Digitalization of Formation Damage Candidate Screening Workflow Improves Process Efficiency

Anish Gupta, Puveneshwari Narayanan, Kukuh Trjangganung, S. J. M. Jeffry, B. C. Tan, M. Awang, Khaled Badawy, Pui Mun Yip
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Abstract

A matrix stimulation candidate screening workflow was developed with the objective to reduce the time and effort in identifying under-performing wells. The workflow was initially tested manually for few fields followed by inclusion in Integrated Operation for an automated screening of wells with suspected formation damage. Analysis done in three fields for stimulation candidate selection will be displayed with actual statistics. The main aim of the work was to digitalize the selection of non-performing candidates rather than manually looking into performance of each well. A concept of Formation Damage Indicator (FDI) was combined with Heterogeneity Index (HI) of the formations to screen out the candidates. Separate database sets of Reservoir engineering, Petrophysicist and Production was integrated with suitable programming algorithms to come up with first set of screened wells evaluating well production performances, FDI and HI trends up to over the last 30 years. The shortlisted candidates were further screened on the basis of practical approach such as gas lift optimization, production trending, OWC-GOC contacts, well integrity and well history to come up with second round of screened candidates. The final candidates were analyzed further using nodal analysis models for skin evaluation and expected gain to come up with type of formation damage and expected remedial solution. For fields A and D with a total of 210 strings each, the initial FDI and HI screening resulted in 70 and 120 strings being shortlisted, respectively. This was followed by a second round of screening with 25 and 35 strings being further shortlisted as stimulation candidates, respectively. Nodal analysis models indicated presence of high skin in 90% of the selected wells indicating a very good efficiency and function-test of the workflow. In addition to selection of the candidates, the identification of formation damage type was compiled on an asset-wise basis rather than field basis which helped in more efficient planning of remedial treatments using a multiple well campaign approach to optimize huge amount of cost. The entire screening process was done in one month which was earlier a herculean task of almost one year and much more man-hours. With effective manual testing of the workflow in two major fields, workflow was included in Integrated Operations for future automation to conduct the same task in minutes rather than months. With this digitalized unique workflow, the selection of under-performing wells due to formation damage is now a one click exercise and a dynamic data. This workflow can be easily operated by any engineer to increase their operational efficiency for flow assurance issues saving tons of cost and time.
地层损伤候选筛选工作流程的数字化提高了流程效率
为了减少识别表现不佳井的时间和精力,开发了一套基质增产候选筛选工作流程。该工作流程最初在少数油田进行了人工测试,随后将其纳入综合作业中,对可能存在地层损害的油井进行了自动筛选。在三个领域进行的增产候选选择分析将与实际统计数据一起显示。这项工作的主要目的是将不良候选井的选择数字化,而不是手动查看每口井的性能。将地层损害指标(FDI)与地层异质性指数(HI)相结合,筛选候选地层。油藏工程、岩石物理学家和生产的独立数据库集与合适的编程算法相结合,得出第一组筛选井,评估井在过去30年的生产动态、FDI和HI趋势。根据实际方法,如气举优化、生产趋势、原油与原油接触面、井完整性和井历史等,对入围候选井进行进一步筛选,以得出第二轮筛选的候选井。最后,利用节点分析模型进行表皮评估和预期收益分析,得出地层损伤类型和预期补救方案。对于A和D油田,各有210个管柱,最初的FDI和HI筛选分别导致70个和120个管柱入围。接下来是第二轮筛选,分别有25个和35个管柱被进一步列入增产候选名单。节点分析模型表明,90%的选定井存在高表皮,这表明工作流程具有非常好的效率和功能测试。除了选择候选井外,地层损害类型的识别是根据资产而不是现场进行的,这有助于更有效地规划使用多井作业方法的补救措施,以优化巨额成本。整个筛选过程在一个月内完成,而在之前,这是一项耗时近一年的艰巨任务,需要花费更多的人力。通过在两个主要领域对工作流进行有效的手动测试,工作流被包含在集成操作中,以便将来自动化地在几分钟内而不是几个月内执行相同的任务。有了这种独特的数字化工作流程,选择由于地层损害而表现不佳的井现在只需一键操作和动态数据。任何工程师都可以轻松操作此工作流程,以提高流程保证问题的操作效率,节省大量成本和时间。
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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