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.

优化油藏开发和资源管理需要对采油动态进行准确预测。岩石物理性质是评价储集层的关键。孔隙度和渗透率是最重要的参数，因为它们分别控制资源估计和流量。在水库的整个生命周期中，许多关键的决定都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，因为在油砂层中获取具有代表性的岩石样品用于渗透率测试是困难和昂贵的。为了提高储层预测的精度，本研究提出了一种高分辨率的渗透率(和孔隙度)预测方法，其目的是利用机器学习技术预测渗透率和孔隙度。CT扫描和成像测井将与常规测井相结合，建立三维指示模型和具有代表性的孔渗关系。下一步将建立高分辨率的孔渗率关系。最后，对整个储集域的渗透率(水平和垂直)、相、孔隙度和含水饱和度进行了建模。校正后的孔隙度-渗透率关系被应用于构造单个网格区块的属性。这项研究将主要有益于加拿大公司中石油加拿大有限公司(前身为布里昂能源公司)。该项目的成果可以通过改进油藏数值模型来降低蒸汽消耗，优化生产设计，最大限度地减少环境足迹，并改善油藏预测。这将通过减少水资源的使用为加拿大带来巨大的环境和经济优势，并可在更多关注环境的情况下开采自然资源。