Artificial Neural Network-Based Surrogate Modeling for Energy-Efficient Operation of the Oilfield Gathering and Transportation System

The oilfield gathering and transportation system (OGTS) accounts for a substantial share of total energy consumption, demonstrating considerable potential for energy-saving optimization. Previous research primarily focused on the independent optimization of gathering pipeline networks or processing stations, with little attention given to their integrated optimization. This study investigates the OGTS of a domestic oilfield block. It develops artificial neural network (ANN) surrogate models for both pipelines and processing station equipment to accurately capture system operating states. An optimization model linking the pipeline network and processing station is established, and a differential evolution algorithm is applied to enhance computational efficiency. The results indicate that optimal predictive performance was achieved when the first hidden layer of the backpropagation neural network (BPNN) contained 50 neurons with a rectified linear unit (ReLU) activation function, with the surrogate models achieving coefficient of determination (R2) values exceeding 0.85. Under a 30 min optimization cycle, total system energy consumption decreased by 2.28%, with computation completed in under 3 min, while daily average optimization led to a 1.55% reduction. These findings demonstrate that the proposed integrated optimization framework offers both a robust methodological foundation and practical engineering guidance for coordinated, low-carbon, and energy-efficient operation of oilfields.