Productivity Decline: Improved Production Forecasting Through Accurate Representation of Well Damage

Yan Li, K. Zaki, Yunhui Tan, Ruiting Wu, Peggy Rijken
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引用次数: 1

Abstract

PI (Productivity Index) degradation is a common issue in many oil fields. To obtain a highly reliable production forecast, it is critical to include well and completion performance in the analysis. A new workflow is developed to assess and incorporate the damage mechanisms at the wellbore, fracture and reservoir into production forecasting. Currently, most reservoir models use a skin factor to represent the combined well damages mechanisms. The skin factor is adjusted based on the user's experience or data analysis instead of physical modeling. In this workflow, a detailed model is built to explicitly simulate the damage mechanisms, assess the dynamic performance of the well and completion with depletion, and generate a physics-based proxy function for reservoir modeling. The new workflow closes the modeling gap in production forecasting and provides insights into which damage mechanisms impact PI degradation. In the workflow, a detailed model is built, which includes an explicit wellbore, an explicit fracture and the reservoir. Subsurface rock and flow damage mechanisms are represented explicitly in the model. Running the model with an optimization tool, the damage mechanisms’ impact on productivity can be assessed separately or in a combination. A physics-based proxy is generated linking the change in productivity to typical well parameters such as cumulative production, drainage region depletion and drawdown. This proxy is then incorporated into a standard reservoir simulator through the utilization of scripts linking the PI evolution of the well to the typical well parameters stated above. The workflow increases the reliability of generated production forecasts by incorporating the best representation of the near wellbore flow patterns. By varying the damage mechanism inputs the workflow is capable of history matching and forecasting the observed field behavior. The workflow has been validated for a high permeability, over pressured deep-water reservoir. The history match, PI prediction and damage mechanism analysis are presented in this paper. The new workflow can help assets to: (1) history match and forecast well performance under varying operating conditions; (2) identify the key damage mechanisms which allows for potential mitigation and remediation solutions and; (3) set operational limits that reduce the likelihood of future PI degradation and maintain current performance.
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生产力下降:通过准确表示井损,提高产量预测
产能指数(PI)下降是许多油田普遍存在的问题。为了获得高度可靠的产量预测,在分析中考虑井和完井性能至关重要。开发了一种新的工作流程,用于评估井眼、裂缝和储层的损害机制,并将其纳入生产预测。目前,大多数油藏模型都使用表皮因子来表示油井联合损伤机制。皮肤因子是根据用户的经验或数据分析而不是物理建模来调整的。在这个工作流程中,建立了一个详细的模型来明确地模拟损害机制,评估井的动态性能和衰竭完井,并生成一个基于物理的油藏建模代理函数。新的工作流程缩小了生产预测中的建模差距,并提供了影响PI退化的损害机制的见解。在工作流程中,建立了一个详细的模型,包括显井眼、显裂缝和储层。在模型中明确地表示了地下岩石和流动损伤机制。使用优化工具运行该模型,可以单独或组合评估损害机制对生产率的影响。生成一个基于物理的代理,将产能变化与典型井参数(如累积产量、泄油区枯竭和下降)联系起来。然后,通过使用将井的PI演变与上述典型井参数联系起来的脚本,将该代理合并到标准油藏模拟器中。该工作流程通过结合近井流动模式的最佳表示,提高了生成的产量预测的可靠性。通过改变损伤机制输入,该工作流程能够进行历史匹配和预测观察到的现场行为。该工作流程已在高渗透率、超压深水油藏中得到验证。本文介绍了历史匹配、PI预测和损伤机理分析。新的工作流程可以帮助资产:(1)历史匹配和预测不同作业条件下的井况;(2)确定可提供潜在缓解和补救办法的关键损害机制;(3)设置操作限制,以降低未来PI退化的可能性并保持当前性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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