Wattenberg Oil Load Dispatch and Hauling Optimization

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Wattenberg Oil Load Dispatch

and Hauling Optimization

Final Report of the Spring 2012 Mathematics Clinic

Sponsor Profile: Noble Energy

Noble Energy, Inc., founded in 1932 and headquartered in Houston, Texas, is one of the nation’s leading independent energy companies engaged in worldwide oil and gas exploration and production. Its broad-based assets include both crude oil and natural gas resources, with exposure in the U.S. and internationally offshore Eastern Mediterranean and West Africa. Noble Energy’s domestic business consists of operations onshore, with key assets in the deepwater Gulf of Mexico areas and the DJ (Denver-Julesburg) Basin including the Wattenberg field located north of the Den-ver metropolitan area. Its onshore portfolio also includes the Haynesville shale of East Texas/North Louisiana and the Piceance Basin of Western Colorado. An S&P 500 company with reserves of 1.2 billion barrels of oil equivalent and assets totaling over $16 billion at year-end 2011, Noble Energy is highly recognized for its innovation, flexibility, and exploration proficiency that helped the company shape the industry and its own future success. For more detailed information about Noble Energy, please visit

the company website athttp://www.nobleenergyinc.com.

Problem Synopsis Noble Energy produces and sells tens of thousands of barrels of oil a day in the Wattenberg field, one of the largest natural gas deposits in the United States. This oil needs to be sold to several different purchasers from thousands of different locations over a hundred square miles radius in north eastern Colorado. How can we support or assist Noble Energy to make business and operational decisions regarding the sales, dispatch, and transportation of its oil to optimize its business? What mathematical models can be created to represent this system for decision analysis, simulation, and optimization purposes?

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The Spring 2012 Clinic Team

Student Participants • Meng Cao • Jennifer L. Diemunsch∗ • Kathryn B. Douglass • Catherine C. Erbes∗ • Christopher W. Fricks • Christian M. Hansen • Daniel C. Kamis • Barry M. Loper • Casey K. Moffatt∗ • Megan E. Morrison • Matthew W. Morrissey • Sasha F. Mushovic • Matthew Nery • Robert Pearson • Meagan M. Power

• Rebecca J. Steck Pituch

• Stephanie R. Tsen

• Taylor J. Williams

∗ _{denotes doctoral student and team leader}

Faculty Advisor

• Alexander Engau, Ph.D., Assistant Professor

UC Denver, Dept. of Mathematical and Statistical Sciences 1250 14th Street Suite 600, Denver, CO 80202

Email:[email protected]

Sponsor Representative

• Wesley Dyk, Supervisor, Research and Development

Noble Energy, Inc., Production Analysis and Optimization Group 1625 Broadway Suite 2200, Denver, CO 80202

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Students and Advisors before and after their final presentation in the Noble Energy Office at World Trade Center Denver on May 10, 2012

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Acknowledgments

The clinic program is a key element in the graduate and undergraduate mathematics education at the University of Colorado Denver and offers our students a unique experience to collaboratively learn, advance, and apply their knowledge and skills to solve a challenging and relevant real-life problem. Its success is in large parts due to the exceptional support by our sponsoring partners who provide us with problems, commit resources, and often dedicate a tremendous amount of their own time to help us, and to help our students. In Spring 2012, we were very fortunate to work withWesley Dyk, Supervisor of the Production Analysis and Optimization Group at Noble Energy, Inc., and I am most thankful both to Wes and to

Noble Energy for making this clinic possible. Wes has taken a fantastic role in getting involved, giving us directions and suggestions, guiding our progress, and providing positive and always constructive feedback. Work-ing with Wes was a true pleasure, and like many of the students I have learned a lot myself and greatly appreciate his willingness and dedication to listen to and patiently answer our questions, and to generously share his incredible expertise and insight into Noble Energy’s operational business.

In addition to our rigorous curriculum, the math clinic is also the cap-stone course to prepare our students for successful careers and to create a realistic learning environment that exemplifies their future responsibilities and opportunities as applied mathematicians in industry or other areas. In accepting this challenge, students often realize that with determination, discipline, and dedication to detail, they can use mathematics to have a true impact and to make things better for those who are willing to listen.

I therefore express my deep gratitude to Dan Kelly, Mike Maguire, Tim

Baumgart, Michelle Blesener, and Jeff Shaffer from Noble Energy for inviting and critiquing our final presentation at the Noble Energy Office at the World Trade Center Denver. Investing their valuable resources and

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time to give such an opportunity to our students shows sincere interest and incites that extra motivation that no classroom setting can ever achieve.

