Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements - 2024 Annual Workshop Presentation

This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS reservoir using three methods: Method 1: Demonstrate complimentary laboratory rock-core stress estimation combined with Machine Learning approach for measuring in-situ stress from field sonic log data; Method 2: Complete field based in-situ measurement (mini-frac); and Method 3: Develop a mechanics-based method for connection near wellbore stress measurements to stresses away from the well-bore. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 14, 2024.

本汇报为匹兹堡大学（The University of Pittsburgh）开展的"多组分表征犹他州地热研究前沿观测站（Utah FORGE）原位应力：实验室建模与现场测量"项目的汇报材料，由安德鲁·邦杰（Andrew Bunger）主讲。该项目采用三类方法对犹他州FORGE增强型地热系统（Enhanced Geothermal System, EGS）储层的应力特征开展表征：方法一：验证实验室岩心应力估算与机器学习（Machine Learning）结合的互补技术路径，以通过野外声波测井数据反演原位应力；方法二：完成基于现场的原位应力测量（小型压裂测试，mini-frac）；方法三：开发基于力学的关联方法，实现井筒附近应力测量结果与井筒远端应力的有效关联。本次汇报于2024年8月14日在犹他州FORGE研发年度研讨会上进行展示。

搜集汇总

背景与挑战

背景概述

该数据集是2024年Utah FORGE研发年度研讨会的演示文稿，由匹兹堡大学Andrew Bunger展示，主题为通过实验室建模和现场测量表征Utah FORGE EGS储层的原位应力。研究采用三种互补方法：结合实验室岩石核心估计与机器学习处理现场声波数据、进行现场小型压裂测量，以及开发力学方法扩展井筒应力测量范围，旨在全面评估地热储层应力状态。