basin, and this application offers a valuable reference for reservoir characterization in other oilfields characterized by extremely high temperatures.
Wu et al. (Sat,) studied this question.