Mawlad, Arwa Ahmed (ADNOC Onshore) | Mohand, Richard (ADNOC) | Agnihotri, Praveen (ADNOC Onshore) | Pamungkas, Setiyo (ADNOC) | Omobude, Osemoahu (ADNOC) | Mustapha, Hussein (Schlumberger) | Freeman, Steve (Schlumberger) | Ghorayeb, Kassem (American University of Beirut) | Razouki, Ali (Schlumberger)
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.
An updated geological and dynamic model for a giant Middle East carbonate reservoir was constructed and history matched with the objective of creating an alternative model which is capable of replicating the reservoir production mechanisms and improving predictability, allowing optimizing the field development plan and water injection strategy. Giant Middle East carbonate fields often have long production history and exhibit high reservoir heterogeneity. It is always challenging to get a robust history matched model aligned with geological concepts and dynamic behavior understanding.
The objective of this paper is to present an improved and integrated reservoir characterization, modeling and history matching procedure for a giant Lower Cretaceous carbonate reservoir in the Middle East. The applied workflow integrates all available geological data (stratigraphy, depositional facies, and diagenesis), petrophysical data (RCA and minipermeameter data, Petrophysical Group definition, cut-off definition) and the extensive database of dynamic data (long production history, well test, RST, open-hole log saturation over more than 40 years of development drilling, and MICP). The process was initiated with the reservoir review by means of a fully integrated study that allowed having better understanding of the reservoir behavior and production mechanisms. The key heterogeneities (high permeability and intra-dense layers) which control the flow behavior were identified during this process. Geological trend maps were generated to control the distribution of high permeability and intra-dense in the model. Well test data, open-hole logs from development wells and time-lapse saturation logs from observation wells were used to calibrate the trend and permeability log data. A phenomenological model was constructed to test the main factors impacting the production mechanism as identified during the reservoir review. Multiple iterations were performed between the static and dynamic models in a way that allowed a quick and efficient work that is consistent with all disciplines assumptions.
Such continuous loop between the dynamic and geological models, with focus on the geological heterogeneities driving the dynamic reservoir behavior, has led to a more robust model capable of replicate the production mechanisms, which represents a major improvement compared to previous model in term of predictability.