石油科学通报2025，Vol.10Issue(5)：892-907,16.DOI:10.3969/j.issn.2096-1693.2025.01.023
多源数据融合的地层可钻性智能评估方法研究及应用
Research and application of an intelligent formation drillability assess-ment method based on multi-source data fusion
摘要
Abstract
Formation drillability assessment is crucial for drilling operations,as it directly influences operational efficiency and cost-effectiveness.Traditional three-dimensional(3D)assessment methods often face challenges due to the unstable integration of multi-source and cross-scale data,resulting in limited spatial generalization and suboptimal prediction performance.To address these limitations,this paper proposes a multi-source data fusion method based on a gated recurrent unit(GRU)network to enhance intelligent formation drillability assessment and improve drilling efficiency in a study area in eastern China.The method consists of two phases:well data training and 3D application.In the first phase,pseudo-depth domain seismic records synthesized from seismic average wavelets and well logging data serve as the foundation.Sensitive attributes related to formation drillability are further extracted as network inputs.These sensitive attributes include a velocity model incorporating geological information and a seismic frequency-fraction attribute that captures multi-scale stratigraphic structure.A corrected drillability index(Dc)is used as a label for model training,ensuring that the network learns to establish an accurate mapping relationship between input attributes and drillability indicators.This training method leverages the temporal and sequential learning capabili-ties of the GRU network to effectively model complex relationships in the data.In the second phase,the pretrained network was extended to 3D applications,constructing a 3D input dataset by extracting the corresponding attributes.This dataset was then fed into a pretrained GRU model to predict formation drillability in the study area.Analysis of five representative wells in the study area validated the effectiveness of Dc in characterizing rock drillability in the study area.Furthermore,experiments using the Marmousi numerical model demonstrated that the method outperformed traditional intelligent prediction methods,such as those relying solely on raw seismic data or a combination of raw seismic and well logging data.Practical application in the study area further confirmed the method's ability to effectively capture variations in formation drillability.By providing reliable predictions,the method becomes a powerful tool for optimizing drilling operations and enhancing drilling engineering decision-making.关键词
地层可钻性/多源数据融合/GRU网络模型/钻前预测Key words
formation drillability/multi-source data fusion/GRU network/prediction before drilling分类
天文与地球科学引用本文复制引用
冯建祥,袁三一,骆春妹,王尚旭..多源数据融合的地层可钻性智能评估方法研究及应用[J].石油科学通报,2025,10(5):892-907,16.基金项目
国家自然科学基金项目(U24B2031)和国家重点研发计划(2018YFA0702504)联合资助 （U24B2031）
