地质导向在定向钻井中起着至关重要的作用，尤其对于水平井和斜井而言。然而，在复杂且难以预测的地质环境中，精准引导井眼轨迹依然是一项挑战。本研究将重点探讨地质导向中的以下三个关键挑战：

• 减少未钻地层岩性参数分布的不确定性，从而影响轨迹精度
• 开发可靠的前瞻岩性参数预测模型，利用多尺度地球物理数据提高预测精度
• 高效整合实时随钻测井(LWD) 数据，不断改进岩性参数预测，最大限度地减少钻井作业期间的不确定性

本研究引入了一种创新方法，利用基于随机森林 (RF) 的框架将随钻测井 (LWD) 和多频地震数据关联起来。该方法通过根据实时随钻测井 (LWD) 输入调整不同频率地震数据的权重，从而实现岩性参数预测的动态更新。
盲井测试验证了该框架，结果表明，全井段声阻抗预测值与实际值之间具有较高的相关性——初始相关性超过 0.85，且随着钻井深度的增加，相关性不断提高，超过 0.9。

RF算法有效地利用了来自多频地震源的阻抗数据，并结合地质先验信息来增强模型的鲁棒性。该方法能够可靠地预测前向岩性参数，预测钻头前方400米范围内的岩性变化。

研究结果表明，该方法能够有效协调测井数据和地震数据之间的分辨率差异。通过将钻前模型的地质先验信息与实时数据驱动的随钻测井洞察相结合，该方法能够提高地质导向应用中模型更新的效率和稳健性。

本文摘要摘自SPE 226223号论文，作者包括中国科学院张建军、霍胜和单晓燕；伦敦帝国理工学院郑伟；中海油研究总院谢荣和袁建军；以及中国石油大学（北京）、油气资源与探测国家重点实验室和中国石油天然气集团公司地球物理勘探重点实验室林恩良。该论文已通过同行评审，并以开放获取的形式在OnePetro平台的SPE期刊上发布。

Geosteering plays a vital role in directional drilling, particularly for horizontal and deviated wells, but challenges persist in accurately guiding well trajectories through complex and unpredictable geological environments. This study tackles the following three critical challenges in geosteering:

• Reducing uncertainties in lithology parameter distribution in undrilled formations, which affects trajectory accuracy
• Developing a reliable look-ahead lithology parameter prediction model that uses multiscale geophysical data to enhance predictive precision
• Integrating real-time logging-while-drilling (LWD) data efficiently to continuously refine lithology parameter predictions, minimizing uncertainty during drilling operations

This research introduces an innovative approach by using a random-forest (RF) -based framework to link LWD and multifrequency seismic data. This method enables dynamic updates to lithology parameter predictions by adjusting the weights of seismic data from various frequencies based on real-time LWD inputs.
Blind well tests validate the framework, demonstrating a high correlation between predicted and actual acoustic impedance values along the full well section—initially exceeding 0.85, with improvements above 0.9 as drilling depth increases.

The RF algorithm effectively uses impedance data from multifrequency seismic sources, incorporating geological prior information to enhance model robustness. This method enables reliable look-ahead lithology parameter prediction, anticipating lithological changes up to 400 m ahead of the drill bit.

The findings demonstrate that this approach effectively reconciles the resolution differences between logging and seismic data. By integrating geological priors from predrilling models with real-time, data-driven LWD insights, the methodology enhances both the efficiency and robustness of model updates in geosteering applications.

This abstract is taken from paper SPE 226223 by J. Zhang, S. Huo, and X. Shan, Chinese Academy of Sciences; W. Zheng, Imperial College London; R. Xie and J. Yuan, CNOOC Research Institute; and L. Enliang, China University of Petroleum-Beijing, State Key Laboratory of Petroleum Resource and Prospecting, and CNPC Key Laboratory of Geophysical Exploration. The paper has been peer reviewed and is available as Open Access in SPE Journal on OnePetro.