复合模型与模糊推理联合的溢流风险分级评估新方法  

A new overflow risk classification assessment method based on composite model and fuzzy reasoning

作　　者：廖华林[1] 屈峰涛 许玉强[1] 魏凯 LIAO Hualin;QU Fengtao;XU Yuqiang;WEI Kai(School of Petroleum Engineering,China University of Petroleum-East China,Qingdao,Shandong 266580,China;Petroleum Engineering College,Yangtze University,Wuhan,Hubei 430100,China)

机构地区：[1]中国石油大学(华东)石油工程学院 [2]长江大学石油工程学院

出　　处：《天然气工业》2025年第3期140-151,共12页Natural Gas Industry

基　　金：国家重点研发计划项目“复杂油气智能钻井理论与方法”(编号:2019YFA0708300)。

摘　　要：溢流作为钻井施工过程中的井喷前兆,对其及时准确识别和评估,对于降低井喷发生概率、保障安全高效钻井具有重要意义。为解决当前数据驱动的溢流风险评估模型在复杂地质环境作业中泛化能力不足和评估结果可解释性较差的问题,构建了具备深度特征挖掘能力的组合卷积神经网络、长短期记忆网络与随机森林算法的复合模型(CNN-LSTM-RF),提取了数据特征、计算风险概率,并采用模糊综合评价方法确定了临界风险概率阈值;然后引入模糊推理,将专家经验转化为模糊规则,优化风险分级边界,提高溢流风险评估的透明度和灵活性;最后形成了一种基于复合模型与模糊推理的溢流风险分级评估方法,并成功将其应用于海上某油田的溢流风险管理。研究结果表明:(1)卷积神经网络(CNN)有效提取了多源数据的局部特征和空间关联,长短期记忆网络(LSTM)则捕捉了数据序列的长短期依赖关系,提升了模型处理复杂数据的能力;(2)模糊综合评价结合正态分布隶属度函数和置信度,能够准确计算临界风险阈值,实现了溢流风险概率的分级标定,提高了评估的可操作性;(3)该方法在低风险和高风险井段钻井溢流识别的准确率达到97.9%,显著降低了固定阈值方法的高风险误判率(降低44.92%)。结论认为,该方法在识别和评估高风险井段及预警方面表现出色,能够提前发出预警信号,在溢流风险分级评估中更加灵活,为实际钻井溢流风险管理提供了可靠的技术支撑。Overflow is a precursor of well blowout in the process of well drilling,and its timely and accurate identification and assessment are of great significance in reducing blowout probability and ensuring safe and efficient drilling.The current data-driven overflow risk assessment model shows insufficient generalization capability in complex geological environments,and its assessment results are of poor interpretability.To address these problems,this paper constructs a composite model of Convolutional Neural Network(CNN),Long Short-Term Memory Network(LSTM)and Random Forest(RF)with deep feature extraction capability,i.e.,CNN-LSTM-RF model,to extract data features and calculate risk probability,and adopts the fuzzy comprehensive evaluation method to determine the critical risk probability thresholds.Then,fuzzy reasoning is introduced,and expert knowledge is converted into fuzzy rules,to optimize risk classification boundaries and enhance the transparency and flexibility of overflow risk assessments.Finally,an overflow risk classification assessment method based on composite model and fuzzy reasoning is developed,and successfully applied to the overflow risk management in an offshore oilfield.The following results are obtained.First,CNN effectively extracts the local features and spatial correlation from historical drilling data,while LSTM captures the long short-term dependency relationship of data sequence,improving the ability of the model to process complex data.Second,the fuzzy comprehensive evaluation,combined with the membership function and confidence of normal distribution,can calculate the critical risk thresholds accurately and realize classification calibration of overflow risk probability,enhancing the operability of the assessment.Third,this method achieves an accuracy rate of 97.9%in overflow identification in low-risk and high-risk well sections,and significantly reduces the high-risk misjudgment rate by 44.92%compared with fixed threshold methods.In conclusion,this method excels in identifying and asse

关 键 词：钻井风险 复合模型 模糊推理 风险分级 溢流风险 CNN-LSTM-RF Mamdani推理 模糊综合评价 

分 类 号：TE242[石油与天然气工程—油气井工程]