344158 Producer Well Placement for Integrated Multi-Reservoir Oil Fields

Wednesday, November 6, 2013: 5:27 PM
Continental 9 (Hilton)
Mohammad Sadegh Tavallali1, Amanda Halim1, Iftekar A. Karimi1 and Kwong Meng Teo2, (1)Department of Chemical & Biomolecular Engineering, National University of Singapore, Singapore, Singapore, (2)Department of Industrial & Systems Engineering, National University of Singapore, Singapore, Singapore

The 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.

In 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.

To 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.

The 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.

Our 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: http://www.opec.org/library/Annual%20Statistical%20Bulletin/interactive/current/FileZ/Main.htm.

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.

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