To work with some of our best students in such an open and interactive learning environment is easily one of the best aspects that a clinic offers also to faculty. Partially highlighted in this report, also personally I like to recognize the wide variety of accomplishments and contributions by the eighteen participating students ranging from literature reviews and class presentations over independent studies and theoretical or methodological research to the development, design, or implementation of new software.

In particular, my special thanks go to Jenny Diemunsch, Cathy Erbes,

and Casey Moffatt who served as mentoring team leaders and fulfilled their roles with an unmatched level of responsibility, commitment to highest quality, and motivational joy. Despite steady encouragement and regular guidance by Jenny, Cathy, Casey, Wes, and myself, however, it is ultimately each student’s individual decision to develop initiative and take personal responsibility that make a clinic successful, and I congratulate those for whom meeting expectations was just the beginning but never enough.

Finally, I thank my colleague Stephen Billups for his service as Math

Clinic Director and his substantial contributions to the math clinic program in our department. While Steve’s repeated recruitment of partners and sponsors keeps this program alive, it is his genuine interest and habitual willingness to share his experiences and offer advice that truly fills it with

life. I also thank our Department ChairMichael Jacobsonand Associate

Chair Lynn Bennethum for this most rewarding teaching assignment, to

our program assistants Angela Beale and Lisa Herbert and all student

workers for their general logistical support, and to Russel Boice for his

continued technical support and for keeping the bugs away.

Alexander Engau Denver, May 2012

Student Acknowledgments to Sponsor

“Thank you, Wes and Noble.”

Catherine Erbes, Christopher Fricks, Matthew Morrissey, Megan Morrison, Matthew Nery, and Sasha Mushovic

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“This is a perfect opportunity for us to learn in the real world. Before this class I was very confused about what can I do with math. However, thanks to this class and Noble’s help I have a better understanding about math. I know that math is useful!”

Meng Cao

“This semester has been a fantastic experience for me. After classes I’ve taken regarding network flows, linear programming, and other model-ing, it was really wonderful to begin with the ideas of how Noble Energy works, and be able to apply what I’ve learned to their operations. I was es-pecially impressed with Wes’s dedication to the success of the clinic. His feedback and advice was essential to our ability to succeed and contribute to Noble Energy. I also was very grateful for the Noble Energy workers who were both supportive of our efforts and receptive to our suggestions and work. They were very kind to us, and were wonderful sponsors. Thank you for this opportunity!”

Jennifer Diemunsch

“This has been a incredible learning experience for me, thank you for providing the Spring 2012 clinic with the opportunity to apply mathematics to a real world problem.”

Kathryn Douglass

“Thank you, Wes, for your time and support in regards to our project. I really appreciate the learning experience that this opportunity provided. It was a great reminder that theory and ideas have to meet the pavement somewhere in order to be effective. I really enjoyed the project - thanks again for giving us this opportunity!”

Christian Hansen

“Thank you to Noble to opening your doors to us. This was a very valuable learning experience for me and without your backing it would not have been possible. I would also like to personally thank Wes for his time and guidance.”

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“Thank you, Wes for convincing Noble to sponsor this clinic. Thanks for enduring our hopelessly ignorant questions as we tried to figure out what the problem was. Thanks for the time you spent prying scraps of data from Noble’s protesting grasp and compiling it all into tidy little ta-bles. Thanks for suffering through our presentations and doing your best to set us straight when we really had no idea what we were talking about. And finally, thanks for telling those Noble employees you knew would have heckled us, that the presentation was on Friday rather than Thursday. Ev-eryone who showed up was very kind. I was honestly expecting a bit more grilling about the business value of our solutions, but it seems you did a great job managing their expectations.”

Barry Loper

“Thank you, Wes and Noble, for giving us this opportunity. I found the problem very interesting and I am glad I was part of this clinic. I learned a lot in the process of creating and implementing our model, and I truly enjoyed working on it.”

Casey Moffatt

“I would like to thank Noble Energy for sponsoring our clinic. It is such a rewarding experience that would not be possible without the significant contributions of someone in industry. To Wes, thank for your very hon-est and timely feedback of the work we were doing. In the beginning we started with a couple of ideas that you felt would not benefit Noble. You could have been ambiguous about this but you were straight forward and helped guide us to an idea that I feel turned out really well. Thank You!”

