Integrated Oil Field Management through Optimal Placement and Scheduling of Drilling and Infrastructural Installations and Activities

High capital expenditures of oil field development, increasing global oil demand, limited oil resources, and multifaceted interactions between production infrastructures have alerted the oil production industry to move towards integrated field management and avoid peacemeal and myopic decisions. In this framework, any decision demands a wholistic analysis of the complex interactions between multiple reservoirs, wells, surface infrastructure network, and energy market. The outcome of this analysis sets several goals and instructions for the operators and contractors in different parts of the production chain, and these optimal decisions have substantial economic impacts. Infrastructure placement and allocation are amongst the most important design decisions over the long planning horizons that demand such analyses. The combinatorial, nonlinear and spatio-temporal dynamic nature of this problem poses several challenges and usually leads to large models with considerable computational costs. Once production and installation scheduling are also integrally tackled with the aforementioned topics, the new problem is even more challenging as the problem size increases much further. However, drilling and  installation scheduling is a must for field development projects; e.g. there are always limited drilling rigs that should be scheduled to drill many wells in a field, and all drillings cannot be done simultaneously. Therefore, in this study, we target an integrated study to consider installation scheduling as well. In our recent studies [1, 2], we addressed joint well and infrastructure placement,  allocation and production scheduling in oil fields with the shared surface network. We formulated dynamic nonconvex MINLP models and modified an outer approximation algorithm (based on the works of Grossmann and coworkers [3]) to solve them. In both of these studies, we assumed that all drillings and installations are done at the beginning of the production horizon.  In this study, we extend our previous works to address the integrated problem of surface network design and placement with production and installation scheduling, which has not been addressed so far in the literature. In this problem, the search space is substantially large due to two components: design, and scheduling. Hence, we solve this joint problem in two stages. In the first stage, we obtain the potential and promising locations and allocations for the design problem using our rigorous dynamic MINLP model. This uses detailed governing equations and employs advanced mathematical techniques to account for the numerous interactions in the field. In the second stage, we solve the drilling and installation scheduling problem to determine the time and order of each activity. In both stages, we employ rigorous dynamic NLPs to model the multiphase flow inside the reservoir. Although the final solution can be sub-optimal, it still can provide much improvement in the financial objective value. In fact, the numerical results show significant improvement in the net present value of a literature problem with two reservoirs. The best solution progresses to the highest NPV of MM$ 240.4 and suggests drilling 6 new wells, establishing 8 new connections and installing two new manifolds over almost 1200 days. The second stage of the solution had a key role in improving the final NPV.

REFERENCES

1.         Tavallali, M.S., I.A. Karimi, A. Halim, D. Baxendale, and K.M. Teo, Well Placement, Infrastructure Design, Facility Allocation, and Production Planning in Multi-Reservoir Oil Fields with Surface Facility Networks. Industrial & Engineering Chemistry Research, 2013. Under review.

2.         Tavallali, M.S., I.A. Karimi, K.M. Teo, D. Baxendale, and S. Ayatollahi, Optimal producer well placement and production planning in an oil reservoir. Computers & Chemical Engineering, 2013. 55: p. 109-125.

3.         Viswanathan, J. and I.E. Grossmann, A combined penalty function and outer-approximation method for MINLP optimization. Computers and Chemical Engineering, 1990. 14(7): p. 769-782.