(723b) 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.