Abstract:

Traditional methods for predicting shale gas well production often struggle to effectively analyze the complex relationship between reservoir parameters, fracturing parameters and production. To address these challenges, a novel approach is introduced, involving the construction of characteristic parameters based on physical meaning and random combination. The small batch gradient descent method（MBGD） is adopted as the training function to develop an improved artificial neural network prediction model for shale gas well production. An example is utilized to demonstrate the effectiveness of the improved artificial neural network model in predicting shale gas well production. The model’s performance is evaluated using the mean squared error（MSE） and the modified determination coefficient（T）. The results indicate that the predictions from the improved network model align well with the actual production data. Moreover, the model exhibits superior prediction accuracy and stability compared to the traditional BP（error backpropagation algorithm） neural network model. With its high accuracy and reliability, the proposed model can provide valuable support for fracturing optimization design and productivity evaluation in shale gas reservoirs.

Key words: artificial neural network, shale gas, fracturing parameter, production forecast, feature construction