数字化转型U-WISE软件技术

Nasser M. Al-Hajri, S. Gilani, Mohammed C. Saloojee, Mohammed A. Atwi, Akram R. Barghouti
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摘要

数字化转型的上游油井完整性监测卓越(U-WISE)软件技术应运而生。U-WISE数据驱动流程提供基于风险的财务优化模型,灵感来自工业4.0的大数据分析。U-WISE软件技术的目标是持续优化与进行井完整性调查频率相关的财务资源。新技术平衡了计算井完整性风险与整个完整性监测项目的相关财务影响。U-WISE软件技术的应用为油田运营商的井完整性监控产品组合带来了范式转变。U-WISE软件技术的开发始于对数千个历史井完整性数据的分析。U-WISE软件技术中的大数据分析优化方案最初是基于来自不同井和流体类型的38104个案例研究而开发的。U-WISE软件技术运行基于人工智能的查询,收集与完整性调查相关的健康和缺陷数据。通过记录运行状况和缺陷时间事件,对数据进行分析。然后,通过统计方案对数据进行运行,得到健康概率、缺陷概率和总体失效概率。该模型的产品是特定于调查和井类型的失效风险百分比,代表所有条件。故障风险百分比用于运行监视优化方案,并量化此类优化的财务影响。U-WISE软件技术继续根据新的现场采集数据对实时数据进行优化。采用U-WISE软件技术的综合优化结果每年可节省大量成本。这种优化还有其他切实的好处,即通过减少油井完整性调查的关井时间,利用更多的原油进行生产。由U-WISE软件技术提供的基于IR 4.0优化的改进井完整性频率,可作为精通和负责财务的资产完整性监测的行业基准。随着基于IR 4.0的U-WISE软件和相应的操作手册所制定的程序、技术和完整性标准的更新,油井完整性的可靠性得到了极大的提高。基于优化的油井监控关井时间,现在的油井生产更加高效和可持续。现在,通过新的U-WISE软件技术,可以对井的安全性和完整性进行量化和平衡,并将其保持在所需的可容忍风险水平。通过减少和消除井底或地面泄漏以及由此产生的含水层和空气污染等不良井完整性事件,井的完整性完整性加强了环境保护。根据最佳油田实践进行了油井完整性调查。有了丰富的历史数据,就可以谨慎地评估井的完整性风险,并平衡这些风险与进行调查的成本,以实现最优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Digitally Transformative U-WISE Software Technology
The digitally transformative Upstream Well Integrity Surveillance Excellence (U-WISE) software technology was built. U-WISE data driven processes provide a risk-based financial optimization model inspired by IR 4.0's big data analytics. The objective of U-WISE software technology is to continuously optimize financial resources related to the frequency of conducting well integrity surveys. The new technology balances the calculated well integrity risk with the associated financial impact for the entire integrity surveillance program. U-WISE software technology application constitute a paradigm shift in the well integrity surveillance portfolio of oilfield operators. The U-WISE software technology development was started by analyzing thousands of historical well integrity data. The big data analytics optimization schemes embedded in U-WISE software technology was initially developed based on a total of 38,104 case studies from different well and fluid types. U-WISE software technology runs artificial-intelligence based queries to collect health and defect data pertaining to integrity surveys. The data were conditioned for the analytics by recording health and defect time events. Then, the data were run through statistical schemes to obtain probability of health, defect, and overall probability of failure. The models’ product is a risk of failure percentage specific to a survey and well type, representative of all conditions. The risk of failure percentages are used to run surveillance optimization scenarios and quantify the financial impacts from such optimization. U-WISE software technology continues to perform the optimization on real-time data based on new field collected data. The overall combined optimization results from applying the U-WISE software technology are substantial annual savings. There are other tangible benefits to this optimization in availing more crude for production by reducing well shut-in time for integrity surveys. The revamped well integrity frequencies based on the IR 4.0 optimization furnished by U-WISE software technology serves as an industry benchmark for proficient and fiscally-responsible asset integrity monitoring. The reliability of wells integrity is now greatly improved with the updated procedures, technologies, and integrity standards set forth by the IR 4.0 based U-WISE software and resulting instruction manual. Wells’ production is now more efficient and sustained based on the optimized well surveillance shut-in times. Safety and integrity of the wells are now quantified and balanced via the new U-WISE software technology and kept at the required tolerable risk levels. Wells intact integrity strengthens environmental protection by reducing and eliminating undesirable well integrity events such as well downhole or surface leaks and the resulting aquifer and air contamination. Well integrity surveys were performed based on best oilfield practices. With the abundance of historical data, it became possible to prudently evaluate the well integrity risks and balance these risks with costs of conducting the surveys to achieve optimization.
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