Abstract:

To address overfitting problem of production forecast model in machine learning and improve accuracy of production forecast in actual oil field, a model for single well production forecasting of reservoir based on Conditional Generative Adversarial Net(CGAN) was proposed. The model hybridizes two types of basic neural network structures, i.e., long short-term memory network and fully connected network, to construct a generative model and a discriminant model. The generative model takes production influencing factors as conditions to generate the forecasting production data. It defines a logarithmic loss function as a residual between predicted and real data, to improve comprehensively the generalization ability of the model. Bayesian hyperparameter optimization algorithm was used to optimize the model structure through game training of CGAN. With numerical simulation software Eclipse, single well production database with same well pattern under different geological and production conditions was established to train CGAN, which can be used to predict rapidly single well production of reservoir by taking geological and production factors as condition input of the model. Experimental results show that compared with prediction results of models FCNN, RF, and LSTM, mean absolute percentage error of CGAN model on the test set is increased by 2.59%, 0.81%, and 1.72%. The overfitting ratio is the smallest(1.027). It indicates that CGAN reduces overfitting degree of the machine-learning-based production forecast model, and improves generalization ability and accuracy of the model as well. This verifies superiority of the algorithm, which is of great significance to guide efficient development of oilfields and ensures security of national energy strategy.

Key words: conditional generative adversarial net, production forecast, machine learning, Bayes hyperparameter optimization, neural network