Robert Pearson

“Thank you, Wes and Noble for providing us with the opportunity to experience a guided real world simulation of the types of situations we may face after graduation. I really learned a great deal during this clinic and I appreciate all the hard work you have put in to make this possible. I understand it was not easy but know that for us, it was worth it. Thank you.”

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“Thank you, Wes, for all the effort you put in that allowed us as a class to work together on the real life application of math for Noble Energy!”

Rebecca Steck Pituch

“Thank you for the opportunity that your organization gave our class. With your help, we were able to learn many new experiences that will help in our future professional knowledge.”

Stephanie Tsen

“I would very much so like to thank Noble Energy and Wes Dyk for the opportunity that they have given the entire Math Clinic this semester. Through their sponsorship, I was able to see how mathematics can be applied to a real world situation in a very large company. Transportation is a concept that was very new to me prior to the start of the semester, but upon completion, I certainly feel that I would have a much better grasp on how to provide a solution if another transportation problem was presented to me in the future. This would not have happened without Noble Energy’s participation in the Math Clinic this semester. I now have a very strong idea of how mathematics can be used in everyday situations, and how I can use the mathematics that I have learned to help solve those problems. Thank you again, very much.”

Taylor Williams

Software Acknowledgment to Simio LLC We like to acknowledge the approval of a generous $39,600 grant from Simio LLC that provided us with academic licenses and technical support to use Simio Simulation Software in our student research and teaching. To learn more about Simio’s capabil-ities for discrete and continuous modeling, object library development, and

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Clinic Introduction

This report describes the activities and outcomes from the Spring 2012 mathematics clinic “Wattenberg Oil Load Dispatch and Hauling Optimiza-tion” that was conducted in partnership between Noble Energy, Inc. and the Department of Mathematical and Statistical Sciences at the University of Colorado Denver (UCD). As one of the nation’s leading independent en-ergy companies, Noble Enen-ergy produces and sells tens of thousands of barrels of oil a day in the Wattenberg field, one of the largest natural gas deposits in the United States and part of the Denver-Julesburg (DJ) Basin just north of the Denver metropolitan area. This oil needs to be loaded and hauled to several different purchasers from thousands of different locations over a hundred square miles radius in north eastern Colorado.

The two aspects that this clinic set out to explore were the following.

• How can we support or assist Noble Energy to make business and

operational decisions regarding the sales, dispatch, and transporta-tion of its oil to optimize its business?

• What mathematical models can be created to represent this system

for decision analysis, simulation, and optimization purposes?

Thus motivated, during the Spring 2012 semester a group of eighteen UCD students with diverse backgrounds in math, physics, economics, and computer science took on the challenge to learn more about Noble En-ergy’s operations in the Wattenberg field and reach or suggest answers to these challenging research questions. Advised by one member from the Operations Research Group in the mathematics faculty of UCD and one industry representative from the Production Analysis and Optimization Group at Noble Energy, these students decided to advance their existing knowledge and skills in using techniques from deterministic and stochas-tic operations research to identify and describe related problem areas,

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for-Clinic Introduction: Wattenberg Oil Dispatch and Hauling Optimization

mulate and analyze new mathematical models, and develop and validate computational techniques or other practical solution approaches that may provide Noble Energy with new insight and strategies to increase the effi-ciency of its current operations and inform decision making in the future.

In the remaining parts of this introduction, we shall provide a general outline of organization and conduct of this term’s clinic including a time line of the different project stages and our respective activities. In particular, we motivate and explain the formation of our three final teams to explore multiple, alternative approaches for several aspects of Noble Energy’s spe-cific operations. This introduction then concludes with brief summaries of our final projects, which are followed by the complete project reports that were written independently by each of the different student teams.

Overview and Conduct of Clinic

The math clinic is a fundamental component in the mathematics program at UCD and a graduation requirement for all our mathematics majors and Ph.D. candidates. Clinics are offered as regular courses MATH 4779/5779 (“Math Clinic”) that are cross-listed in our undergraduate and graduate cur-ricula but also open to students in the natural sciences, computer science, engineering, economics, and other disciplines related to the specific clinic topic. The common prerequisites for successful participation in a math clinic include a strong mathematical foundation in several areas such as linear algebra, differential equations, vector analysis, probability, statis-tics, optimization, and math modeling, in addition to solid computer skills and some basic programming experience. While students are expected to have learned the majority of these technical skills in their former classes, the clinic also emphasizes a high level of personal maturity and expects successful students to have developed high work ethics and the willing-ness to study and learn both independently and collaboratively in teams.

