用自监督模型预测钻井数据中的地下参数，解决标签稀缺难题。
Assessing the Potential of Masked Autoencoder Foundation Models in Predicting Downhole Metrics from Surface Drilling Data
- 利用掩码自编码器预训练，从无标签表面数据中学习特征
- 识别出8种常见地表指标与7种目标地下指标的映射关系
- 为油气钻井智能分析提供未被探索的新方法，适合工业界研究者
油气钻井过程产生大量地表传感器的时间序列数据，但因地下测量标签稀缺，实时预测关键地下参数仍具挑战。本系统综述分析了2015至2025年间发表的十三篇论文，评估掩码自编码器基础模型（MAEFMs）从地表钻井数据预测地下指标的潜力。研究识别出八种常见采集的地表指标和七种目标地下指标。当前方法多采用人工神经网络（ANNs）和长短期记忆（LSTM）网络等神经网络架构，但尚未有研究探索MAEFMs的应用，尽管其在时间序列建模中已表现出优异性能。MAEFMs通过在丰富无标签数据上进行自监督预训练，支持多任务预测并提升跨井泛化能力。研究证实MAEFMs在技术上可行且尚未被探索，建议未来开展实证验证其性能，并拓展其在油气作业中的应用范围。
原文摘要 · Abstract (English)
Oil and gas drilling operations generate extensive time-series data from surface sensors, yet accurate real-time prediction of critical downhole metrics remains challenging due to the scarcity of labelled downhole measurements. This systematic mapping study reviews thirteen papers published between 2015 and 2025 to assess the potential of Masked Autoencoder Foundation Models (MAEFMs) for predicting downhole metrics from surface drilling data. The review identifies eight commonly collected surface metrics and seven target downhole metrics. Current approaches predominantly employ neural network architectures such as artificial neural networks (ANNs) and long short-term memory (LSTM) networks, yet no studies have explored MAEFMs despite their demonstrated effectiveness in time-series modeling. MAEFMs offer distinct advantages through self-supervised pre-training on abundant unlabeled data, enabling multi-task prediction and improved generalization across wells. This research establishes that MAEFMs represent a technically feasible but unexplored opportunity for drilling analytics, recommending future empirical validation of their performance against existing models and exploration of their broader applicability in oil and gas operations.
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