基于大语言模型的潜油电泵异常井智能诊断方法

Intelligent Diagnosis Method for Abnormal Wells of Electric Submersible Pump Based on Large Language Model

摘要:

针对潜油电泵（ESP）在复杂工况和油井环境下，早期异常特征微弱、井间数据分布漂移显著与单一模型容易误检漏检等问题，提出一种基于大语言模型的ESP异常检测方法；结合多种工况数据与对应油井背景知识，构建上下文输入；建立可扩展的ESP领域指令微调数据集，基于多种中小参数开源模型，通过LoRA指令微调构建差异化专家ESP异常诊断模型集合，并由单独的仲裁系统对专家输出集合进行链式推理与一致性校验，生成最终判别结果；以某钻井平台ESP数据为对象进行了实验研究，验证了所提出方法的可行性与稳定性，准确率远优于其他主流的机器学习或深度学习方法。

Abstract:

To address the issues of electric submersible pumps (ESP) operating under complex working conditions and oilwell environments, including weak early abnormal features, significant inter-well data distribution drift, and the tendency of a single model to produce false positives and false negatives, we propose an ESP anomaly detection method based on large language models. By integrating data from multiple operating conditions and the corresponding oilwell background knowledge, we construct contextual inputs. We build a scalable ESP-domain instruction fine-tuning dataset, and based on multiple open-source small- and medium-parameter models, we build a collection of differentiated expert ESP anomaly diagnosis models via LoRA-based instruction fine-tuning, and employ a separate arbitration system to perform chain-of-thought reasoning and consistency verification over the set of expert outputs to generate the final decision. Experimental studies were conducted on ESP data from a drilling platform, validating the feasibility and stability of the proposed method; its accuracy is significantly superior to that of other mainstream machine learning or deep learning methods.