(538g) Producer Well Placement for Integrated Multi-Reservoir Oil Fields | AIChE

(538g) Producer Well Placement for Integrated Multi-Reservoir Oil Fields


Karimi, I. A. - Presenter, National University of Singapore
Tavallali, M. S., National University of Singapore
Halim, A., National University of Singapore
Teo, K. M., National University of Singapore

growing world energy demand makes the optimal exploitation of existing oil and
gas reserves essential. Well drilling is the first stage in exploiting a given
hydrocarbon reservoir and that is important not only for new reservoirs, but
also for mature and marginal reservoirs. The increasing oil price over the last
decade has generally motivated oil and gas exploration and production companies
to increase their drilling activities worldwide [1] (even in marginal fields).
Wells connect the subsurface reservoir to the surface facilities; the recovery
from a reservoir depends strongly on its surface and subsurface conditions,
which change with time. These changes are drastic, when new wells are drilled
and opened to production.  In spite of
the possible financial and environmental risks especially in offshore ventures,
well drilling is an activity that is critical to the energy needs of the world.

our earlier work [2], we addressed joint well placement and production planning
in a single reservoir with a rectangular shape. It is already a challenging
dynamic optimization problem requiring a spatiotemporal and dynamic nonconvex MINLP model. However, in practice, fields often
have multiple reservoirs that share common surface infrastructure and
production facilities, which strongly interlink the operations of these
reservoirs. Clearly, the well placement and production planning become more
challenging, as each reservoir cannot be studied separately. Firstly, each
reservoir may have different geological characteristics and production
mechanism. While one reservoir might be producing via a secondary or tertiary
mechanism, another reservoir may be using the primary mechanism. Treating these
separate reservoirs as one aggregated reservoir with several inactive portions
is very inefficient [3]. Secondly, the surface settings would depend on the
conditions of the connected wells from multiple reservoirs. Clearly, addressing
well placement jointly with the operational planning of surface facilities at
the field level rather than the reservoir level is of paramount importance, and
poses special challenges.

our limited knowledge, very few studies have partially addressed this problem,
and with several limitations. Grossman and coworkers [4, 5] studied field wide
well placement, offshore infrastructure design, and production planning.
Although they addressed drilling and scheduling in multi-reservoirs, they
simplified the subsurface and pipe flow models drastically. In another study, Kosmidis et al. [6] addressed production scheduling and
well-to-surface facility allocation on a daily basis, so the reservoir dynamics
were irrelevant. Moreover, they did not address well drilling. In our recent
work [2], we incorporated, for the first time to our knowledge, rigorous
subsurface and well flow models to address long-term production planning and
well placement simultaneously.

aim of this work is to extend our single-rectangular-reservoir work to address
a field with multiple irregular-shaped reservoirs supplying to a shared surface
production facility. Some reservoirs may be marginal with no existing wells.
The field has multiple manifolds, with each dedicated to a set of wells. Each
manifold supplies oil to one or more separation centers. This network of
interconnected pipelines along with its multiphase flows make it a challenging
problem, as the tubing head pressure of each well not only depends on its own
production, but also on the pressures at its manifold and separation center. In
this work, we use an integrated and holistic view of the field to make
placement and production decisions. We simultaneously address all the dynamic,
economic, and operational inter-dependencies of the entire field and its
reservoirs. Of course, this requires some level of approximation, but we do it
with minimum loss of accuracy. We also modify our outer-approximation algorithm
for a single-reservoir problem.

work aids decision-making for (a) number and locations of new producer wells
(hence the eligible reservoirs for new drilling), (b) production/injection
planning for each well, (c) pressure settings at various valves, manifolds, and
separation centers over time, and (d) the spatiotemporal profiles of pressure
and saturation (hence the oil in place and water front maps) in each reservoir.
By allowing irregular shaped reservoirs, this work expands the realism and
application of our work. It also allows multiple production mechanisms with a
more extensive surface model. In addition, we improve well placement
constraints by defining tighter upper bounds on well flow rates. We also add
new constraints to make our MINLP tighter.

Acknowledgments: We
would like to thank the National University of Singapore and the Singapore
International Graduate Award program of Agency for Science, Technology and
Research (A*STAR) for financial support of this research. Moreover, we would
like to extend our gratitude to Schlumberger Wellog
(M) Sdn Bhd for granting us
the complimentary use of their ECLIPSE reservoir simulator. We are also
thankful to Mr. David Baxendale from RPS Energy
Limited and Professor Sh. Ayatollahi from Shiraz
University for their valuable industrial and academic insights.


1.       OPEC.
OPEC Annual Statistical Bulletin 2010/2011. 2011; Available from:

2.       Tavallali,
M.S., et al., Optimal Producer Well Placement and Production Planning in an Oil
Reservoir. Computers & Chemical Engineering - in print, 2013.,   10.1016/j.compchemeng.2013.04.002.

3.       Schlumberger,
ECLIPSE Manual, Technical Description 2009.1, Schlumberger, Editor. 2009.

4.       Iyer,
R.R., et al., Optimal Planning and Scheduling of Offshore Oil Field
Infrastructure Investment and Operations. Industrial and Engineering Chemistry
Research, 1998. 37(4): p. 1380-1397.

5.       Van
den Heever, S.A., et al., A lagrangean
decomposition heuristic for the design and planning of offshore hydrocarbon
field infrastructures with complex economic objectives. Industrial and
Engineering Chemistry Research, 2001. 40(13): p. 2857-2875.

6.       Kosmidis,
V.D., J.D. Perkins, and E.N. Pistikopoulos, A mixed
integer optimization formulation for the well scheduling problem on petroleum
fields. Computers and Chemical Engineering, 2005. 29(7): p. 1523-1541.