(343b) From Academia to Industry: Optimization Models for Shale Gas Development

 From Academia to Industry: Optimization Models for Shale Gas Development

Markus
G. Drouven¹, Ignacio E. Grossmann²

¹Optimization Engineering

EQT Corporation

Pittsburgh, PA 15222

²Department of Chemical Engineering

Carnegie Mellon University

Pittsburgh, PA 15213

¹mdrouven@eqt.com, ²grossmann@cmu.edu,

Abstract

For
the past four years the Center for Advanced Process Decision-Making (CAPD) at
Carnegie Mellon University (CMU) and EQT Corporation have been actively
collaborating to develop optimization models for shale gas development. The
CAPD is a research center at CMU that is engaged in Process Systems Engineering
research for the process industries. The CAPD focuses on a number of different
research areas including a) modeling and optimization algorithms, b) process
synthesis and product design, c) enterprise-wide optimization and process
operations, and d) molecular computing. EQT, on the other hand, is the leading
natural gas producer in the United States with a strategic focus on shale gas
development in the Marcellus Play. The company locates natural gas, develops
acreage and transports produced gas to distribution systems.

In
this presentation, we review how the shale gas development research project at
CMU started and how it progressed over time. We show that a number of
researchers associated with the CAPD made important contributions to this
domain. In particular, Knudsen et al. (2014) developed a Lagrangean relaxation
scheme for shut-in scheduling in shale-gas multi-pad systems. Thereafter,
Cafaro et al. (2014) proposed a mixed-integer nonlinear programming (MINLP)
model for the strategic planning of the shale gas supply chain. Next, Yang et
al. (2014) developed a stochastic two-stage mixed-integer linear programming (MILP)
scheduling models for shale plays water management. Most recently, Drouven et
al. (2016) developed a multiperiod MINLP model for long-term, quality-sensitive
shale gas development. Finally, Ondeck et al. (2018) developed a bi-criterion optimization
approach for simultaneous planning and scheduling of shale gas production. In
this talk we review the key findings of the aforementioned work, and we discuss
their significance for the shale gas industry as a whole.

We
also review how the research within the CAPD led to the formation of the
Optimization Engineering team at EQT. We show how this team currently
translates practical business and/or engineering problems within the company
into mixed-integer programming models; and we demonstrate how these models are used
to support faster and better decision-making across the organization.

Finally,
we present a real-world case study to demonstrate how optimization models at EQT
are currently being used to support safer and more economic shale gas
development. The case study reveals how a rigorous optimization model for water
management applied to an active shale gas development area can reduce the
number of water hauling trucks on the road (a safety benefit), limit impaired
water disposal volumes (an environmental benefit) and how it minimizes
operational costs (an economic benefit).

References

[1]
Knudsen BR, Grossmann IE, Foss B, Conn AR. Lagrangian relaxation based
decomposition for well scheduling in shale-gas systems. Computers & Chemical Engineering. 2014; 63:234-249.

[2]
Cafaro, D. C.; Grossmann, I. E. Strategic Planning, Design, and Development of
the Shale Gas Supply Chain Network. AIChE Journal. 2014. doi: 10.1002/aic.14405. 

[3]
Yang L, Grossmann IE, Manno J. Optimization models for shale gas water
management. AIChE Journal. 2014; 60
(10):3490-3501.

[4]
Drouven, M. G.; Grossmann, I. E. Multi-Period Planning, Design and Strategic
Models for Long-Term Quality-Sensitive Shale Gas Development. AIChE Journal. 2016;62(7): 2296-2323.

[5]
Ondeck, A., M.G. Drouven, N. Blandino, I.E. Grossmann, “Multi-System
Development Planning for Shale Gas Production,” Proceedings of the 13th International Symposium on Process Systems
Engineering – PSE 2018, July 1-5, 2018, San Diego, California, USA