Using the Results from Automated Petroleum Engineering Calculations to Accelerate Decision Workflows

Author:

Fair Chris1,Flores Ricardo1,Hakim Bilal1,Nande Soumitra1

Affiliation:

1. Oilfield Data Services, Inc.

Abstract

Abstract With the advent and common usage of high resolution tree gauges, downhole permanent pressure/temperature gauges, and continuous flow measurement, many of the common petroleum engineering calculations and analysis tools for production systems can and have been automated. These include: a) Well Test Analysis, b) Well Productivity, c) Static and Flowing Material Balances and Energy Balances. In addition, the use of rigorous wellbore thermal and phase behavior models allows the two gauges (wellhead and downhole) to be used as a giant differential pressure meter – making it possible to calculate gas rates and water cuts independently of the flow measurements, as well as mid-completion bottomhole pressure (BHP). The purpose of this paper is to present the basic physics involved in these calculations/analyses, as well as to discuss the implications that these processes will have on instrumentation selection and on engineering work flows. The crux of the argument for these types of automated systems is that it is much easier for an engineer to check the results than to spend his/her entire day just looking for useful information in the database, then analyzing it (or getting someone else to analyze it). Furthermore, being able to see the “big picture” – seeing what skin, perm, productivity and apparent hydrocarbon reservoir volumes are now and how they have changed with time, allows engineers to make quicker, more accurate decisions. The use of automated analysis also reduces bias – the computer doesn't care what the answer is. This paper will also include several case studies for both oil and gas wells.

Publisher

SPE

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Engineered flowback and reservoir evaluation for unconventional wells;The APPEA Journal;2023-05-11

2. Lessons Learned in a Decade of Automated Reservoir and Production Engineering Surveillance;Day 2 Tue, October 04, 2022;2022-09-26

3. Structured Noise Detection;Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining;2019-07-25

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