Dynamic Fracture Characterization and Integrated Optimization of Enhanced Oil Recovery Performance in Tight Formations under Uncertainty

不确定性条件下致密地层动态裂缝表征及提高采收率综合优化

• 批准号: RGPIN-2019-07150
• 负责人: Yang, Daoyong
• 金额: $ 2.84万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715651
• 关键词: Dynamic; Fracture; Characterization; Integrated; Optimization

The primary recovery factor is very low (around 5-10%) for unconventional resources trapped in a tight formation, even after long horizontal wells have been drilled and massively fractured. Due to its rapid depletion on primary energy, enhanced oil recovery (EOR) methods are urgently needed to boost production; however, it is still a challenging task to increase the ultimate oil recovery because fluid flow in tight formations can be completely dependent on the fracture network while the matrix only plays a source role. Laboratory experiments and field tests have shown that waterflooding does not normally result in the expected performance as in the conventional reservoirs, that gas (e.g., hydrocarbon gas, CO2, and N2) injection, especially huff-n-puff processes, performs favourably, and that optimization of production-injection modes is found to be fundamentally important to exploit tight oil and shale gas reservoirs. Physically, fracture network, multiscale pore-throat/fracture structure and confined mass transfer make flow behaviour very complicated in tight formations. Furthermore, production or injection through a hydraulically fractured horizontal well leads to changes in the stress field, and hence induces changes in matrix permeability and fracture conductivity. Therefore, it is essential to integrally characterize fracture dynamics, determine residual oil saturation distribution within matrices and fractures, and optimize reservoir performance in the presence of geological and economic uncertainty for designing a technically feasible and economically sound EOR scheme and CO2 storage capacity in a tight formation. The major objectives of this proposed research are: 1) to experimentally and theoretically quantify phase behaviour and two-way mass transfer for the injected gases-light oil systems; 2) to perform network modelling of complex flow behaviour between matrix and fractures with the reconstructed pore-network obtained by using the micro-CT scanning images; 3) to develop an integrated inversion technique to characterize dynamic fracture network by performing transient pressure/rate analysis and matching production history together with monitoring and surveillance data; and 4) to develop robust and parallelized optimizers to efficiently optimize large-scale and closed-loop nonlinear systems in the presence of economic and geological uncertainty. Finally, we will provide the oil and gas industry with an integrated and pragmatic technique that can be applied to accurately quantify phase behaviour and fluid properties, characterize the dynamic fracture network, identify the underlying EOR mechanisms, and thus maximize oil recovery and CO2 storage capacity in tight formations with complex fracture networks under uncertainty in a cost-effective and sustainable manner.

即使在钻长水平威尔斯井和大规模压裂之后，对于被困在致密地层中的非常规资源，一次采收率也非常低（约5-10%）。由于其对一次能源的快速消耗，迫切需要提高石油采收率（EOR）的方法来提高产量;然而，由于致密地层中的流体流动可以完全依赖于裂缝网络，而基质仅起源作用，因此提高最终石油采收率仍然是一项具有挑战性的任务。实验室实验和现场试验已经表明，注水通常不会像常规油藏那样产生预期的性能，气体（例如，烃类气体、CO2和N2）注入，特别是吞吐工艺，表现良好，且发现生产-注入模式优化对于开采致密油和页岩气储层至关重要。物理上，裂缝网络、多尺度孔喉/裂缝结构和受限传质使得致密地层中的流动行为非常复杂。此外，通过水力压裂水平井的生产或注入导致应力场的变化，并因此引起基质渗透率和裂缝导流能力的变化。因此，在地质和经济不确定性的情况下，为了设计技术上可行且经济上合理的致密地层中的EOR方案和CO2存储容量，必须完整地表征裂缝动态，确定基质和裂缝内的剩余油饱和度分布，并优化储层动态。 本研究的主要目的是：1）从实验和理论上定量研究注入气-轻质油体系的相态和双向传质，2）利用显微CT扫描图像重建孔隙网络，对基质与裂缝之间的复杂流动行为进行网络模拟;（3）开发综合反演技术，通过进行瞬态压力/流量分析和将生产历史与监测和监督数据相匹配来表征动态裂缝网络;以及4）开发鲁棒的和并行的优化器以在经济和地质不确定性的存在下有效地优化大规模和闭环非线性系统。最后，我们将为石油和天然气行业提供一种综合实用的技术，该技术可用于准确量化相态和流体性质，表征动态裂缝网络，确定潜在的EOR机制，从而以具有成本效益和可持续的方式在不确定性的复杂裂缝网络中最大限度地提高致密地层的石油采收率和CO2储存能力。