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Abstract Laboratory studies of unconventional reservoirs are faced with considerably more challenges than those of conventional reservoirs. The assessment of Enhanced Oil Recovery potential in unconventional reservoirs (UCR EOR) in particular needs to address the characterization of static and dynamic properties given the tightness of the rocks, available sample size and simulation of EOR under elevated pressure and temperature conditions. This paper summarizes a laboratory study designed and performed for a potential EOR pilot utilizing cyclic gas injection (Huff-n-Puff) in the Sooner Trend Anadarko Canadian Kingfisher (STACK) shale play in Oklahoma. The lab study focuses on characterizing the rock-fluid interactions as well as upscaling key parameters for the field-scale modeling and simulation. A systematic approach was followed in the design of a laboratory program specific to the characteristics of rock/fluid interaction and the proposed injection scheme of a cyclic gas injection pilot. Digital Core Analysis (DCA) incorporating micro CT, SEM and FIB-SEM analyses were performed in order to determine basic petrophysical properties at micro scale, with capillary pressure and relative permeability curves simulated digitally. Porosity and relative permeability end points were also measured on preserved STACK core plugs. Minimum miscibility pressure (MMP) measurements of field separator gas and STACK crude oil was performed with a rising bubble apparatus (RBA). Finally, a huff-n-puff experiment was designed and performed within a custom pressure cell to study the recovery efficiency at the existing core sample scale. Digital Core Analysis (DCA) has been shown to reliably produce petrophysical properties for tight STACK cores. Laboratory miscibility pressure measurements were conducted at reservoir conditions (4,500 psi and 183 °F) using field crude samples and the associated gas composition. Seven injection/production cycles were applied to a re-saturated standard core plug with oil production observed and measured in the effluent. Cyclic injection continued until no further oil could be visually observed in the effluent. A customized 2-stage drawdown was incorporated to provide input for the recovery process. The total recovery after seven cycles reached 82 %OOIP. This work provides the first rock and fluid analysis integrating digital and traditional approaches for assessment of EOR potential in unconventional reservoirs such as those found in the STACK. This systematic approach presents properly designed and executed laboratory experiments without leaving out key formation and fluid variables. This workflow can be applied in similar UCR EOR studies to lay a solid foundation for appraising UCR EOR potential and providing reliable inputs for upscaling to the field level studies.
- Geology > Mineral (0.69)
- Geology > Petroleum Play Type > Unconventional Play (0.34)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Thermal methods (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Gas-injection methods (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Core analysis (1.00)
Abstract Given limited CO2 supply, operational constraints, and pattern specific reservoir performance, WAG schedule can be customized such that NPV or other metrics are optimized. Depending on the WAG schedule, recovery can fluctuate between 5–15% at the pattern scale due to reservoir heterogeneity causing variations in sweep efficiency. An analytical method was developed to optimize WAG schedules that couples traditional reservoir modeling and simulation with machine learning, enabling the discovery of optimal WAG schedules that increase recovery at the pattern level. A history-matched reservoir model of Chaparral Energy's Farnsworth Field, Ochiltree County, TX was sampled intelligently to perform predictive reservoir flow simulations and artificially build an intelligent reservoir model that samples a broad range of possible WAG scenarios for optimization. The intelligent model generates the next "best" sample to investigate in the numerical simulator and converges on the optima, quickly reducing the number of runs investigated. Results in this paper demonstrate that there can be significant improvements in net present value as well as net utilization rates of CO2 using this analytical technique. The WAG design generated by the intelligent reservoir model should be deployed in the field in early 2016 for validation. It is intended that the intelligent reservoir model will be updated on a regular basis as injection and production data is obtained. This effort represents the beginning of a paradigm shift in the application of modeling and simulation tools for significant improvements in field production operations.
- North America > United States > Texas > Ochiltree County (0.24)
- North America > United States > Texas > Jones County (0.24)
- North America > United States > Texas > Fort Worth Basin > Farnsworth Field (0.99)
- North America > United States > Texas > Anadarko Basin (0.89)
- North America > United States > Oklahoma > Anadarko Basin (0.89)
- (2 more...)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Miscible methods (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Chemical flooding methods (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (0.86)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (0.68)