Field Surveillance and AI based Steam Allocation Optimization Workflow for Mature Brownfield Steam Floods
- Anjani Kumar (Computer Modelling Group Ltd) | Alex Novlesky (Computer Modelling Group Ltd) | Erykah Bityutsky (Computer Modelling Group Ltd) | Paul Koci (Consultant for Occidental Petroleum Corporation) | Jeff Wightman (Occidental Petroleum Corporation)
- Document ID
- Society of Petroleum Engineers
- SPE International Heavy Oil Conference and Exhibition, 10-12 December, Kuwait City, Kuwait
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 0.2 Wellbore Design, 1.6 Drilling Operations, 3 Production and Well Operations, 5.4 Improved and Enhanced Recovery, 7.6.6 Artificial Intelligence, 4.1.5 Processing Equipment, 5 Reservoir Desciption & Dynamics, 1.6.6 Directional Drilling, 5.4.6 Thermal Methods, 5.5 Reservoir Simulation, 3 Production and Well Operations, 5.4 Improved and Enhanced Recovery
- Artificial Intelligence, Field Surveillance, Steam Optimization, Steam Allocation, Brownfield steam flood
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- 149 since 2007
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|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
Heavy oil reservoirs often require thermal enhanced oil recovery (EOR) processes to improve the mobility of the highly viscous oil. When working with steam flooding operations, finding the optimal steam injection rates is very important given the high cost of steam generation and the current low oil price environment. Steam injection and allocation then becomes an exercise of optimizing cost, improving productivity and net present value (NPV). As the field matures, producers are faced with declining oil rates and increasing steam oil ratios (SOR). Operators must work to reduce injection rates on declining groups of wells to maintain a low SOR and free up capacity for newer, more productive groups of wells. Operators also need a strong surveillance program to monitor field operational parameters like SOR, remaining Oil-in-Place (OIP) distribution in the reservoir, steam breakthrough in the producers, temperature surveys in observation wells etc. Using the surveillance data in conjunction with reservoir simulation, operators must determine a go-forward operating strategy for the steam injection process.
The proposed steam flood optimization workflow incorporates field surveillance data and numerical simulation, driven by machine learning and AI enabled Algorithms, to predict future steam flood reservoir performance and maximize NPV for the reservoir. The process intelligently determines an optimal current field level and well level injection rates, how long to inject at that rate, how fast to reduce rates on mature wells so that it can be reallocated to newly developed regions of the field. A case study has been performed on a subsection of a Middle Eastern reservoir containing eight vertical injectors and four sets of horizontal producers with laterals landed in multiple reservoir zones. Following just the steam reallocation optimization process, NPV for the section improved by 42.4% with corresponding decrease in cumulative SOR by 24%. However, if workover and alternate wellbore design is considered in the optimization process, the NPV for the section has the potential to be improved by 94.7% with a corresponding decrease in cumulative SOR by 32%. This workflow can be extended and applied to a full field steam injection project.
|File Size||3 MB||Number of Pages||20|
Dang, C., Nghiem, L., Fedutenko, E.. 2018. "Application of Artificial Intelligence for Mechanistic Modeling and Probabilistic Forecasting of Hybrid Low Salinity Chemical Flooding". Paper SPE 191474 to be presented at the 2018 SPE Annual Technical Conference and Exhibition, Dallas, TX, USA, 24-26 September 2018.