This math clinic concept has the educational purpose to provide our most advanced students with an interdisciplinary capstone experience to apply knowledge and skills from their previous studies to a challenging and relevant real-life problem provided by our sponsoring partner from industry, government, or academia. To better prepare our students for successful careers in any of these sectors, a particular focus of each clinic is to also learn and enhance effective communication and management skills. In

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Conduct of Clinic Stage 1: Preparation and Problem Definition

consequence, most clinics have no or only very few traditional lectures and are typically run as much as possible like real-world projects focused around activities and initiatives led by the students. Accordingly, students are evaluated not only by their mathematical strength but also based on their timely communication, good teamwork and citizenship, regular and prepared attendance of meetings, active and constructive participation in discussions and planning, timeliness and high quality of completed work assignments, and - encompassing all of the former - an early willingness to take on and successfully meet responsibilities and a subsequent personal growth to also independently develop initiative and leadership.

In addition to this educational objective, however, the ultimate goal of each clinic is to demonstrate a good understanding and a clear step toward an acceptable solution of the originally posed problem by our sponsoring partner. This “reality” aspect has proven to be invaluable, and the realiza-tion that a successful company like Noble Energy is willing to listen and of-fer students that early in their career an opportunity to showcase how they can use their mathematical training for a potential impact on actual opera-tions in practice is often one of the biggest motivaopera-tions for our students to take such a class seriously, improve, and perform well. In particular, one of the key aspects that makes this format successful is the ability to regularly correspond, ask questions, and receive constructive feedback from fac-ulty members and one or more sponsor representatives who are involved as technical contacts and treated as equal partners in all conducted clinic activities. For the Spring 2012 clinic, these were divided into four stages.

Stage 1: Preparation and Problem Definition

The goals during the first stage of this clinic project were, first, to introduce students to both the clinic concept and some of the current operational problems faced by Noble Energy, and second, to review or learn basic project skills like searching the literature, posting findings and comments to an online wiki page, gaining proficiency in mathematical typesetting and

preparing slides using LA_{TEX, and establishing formal guidelines for oral}

presentations. In addition to these technical learning objectives, the ex-pected final outcomes from this stage were detailed problem statements written by each of the students to define the scope of our later activities and identify mathematical areas or techniques for further exploration.

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Clinic Introduction: Wattenberg Oil Dispatch and Hauling Optimization

Day 1 (January 17)

During the first day of class, students were introduced to the clinic concept and given the course syllabus, that was discussed in detail with a partic-ular emphasis on expectations and grading guidelines. At the same time,

students were asked to familiarize themselves with LA_{TEX and complete an}

initial clinic statement to describe their own expectations, background, and

objectives. A LA_{TEX template for this statement was was sent out by email,}

and this short initial writing assignment was due the next class meeting.

Day 2 (January 19)

After handing in their clinic statements, students met with the sponsor rep-resentative (Wesley Dyk) and were introduced to some of the current op-erational challenges faced by Noble Energy with a first opportunity to ask specific questions regarding the problem at hand. The next step was for each student to identify at least three sources in the literature (articles, books, conference papers, dissertations, news reports, web sites, etc.) of potential relevance to build further background knowledge, develop techni-cal vocabulary, learn about related problems, and possibly identify existing modeling or solution approaches that we could use as a starting point. These references were to be created as BiBTEX entries to further students

knowledge with LA_{TEX; templates for all required files were again sent out}

by email and posted to an internal wiki page that had been created from own initiative by two of our students (Christopher Fricks and Barry Loper).

Day 3 (January 24)

After a brief presentation of our new wiki by Chris and Barry, we compared our literature findings and evaluated the different search strategies that had been used by different students. Together with Wes, we then identi-fied a subset of search results that seemed worthwhile for further study, and each student was asked to select one of the corresponding articles and prepare a brief summary to be presented to the class the following week. To avoid two or more students working on the same article, stu-dents had to post their selections on a first-come first-served basis on the class wiki. During the remaining class, the students received basic guide-lines for this preliminary literature review and oral presentations in general,

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Conduct of Clinic Stage 1: Preparation and Problem Definition

together with a brief introduction to the LA_{TEX presentation package Beamer}

that would also be used throughout the rest of class. The full list of refer-ences (given at the end of this report) and all corresponding templates were posted on the class wiki that would also become the major platform to communicate or exchange ideas and information outside of class.

