Image-based Permeability Assessment for Oil Sands and Application to Reservoir Forecast

优化油藏开发和资源管理需要对采收率进行准确预测。岩石物性对储层评价至关重要。孔隙度和渗透率是最重要的参数，它们分别控制着资源估算和流量。在水库的整个生命周期中，许多关键的决定取决于这些预测的准确性。然而，这些性质的估计，特别是渗透率的估计，是具有挑战性的，因为获取具有代表性的岩石样品用于油砂储层的渗透率测试是困难和昂贵的。本研究提出了一种精确的高分辨率渗透率（和孔隙度）估算方法，以改善储层预测。本研究计划的目的是通过机器学习技术预测渗透率和孔隙度。ct扫描和图像测井将与常规测井相结合，建立三维指示模型和代表性的孔隙度-渗透率关系。接下来，将建立高分辨率的孔隙度-渗透率关系。最后，对整个储层域的渗透率（水平和垂直）、相、孔隙度和含水饱和度进行建模。校正后的孔隙度-渗透率关系应用于构建单个网格块的属性。这项研究将主要有利于加拿大公司中石油加拿大有限公司（前身为Brion能源公司）。该项目的研究结果可以降低蒸汽消耗，优化生产设计，最大限度地减少环境足迹，并通过改进油藏数值模型来改进油藏预测。通过减少水资源的使用，这将给加拿大带来巨大的环境和经济优势，并且可以在更加关注环境的情况下提取自然资源。

Optimizing reservoir development and resource management requires an accurate forecast of recovery performance. Petrophysical properties are essential to the evaluation of the reservoir formation. Porosity and permeability are the most important parameters since they control resource estimation and flow rate, respectively. Over the life of the reservoir, many crucial decisions depend on the accuracy of these predictions. However, the estimation of such properties, especially permeability, is challenging as it is difficult and expensive to acquire representative rock samples for permeability testing in oil sand deposits. This research proposes a methodology to estimate accurate high-resolution permeability (and porosity) for improving reservoir forecasts.The aim of this research proposal is to predict permeability and porosity by machine learning techniques. CT-Scans and image logs will be combined with the conventional well logs to build a 3D indicator model and representative porosity-permeability relationships. Next, a high-resolution porosity-permeability relationship will be built. Finally, permeability (horizontal and vertical), facies, porosity and water saturation are modeled for the entire reservoir domain. The calibrated porosity-permeability relationships are applied to construct properties at individual grid block. This research will be primarily beneficial to the Canadian company PetroChina Canada Ltd. (formerly known as Brion Energy Corporation). The results of this project could lead to reduced steam consumption, optimized production design, minimized environmental footprint and improved reservoir forecast by improving numerical reservoir models. This will lead to substantial environmental and economic advantages to Canada by mitigating water resource usage, and natural resource could be extracted with more attention to environment.