Development Index Prediction Through Big Data Analysis for QX Ultra-Deep Permian Marine Carbonate Gas Reservoir in Sichuan Basin, China

Uncertainties in the characterization of new-found, ultra-deep, thin and low porosity Permian gas reservoir reduce feasibility for development index (DI) prediction through reservoir simulation. DI prediction with big data analysis approach are studied. Geology and production data from 30 mature gas fields are reviewed and 13 parameters are selected to represent geological features, deliverability and DI of individual reservoir. Based on the BP neural network algorithm, proxy models are established to correlate DI with geology and deliverability data, and the bagging method is used to effectively improve the experimental accuracy and stability while avoiding over-fitting phenomenon in the case of limited sample data. The coefficient of determination coefficient (R2) are selected to evaluate the prediction effect of DI. The mean value of the prediction results of the model with higher R2 value in 2000 numerical experiments was selected as the final prediction result. With the established proxy model, DI for QX reservoir in Permian formation are predicted and the influence of heterogeneity are also evaluated.