中国科技核心期刊
(中国科技论文统计源期刊)
  Scopus收录期刊

石油科学通报 ›› 2025, Vol. 10 ›› Issue (5): 892-907. doi: 10.3969/j.issn.2096-1693.2025.01.023

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多源数据融合的地层可钻性智能评估方法研究及应用

冯建祥(), 袁三一*(), 骆春妹, 王尚旭*()   

  1. 中国免费靠逼视频(北京)油气资源与工程全国重点实验室,北京 102249
  • 收稿日期:2025-07-08 修回日期:2025-08-20 出版日期:2025-10-15 发布日期:2025-10-21
  • 通讯作者: *袁三一(1983年—),博士,教授,从事地球物理勘探、油气人工智能以及地震地质工程一体化等方面的研究,yuansy@dgqinyehang.com
    王尚旭(1962年—),博士,教授,从事岩石物理实验、地震物理模型试验、地震信号分析与反演等方面的研究,wangsx@dgqinyehang.com
  • 作者简介:冯建祥(1995年—),博士研究生,从事地震储层解释与地震导向钻井等方面研究,fjxf@outlook.com
  • 基金资助:
    国家自然科学基金项目(U24B2031);国家重点研发计划(2018YFA0702504)

Research and application of an intelligent formation drillability assessment method based on multi-source data fusion

FENG Jianxiang(), YUAN Sanyi*(), LUO Chunmei, WANG Shangxu*()   

  1. State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, China
  • Received:2025-07-08 Revised:2025-08-20 Online:2025-10-15 Published:2025-10-21
  • Contact: *yuansy@dgqinyehang.com;wangsx@dgqinyehang.com

摘要:

地层可钻性评估对钻井工程至关重要,影响着钻井作业效率和成本效益。传统的三维评估方法往往面临跨尺度多源数据的不稳定融合问题,导致空间泛化能力有限且预测效果欠佳。为解决上述局限性,本文提出了一种基于门控循环单元(GRU)网络的多源数据融合方法,优化地层可钻性的智能评估并提高我国东部某研究区的钻井效率。该方法分为两个阶段:井数据训练阶段和三维应用阶段。在第一阶段,利用地震平均子波和测井资料合成伪深度域地震记录作为基础,并进一步提取地层可钻性敏感属性作为网络输入。具体敏感属性包括融入地质信息的速度模型和用于捕捉多尺度地层结构的分频地震属性。采用改进的可钻性指数Dc作为标签训练模型,确保网络模型学习建立输入属性与可钻性指标的准确映射关系。这种训练方法利用GRU网络的时序学习能力,对数据中的复杂关系进行有效建模。在第二阶段,预训练网络推广至三维应用,提取相应的属性构建三维输入数据集并输入至预训练GRU模型中来预测评估研究区域的地层可钻性。通过对研究区5口代表井的分析,验证了Dc指数有效表征研究区的地层可钻性。此外,Marmousi数值模型实验表明,该方法优于传统智能预测方法,例如仅依赖原始地震数据或原始地震融合测井数据输入的方法。在研究区的实际应用进一步证实了本方法能有效地捕捉地层可钻性变化。该方法通过提供可靠的预测,成为优化钻井作业和增强钻井工程决策的有力工具。

关键词: 地层可钻性, 多源数据融合, GRU网络模型, 钻前预测

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 capabilities 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.

Key words: formation drillability, multi-source data fusion, GRU network, prediction before drilling

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