(715e) Multi-Operational Development Planning for Multi-System Shale Gas Production

Shale gas production is expected to increase by more than 50% over the next 34 years, from 14 trillion cubic feet (TCF) in 2016 to approximately 29 TCF in 2040 [1]. This increase in production will make shale gas a major player in the natural gas field, with shale gas production fulfilling 69% of the expected total dry gas natural gas demand [1]. With this expected growth in shale gas, informed decisions, as to where, when, and how to develop assets, must be made to keep the development and production economical. This includes development scheduling, such as when and where to drill, and strategic planning, such as when and which contracts should be made for gas transportation and resource utilization.

Due to the increased impact of shale gas on the energy sector, the optimization of the development and transportation of shale gas has grown in popularity during the past decade. This research has concentrated on adding breadth to the supply chain optimization problem [2,3], and depth to the development and production scheduling [2,4]. However, previous models lumped all pre-production operations (i.e., top-setting, horizontal drilling, fracturing, and turning-in-line) into the generic group of development operations. While this does provide a method of tracking well production as a function of development, it does not allow for the consideration of drilling rig and fracturing crew contracts or mobilization costs, which may significantly impact the development of a shale gas region.

Motivated by the above, in this paper, we present the planning of the multi-operational asset development and shale gas production for a set of prospective capacity systems in an expansive shale gas region. By explicitly accounting for the sequencing of development operations at the pad level, the planning considers the time to perform individual operations, the length of contracts and the number of resources (rigs, crews) needed for development, and costs for moving rigs from one pad to another. By initially formulating the multi-system development planning using General Disjunctive Programming (GDP), we can systematically derive mixed-integer inequalities for all disjunctions and logic propositions to obtain a discrete-time mixed integer programming (MILP) formulation [5,6]. The MILP can be used to optimize the planning of asset development and infrastructure commitments, considering the long-term impacts of gas production and transportation for multiple shale gas systems. To demonstrate the validity of this formulation and the impact of rig considerations, we apply the planning to an industrial case study based on real data, quantifying the value of optimizing shale gas development and production.

References:

[1] EIA (2016). Most natural gas production growth is expected to come from shale gas and tight oil plays. https://www.eia.gov/todayinenergy/detail.php?id=26552

[2] Drouven, M. G., & Grossmann, I. E. (2016). Multi‐period planning, design, and strategic models for long‐term, quality‐sensitive shale gas development. AIChE Journal, 62(7), 2296-2323.

[3] Cafaro, D. C., & Grossmann, I. E. (2014). Strategic planning, design, and development of the shale gas supply chain network. AIChE Journal, 60(6), 2122-2142.

[4] Gao, J., & You, F. (2015). Deciphering and handling uncertainty in shale gas supply chain design and optimization: Novel modeling framework and computationally efficient solution algorithm. AIChE Journal, 61(11), 3739-3755.

[5] Grossmann, I. E., & Trespalacios, F. (2013). Systematic modeling of discrete‐continuous optimization models through generalized disjunctive programming. AIChE Journal, 59(9), 3276-3295.

[6] Williams, H. P. "Mathematical building in mathematical programming." (1985).