Day 4 (January 26)

After an opportunity to ask general questions regarding the use of LA_TEX

and Beamer, students were given a small case study with a preliminary data analysis of battery tanks and oil production rate curves that had been prepared by Wesley Dyk. The data set contained information on 4390 battery tanks including their location, capacity, current inventory, and aver-age oil inflow rates, and the location of 3 possible markets (rail, pipeline, refinery). Students were asked to form small teams and work on three open-ended questions to predict (maximum) time to dispatch or (minimum) time to shut-in, compute (surface) distances between batteries and mar-kets based on certain restrictions on transportation paths of haulers, and brainstorm on strategies to decide which loads to ship where and what additional data may be useful. This exercise alerted students that many details about the problem that seemed clear the week before required fur-ther clarification and nicely complemented the simultaneous, individual lit-erature reviews. The early realization that our underlying data sets are large was also helpful to reinforce the necessity to use or develop good computer skills.

Week 3 (January 31 and February 2)

Students gave their oral presentations on the various literature findings, with 3-5 minutes each for the actual presentation and additional time for questions and discussion. Although several of the articles that were dis-cussed seemed of no direct relevance, this activity proved very useful for students to identify operations research as key discipline (rather than being told so by a biased faculty member), to become comfortable to stand up, talk in front of the class and take questions, and to further accelerate their learning process of LA_{TEX. At the end of this week, students also received a}

short LA_{TEX quiz that evaluated basic proficiency in creating BibTEX entries}

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Day 7 (February 7)

A final discussion of previous week’s presentations suggested we focus on three specific model techniques to represent Noble Energy’s operations for decision analysis, simulation, and optimization. After a brief review of the purpose (“gain insight”) and process (“start simple and build up”) of mathe-matical modeling in general, we decided to form three groups: one to focus on a deterministic network flow model to represent Noble’s dispatch and transportation system, one to create a stochastic simulation model to han-dle and analyze the effects of uncertainty, and one to devise a dynamic op-timization model to consider time dependencies and thus inform long-term planning and strategic decision making. Based on previous experiences, course work, and personal interests, the three doctoral students (Jennifer Diemunsch, Catherine Erbes, and Casey Moffatt) were appointed as group mentors to supervise and provide guidance to their respective teams.

Day 8 (February 9)

To help the other students choose a topic of their own interest, Jenny, Cathy, and Casey prepared excellent mini-tutorials that introduced some of the basic principles and examples of linear programming and network flows, stochastic processes and simulation, and dynamic programming, respectively. Thus having had chosen the basic route for the rest of the semester, students were asked to complete a second clinic statement to reflect on their initial expectations and objectives, to identify those aspects they would like to work on, and to offer a formal problem definition and de-tailed description to prepare themselves for the subsequent second stage.

Stage 2: Planning and Project Proposals

The specific goal of this second stage was to divide students into groups with the assignment to develop and present a formal proposal that would identify specific project goals for the rest of the semester and break these goals into smaller well-defined tasks (technical description), and to assign project teams or subteams who would perform these tasks (project man-agement plan). The expected outcomes of this stage also included a set of well-defined research questions and justification of their relevance, a detailed plan of tasks and milestones, clear strategies to measure and

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Conduct of Clinic Stage 2: Planning and Project Proposals

monitor progress with possible fall-back options, and a tentative outline for the contributions to the final clinic report. Deadlines were discussed and set as February 23 for an initial oral project proposal to receive early feed-back and detect potential overlaps between different groups, March 1 for the final completed project proposal, and May 12 for all final group reports.

Weeks 5 and 6 (February 14-16 and 21-23)

At the beginning of stage 2, all students chose their respective group and received a written outline with clear proposal guidelines and instructions. After few changes later in the term, our group assignments were as follows.

• Network Modeling Group: Meng Cao, Jennifer Diemunsch (group leader), Christopher Fricks, Daniel Kamis, Meagan Power, Stephanie Tsen

• Stochastic Modeling Group: Kathryn Douglass, Catherine Erbes (group leader), Barry Loper, Megan Morrison, Sasha Mushovic, Rebecca Steck Pituch, Taylor Williams

• Dynamic Modeling Group: Christian Hansen, Casey Moffatt (group leader), Matthew Morrissey, Matthew Nery, Robert Pearson

During the subsequent two weeks, all groups worked mostly indepen-dently to brainstorm and develop the ideas for their proposals. The full class met again on February 23 when every group was asked to give a 15-minute presentation with a preliminary project description and tentative proposal outline. Each project was discussed in detail and critiqued by the full class to help each team broaden, narrow, or rethink its current focus.

