岩屑分析中的自动化:地质101数字实现的未来预览

C. Shrivastava, K. Bondabou, Mahdi Ammar, Simone Di Santo, Tetsushi Yamada, Sophie Androvandi
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引用次数: 0

摘要

对钻机上收集的钻屑进行分析一直是在其自身限制下了解地下情况的最基本、也是最直接的手段。然而,泥浆测井这方面的数字化转型所带来的自动化大大增加了这些数据的重要性。一个未来的预览正在呈现,用于重新定位和价值展示最基本的和广泛可用的数据,即数字支持的切割。低成本的样品制备技术,降低了对近实时地层评价的不利环境影响,从而提高了井位和完井设计,通过直接检测和量化矿物、总有机碳(TOC)、干酪根含量和元素组成,重塑了传统的泥浆测井方法;通常最大限度地减少对时间和成本密集的电缆测井的要求。劳动密集型的样品采集正在实现自动化,泥浆记录仪的主观和描述性解释正在让位于数字化、客观的解释,随时准备与随钻测井数据实时集成。除x射线荧光和衍射外;漫反射红外傅立叶变换光谱(DRIFTS)等新技术正在被纳入井场设置,减少了钻机的占地面积,并最大限度地减少了化学品的使用。独特的自动化流程可以分析高分辨率的数字图像,在最短的时间内提供大量的信息;通常在人工智能的帮助下增强。未来的观点与构建模块的自动解释过程提出。本文提供了实现自动化所需的不同步骤的示例,从样品准备到数字分析,再到机器学习,以整体的未来愿景,突出数字实现,以经济高效和及时的方式实现井目标,以适应不断变化的市场动态。这种基础切割分析(地质学101)的愿景将推动该领域的进一步发展。
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Automation in Cuttings Analysis: Futuristic Preview of Digital Enablement for Geology 101
Analysis of drill-cuttings collected on the rig has always been the most basic, yet most direct means of understanding the subsurface within its own limitations. However, automation enabled by digital transformation of this aspect of mud logging has greatly increased the importance of this data. A futuristic preview is being presented for the repositioning and value showcasing of most basic and widely available data, i.e., cuttings with digital enablement. Cost-efficient characterization with lean sample preparation, reducing the adverse environmental imprint to near real-time formation evaluation leading to enhanced well placement and completion design is reshaping the old-school mudlogging with direct detection and quantification of minerals, total organic carbon (TOC), kerogen content and elemental composition; often minimizing the requirement for time-and-cost intensive wireline logging. Labor-intensive sample collection is getting automated, and subjective and descriptive interpretation per experience of mud-logger is giving way to digital, objective interpretation, ready to be integrated with logging-while-drilling data in real-time. In addition to the X-Ray Fluorescence & Diffraction; newer technologies like Diffused Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) are being incorporated in wellsite set-up with reduced footprint on rig and minimized usage of chemicals. Unique automated process can analyze high resolution digital images to deliver plethora of information in minimum time; often augmented with the help of artificial intelligence. A futuristic view with building blocks of the automated interpretation process is presented. Examples from different steps needed to achieve automation are provided, from sample preparation to digital analysis through machine learning for a holistic futuristic vision to highlight digital enablement in delivering the well-objectives in cost-efficient and timely manner honoring the changing market dynamics. This foundational cutting analysis (Geology 101) vision would drive further adavnces in this field.
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