由于岩石性质和孔隙系统复杂，难以准确表征，因此非均质碳酸盐地层的渗透率预测或计算是一项具有挑战性的任务。在完整论文中详述的研究中，开发了一种有效的方法来克服这一挑战，即结合岩石类型和机器学习神经网络 (MLNN) 技术来准确预测非均质碳酸盐地层的渗透率。该方法适用于广泛的碳酸盐地层，并有可能显著改善油藏表征和生产优化。
正在研究的阿布扎比近海油藏代表着复杂的沉积环境，从潮滩到泻湖和内斜坡，偶尔过渡到中斜坡沉积物。油藏内这种复杂的沉积循环层次反映了各种沉积过程和水深波动的影响，最终形成了油藏的异质性和动态流体流动路径。
这一环境中一个显著的地质特征是白云石化的普遍存在，主要在储层的中部和下部产生明显的影响。
Permeability prediction or calculation in heterogeneous carbonate formations is a challenging task because of the complexity of rock properties and pore systems that are difficult to characterize accurately. In the study detailed in the complete paper, an efficient approach was developed and used to overcome this challenge by combining rock typing and machine-learning neural-network (MLNN) techniques to predict the permeability of heterogeneous carbonate formations accurately. The approach is applicable to a wide range of carbonate formations and has the potential to improve reservoir characterization and production optimization significantly.
The reservoir under investigation offshore Abu Dhabi represents a complex amalgamation of sedimentary environments, ranging from tidal flat to lagoon and inner ramp, occasionally transitioning to midramp deposits. This intricate hierarchy of sedimentary cyclicity within the reservoir reflects the influence of diverse depositional processes and water-depth fluctuations, ultimately shaping the reservoir’s heterogeneity and dynamic fluid-flow pathways.
A notable geological feature within this setting is the prevalence of dolomitization, exerting a pronounced effect, primarily in the middle and lower sections of the reservoir.