Week 7 (February 28 and March 1)

Based on the feedback and suggestions received during previous week’s oral presentations, all groups continued to complete their final written pro-posal in group meetings both inside and outside of our regular class time. At the same time, each student was asked to also meet personally with the clinic instructor to discuss individual progress, address any potential ques-tions or concerns, and ensure that each student was able to contribute efficiently within their current group. All proposals were submitted on time on March 1 and distributed to the sponsor for feedback and discussion.

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Stage 3: Project Execution

In this main stage of the clinic course, all students continued to work in their respective groups to achieve the project goals outlined in their respective proposals. Equipped with basic research and project management skills and receiving regular input and guidance from the graduate students, fac-ulty, and sponsor advisor, some of the groups’ activities included search-ing and reviewsearch-ing the literature for further information, brainstormsearch-ing ideas both within and between groups, learning about new mathematics and possible new solutions approaches including new software, developing new mathematical models and computer programs to implement and an-alyze these models, posting regular updates and weekly progress reports to the wiki, and giving a biweekly progress presentation to the full class that summarized any accomplishments since the last time, and defined new goals for each next reporting period. The expected outcome of this stage was a draft version of each group’s final clinic report that was to be

submitted on April 23; a LA_{TEX template for these final reports was made}

available on April 12, and the final deadline was confirmed as May 12.

Weeks 8 and 9 (March 7-9 and 14-16)

These two weeks were conducted similarly to Weeks 5 and 6 in Stage 2, with groups working mostly independently both inside and outside of our regularly scheduled class time, and first progress updates on February 16. In addition, each group had an opportunity to meet for a full class period with sponsor representative Wesley Dyk to receive further feedback on its written project proposal and better steer its project and focus into a direction that would be of true interest and potential use to Noble Energy.

Week 10 (March 19-21)

Due to Spring Break, no class meetings were held during this week.

Weeks 11-14 (March 26-28, April 3-5, 10-12, and 15-19)

The format of these four weeks was kept analogous to Weeks 8 and 9 with progress updates on April 5 and 19. The submission of all draft reports by April 23 successfully completed this third stage of the clinic project.

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Conduct of Clinic Stage 4: Final Reporting and Review

Stage 4: Final Reporting and Review

Three weeks before the end of the semester, students were encouraged to complete all major initiatives and focus primarily on collecting, preparing, and reporting all final results in polished form both for the final project pre-sentation that was scheduled for May 10 during finals week, and the final written report with a set deadline of May 12. At the same time, students received score sheets to be returned on May 11 with commented self and peer evaluations in the six categories participation and attendance, com-munication and reliability, initiative and leadership, teamwork and citizen-ship, timeliness and quality of work, and overall contribution. Together with this evaluation, all students were also asked to complete a third and final clinic statement to review their initial expectations and objectives and re-flect whether they had been meet, to describe their work and what they had learned from own initiative, and to compile a detailed list of their own specific contributions that had helped to make this clinic successful.

Week 15 (April 24 and 26)

While all groups were advised to collect results, prepare slides, and prac-tice their final presentations, each individual student was again asked to meet personally with the clinic instructor for another round of “clinic chats” to specifically address questions or concerns about the evaluations and final grading scheme. The remaining class time on April 26 was used to provide general feedback on the submitted draft reports, that were handed back to each group with specific comments and suggestions.

Week 16 (May 1-3)

During the last regular week of classes, both periods were used to cri-tique and give feedback on practice presentations, and to address possible questions that students may receive when presenting to Noble Energy.

Final’s Week (May 10-12)

The final presentation was given on May 10 at the Noble Energy Office at the World Trade Center Denver, followed by an opportunity for the students to network with several representative from Noble Energy (Dan Kelly, Mike

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Maguire, Tim Baumgart, Michelle Blesener, Jeff Shaffer, and Wes Dyk). Reminding students of the remaining deadlines for evaluations, final state-ments, and reports, the evening and class ended with a small celebration at Rock Bottom Brewery in Downtown Denver. With only few exceptions, all statements, evaluations, and final reports were submitted on time by May 12, and after small edits and another last review this final report was finally completed and submitted to the sponsor one week later on May 19.

