利用油藏模拟和流线示踪优化孔隙置换计算

Osama A. Abdelhamid, Shu Zhang, M. Maučec, Brett Fischbuch
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引用次数: 0

摘要

在油藏的整个生产周期内,有效地管理空隙置换比(VRR)对于实现最佳采收率至关重要。VRR被定量定义为油藏条件下的注入/生产流体体积比。管理空隙置换的主要目标是在一定程度上补充储层中的能量,使生产井以经济的速度产出碳氢化合物。然而,当储层受到流体流入的显著影响时,VRR的确定变得更加复杂。本文提出了一种利用有限差分油藏模拟模型输出的流线来优化VRR计算的方法。良好的油藏管理实践要求常规容积比保持在1或1以上。因此,在二次采油过程中,保持适当的注入性能是实现最佳采收率的基本要求。这可以通过有效的VRR监测、破水监测和油藏压力维持来实现。本文提出了一种新的技术和相关的工作流程来严格确定VRR,解决了传统VRR分析中固有的一些缺点。这种严格的VRRR确定方法应用于现有油田,该油田具有丰富的作业历史,包括多种驱油和采收率过程:初级枯竭、含水层涌入、气体回注、重力注水和动力注水。这种新方法利用有限差分油藏模拟模型从压力场和通量中生成流线。流线表示注入器和采油器之间的流动路径。由此获得的具有相关飞行时间值的流线轨迹考虑了地质复杂性、外部通量、井位、相行为和油藏流动行为。通过考虑流量和井分配系数(WAF),可以获得严格的VRR估计,WAF代表了具有相关流量的特定注入器/采油器对之间连通性的度量。在对相关时间步长进行流线跟踪时,根据历史匹配油藏模拟模型自动计算通量和WAF值。传统上,井的VRR值是通过井流入动态关系(IPR)公式来计算的,由于没有考虑外部能源,例如邻近层的流入,可能会导致不理想的估计。该方法可以提高注水效率,通过储层物质平衡(MB)计算和流线生成的井分配系数,可以直接获得偏移油产量。为了方便动态仿真区域的VRR计算,我们提出了一种基于流线(SLN)的VRR计算工作流,该工作流使用基于时间相关流的SLN条件排水体积,自动从仿真网格中提取,并作为仿真运行时间步长的函数迭代地纳入仿真模型约束。
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Optimisation of Voidage Replacement Calculation Using Reservoir Simulations and Streamline Tracing
Effective management of Voidage Replacement Ratio (VRR) throughout the producing life of an oil reservoir is essential for achieving optimal oil recovery. VRR is quantitatively defined as injection/production fluid volume ratio at reservoir conditions. The primary goal in managing voidage replacement is to replenish the energy in a reservoir to a degree that the producing wells yield hydrocarbons at economical rates. The determination of VRR, however, becomes more complicated when reservoirs are significantly affected by fluid influxes. This paper presents a method developed to optimize VRR calculations using streamlines, traced from finite-difference reservoir simulation model outputs. Good reservoir management practice necessitates that conventional VRR should be maintained at or above unity. Maintaining appropriate injection performance is therefore an essential requirement for achieving optimal oil recovery in secondary recovery processes. This can be achieved through effective VRR surveillance, water breakthrough monitoring, and reservoir pressure maintenance. This paper presents a new technique and associated workflow for rigorous VRR determination that resolves a number of shortcomings inherent in conventional VRR analysis. This rigorous VRRR determination methodology was applied to an existing field with considerable operating history including multiple displacement and recovery processes: primary depletion, aquifer influx, gas re-injection, gravity water injection, and power water injection. This new methodology utilizes finite difference reservoir simulation models to generate streamlines from the pressure field and fluxes. Streamlines represent flow paths between injectors and producers. The streamline trajectories with associated time-of-flight values thus obtained take into account geologic complexity, external fluxes, well locations, phase behavior, and reservoir flow behavior. Rigorous VRR estimates are obtained by accounting for the influxes and well allocation factors (WAF), which represent a measure of connectivity between specific injector/producer pairs with associated fluxes. The fluxes and WAF values are calculated automatically from the history-matched reservoir simulation model during streamline tracing for associated time steps. Traditionally, the well VRR values are calculated via the formulation of well inflow performance relationship (IPR), which may result in suboptimal estimations by not accounting for external sources of energy, such as influx from neighboring zones. The presented approach allows for improved optimisation of waterflood injection efficiency, where the off-set oil production can be derived directly from reservoir material balance (MB) calculations and streamline-generated well allocation factors. In order to facilitate VRR calculations with dynamic simulation regions, we propose a workflow for streamline (SLN) based VRR calculations using the time-dependent flow-based SLN-conditioned drainage volumes, automatically extracted from the simulation grid and iteratively incorporated into simulation model constraints as a function of simulation run time-steps.
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