@inbook{1124dabd28a7463ebb9b41d8c295a484,
title = "Decision Automation for Oil and Gas Well Startup Scheduling Using MILP",
abstract = "A novel approach to scheduling the startup of oil and gas wells in multiple fields over a decade-plus discrete-time horizon is presented. The major innovation of our formulation is to treat each well or well type as a batch-process with time-varying yields or production rates that follow the declining, decaying or diminishing curve profile. Side or resource constraints such as process plant capacities, utilities and rigs to place the wells are included in the model. Current approaches to this long-term planning problem in a monthly time-step use manual decision-making with simulators where many scenarios, samples or cases are required to facilitate the development of possible feasible solutions. Our solution to this problem uses mixed-integer linear programming (MILP) which automates the decision-making of deciding on which well to startup next to find optimized solutions. Plots of an illustrative example highlight the operation of the well startup system and the decaying production of wells.",
keywords = "Well startup optimization, long-term planning, oil and gas production",
author = "Kelly, {Jeffrey D.} and Menezes, {Brenno C.} and Grossmann, {Ignacio E.}",
note = "Publisher Copyright: {\textcopyright} 2017 Elsevier B.V.",
year = "2017",
month = oct,
doi = "10.1016/B978-0-444-63965-3.50235-X",
language = "English",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier B.V.",
pages = "1399--1404",
booktitle = "Computer Aided Chemical Engineering",
address = "Netherlands",
}