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.

了解地应力对于最大限度地开发能源、减轻井眼坍塌和天然气泄漏等地质和环境灾害至关重要。在世界范围内，地下能源行业正在寻求有效和廉价的方法来量化地应力。传统的方法依赖于井眼压力信息，然而，这种方法容易产生不准确性，因为流体输送和裂缝动力学之间相互作用的复杂性导致了地应力反演中使用的压力解释的不确定性。由于泄漏试验的性质，泄漏压力数据很少，水力压裂试验获得所需井眼压力的成本很高。我花了10年的时间研究一种方法，利用井眼变形信息，通过基于软计算的逆分析来估计地应力，这种方法成本低，精度高，并取得了初步成功。然而，在接下来的五年里，仍有三个挑战需要作为研究目标：(1)井眼变形的机制需要区分和考虑；(2)逆分析中的高维优化问题需要先进的软计算技术；(3)井眼在地应力作用下的场尺度变形的不可重复性需要先进的数值模型进行模拟。该方案的目标是：(1)开展实验室真三轴试验，重点研究钻孔变形的不同机制。(2)提高高维数优化算法在地应力反演分析中的性能。(3)建立先进的数值模型，在实验室观测的基础上模拟钻孔变形。提出的研究计划将提供一种准确有效的测量地应力大小的方法，这是地下能源行业长期以来所期望的。该项目将推动岩石力学和构造地质学地应力测定的基础研究，同时降低页岩开发勘探阶段的成本，提高勘探精度。从更广泛的角度来看，不仅石油/天然气行业将从该计划中受益，采矿、地质二氧化碳储存、地下能源储存和地热能行业也将从该计划中受益。所有这些行业都与盖层完整性有关，包括相关的环境和地震问题，而了解地应力是了解盖层完整性的关键。拟议的研究项目还为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。