Pump pressure prediction and application based on mechanism and intelligence

Efficient development of shale oil and gas in China relies on factory operations and large-scale fracturing technology. Large-scale fracturing of shale oil and gas requires a long time and numerous equipment and facilities, with frequent and severe incidents of fracturing sand blockage. The research on early warning research in these incidents is crucial for the safety of shale oil and gas fracturing operations. However, the effective methods for analyzing the main control factors of fracturing sand blockage and predicting the pump pressure during operations are lacked. To study this issue, considering the fracturing mechanism and pump pressure variation characteristics, a method for real-time prediction of pump pressure during fracturing operations has been established to conduct sand blockage early warning research here.

First, a fracturing simulator was used to simulate the entire process of pump pressure changes during fracturing. By altering different fluid properties and formation parameters, the main control factors of pump pressure variation were analyzed, and the grey correlation analysis method was used to rank these factors. Secondly, based on fracture mechanics, proppant transport theory, and the Long Short-Term Memory (LSTM) neural network model, a framework and model for predicting pump pressure during operations was established, forming a method for early warning of fracturing sand blockage under the integration of mechanism and intelligence. Finally, the early warning method for sand blockage was applied to actual field fracturing operations.

Results indicate that the factors affecting the pump pressure of a typical well, from most to least significant, are discharge rate, fluid viscosity, differential principal stress, sand concentration, number of fracture clusters, and number of perforations. When other parameters remain constant, as fluid viscosity, differential principal stress, and discharge rate increase, the pump pressure increases; as the number of fracture clusters, perforations, and sand concentration increase, the pump pressure decreases. This method can be used for the identification and early warning of fracturing sand blockage incidents in the actual field operations, which is 19 seconds earlier than on-site manual identification, with a relative error of about 6.8%. The predicted pump pressure is friendly matched with the actual field one, which is helpful in accurate early warning of fracturing sand blockage.