Determination of in-situ stress from borehole deformation

通过钻孔变形确定地应力

• 批准号: RGPIN-2019-04765
• 负责人: Yin, Shunde
• 金额: $ 1.89万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737844
• 关键词: Determination; situ; stress; borehole; deformation

Knowledge of in-situ stress is critical to maximize the energy resource exploitation and to mitigate geologic and environmental disasters such as borehole collapse and gas leakage. Worldwide, subsurface energy resource industries are seeking effective and inexpensive means to quantify in-situ earth stress. Traditional approaches rely on information on borehole pressure, however, this method is prone to inaccuracies, because the complicated nature of the interaction between fluid transport and fracture dynamics causes uncertainty in the interpretation of the pressures that are used in in-situ stress inversion. Leak-off pressure data are scarce due to the nature of leak-off tests, and hydraulic fracturing tests incur high cost to obtain necessary borehole pressures.          I have spent 10 years investigating an approach to use information on borehole deformation to estimate in-situ stress via soft-computing based inverse analysis, which is inexpensive and accurate, and has had initial success. However, there are still three challenges to investigate as objectives over the next five years: (1) the mechanisms for borehole deformation need to be differentiated and accounted for accordingly; (2) the high dimensionality of the optimization problem in the inverse analysis requires advanced soft-computing techniques; and (3) the non-repeatability of field-scale deformation of a borehole subjected to in-situ stress requires advanced numerical models for simulation.                     The objectives for the proposed program are:          (1) To carry out laboratory true tri-axial tests focusing on study on the different mechanisms of borehole deformation.          (2) To enhance the performance of high dimensionality of the optimization in inverse analysis for in-situ stress determination.          (3) To develop advanced numerical models to simulate the borehole deformation based on mechanisms observed in laboratory.          The proposed research program will provide an accurate and effective way of measuring in-situ stress magnitude, which has long been desired in subsurface energy resource industries. This program will boost the fundamental research on in-situ stress determination in rock mechanics and structural geology, while reducing the cost and enhancing the accuracy in the exploration stage of shale developments. In a broader view, not only will the oil/gas industry benefit from this program, but the mining, geologic CO2 storage, subsurface energy storage, and geothermal energy industries will also benefit from this program. All of these industries are concerned with caprock integrity, including the associated environmental and seismic concerns, and knowledge of in-situ stress is key to understanding caprock integrity.         The proposed research program also provides unique training opportunities to for HQP to develop artificial intelligence based techniques that is highly desired by the subsurface energy industries.

地应力的认识对于最大限度地开发能源资源和减轻地质和环境灾害（如井眼坍塌和气体泄漏）至关重要。在世界范围内，地下能源工业正在寻求有效和廉价的手段来量化原位地球应力。传统的方法依赖于钻孔压力的信息，然而，这种方法容易不准确，因为流体输运和断裂动力学之间的相互作用的复杂性质导致在解释用于原地应力反演的压力中的不确定性。由于泄漏测试的性质，泄漏压力数据是稀缺的，并且水力压裂测试产生高成本以获得必要的钻孔压力。 我花了10年时间研究一种方法，利用钻孔变形信息通过基于软计算的反分析来估计原地应力，这种方法既便宜又准确，并且已经取得了初步成功。然而，在未来五年内，仍有三个方面的挑战需要研究：（1）井眼变形机理需要区分和解释：（2）反分析中的优化问题是高维问题，需要先进的软计算技术;以及（3）受到原地应力的钻孔的现场规模变形的不可重复性需要用于模拟的先进的数值模型。 拟议方案的目标是： （1）开展室内真三轴试验，重点研究不同的钻孔变形机理。 (2)提高地应力反演分析中优化问题的高维性能。 (3)根据实验室观测到的井眼变形机理，建立先进的数值模型，模拟井眼变形。 建议的研究计划将提供一个准确和有效的方法来测量原地应力大小，这一直是地下能源行业所期望的。该项目将促进岩石力学和构造地质学中地应力测量的基础研究，同时降低页岩开发勘探阶段的成本并提高精度。从更广泛的角度来看，不仅石油/天然气行业将受益于该计划，采矿，地质二氧化碳储存，地下能源储存和地热能源行业也将受益于该计划。所有这些行业都关注盖层的完整性，包括相关的环境和地震问题，了解原地应力是了解盖层完整性的关键。 拟议的研究计划还为HQP提供了独特的培训机会，以开发地下能源行业高度期望的基于人工智能的技术。