周舟,李犇,耿宇迪,等. 超深破碎型地层岩石力学参数的大数据预测模型[J]. 石油钻探技术,2024,52(5):1-6. DOI: 10.11911/syztjs.2024084
引用本文: 周舟,李犇,耿宇迪,等. 超深破碎型地层岩石力学参数的大数据预测模型[J]. 石油钻探技术,2024,52(5):1-6. DOI: 10.11911/syztjs.2024084
ZHOU Zhou, LI Ben, GENG Yudi, et al. Study on Prediction of Rock Mechanics in Ultra-Deep Naturally Fractured Formations through Big Data Analysis[J]. Petroleum Drilling Techniques, 2024, 52(5):1-6. DOI: 10.11911/syztjs.2024084
Citation: ZHOU Zhou, LI Ben, GENG Yudi, et al. Study on Prediction of Rock Mechanics in Ultra-Deep Naturally Fractured Formations through Big Data Analysis[J]. Petroleum Drilling Techniques, 2024, 52(5):1-6. DOI: 10.11911/syztjs.2024084

超深破碎型地层岩石力学参数的大数据预测模型

Study on Prediction of Rock Mechanics in Ultra-Deep Naturally Fractured Formations through Big Data Analysis

  • 摘要: 超深储层油气资源丰富是目前油气开发的重点,但是因为储层岩体破碎和非均质性强,传统预测储层力学参数的方法误差大,对工程设计和施工造成很大的困难,因此本文建立了超深破碎型储层全储层段岩石力学参数预测模型。模型基于大量的实验和现场施工数据,分析储层测井数据、储层裂缝数据、岩石力学数据的相互联系,建立了基于岩石力学性质-地质储层特征-测井解释之间物理关联的多参数约束,开发了多元非线性回归拟合算法模型预测超深破碎型储层全储层段的岩石力学参数。该预测模型克服了破碎型地层数据量少计算误差大的难题,能明确全储层段岩石力学参数,与实际工程施工参数进行比较,预测准确度达90%以上。研究结果为超深破碎型地层钻完井安全施工提供了技术支撑。

     

    Abstract: The development of oil and gas in ultra-deep reservoirs has grown by leaps and bounds. The predication of rock mechanics in ultra-deep formations, however, is a challenge because the geological structure is complicated, and cracks are huge so that the accuracy based on traditional methodology is difficult to accept. Hence, this paper developed a predication model to predicate rock mechanics in the whole ultra-deep reservoir. This model, based on laboratory data and on-site field data, was a multivariate nonlinear regression fitting model which was constrained of the relationship between logging respond, natural fracture conditions, and rock mechanics properties. Comparing with the achieved date after actual drilling and fracturing work in the field, the accuracy of the results in the model can be up to 90%. This predication model provides reliable rock mechanical data to support for field drilling and completion work in ultra-deep formations.

     

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