Simultaneous Well Spacing and Completion Optimization Using Automated Machine Learning Approach. A Case Study of Marcellus Shale Reservoir in the North-Eastern United States
E. Fathi, Ali Takbiri-Borujeni, F. Belyadi, M. F. Adenan
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引用次数: 0
Abstract
Optimizing unconventional field development requires simultaneous optimization of well spacing and completion design. However, the conventional practice of using cross plots and sensitivity analysis via Monte Carlo simulations (MCS) for independent optimization of well spacing and completion design has proved inadequate for unconventional reservoirs. This is due to the inability of cross plots to capture non-linear cross-correlations between parameters affecting hydrocarbon production, and the computational expense and difficulty of Monte Carlo simulations. Recently, automated machine learning (AutoML) workflows have been used to tackle complex problems. However, applying AutoML workflows to engineering problems presents unique challenges, as achieving high accuracy in forecasting the physics of the problem is crucial. To address this issue, a new physics-informed AutoML workflow based on the TPOT open-source tool developed that guarantees the physical plausibility of the optimum model while minimizing human bias and uncertainty. The workflow has been implemented in a Marcellus Shale reservoir with over 1,500 wells to determine the optimal well spacing and completion design parameters for both the field and each well. The results show that using a shorter stage length (SSL) and a higher sand-to-water ratio (SWR) is preferable for this field, as it can increase cumulative gas production by up to 8%. Additionally, it is observed that fifty-percentile cumulative gas predictions are in close agreement with actual field productions.
Thematic collection:
This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at:
https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows
期刊介绍:
Petroleum Geoscience is the international journal of geoenergy and applied earth science, and is co-owned by the Geological Society of London and the European Association of Geoscientists and Engineers (EAGE).
Petroleum Geoscience transcends disciplinary boundaries and publishes a balanced mix of articles covering exploration, exploitation, appraisal, development and enhancement of sub-surface hydrocarbon resources and carbon repositories. The integration of disciplines in an applied context, whether for fluid production, carbon storage or related geoenergy applications, is a particular strength of the journal. Articles on enhancing exploration efficiency, lowering technological and environmental risk, and improving hydrocarbon recovery communicate the latest developments in sub-surface geoscience to a wide readership.
Petroleum Geoscience provides a multidisciplinary forum for those engaged in the science and technology of the rock-related sub-surface disciplines. The journal reaches some 8000 individual subscribers, and a further 1100 institutional subscriptions provide global access to readers including geologists, geophysicists, petroleum and reservoir engineers, petrophysicists and geochemists in both academia and industry. The journal aims to share knowledge of reservoir geoscience and to reflect the international nature of its development.