结合机器学习和油藏物理技术优化注水在不影响产油量和提高碳强度的情况下减少淡水注入的现场应用

P. Sarma, J. Rafiee, F. Gutiérrez, C. Calad, Ryan Hilliard, Sebastian Plotno, E. Mamani, O. Angulo, Gabriel Quintero
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引用次数: 0

摘要

随着油气行业走上能源转型之路,来自政府监管机构、投资者和公众的压力要求企业制定清晰透明的净零排放目标,并要求其运营举措和计划支持这种转型努力。成熟油田提供了通过优化操作来提高产量的机会,同时也可以提高温室气体(GHG)排放效率。本文介绍了一种新的建模和优化技术在成熟注水环境中的应用。数据物理学是机器学习中最先进的技术和油藏模拟器中相同的基础物理学的融合。这些模型可以像机器学习模型一样高效地创建,可以整合各种数据,并且可以比全尺寸模拟模型更快地进行评估,并且由于它们包含与模拟器相似的底层物理,因此它们具有良好的长期预测能力,甚至可以在没有任何历史数据的情况下用于预测新井的性能。该技术被应用于阿根廷Neuquen盆地的一个成熟油田,有效地减少了注入油藏的水量,同时没有对产量产生负面影响。此外,利用一种新的碳强度(CI)建模工具对优化前后的排放强度进行了比较,结果表明,在一个单一决策中,CI显著提高,实现了三个目标:1)获得显著的注水减少,并相应影响注水和水处理成本;2)相对于油田最初的产量下降,维持产量,提高收入;3)提高温室气体排放强度,从而获得长期的环境效益。本文更多地讨论技术的实现,而不是技术本身,假设不熟悉数据物理和碳强度工具的读者将参考参考资料部分来熟悉这些工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing a Waterflood Using a Combination of Machine Learning and Reservoir Physics. A Field Application for Reducing Fresh Water Injection with no Impact on Oil Production and Improved Carbon Intensity
As the oil and gas industry embarks on the path to energy transition, pressure from government regulators, investors, and the public in general demand that companies have clear and transparent net-zero goals and that their operational initiatives and plans support such transition efforts. Mature fields present an opportunity to increase production through operational optimization, which at the same time, can also lead to greenhouse gas (GHG) emissions efficiency. This paper presents the application of a novel modeling and optimization technique in a mature waterflood environment. Data Physics is the amalgamation of the state-of-the-art in machine learning and the same underlying physics present in reservoir simulators. These models can be created as efficiently as machine learning models, integrate all kinds of data, and can be evaluated orders of magnitude faster than full scale simulation models, and since they include similar underlying physics as simulators, they have good long term predictive capacity and can even be used to predict performance of new wells without any historical data. The technology was applied to a mature field in the Neuquen basin in Argentina to effectively reduce the amount of water injected into the reservoir with no negative impact on the production. Additionally, a new Carbon Intensity (CI) modeling tool was used to compare the emissions intensity before and after optimization showing a significant improvement in CI achieving three objectives in one single decision: 1) obtain significant water injection reduction with its corresponding impact in injection and water treatment costs; 2) maintaining production compared to the initial decline of the field, improving the top line; and 3) improving the GHG emissions intensity hence the long term benefit to the environment. The paper deals more with the implementation of the technologies than the technologies themselves, assuming that readers unfamiliar with both Data Physics and Carbon Intensity tools will refer to the references section to gain familiarity with these.
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