(752c) Well Placement Optimisation in a Heterogeneous Petroleum Reservoir with Geological Uncertainty

Huge investments are required in the development of oil and gas fields; this implies that decisions at any stage of the development process must be aided by sound and proven mathematical-oriented methods, because the use of intuitive engineering judgement alone cannot guarantee sustainable profitability over long periods [1]. This presents an ever-increasing motivation to apply optimisation techniques for the enhancement of petroleum production from both planning and operational/scheduling perspectives. The planning aspect typically involves activities such determining the number and locations of wells, drilling the wells and infrastructure installation. On the other hand, the operational/scheduling aspect comprises of well rate allocation, flow scheduling and routing problems [2]. Incorporating optimisation at the planning stage is a pre-emptive step that mitigates subsequent operational difficulties over the field’s lifetime. However, embedding optimisation in a planification problem can be more challenging compared to production and injection scheduling optimisation in an oil and gas field. Although many studies [3, 4] have addressed the operational/scheduling aspect of the field development problem, this work is premised on the proposition that, jointly optimising the planning and scheduling problem better enhances field operability.

The uncertainty of subsurface geology is another constraint to take into consideration when optimising field productivity [5]. To capture the uncertainties in the subsurface geological (reservoir) model, geostatistical realisations of the most accurate reservoir representation are obtained using available information (permeabilities, porosities, fluid saturation) [6]. These uncertainties in the model are often reflected in the diverse outcomes of well productivities and ultimately the field’s Net Present Value (the objective function) [7, 8].Given the numerous number of possible realisations, it is impractical to consider all realisations in the optimisation; hence, it is important to apply classical reduction methods to ensure computational feasibility.

In this work, we specifically address the well placement planning and rate control optimisation problem under geological uncertainty. The difficulty of this problem is characterised by the presence of discrete variables, nonlinear and nonconvex objective function and constraints. Increased complexity arises from the computationally demanding functional evaluations from full-scale reservoir simulation, which is done in ECLIPSE^®. We also exploit the MATLAB^® Reservoir Simulation Toolbox – MRST^® [9] for interfacing with ECLIPSE^® and performing derivative-free optimisation tasks. The developed workflow is applied to a synthetic case study, for which it is demonstrated using several operational scenarios that using optimally selected realisations reduces computational time while providing best operating well locations. A comparative analysis of the performance of different derivative-free optimisation algorithms is also presented.

REFERENCES

1. Epelle, E.I. and Gerogiorgis, D.I., 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. Tavallali, M.S. et al., 2014. Well placement, infrastructure design, facility allocation, and production planning in multireservoir oil fields with surface facility networks. Ind. Eng. Chem. Res., 53(27), 11033-11049.
3. Gunnerud, V., Conn, A. and Foss, B., 2013. Embedding structural information in simulation-based optimization. Comput. Chem. Eng., 53, 35-43.
4. Kosmidis, V.D., Perkins, J.D. and Pistikopoulos, E.N., 2004. Optimization of well oil rate allocations in petroleum fields. Ind. Eng. Chem. Res., 43(14), 3513-3527
5. Li, L., Jafarpour, B. and Mohammad-Khaninezhad, M.R., 2013. A simultaneous perturbation stochastic approximation algorithm for coupled well placement and control optimization under geologic uncertainty. Comput. Geosci., 17(1), 167-188.
6. Wang, H., Echeverría-Ciaurri, D., Durlofsky, L. and Cominelli, A., 2012. Optimal well placement under uncertainty using a retrospective optimization framework. SPE J., 17(01), 112-121.
7. Bellout, M.C. et al., 2012. Joint optimization of oil well placement and controls. Comput. Geosci., 16(4), 1061-1079.
8. 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.
9. Lie, K.A., 2014. An introduction to reservoir simulation using MATLAB: user guide for the Matlab Reservoir Simulation Toolbox (MRST). Sintef ICT, Trondheim, Norway.