Summary
Model-based dynamic optimization of oil production has a significant
potential to improve economic life-cycle performance, as has been shown in
various studies. However, within these studies, short-term operational
objectives are generally neglected. As a result, the optimized injection and
production rates often result in a considerable decrease in short-term
production performance. In reality, however, it is often these short-term
objectives that dictate the course of the operational strategy. Incorporating
short-term goals into the life-cycle optimization problem, therefore, is an
essential step in model-based life-cycle optimization. We propose a
hierarchical optimization structure with multiple objectives. Within this
framework, the life-cycle performance in terms of net present value (NPV)
serves as the primary objective and short-term operational performance is the
secondary objective, such that optimality of the primary objective constrains
the secondary optimization problem. This requires that optimality of the
primary objective does not fix all degrees of freedom (DOF) of the decision
variable space. Fortunately, the life-cycle optimization problem is generally
ill-posed and contains many more decision variables than necessary. We present
a method that identifies the redundant DOF in the life-cycle optimization
problem, which can subsequently be used in the secondary optimization problem.
In our study, we used a 3D reservoir in a fluvial depositional environment with
a production life of 7 years. The primary objective is undiscounted NPV, while
the secondary objective is aimed at maximizing short-term production. The
optimal life-cycle waterflooding strategy that includes short-term performance
is compared to the optimal strategy that disregards short-term performance. The
experiment shows a very large increase in short-term production, boosting
first-year production by a factor of 2, without significantly compromising
optimality of the primary objective, showing a slight drop in NPV of only
?0.3%. Our method to determine the redundant DOF in the primary objective
function relies on the computation of the Hessian matrix of the objective
function with respect to the control variables. Although theoretically
rigorous, this method is computationally infeasible for realistically sized
problems. Therefore, we also developed a second, more pragmatic, method relying
on an alternating sequence of optimizing the primary- and secondary-objective
functions. Subsequently, we demonstrated that both methods lead to nearly
identical results, which offers scope for application of hierarchical long-term
and short-term production optimization to realistically sized
flooding-optimization problems.
© 2010. Society of Petroleum Engineers
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History
- Original manuscript received:
3 November 2009
- Meeting paper published:
5 October 2009
- Revised manuscript received:
12 March 2010
- Manuscript approved:
27 April 2010
- Published online:
30 September 2010
- Version of record:
15 March 2011