## 1 Introduction  
The current pandemic, downturn, and low oil prices result in a larger global energy demand for hydrocarbon [1]. As the conventional onshore oil resources decrease, the interest in developing heavy oil resources from offshore reservoirs increases [2, 3]. The proven heavy oil resources in Bohai Bay, China, are more than 4 billion tons [4]. Cyclic steam stimulation (CSS) is one of the main methods used for offshore heavy oil reservoirs [5].  
Predicting the production of horizontal CSS wells is significant for developing offshore heavy oil reservoirs. Currently, the existing reservoir numerical simulation and analytical models are the two major methods to predict the production of horizontal CSS wells [6].  
However, the reservoir numerical simulation is tedious and time-consuming, because it requires comprehensive data collection, a reliable simulation model, and history matching [7; 8], while the analytical models need many assumptions to simplify the production process of horizontal CSS wells, decreasing the models' accuracy [9; 10].  
Recently, artificial intelligence algorithms, such as back propagation (BP) neural networks, support vector machines (SVM), and autoregressive integrated moving average (ARIMA) have been commonly used in the petroleum industry to solve some unsettled problems by conventional methods.  
[PERSON] et al. built an ARIMA model to predict oil well production [11]. [PERSON] et al. studied the profile control of the 32 wells of the Dongxin block by using the SVM method instead of numerical simulations [12] Based on the historical data of 15 oilfields in China, [PERSON] et al. improved the BP neural network by the combination of fuzzy clustering algorithm and a genetic algorithm and used the network for oil production prediction [13]. [PERSON] et al. coupled the particle swarm optimization algorithm (PA) and machine learning to form a computational framework, which was used to predict shale oil production and optimize the fracturing parameters [14].  
Compared with the aforementioned methods, recurrent neural networks (RNN) can predict time-series data by introducing the concept of timing sequence [15]. To solve the shortcomings of the traditional RNN model, such as gradient explosion and disappearance, [PERSON] et al. developed a long short-term memory (LM) model by designing the cell state and gate structure in the RNN model [16; 17]. Currently, the LM model has been successfully used in many areas, such as climate prediction, machine life prediction, and disease prediction [18; 19; 20; 21].  
[PERSON] et al. developed an LM-based encoder-decoder scheme to estimate the remaining useful life of a machine [18]. [PERSON] et al. used the LM model for predicting the 24 h PM2.5 and confirmed that the LM model performed well with a short training time and low errors [19]. [PERSON] et al. simulated the COVID-19 cases by using the ARIMA, RNN, and LM models and found that the LM model was the most accurate [20]. [PERSON] et al. observed that the LM model could exactly characterize the trend of time-sequence data and was utilized to predict the rainfall-runoff [21].  
At present, the LM method has not been used to predict the cumulative oil production (COP) of horizontal CSS wells in offshore heavy oil reservoirs. In addition, as mentioned above, the LM model solved the shortcomings of the RNN model by introducing the cell state and gate structure. However, this improvement also increases some hyperparameters in the LM model. Currently, the empirical method was commonly used to manually determine these hyperparameters, which results in low prediction accuracy. Therefore, the PA was utilized to determine the optimal hyperparameters of the LM model in this study. Then, a novel methodology combining the PA and LM model was developed to predict the COP of horizontal CSS wells in offshore heavy oil reservoirs located at Bohai Bay, China.