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Uncertainty Reduction in Geomechanical Modeling Using Continuous Core Based Data in a Giant Field, Abu Dhabi, UAE

机译:在巨大田间中使用连续核心数据的地质力学建模的不确定性降低,阿布扎比,阿联酋

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This paper focuses on a tight carbonate reservoir in a giant field in Abu Dhabi by identifying shortcomings in conventional modeling strategies for geomechanics and demonstrating the benefits of continuous core data to build more reliable 1-D Mechanical Earth Models (MEM). A 1-D MEM was built from the sonic wireline log, which shows significant difference with a profile of ultrasonic P-wave velocity (Vp) measured on cores. However, results of rock mechanical tests (RMT) on plug samples (including ultrasonic Vp measurements at different stress conditions, and stress-strain curves from triaxial tests) are consistent with the core-based Vp profile. We investigate the impact of stresses, resolution and fluid saturation on sonic velocities to reveal the possible shortcomings of sonic wireline logs as an input for geomechanical models and the greater relevance of using core based ultrasonic velocities measured on dry cores for the upscaling of static elastic moduli. Finally we propose an empirical relation to correct sonic wireline logs for geomechanical modeling in offset wells. The following conclusions can be drawn from this study: 1. The core based Vp profile, which is highly consistent with the RMT results, ultimately leads to opposed trends in the in-situ horizontal stresses predictions compared to those of a 1-D MEM based on the non-calibrated wireline sonic log. 2. Only unrealistic reservoir stress conditions could reconcile ultrasonic Vp measured on plugs at different stress states with wireline sonic velocities; 3. Using a low resolution Vp profile at reservoir stress conditions (combining Vp from plug samples and core based continuous Vp profile), we show that differences in stress only partially explain the discrepancy between velocities measured on plugs and wireline sonic velocities. 4. Although a conventional Gassman fluid correction could explain the remaining differences between core measurements and the wireline sonic, its practical application would require the detailed knowledge of the rock mineralogy and of the saturation along the well. 5. Conversely, a profile of the bulk modulus of the rock mineral fraction can be derived from the sonic log and the ultrasonic P-wave velocities measured on dry cores corrected for stresses effects. 6. Evidences in the drilling data suggest that the discrepancies between the core based sonic velocities and the wireline sonic could be due to natural fractures in the borehole vicinity. 7. An empirical relationship involving wireline logs only was established to correct the sonic wireline log to enhance the reliability of geomechanical models for offset wells. These findings have important implications for the practical applications of 1-D MEM, such as the design of hydraulic fractures. Quality control of the sonic logs with extensive data acquired on dry cores reduces the uncertainty when upscaling static elastic properties. Continuous velocity profiles acquired on dry cores are therefore highly valuable to calibrate empirical corrections of sonic logs for geomechanical modeling in offset wells.
机译:本文通过识别在常规建模策略的缺点为地质力学和演示连续核心数据的好处建立更可靠的1-d机械地球模型(MEM)着重于在阿布扎比巨字段紧密碳酸盐贮存器。 A 1-d MEM从声波电缆测井,其示出了与上芯测量超声纵波速度(VP)的轮廓显著差异建造。然而,对插头样本(包括超声VP测量在不同胁迫条件下,和从三轴试验的应力 - 应变曲线)岩机械测试(RMT)结果与VP基于芯轮廓相一致。我们调查的压力,分辨率和流体饱和度对声波速度的影响,揭示声波测井资料的可能存在的缺点,作为地质力学模型的输入和干燥的内核测量静态弹性模量的倍增使用基于核心超声波速度的更大的相关性。最后,我们提出了一个经验关系,以正确的声波测井资料对邻井的地质力学模型。下列结论可从该研究可以得出:1.基于VP配置文件中的核心,这是与RMT结果,最终导致在原位的水平应力的预测相对的趋势与基于那些有1 d MEM的高度一致对非标定线缆声波日志。 2.只有不切实际储胁迫条件可以调和在上结合有线声速不同应力状态的插头测量超声波VP; 3.使用在储层压力条件(从插头样品和芯基连续VP配置文件组合VP)低分辨率VP轮廓,我们表明,在应力的差异仅部分地解释上的插头和有线声速测量的速度之间的差异。 4.虽然传统加斯曼流体校正可以解释的核心测量和线缆声波之间尚存分歧,但其实际应用需要岩石矿物学和沿井饱和的详细知识。 5.相反,岩石矿物馏分的体积弹性模量的轮廓可从声波测井和对应力的影响校正干核测量的超声波P波速度来导出。 6.在钻井数据的证据表明,基于核心声速和线缆声波之间的差异可能是由于在钻孔附近的天然裂缝。 7.只涉及电缆测井的经验关系的建立是为了纠正声波电缆测井,加强地质力学模型对邻井的可靠性。这些发现具有诸如水力裂缝的设计1-d MEM,的实际应用中具有重要意义。升频静态弹性性质时与干燥芯获得了广泛的数据中的声波测井的质量控制降低的不确定性。干核获取的连续的速度分布,因此校准声波测井的经验修正为在邻井的地质力学模型高度有价值的。

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