Video: A Large-Scale Study for a Multi-Basin Machine Learning Model Predicting Horizontal Well Production
- Salma Amr (Raisa Energy) | Hadeer El Ashhab (Raisa Energy) | Motaz El-Saban (Raisa Energy) | Paul Schietinger (Raisa Energy) | Curtis Caile (Raisa Energy) | Ayman Kaheel (Raisa Energy) | Luis Rodriguez (Raisa Energy)
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- Society of Petroleum Engineers
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- 2018. Copyright is retained by the author. This presentation is distributed by SPE with the permission of the author. Contact the author for permission to use material from this video.
- 6.1 HSSE & Social Responsibility Management, 1.6.6 Directional Drilling, 1.6 Drilling Operations, 6 Health, Safety, Security, Environment and Social Responsibility, 7.6.6 Artificial Intelligence, 6.1.5 Human Resources, Competence and Training, 4.3.4 Scale
- Prodcution Forecasting, PDP, PUD, artificial intelligence, machine learning
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This paper proposes a set of data driven models that use state of the art machine learning techniques and algorithms to predict monthly production of unconventional horizontal wells. The developed models are intended to forecast both producing locations (PLs) and non-producing well locations (NPLs). Furthermore, results of extensive experiments are presented that were conducted using different methodologies and features combinations. Results are measured against conventional Arps's decline curve analysis showing significant boost in prediction accuracy for both NPLs and PLs. The most accurate model outperforms Arps's-based estimates by almost 23% for NPLs and 36% for PLs. Results also show that using data from multiple basins in training models for another basin yields gains in accuracy, especially for basins with relatively small data.