阿曼Yibal油田Khuff地层致密裂缝型碳酸盐岩油环开发优化

Andrea C. Knoerich, A. Dhahli, Sonia Lai, Sumaiya Habsi, S. Farsi
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引用次数: 0

摘要

Yibal油田的Khuff地层目前正在进行PDO最大的油田开发活动之一。虽然主要的项目驱动因素是确保稳定的平台速率下的天然气产量(高酸性),但最大限度地提高油环采收率和产量是本研究的主要目标。这是一个挑战,因为所有的井都是根据FDP建议进行预钻的,任何后续的开发优化都是困难和昂贵的。新获得的静态(地震、测井、井眼图像)和动态(试井)数据被用于决策驱动建模方法,以更新现有的静态和动态模型,以确认地下开发决策、石油生产承诺和EUR的稳健性。更新的结构和属性建模结果用于优化剩余井的2/3,以改善排水。储层岩石类型和裂缝解释,以及所有井的套管井监测数据,用于优化生产层段,并确定不同层段(白云岩/灰岩/裂缝)的产能;然后进行整合,以确定下一步的射孔策略。本研究描述了阿曼苏丹国Khuff碳酸盐岩的首次开发,历史生产数据有限,在阿曼苏丹国没有类似的数据。该研究强调了在决策驱动的建模方法中持续集成新数据的重要性,以确保项目决策的稳健性,及时进行项目调整,以防止NPV侵蚀。
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Optimizing Oil Rim Development in a Tight, Fractured Carbonate Field: Khuff Formation, Yibal Field, Sultanate of Oman
The Khuff formation in the Yibal field is currently undergoing one of the largest field development campaigns in PDO. While the main project driver is securing gas production (highly sour) at a stable plateau rate, maximizing oil rim recovery and production is the main objective of the presented study. This is challenging as all wells are pre-drilled as per FDP recommendation and any later development optimization is expected to be difficult and costly. Newly available static (seismic, well log, borehole images) and dynamic (well test) data were utilized in a decision driven modelling approach to update existing static and dynamic models to confirm the robustness of subsurface development decisions, oil production promises and EUR. Updated structural and property modeling results were utilized to optimize placement of 2/3 of the remaining wells in order to improve drainage. Reservoir rock typing and fracture interpretation, along with cased-hole surveillance data acquired in all wells, were used to optimize production intervals and to confirm the productivity of the different intervals (dolostone/ limestone/ fractures); which were then integrated to confirm the perforation strategy going forward. This study describes the first development of Khuff carbonates in Sultanate of Oman, with limited historical production data and no analogues in Sultanate of Oman. The study highlights the importance of continuous integration of new data in a decision driven modelling approach to ensure robustness of project decisions with timely project adjustments to prevent NPV erosion.
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