Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

Shale gas, a low-permeability, unconventional resource, requires horizontal drilling and multi-stage fracturing for commercial production. This study develops a trilinear flow model for fractured horizontal wells in shale gas formations, incorporating key mechanisms such as adsorption, desorption, diffusion, wellbore storage, and skin effects. The model delineates seven distinct flow regimes, providing insights into gas migration processes and the factors controlling production. Sensitivity analyses reveal that desorption plays a critical role under low-pressure and low-production conditions, significantly enhancing gas transfer rates from the matrix to the fracture network and contributing to overall production. Monte Carlo simulations further highlight the variability in pressure responses under different input conditions, offering a comprehensive understanding of the model’s behavior in complex reservoir environments. These findings advance the characterization of shale gas flow dynamics and inform the optimization of production strategies.

Summary: Traditional methods for forecasting production rate, such as Arps, analytical techniques, and recurrent neural network (RNN)–based deep learning, are mainly point prediction techniques developed within the framework of single-well forecasting. These methods often face limitations stemming from single-well historical production data and model assumptions, hindering their ability to consider the influence of development patterns of other production wells within the block on the target well. In addition, they struggle to predict the multiple production rate time series simultaneously and often fail to quantify uncertainty in predictions or adequately exploit extensive relevant historical production data. To tackle these challenges, we propose a model based on the deep autoregressive recurrent neural network (DeepAR), leveraging related multiwell production rate data to enable global modeling and probabilistic forecasting. This model incorporates dynamic covariate and static categorical variable data, integrating Bayesian inference and using gradient descent algorithms and maximum likelihood estimation methods to derive a comprehensive historical-future production probability evolution pattern shared across multiple wells. Leveraging data from 943 tight gas wells, a comprehensive evaluation of the DeepAR model’s performance was undertaken, encompassing the comparison of prediction accuracy with long short-term memory (LSTM), classification prediction, cold-start prediction, and single-well multitarget prediction scenarios, summarizing the applicability conditions for each. The research findings highlight that DeepAR integrates the acquired comprehensive production probability evolution pattern with specific production historical data of the target well to formulate a “comprehensive + specific” production probability prediction approach, resulting in improved stability and accuracy. On average, DeepAR demonstrates a 58.79% reduction in normalized deviation (ND) compared to the LSTM model, showcasing enhanced stability, particularly in scenarios involving frequent well shut-ins and openings. Moreover, DeepAR can learn static categorical features, with the classification model resulting in a 27.15% reduction in the ND compared to the unclassified model. Furthermore, DeepAR adeptly addresses the challenge of limited data availability, achieving cold-start prediction and facilitating multitarget single-well training and prediction while considering the interdependency among multiple variables over time and effectively mitigating the issue of missing auxiliary variables during the prediction phase. This study contributes to a broader understanding of production forecasting and analysis of production dynamics methods from a big data perspective.