Embracing the Digital and Artificial Intelligence Revolution for Reservoir Management - Intelligent Integrated Subsurface Modelling IISM
- Arwa Ahmed Mawlad (ADNOC Onshore) | Richard Mohand (ADNOC) | Praveen Agnihotri (ADNOC Onshore) | Setiyo Pamungkas (ADNOC) | Osemoahu Omobude (ADNOC) | Hussein Mustapha (Schlumberger) | Steve Freeman (Schlumberger) | Kassem Ghorayeb (American University of Beirut) | Ali Razouki (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Exhibition & Conference, 11-14 November, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Intelligent Integrated Subsurface Modelling, evergreen subsurface models, AI subsurface workflows, managing Hydrocarbon resources, oil and gas 4.0, digital revolution
- 15 in the last 30 days
- 221 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
Challenges associated with volatile oil and gas prices and an enhanced emphasis on a cleaner energy world are pushing the oil and gas industry to re-consider its fundamental existing business-models and establish a long-term, more sustainable vision for the future. That vision needs to be more competitive, innovative, sustainable and profitable. To move along that path the oil and gas industry must proactively embrace the 4th Industrial Revolution (oil and gas 4.0) across every part of its business. This will help to overcome time constraints in the understanding and utilization of the terabytes of data that have been and are continuously being produced. There is a clear need to streamline and enhance the critical decision-making processes to deliver on key value drivers, reducing the cost per barrel, enabling greater efficiencies, enhanced sustainability and more predictable production.
Latest advances in software and hardware technologies enabled by virtually unlimited cloud compute and artificial intelligence (AI) capabilities are used to integrate the different petro-technical disciplines that feed into massive reservoir management programs. The presented work in this paper is the foundation of a future ADNOC digital reservoir management system that can power the business for the next several decades. In order to achieve that goal, we are integrating next generation data management systems, reservoir modeling workflows and AI assisted interpretation systems across all domains through the Intelligent Integrated Subsurface Modelling (IISM) program. The IISM is a multi-stage program, aimed at establishing a synergy between all domains including drilling, petrophysics, geology, geophysics, fluid modeling and reservoir engineering. A continuous feedback loop helps identify and deliver optimum solutions across the entire reservoir characterization and management workflow. The intent is to dramatically reduce the turnaround time, improve accuracy and understanding of the reservoir for better and more timely reservoir management decisions. This would ultimately make the management of the resources more efficient, agile and sustainable.
Data-driven machine learning (ML) workflows are currently being built across numerous petro-technical domains to enable quicker data processing, interpretation and insights from both structured and unstructured data. Automated quality controls and cross domain integration are integral to the system. This would ensure a better performance and deliver improvements in safety, efficiency and economics. This paper highlights how applying artificial intelligence, automation and cloud computing to complex reservoir management processes can transform a traditionally slow and disconnected set of processes into a near real time, fully integrated, workflow that can optimize efficiency, safety, performance and drive long term sustainability of the resource.
|File Size||958 KB||Number of Pages||11|
Arief, I. H., Forest, T., & Meisingset, K. K. (2017, April 5). Estimating Fluid Properties Using Surrogate Models and Fluid Database. Society of Petroleum Engineers. doi:10.2118/185937-MS.
Bravo, C. E., Saputelli, L., Rivas, F., Pérez, A. G., Nickolaou, M., Zangl, G., De Guzmán, N., Mohaghegh, S.D.Nunez, G. (2014, August 1). State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey. Society of Petroleum Engineers. doi:10.2118/150314-PA.
Cao, Q., Banerjee, R., Gupta, S., Li, J., Zhou, W., & Jeyachandra, B. (2016, June 1). Data Driven Production Forecasting Using Machine Learning. Society of Petroleum Engineers. doi:10.2118/180984-MS.
Gupta, S., Sharma, A., & Abubakar, A. (2018, September 24). Artificial Intelligence–Driven Asset Optimizer. Society of Petroleum Engineers. doi:10.2118/191551-MS.
Alpak, F. O., Araya-Polo, M., & Onyeagoro, K. (2019, August 1). Simplified Dynamic Modeling of Faulted Turbidite Reservoirs: A Deep-Learning Approach to Recovery-Factor Forecasting for Exploration. Society of Petroleum Engineers. doi:10.2118/197053-PA.
Guevara, J., Zadrozny, B., Buoro, A., Lu, L., Tolle, J., Limbeck, J. W., & Hohl, D. (2019, July 1). A Machine-Learning Methodology Using Domain-Knowledge Constraints for Well-Data Integration and Well-Production Prediction. Society of Petroleum Engineers. doi:10.2118/195690-PA.
Lee, K., Lim, J., Yoon, D., & Jung, H. (2019, July 1). Prediction of Shale-Gas Production at Duvernay Formation Using Deep- Learning Algorithm. Society of Petroleum Engineers. doi:10.2118/195698-PA.
Noshi, C. I., & Schubert, J.J. (2019, April 26). Application of Data Science and Machine Learning Algorithms for ROP Prediction: Turning Data into Knowledge. Offshore Technology Conference. doi:10.4043/29288-MS.
Schuetter, J., Mishra, S., Zhong, M., & LaFollette, R. (2018, August 1). A Data-Analytics Tutorial: Building Predictive Models for Oil Production in an Unconventional Shale Reservoir. Society of Petroleum Engineers. doi:10.2118/189969-PA.
Vallabhaneni, S., Saraf, R., & Priyadarshy, S. (2019, June 3). Machine-Learning-Based Petrophysical Property Modeling. Society of Petroleum Engineers. doi:10.2118/195436-MS.
Wang, L., & Oliver, D. S. (2019, August 1). Efficient Optimization of Well-Drilling Sequence with Learned Heuristics. Society of Petroleum Engineers. doi:10.2118/195640-PA.
Xu, C., Misra, S., Srinivasan, P., & Ma, S. (2019, March 15). When Petrophysics Meets Big Data: What can Machine Do? Society of Petroleum Engineers. doi:10.2118/195068-MS.
Zhang, Y., He, J., Yang, C., Xie, J., Fitzmorris, R., & Wen, X.-H. (2018, August 1). A Physics-Based Data-Driven Model for History Matching, Prediction, and Characterization of Unconventional Reservoirs. Society of Petroleum Engineers. doi:10.2118/191126-PA.