Production prediction plays an important role in decision making, development planning, and economic evaluation during the exploration and development period. However, applying traditional methods for production forecasting of newly developed wells in the conglomerate reservoir is restricted by limited historical data, complex fracture propagation, and frequent operational changes. This study proposed a Gated Recurrent Unit (GRU) neural network-based model to achieve batch production forecasting in M conglomerate reservoir of China, which tackles the limitations of traditional decline curve analysis and conventional time-series prediction methods. The model is trained by four features of production rate, tubing pressure (TP), choke size (CS), and shut-in period (SI) from 70 multistage hydraulic fractured horizontal wells. Firstly, a comprehensive data preprocessing is implemented, including excluding unfit wells, data screening, feature selection, partitioning data set, z-score normalization, and format conversion. Then, the four-feature model is compared with the model considering production only, and it is found that with frequent oilfield operations changes, the four-feature model could accurately capture the complex variance pattern of production rate. Further, Random Forest (RF) is employed to optimize the prediction results of GRU. For a fair evaluation, the performance of the proposed model is compared with that of simple Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) neural network. The results show that the proposed approach outperforms the others in prediction accuracy and generalization ability. It is worth mentioning that under the guidance of continuous learning, the GRU model can be updated as soon as more wells become available.

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Summary Relying on its strong nonlinear mapping ability, machine learning is found to be efficient and accurate for production prediction of fractured wells compared with conventional analytical methods, numerical simulations, and traditional decline curve analysis. However, its application in forecasting future multistep time series production remains challenging, with complications of error accumulation, growing uncertainty, and degraded accuracy. To this end, we propose a novel multistep ahead production prediction framework based on a bidirectional gated recurrent unit (BiGRU) and multitask learning (MTL) combined neural network (BiGRU-MTL), which can improve prediction performance by sharing task-dependent representations among tasks of multiphase production prediction. The forecasting strategies and evaluation setups for multiple timesteps are elaborated to avoid unfair assessment caused by mixing different prediction confidences over several days. In this framework, BiGRU is in charge of capturing nonlinear patterns of production variation by utilizing both forward and backward sequence information. MTL methods including cross-stitch network (CSN) and weighting losses with homoscedastic uncertainty are incorporated to automatically determine the sharing degree of multiple tasks and the weight ratio of the total loss function. By this means, domain knowledge contained in tasks of multiphase production prediction is deeply leveraged, shared, and coupled to enhance multistep ahead prediction accuracy while meeting the need for multiphase production forecasting. The proposed framework is applied to a synthetic well case, a field well case, and a field multiwell case to progressively prove the feasibility, robustness, and generalization of the BiGRU-MTL model. Experiment results show that the proposed framework outperforms conventional single-task models and commonly used recurrent neural networks (RNNs), furnishing a reliable and stable tool for accurate multistep ahead production prediction. This work promises to provide insights into dynamic production optimization and management in oil- and gasfield sites.