基于图像测井资料的应力反演算法的发展与应用

Temirlan Zhekenov, K. Chettykbayeva, A. Cheremisin, Alexey Sobolev, Y. Petrakov
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引用次数: 0

摘要

在复杂的地质环境中(特别是水平井和大斜度井),成功的井规划和增产与地质力学评估息息相关。应力状态的识别是地质力学建模的重要基础,对储层开采方案的改变具有重要意义。此外,对应力状态的了解直接影响水力压裂程序的效率和井筒稳定性。例如,在应力状态相反的情况下,由于裂缝起裂、高注入压力以及支撑剂沉降在井筒中的风险,水力压裂作业可能效率低下。遇到水力压裂问题的油田应通过地质力学参考框架进行评估,以全面了解问题。由于缺乏最大水平应力的直接测量工具,对应力状态的评估受到了挑战。业内常用的应力估计方法(包括裂缝宽度、声波各向异性反演和假设构造系数的孔隙弹性建模)的应用存在一定的局限性,这往往导致获得的最大水平应力值范围很广,从而增加了钻井和水力压裂建议的不确定性。因此,这项工作的主要目标是开发和应用一种对应力状态和方向进行定性评估的工具。建立在最小数据集基础上的可靠数学模型,能够预测远场和近井压力下的岩石行为,对于有效的钻井、压裂、井位和油藏开发作业来说,是非常有用的工具。开发应力反演算法的基本方法是基于限制水平应力可能值的范围,使用安德森的应力状态定义,Mohr-Coulomb和Kirch方程的摩擦理论。随后,根据图像测井数据对几口斜井井壁的破裂方位角进行了分析,从而可靠地预测了储层应力状态和方向。该方法的最佳应用需要了解垂直应力和斜度至少为20°且方位方向不同的斜井的井眼失效情况。然后将开发的改进的应力状态识别算法应用于实际的现场案例,以了解水力压裂处理过程中遇到的问题的地质力学根源。了解应力状态和水平应力方向可以建立可靠的地质力学模型,这对于优化水力压裂方案和提高油井作业效率是必要的。根据所进行的工作得出的结论是,对于数据不足的情况(例如未记录斜井眼),应进一步研究水平应力检测方法。此外,由于所有估算SHmax的方法都是相反的,因此只有创建一个将所有技术(现有的和正在开发的)集成在一起的工具,才能产生最大的价值。
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Development and Application of Algorithm for Stress Inversion Based on Image Log Data
Successful well planning and stimulation in complex geological settings (especially in the horizontal wells and wells with a high degree of deviation) is bound with conducting geomechanical estimations. Identification of the stress regime, which is an imperative basis for the geomechanical modeling, can significantly alter the reservoir production scheme. Moreover, knowledge of the stress regime directly impacts the efficiency of hydraulic fracturing procedures and wellbore stability. For example, in case of reverse stress regime, the hydraulic fracturing operations could be inefficient due to the problems with the fracture initiation, high injection pressures, and risks associated with the proppant fallout in the wellbore. Fields experiencing hydraulic fracturing problems should be assessed via the geomechanical frame of reference for the comprehensive understanding of the issue. Assessing the state of stress is challenged by the absence of direct measurement tools of maximum horizontal stress. Application of the stress estimation methods commonly used in the industry (including the breakout width, acoustic anisotropy inversion and poroelastic modeling with the assumption of tectonic coefficients) have certain limitations which often lead to a broad range of obtained values of maximum horizontal stress, thus adding uncertainty to the drilling and hydraulic fracturing recommendations. Thus, the main goal of this work is to develop and apply an instrument for qualitative assessment of stress regime and direction. The reliable mathematical model, built upon the minimal set of required data, which is able to forecast the rock behavior under far-field and near wellbore stresses can be an extremely useful instrument for effective operations of drilling, fracturing, well placement and reservoir development. The underlying method for the development of the stress inversion algorithm was based on limiting the range of possible values of horizontal stresses using Anderson's definition of stress regimes, the frictional theory of Mohr-Coulomb and Kirch equations. The subsequent analysis of the breakout azimuths at the wellbore walls of several inclined wells from the image log data results in a reliable prediction of reservoir stress regime and direction. The optimal usage of the method required knowledge of vertical stress and the borehole failures logged in the deviated wells with the inclination of at least 20° and varying azimuthal directions. The developed algorithm of the improved identification of stress regime was then applied for a real field case in order to understand the geomechanical roots of the problems experienced during hydraulic fracturing treatment. Learning the stress regime and the orientation of the horizontal stresses allowed building reliable geomechanical models, necessary for the optimization of the hydraulic fracturing program and improvement of well operating efficiency. The conclusion upon the conducted work was that the methods of horizontal stress detection should be further studied for the cases where the data is not enough (for example no deviated bores logged). Moreover since all methods of SHmax estimation are inverse, the most value can be brought only by creating a tool where all the techniques (existing and the ones under development) are integrated.
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