(373s) Enhancing Petroleum Field Profitability Via Optimisation of Production and Injection Operations

Epelle, E. I., University of Edinburgh
Gerogiorgis, D. I., University of Edinburgh

An oil and gas field requires careful operational planning and management via production optimization for increased recovery and long-term project profitability. With inevitably high environmental and financial stakes associated with the exploration and production of mature oil and gas fields, there is a strong incentive to enhance hydrocarbon recovery and production via systematic and mathematical-oriented approaches [1]. In further response to this challenge, the application of sophisticated simulation methodologies to integratedly capture the reservoir behavior, multiphase flows (in wellbores and flowlines), and gas–oil–water separation in the processing facilities is constantly increasing. Liquid loading in gas wells and artificial lift design considerations, reservoir pressure maintenance via water injection, gas–water coning during production from vertical and horizontal wells [2], pressure drop, and liquid holdup of multiphase mixtures in highly deviated flowlines are some of the specific complexities associated with this system. The effects of these constraints span several timescales over the entire time horizon [3-5].

Simulation of these prevalent subsurface and surface phenomena does not always guarantee an accurate prediction of the onset of these problems, and even more so, problem elimination and fault-free operation in an oil and gas production field. To tackle the insufficiencies and thus reduce the uncertainties of the current state-of-the-art models, it is necessary to also combine robust optimization methods with these flow simulations. This combination of simulation and optimization algorithms increases complexity due to function evaluations, lack of gradient information, model nonlinearity, non-convexity, and the presence of discrete and binary routing variables [6].

This article addresses the challenge of production optimization in a field undergoing secondary recovery by water flooding. The field operates with limited processing capacity at the surface separators, pipeline pressure constraints, and water injection constraints; an economic indicator (net present value, NPV) is used as the objective function. The formulated optimization framework adequately integrates slow-paced subsurface dynamics using reservoir simulation, and fast-paced surface dynamics using sophisticated multiphase flow simulation in the upstream facilities. Optimization of this holistic long-term model is made possible by developing accurate second-order polynomial proxy models at each time step [7].

The resulting formulation is solved as a nonlinear program using commercially available solvers. A comparative analysis is performed using MATLAB's fmincon solver and the IPOPT solver [8] for their robustness, speed, and convergence stability in solving the proposed problem. By implementing two synthetic case studies, our mathematical programming approach determines the optimal production and injection rates of all wells and further demonstrates considerable improvement to the NPV obtained by simultaneously applying the tools of streamline, reservoir, and surface facility simulation for well rate allocation. Our efforts thus coincide with software developments, which foster interoperability between proprietary system simulation suites and open-source optimization solvers paving the way for new advances.


  1. Epelle, E.I. and Gerogiorgis, D.I., 2019. Optimal Rate Allocation for Production and Injection Wells in an Oil and Gas Field for Enhanced Profitability. AIChE J., in press (DOI: 10.1002/aic.16592).
  2. Epelle, E.I. and Gerogiorgis, D.I., 2018. A CFD investigation of the effect of particle sphericity on wellbore cleaning efficiency during oil and gas drilling. Comput. Aided Chem. Eng., 43, 127-132.
  3. Gunnerud, V. and Foss, B., 2010. Oil production optimization—A piecewise linear model, solved with two decomposition strategies. Comput. Chem. Eng., 34(11), 1803-1812.
  4. Gerogiorgis, D.I., Georgiadis, M., Bowen, G., Pantelides, C.C. and Pistikopoulos, E.N., 2006. Dynamic oil and gas production optimization via explicit reservoir simulation. Comput. Aided Chem. Eng.21, 179-184.
  5. Nikolaou, M., Cullick, A.S., Saputelli, L.A., Mijares, G., Sankaran, S. and Reis, L.C., 2006, January. A consistent approach towards reservoir simulation at different time scales. Intelligent Energy Conference and Exhibition. Society of Petroleum Engineers.
  6. Silva, T.L. and Camponogara, E., 2014. A computational analysis of multidimensional piecewise-linear models with applications to oil production optimization. Eur. J. Operational Res., 232(3), 630-642.
  7. Yeten, B., Castellini, A., Guyaguler, B. and Chen, W.H., 2005, January. A comparison study on experimental design and response surface methodologies. SPE Reservoir Simulation Symposium. Society of Petroleum Engineers.
  8. Wächter, A. and Biegler, L.T., 2006. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Prog., 106(1), 25-57.