在完整的论文中，作者提出了一种人工智能 (AI) 辅助的工作流程，使用机器学习 (ML) 技术来识别碳酸盐岩储层中的最佳点。该过程涉及在主题专家 (SME) 的监督下使用井数据库对地质特征进行注释。生成的机器学习模型在新井上进行了测试，可以识别产层、射孔间隔和应力分析。该模型以像素级精度成功检测裂缝、破裂、层理面、孔洞和滑移通道，减少了钻孔图像 (BHI) 分析时间。
BHI的使用需要人工解读和数据识别，严重依赖中小企业的专业知识和时间。解决这一挑战的一种广泛采用的方法是使用监督计算机视觉算法，这是人工智能的一个子领域。这些算法根据在训练期间从数据中学到的示例来优化任务函数或模型。
In the complete paper, the authors propose an artificial-intelligence (AI)-assisted work flow that uses machine-learning (ML) techniques to identify sweet spots in carbonate reservoirs. This process involves annotation of geologic features using a well database, with supervision from subject-matter experts (SMEs). The resulting ML model is tested on new wells and can identify pay zones, perforation intervals, and stress analysis. The models successfully detect fractures, breakouts, bedding planes, vugs, and slippage passages with pixel-level precision, reducing borehole-image (BHI) analysis time.
The use of BHIs requires manual interpretation and data identification, heavily relying on the expertise and time of SMEs. A widely adopted approach to address this challenge is the use of supervised computer-vision algorithms, a subfield of AI. These algorithms optimize the task function or model based on examples they have learned from data during training.
