Predicting geological and geomechanical rock properties using data analytics and multi-sensor core logging data

使用数据分析和多传感器岩心测井数据预测地质和地质力学岩石特性

• 批准号: RGPIN-2020-06196
• 负责人: Esmaeili, Kamran
• 金额: $ 1.89万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749336
• 关键词: Predicting; geological; geomechanical; rock; properties

A detailed modelling of geological and geotechnical rock mass properties is essential for a cost effective and safe mining operation. Despite improvements in almost every technological aspect of mine planning and design, lack of sufficient and consistently measured data for the development of a detailed geological and geotechnical model remains a challenge. A higher quality and quantity of geological and geotechnical data is required to model the spatial variation of rock mass properties and to lower uncertainties in mine planning and design. Core logging is a fundamental method in obtaining geological and geotechnical data. However, the manual core logging methods are subjective, time consuming and inconsistent, which can significantly reduce the reliability of the resulting geological and geotechnical models. To overcome these limitations, new rock measurement and analysis techniques that automate the collection and analysis of data are required. This research program aims to advance prediction of rock geological and geomechanical properties using data from a multi--sensor core logging system. This will be achieved through the collection of multi-variate rock physical, mechanical, mineralogical and structural properties from non--destructive tests, together with high resolution images of the core samples. The data will then be used to develop machine learning models to predict geological and geomechanical rock properties. Core samples from geotechnical boreholes will be logged using both the manual and the multi--sensor core logging system. The high- quality images of the core samples along with the multi--variate core logging data will be processed to develop a multi--modal dataset. The dataset will be used for predictive models of geological and geomechanical rock properties. Supervised machine learning techniques will be used to train predictive models using the multi--modal mixed data. The models will be employed to automatically: predict and classify lithological rock units and their mechanical properties; detect and characterize geometrical attributes of natural discontinuities using image analysis; predict shear behaviour of discontinuities using digital core logging data; classify geomechanical rock mass properties based on multi--parameter digital core logging data; and to investigate the influence of data quality and quantity on geotechnical models used for mine design. The predictive geomechanical models from the digital core logging system will be compared to the models developed based on manual core logging data. The results of the research are expected to improve rock characterization and classification; allow informed decision making in mine planning and optimization; reduce the time and cost associated with unpredicted ground conditions; and reduce the risk of mine excavation failure and its associated costs.

地质和岩土工程岩体特性的详细建模对于具有成本效益和安全的采矿作业至关重要。尽管采矿规划和设计的几乎每一个技术方面都有改进，但缺乏足够和一致的测量数据来建立详细的地质和岩土模型仍然是一个挑战。需要更高质量和数量的地质和岩土数据来模拟岩体性质的空间变化并降低矿山规划和设计中的不确定性。岩心录井是获取地质和岩土工程资料的基本方法。然而，人工岩心测井方法是主观的，耗时的和不一致的，这可以显着降低所产生的地质和岩土模型的可靠性。为了克服这些限制，需要新的岩石测量和分析技术，自动收集和分析数据。该研究计划旨在利用多传感器岩心测井系统的数据提前预测岩石地质和地质力学性质。这将通过收集非破坏性试验的多变量岩石物理、机械、矿物学和结构特性以及岩心样品的高分辨率图像来实现。然后，这些数据将用于开发机器学习模型，以预测地质和地质力学岩石特性。将使用手动和多传感器岩心测井系统记录岩土钻孔的岩心样本。岩心样品的高质量图像沿着多变量岩心测井数据将被处理以开发多模态数据集。该数据集将用于地质和地质力学岩石特性的预测模型。监督机器学习技术将用于使用多模态混合数据来训练预测模型。这些模型将自动用于：预测和分类岩性岩石单元及其力学性质;利用图像分析检测和表征天然不连续面的几何属性;利用数字岩心测井数据预测不连续面的剪切特性;根据多参数数字岩心测井数据分类地质力学岩体性质;研究数据质量和数量对矿山设计所用岩土模型的影响。将数字岩心测井系统的预测地质力学模型与基于手动岩心测井数据开发的模型进行比较。预计研究结果将改善岩石表征和分类;允许在矿山规划和优化中做出明智的决策;减少与不可预测的地面条件相关的时间和成本;并降低矿山挖掘失败的风险及其相关成本。