• Research Areas
• Optimal Well Placement & Control
  • Water Flood Management and Optimization (*)
  • Production Optimization Using Gradient-Free Methods
  • A Well Pattern Optimization Algorithm for Large-Scale Field Development
  • Retrospective Optimization of Well Location under Uncertainty
  • Reservoir Management Optimization based on Approximate Dynamic Programming
  • Linear Programming in Streamline Model
• Model Order Reduction
• Reservoir Inverse Modelling & Uncertainty Quantification
• Carbon Dioxide Sequestration
• Data Assimilation and Integration
• Decision Analysis under Uncertainty
• Gradient-based History Matching

Water Flood Management and Optimization (*)

D. Echeverría Ciaurri and M. Thiele

With 70% of oil production coming from fields that are 30 years or older, the optimal management of reservoir floods, and of water floods in particular, is a specially important topic. Despite many advances in reservoir simulation and management, reservoir engineers still struggle in setting optimal injection/production target rates for such fields.

Streamline-based flow simulation (see Figure 1) is generally associated with fast simulations. Therefore, they are a good proxy (surrogate) to use in any workflow that requires many simulations. An overlooked factor of streamline-based simulation is the novel data that is generated. Examples of this are the identification of injector-producer pairs and the quantification of the reservoir volumes associated with these pairs. Armed with this information it is possible to promote the more efficient injector/producer pairs and demote the less efficient ones in a manner computationally very efficient. This can lead to a more successful field management strategy.

In this project, we propose to verify how well the streamline-based heuristic flood management approach presented in Thiele and Batycky (2006) compares to a solution obtained using a more rigorously framed optimization scheme. The project will be helpful in shedding light on:

• the quality of the flood management solution given by the streamline-based method; a direct comparison of approaches should in turn reveal a number of hybrid efficient methodologies for the management of water floods (for example, a good initial guess for a gradient-based optimization algorithm might be quickly obtained by streamlines);
• delineate situations where streamlines can/cannot be used for flood management optimization;
• investigate if streamline specific information can be directly incorporated in the more rigorous flood optimization and management problem formulation, in the hope of improving convergence and solution accuracy.

Initially, the same problem presented in Thiele and Batycky (2006) will be used. This is a small but real reservoir with nine producers and eight injectors (see Figure 2) with a grid of size 80x81x20 (129,600 grid points). The goal is to improve (optimize) sweep over a period of five years by setting target rates every three months on both producers and injectors (this yields a total of 340 optimization variables). Additionally, the problem can be constrained to total available injection rate, for example. After this first test, the reservoir considered can be larger and more realistic.

The project aims at a formal study of a streamline-based optimization methodology and assessment of its use in water flood management scenarios, with several applications of this methodology in cases of practical interest.

References

Thiele, M.R., and Batycky, R.P., Using Streamline-Derived Injection Efficiencies for Improved WaterFlood Management, SPE Reservoir Evaluation & Engineering (SPEREE), Vol 9, No 2, 187-196, April 2006 (SPE84080-PA).