现实世界中的生产优化:无人平台上天然气生产监控、干预计划和决策的案例研究

Antoni Kourakis, A. Nagel
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引用次数: 0

摘要

在复杂的情况下,生产优化往往偏离计划和现实。理想情况下,利用一组已知因素来确定生产井的最佳作业方案。然而,在现实中,许多因素仍然是未知的,在那些未知的因素中,许多因素只是在不确定的范围内才知道。不确定性持续存在;无论是仪器故障、计量错误还是混合完井中多个储层的产量调整。此外,井的优化总是受到经济和操作限制的制约。这些限制因素限制了油井监测活动和复合不确定性。当Otway盆地一个小型无人海上平台上的一口大井径斜度枯竭驱动气井开始出现意外的产量下降时,这些挑战结合在一起。对混合储层完井的大井眼气井进行了诊断,认为该井具有液体加载特征。干预措施的目标是隔离可能的地层水源,恢复无水气的生产。需要使用生产日志来确认水的存在,并从三组已完成的井段中确定水的来源,每组井段都使用封隔器和机械滑动侧门分开。在修井过程中进行风险评估后,作业者决定中止作业,不隔离水源,而是继续循环生产,以最大限度地提高天然气采收率。引入未知的生产测井,发现三个已完成的储层段中有一个被封闭的滑动侧门隔离,之前认为该侧门是打开的,提供了增加生产的机会。后续的干预措施保留了隔离水源的目标,并附加了进入隔离油藏层段的目标。在作业前进行了详细的规划和不确定性分析,主要风险是目标段内可能存在的压力范围以及进入目标段后立即产生的井筒过流。虽然第二次修井出现了机械故障,但随后采取的务实决策最终取得了成功的生产结果。隔离了水源,并通过对目标层段的爆破缝射孔获得了增量速率和储量。本文介绍了用于诊断产量下降并优化该井产量的工作流程、工具和干预措施。这是一个生产监控的案例研究,利用有限的数据、决策树分析和应急计划,对具有重大操作限制的干预措施进行了研究。它包括在强交叉流井筒环境中使用钢丝绳生产测井、油管塞和电缆射孔。对于在恶劣环境、混合完井或生产数据受损的井中使用无人平台的操作人员来说,这篇论文将会很有意义。通过整合所展示的知识,工程师们将在解决类似的不确定性和他们自己具有挑战性的生产优化活动时取得先机。
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Production Optimisation in the Real World: A Case Study in Gas Production Surveillance, Intervention Planning and Decision Making on an Unmanned Platform
In complex situations production optimisation often differs from plan to reality. Ideally a set of known factors are used to determine the optimal course of action for a production well. However, in reality many factors remain unknown and of those that are, many are only known within a range of uncertainty. Uncertainty is persistent; whether in the form of failed instrumentation, erroneous metering or production reconciliation to multiple reservoirs in commingled completions. Further, well optimisation is always governed by economics and operational constraints. Such constraints limit well surveillance activities and compound uncertainty. These challenges united when a large bore deviated depletion drive gas well on a small unmanned offshore platform in the Otway Basin began to exhibit unexpected production decline. The large bore gas well with commingled reservoir completions was diagnosed as exhibiting liquid loading behavior. The intervention objective was to isolate the probable formation water source and restore water free gas production. A production log was required to confirm water was present and identify the source from three groups of completed intervals, each separated from one another using packers and mechanical sliding side doors. After risk assessments conducted during the intervention an active decision was taken to abort the work and not isolate the water source in favour of continuing cycled production to maximise gas recovery. Introducing an unknown, production logging identified that one of the three completed reservoir intervals was isolated by a closed sliding side door, previously believed to be open, presenting an incremental production opportunity. A follow-up intervention retained an objective of isolating the water source, with the additional objective of accessing the isolated reservoir interval. Detailed planning and uncertainty analysis was conducted ahead of the campaign with a key risk being the range of pressure possibly present within this target interval and the resultant wellbore cross-flow immediately after accessing it. Whilst the second intervention experienced mechanical failure, the ensuing pragmatic decisions that were taken "on the fly" ultimately resulted in a successful production outcome. The water source was isolated and incremental rate and reserves were achieved through perforation of blast joints opposite the target interval. This paper presents the workflows, tools & interventions used to diagnose production decline and optimise production from this challenging well. It is a case study in production surveillance utilising limited data, decision tree analysis and contingency planning for interventions performed with significant operational limitations. It includes the use of slickline production logging, tubing plugs, and electric wireline perforating in a strong cross-flow wellbore environment. This paper will be of interest to operators of unmanned platforms in hostile environments, commingled completions or wells with compromised production data. By integrating the learnings presented, engineers will get a head start when tackling similar uncertainties with their own challenging production optimisation activities.
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