摘要：

在深水气田生产过程中，通常需要根据未来配产计划，即产气需求，预测井底/井口压力与温度以及产气量、产油量、产水量等关键生产参数，以快速评估和制定配产方案。然而，深水气井生产动态参数预测仍面临两方面挑战：一方面，随着气田持续开发，储层与井筒状态不断演化，关键生产参数的统计特征及多参数耦合响应关系在不同开发阶段呈现非平稳变化，影响预测模型在后续生产阶段的泛化能力和长期适应性；另一方面，生产参数变化受未来配产计划约束，如何有效提取并建模配产计划与多维生产参数之间的关联特征仍是制约预测性能提升的关键因素。针对上述问题，本文提出一种基于多维时序特征融合的气井生产动态参数预测模型X-NLinear。该模型沿袭NLinear的轻量化线性结构及其基于样本自身统计特征的动态归一化处理机制，以降低生产参数非平稳变化对预测结果的影响；在此基础上，进一步设计跨变量特征提取模块，将未来配产计划与多类生产参数信息进行融合，刻画产气需求变化对压力、温度及油气水产量等参数的动态影响。基于实际深水气井生产数据，本文将X-NLinear与典型时间序列预测模型进行对比分析。实验结果表明，X-NLinear在保持计算效率的同时，能够提升深水气井多参数联合预测的准确性和稳定性；跨变量特征提取模块进一步降低了模型在平均绝对误差和对称平均绝对百分比误差指标上的预测误差。本文所提方法可为深水气田配产方案快速评估和生产优化提供技术支撑。

关键词: 深水气井, 生产动态预测, 代理模型, 时间序列, 统计特征变化, 多维时序特征融合

Abstract:

During the production of deepwater gas fields, it is usually necessary to predict a series of key production parameters according to future production allocation plans, namely future gas demand. These parameters include downhole pressure and temperature, wellhead pressure and temperature, gas production rate, oil production rate, and water production rate. Accurate prediction of these parameters can assist engineers in rapidly evaluating existing production allocation schemes and formulating new ones, thereby providing direct support for production decision-making in deepwater gas fields. However, dynamic production parameter prediction for deepwater gas wells still faces two major challenges. On the one hand, as gas field development continues, reservoir and wellbore conditions continuously evolve, and the statistical characteristics of key production parameters as well as the coupled response relationships among multiple parameters exhibit non-stationary changes at different development stages. Such time-varying shifts significantly affect the generalization capability and long-term adaptability of prediction models. On the other hand, changes in production parameters are constrained by future production allocation plans. Therefore, effectively extracting and modeling the correlations between production allocation plans and multidimensional production parameters remains a key factor limiting further improvement in prediction performance. To address the above problems, this paper proposes X-NLinear, a dynamic production parameter prediction model for gas wells based on multidimensional temporal feature fusion. The model inherits the lightweight linear structure of NLinear and its dynamic normalization mechanism based on sample-specific statistical characteristics, thereby reducing the influence of non-stationary changes in production parameters on prediction results. On this basis, a cross-variable feature extraction module is further designed to integrate future production allocation plans with multiple types of production parameter information, capturing the dynamic influence of gas demand changes on pressure, temperature, and oil, gas, and water production rates. Based on real production data from deepwater gas wells, X-NLinear is compared with representative time-series forecasting models. The experimental results show that X-NLinear improves the accuracy and stability of multi-parameter joint prediction for deepwater gas wells while maintaining computational efficiency. In addition, the cross-variable feature extraction module further reduces prediction errors in terms of mean absolute error and symmetric mean absolute percentage error. The proposed method can provide technical support for rapid evaluation of production allocation schemes and production optimization in deepwater gas fields.

Key words: deepwater gas well, production dynamic prediction, surrogate model, time series, statistical feature shift, multivariate temporal feature fusion

中图分类号: 

• TE37

• P618.13