Unlocking Completion Design Optimization Using an Augmented AI Approach
- Zheren Ma (Quantum Reservoir Impact LLC) | Ehsan Davani (Quantum Reservoir Impact LLC) | Xiaodan Ma (Quantum Reservoir Impact LLC) | Hanna Lee (Quantum Reservoir Impact LLC) | Izzet Arslan (Quantum Reservoir Impact LLC) | Xiang Zhai (Quantum Reservoir Impact LLC) | Hamed Darabi (Quantum Reservoir Impact LLC) | David Castineira (Quantum Reservoir Impact LLC)
- Document ID
- Society of Petroleum Engineers
- SPE Canada Unconventional Resources Conference, 29 September - 2 October, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 7.2.1 Risk, Uncertainty and Risk Assessment, 2.3 Completion Monitoring Systems/Intelligent Wells, 7 Management and Information, 2 Well completion, 7.6.1 Knowledge Management, 2.1 Completion Selection and Design, 7.6.6 Artificial Intelligence, 2.3.6 Tubular Optimisation, 7.2 Risk Management and Decision-Making, 2.1 Completion Selection and Design
- Artificial Intelligence, Optimization
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- 81 since 2007
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An Augmented AI approach has been developed to optimize completion design parameters and access the full potential of unconventional assets by leveraging big data sculpting, domain-induced feature engineering, and robust and explainable machine learning models with quantified uncertainty. This method unlocks the full potential of a well using completion design parameters optimization that considers all the factors that impact well performance, geological characteristics, well trajectory, spacing, etc.
By leveraging basin-level knowledge captured by big data sculpting with the use of uncertainty quantification, Augmented AI can provide quick and science-based answers for completion optimization, and also assess the full potential of an asset in unconventional reservoirs.
By leveraging computer vision and natural language processing techniques, unstructured data from various sources were deciphered, combined and organized into a structured database. Imputation techniques were used to fill the gaps of missing data. With the Augmented AI approach, the median accuracies of IP and EUR predictions for new drills is around 90%, which often outperforms industry-standard type curving methods. With the explainable machine learning (ML) model, the direct impact of completion design parameters on well performance is deconvoluted among other parameters, such as engineering and geological attributes. The prediction also comes with an 80% confidence interval to quantify the prediction uncertainties, which allows for better risk management and confident business decision making. With the ML model and given economic inputs and metrics, many sensitivity analyses are performed to evaluate optimized completion design parameters. The proposed Augmented AI approach has been deployed to Eagle Ford wells.
|File Size||820 KB||Number of Pages||12|
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