The shale gas revolution that started at the dawn of the twenty-first century has transformed the American energy landscape, allowing drillers and pipeline operators to quickly and cheaply extract natural gas from areas that were previously economically unviable. However, shale gas production requires drilling and well-pads, gathering pipelines, intrastate and interstate transmission pipelines, compressor stations, and roads—all of which have an impact on the surrounding natural environment.
A classic tradeoff situation arises between minimizing costs of shale gas infrastructure and avoiding environmental impacts. While pipeline companies likely take some natural features into account in deciding on the infrastructure design, maximizing environmental protection would likely require further modifications to the design such as routing pipelines around forests and wetlands. It is unlikely that—in the absence of regulation—companies would account for the full value that society places on the environment, as these environmental protections would come at a direct private cost to the industry.
A better understanding of the tradeoffs between infrastructure costs and environmental impacts can provide insight for policymakers into the potential costs and benefits of regulatory policy design. In our paper, "Systematically Incorporating Environmental Objectives into Shale Gas Pipeline Development: A Binary Integer, Multi-Objective Spatial Optimization Model," we focus on the environmental impacts of pipelines that move gas and oil from drilling sites to production sites (i.e., gathering pipelines) and develop a mathematical model able to optimally site pipeline routes and determine the associated construction cost under varying levels of environmental protection. We call the model the Multi-Objective Pipeline Siting (MOPS) model.
The most simplistic portrayal of the choice between costs and impacts is below: Image 1a shows a pipeline design option that minimizes gathering line distances (and costs) from each well, but cuts through blocks of forest to reach the main transmission lines. Image 1b shows an option that minimizes environmental impact by routing all gathering lines from wells to avoid forests, but requires greater distance and increased costs.
Gathering pipeline system design is a challenging optimization problem because each length of gathering pipeline must be connected to other gathering pipeline in such a way that all wells are connected to large transmission pipelines. To address these challenges, our MOPS model formulates the pipeline design problem as a multi-origin, multi-destination problem, with nodes representing the wells and the transmission pipeline connectors. The landscape is represented as a grid, with attributes such as land cover associated with each grid cell.