A Quasi Exact Solution Approach for Scheduling Enhanced Coal Bed Methane Production Through CO2 Injection

The purpose of this research is to provide an easily accessible, effective and efficient solution method for the profit-maximization scheduling problem for CO2-enhanced coal bed methane (ECBM) production. ECBM has been a mature technology which can further extend the value of the unminable coal mines. The total profit of the production is based on the revenue generated by the methane produced as well as CO2 credits earned from the carbon market, and the production cost of the methane along with the CO2 operational cost. This scheduling problem is formulated as a nonlinear optimization program which includes bilinear terms in the constraints due to the modeling of physical reactions. In this paper, we use a quasi exact solution technique to solve the problem, where bilinear terms are eliminated by a discretization and linearization procedure. The original problem is then transformed to a mixed integer linear program with binary variables representing the discretization of the continuous fractional variables involved in those nonlinear constraints. This approach is yielding an equivalent program when enough binary variables are used. Numerical experiments show that this method can be easily implemented and yield almost exact solutions in reasonable computational times.

[1]  Panos M. Pardalos,et al.  State of the Art in Global Optimization , 1996 .

[2]  P. Pardalos,et al.  State of the art in global optimization: computational methods and applications , 1996 .

[3]  M. Shahidehpour,et al.  Security-Constrained Unit Commitment With Volatile Wind Power Generation , 2008, IEEE Transactions on Power Systems.

[4]  Christopher Nichols,et al.  Storing CO2 with enhanced oil recovery , 2009 .

[5]  E. Robertson Enhanced Coal Bed Methane Recovery and CO2 Sequestration in the Powder River Basin , 2010 .

[6]  Abbas Firoozabadi,et al.  Prospects for subsurface CO2 sequestration , 2010 .

[7]  Panos M. Pardalos,et al.  Optimization Models in The Natural Gas Industry , 2010 .

[8]  Vladimir Alvarado,et al.  Enhanced Oil Recovery: An Update Review , 2010 .

[9]  Adela Pagès-Bernaus,et al.  Value chains for carbon storage and enhanced oil recovery: optimal investment under uncertainty , 2010 .

[10]  Oleg A. Prokopyev,et al.  Optimization of minimum set of protein–DNA interactions: a quasi exact solution with minimum over-fitting , 2009, Bioinform..

[11]  Panos M. Pardalos,et al.  Stochastic and Risk Management Models and Solution Algorithm for Natural Gas Transmission Network Expansion and LNG Terminal Location Planning , 2010, J. Optim. Theory Appl..

[12]  Panos M. Pardalos,et al.  Handbook of CO₂ in Power Systems , 2012 .

[13]  Panos M. Pardalos,et al.  A decomposition approach to the two-stage stochastic unit commitment problem , 2012, Annals of Operations Research.

[14]  Steffen Rebennack,et al.  Techno-economic analysis and optimization models for carbon capture and storage: a survey , 2013 .

[15]  Yuping Huang,et al.  Optimal scheduling for enhanced coal bed methane production through CO2 injection , 2014 .