Problem Statement and Project Summaries

This project addresses Noble Energy’s operations in the Wattenberg field with a specific focus on resource (truck) allocation and scheduling for the planned dispatch and transportation of its oil production. The Wattenberg field belongs to the Denver-Julesberg (DJ) Basin and is situated north of the Denver metropolitan area on the eastern side of the Rocky Mountains. It is classified as a natural gas field in which gas can be sold and transfered directly via pipeline to midstream companies where it is processed and distributed to utilities and other purchasers. However, similar to other gas fields there is no infrastructure to also transfer oil to markets directly via pipeline, so that wells must be equipped with additional equipment to allow for the temporary storage and the subsequent pick-up and transfer of their products by truck. We refer to these storage locations as batteries.

Noble Energy operates roughly 4100 of such batteries and more than 7500 wells in the DJ Basin. These batteries typically consist of a separator to divide gas and oil products into separate streams, meters to measure produced and transferred gas and oil products, a vapor recovery unit to capture evaporation from produced oil for reinjection into the gas line, a compressor to raise the pressure of the gas to exceed the pressure of the gas in the sales line, and one or more tanks that store the oil and any other liquid products, including both bottom sediment and water (BS&W) that subsequently separate from the lighter oil on top. A lease operator (or pumper) is responsible to monitor this process, measure and control oil quality, grade BS&W content based on industry standards and contracts with haulers or purchasers, and ultimately decide whether to pump the oil and dispatch for market delivery or to request haul for treatment at a separate facility. If the liquid tanks at a battery become full, the wells feeding the battery must be shut in to prevent the tanks from overflowing.

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Problem Statement and Project Summaries

This general description of the logistical oil production process gives a basic understanding of some of the fundamental problems at Noble En-ergy and informs several of the short-term and long-term decisions that pumpers, dispatchers, haulers, and operators face on different operational, tactical, or strategical levels. These include the following, among many others.

• Haul Date: Day or time to have a truck dispatched to pick up a load.

• Hauler and Market: Hauler to pick up the load and market to sell to.

• Split Loads: On occasion, the option exists to have a partial load picked up from one battery and topped off with oil from another bat-tery (although this is discouraged by haulers for overhead reasons).

• Inventory On Hand: The minimum inventory to be kept on location.

• Contract: Agreements with lease owners, haulers, or markets that involve decisions such as minimum and maximum number of loads hauled per day, or minimum amounts of oil sold to a specific market.

• Infrastructure: Construction of oil pipelines to transport oil between locations when the production and capacity make economic sense. For our projects, we decided to focus on the current operations at Noble Energy to provide analytical support primarily for its operational decisions including haul dates, choice of haulers and markets, acceptance or re-jection of split loads, and planning of optimal inventory levels. We also considered contractual agreements and obligations as well as the existing infrastructure, that are featured as fixed constraints and thus included as given requirements in the majority of the models that we have developed. To further assist Noble Energy in its tactical or strategic decision making regarding long-term contracts and development of infrastructure would re-quire a deeper understanding of Noble Energy internal business model, and was found to be out of scope at least for this initial clinic project.

Based on information from Noble Energy, in current operations many decisions are made without prior analytical analysis, and trucks are dis-patched to individual locations based on priority lists of batteries that are compiled and renewed manually on a daily basis. While certain business agreements dictate who can haul from a particular battery to a particular market, which we can model as fixed constraints, this process may cre-ate potential inefficiencies due to its high degree of complexity, especially

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when different oil markets are competing and when the number of available trucking companies and trucks is either large or limited. Different mathe-matical models are able to capture this underlying complexity and utilize the resulting degrees of freedom to optimally increase efficiencies by ex-plicitly considering one or more decision criteria and goals. The following objectives were found to be of particular importance for Noble Energy.

• Safety: Safety of employees, contractors, partners and third parties impacted by operations is the number one priority with no exceptions.

• Stewardship: As an operator, minimal environmental impact of op-erations (land use, air quality, noise pollution, etc.) is very important.

• Profitability: Maximize profitability by minimizing operational costs.

• Never Shut In: The shut-in of a well should be avoided at all times.

• Maintain Production: The production volume is to be maximized, which goes hand-in-hand with profitability and avoidance of shut-ins.

