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

基于图像的油砂渗透率评估及其在储层预测中的应用

• 批准号: 518072-2017
• 负责人: Nouri, Alireza
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640647
• 关键词: Image; based; Permeability; Assessment; Oil

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.

优化储层开发和资源管理需要准确的恢复性能预测。岩石物理特性对于评估储层形成至关重要。孔隙率和渗透性是最重要的参数，因为它们分别控制资源估计和流速。在水库的生活中，许多关键决定取决于这些预测的准确性。但是，对此类特性的估计，尤其是渗透性，这是具有挑战性的，因为购买代表性的岩石样品以在油砂沉积物中进行渗透性测试是困难且昂贵的。这项研究提出了一种方法，以估算改善储层预测的准确高分辨率渗透性（和孔隙率）。该研究建议的目的是通过机器学习技术来预测通透性和孔隙率。 CT扫描和图像日志将与常规的井日志结合使用，以构建3D指示器模型和代表性的孔隙率 - 渗透关系。接下来，将建立高分辨率的孔隙率 - 渗透关系。最后，为整个储层结构域建模了渗透性（水平和垂直），相，孔隙率和水饱和度。校准的孔隙率 - 渗透性关系用于在单个网格块上构建特性。这项研究将主要对加拿大公司Petrochina Canada Ltd.（以前称为Brion Energy Corporation）有益。该项目的结果可能导致蒸汽消耗减少，优化的生产设计，最小化的环境足迹以及改进的储层预测，通过改善数值储层模型。这将通过减轻水资源的使用来带来对加拿大的实质性环境和经济优势，并且可以更关注环境，从而提取自然资源。