• Guaranteed Deliverability: The ability of operators to deliver guar-anteed amounts of product to markets is what keeps them in busi-ness. This aspect is what drives the decision on minimum inventory. Specifically, to inform the previously described operational decisions at Noble Energy we decided to explicitly take into account considerations re-garding their impact on profitability, avoidance of shut-ins, production and deliverability. While we recognize that safety and stewardship including environmental protection are key requirements for Noble Energy’s busi-ness and sustainable operations, the analytic assessment and quantita-tive measure of these aspects was much more difficult so that we felt our alternative focus would yield much more accurate and useful results. Also in general, it is understood that all mathematical models are limited in their real-life verisimilitude. In consequence, we advise and encourage Noble Energy to use our models and findings not to replace experience and ex-pert opinion, but as helpful tools that may provide analytic decision support to gain insights, develop and experiment with new strategies, and success-fully simulate and optimize certain aspects of its business to increase the efficiency of its current operations and inform decision making in the future. Based on our previous discussion, we decided to split the class into three groups whose projects would focus on different approaches that

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to-Problem Statement and Project Summaries

gether would address all of the above decisions and objectives. The spe-cific aspects considered in each of the individual projects are collected in the table below and followed by a list of group members and brief summary statements. The rest of this report then highlights the full project reports that were written by each of the three individual student groups.

Decisions/Objectives Project 1 Project 2 Project 3

Haul Date X X

Hauler and Market X X

Split Loads X X Inventory on Hand X Profitability X X Never Shut In X X Maintain Production X X X Guaranteed Deliverability X X X

• Project 1 (Network Modeling Group): Meng Cao, Jennifer Diemunsch (group leader), Christopher Fricks, Daniel Kamis, Meagan Power, Stephanie Tsen

• Project 2 (Stochastic Modeling Group): Kathryn Douglass, Catherine Erbes (group leader), Barry Loper, Megan Morrison, Sasha Mushovic, Rebecca Steck Pituch, Taylor Williams

• Project 3 (Dynamic Modeling Group): Christian Hansen, Casey Moffatt (group leader), Matthew Morrissey, Matthew Nery, Robert Pearson

Project 1: Network Modeling Group

The specific focus of this first student group was to create a deterministic network flow model to represent the current infrastructure of Noble En-ergy’s oil field operations and to develop functional relationships for the various geographic distances, travel times, and transportation costs when using different trucking companies to haul between wells or batteries and markets. The final model supports dispatch and transportation decisions regarding the allocation and routing of trucks and is able to provide dis-patchers with detailed schedules that suggest which batteries to service, where to send the collected loads of oil, and what trucking companies to

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use in order to maximize profit or production. The rejection or acceptance of split loads can be incorporated easily by respectively turning on or off integrality constraints on the corresponding decision variables. Contrac-tual agreements with well owners, haulers, and markets are guaranteed using additional sets of constraints and penalty parameters, and a further analysis of their associated shadow prices may provide additional insights about their ultimate value to Noble Energy’s operations.

The model is cast as a transportation problem and was implemented as a linear program (LP) using the mathematical optimization software GAMS for validation. The data input was achieved automatically from databases provided in Microsoft Excel, and the resulting LPs were solved using the IBM/ILOG solver CPLEX. Together with the full model implementation, a specific example for data input and the corresponding output are provided in the final report’s appendix. Under the assumptions, constraints and data used to formulate and solve this model, this solution provides an opti-mal dispatch list to maximize profit or equivalently, to minimize costs from dispatch and transportation. Clearly, further validation and expert opinion from Noble Energy may be needed to modify these tentative solutions to account for additional requirements or other real-life complexities that were not already included in the mathematical model.

Two specific aspects that are also pointed out in the respective report are the assumptions that all data is known or can be estimated with suffi-cient accuracy, and does not change over time. While these assumptions are common to all static deterministic models and reasonable to inform daily or short-term decisions, further insight for long-term planning also requires to consider uncertainties and dynamic changes to the operating environment. Accordingly, to explore these two additional factors was the focus of the stochastic and dynamic modeling groups, respectively.

Project 2: Stochastic Modeling Group

This group started with a broader and less well-defined task to learn about quantitative approaches for decision making under uncertainty and explore their capabilities and potential benefit for Noble Energy. Specifically, the students decided to form two smaller teams and independently of each other focus on two different approaches to apply stochastic processes and queueing theory to simulate certain aspects of Noble Energy’s operations.

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Problem Statement and Project Summaries

The first group (Kathryn Douglass, Barry Loper, and Sasha Mushovic) was interested in investigating the use of a simulation software developed by Simio LLC and initiated our application for a Simio research and teach-ing grant that provided sponsored academic licenses to our faculty and students. Using Simio, these students created a simulation model for No-ble Energy’s current operations in the DJ Basin based on queuing theory, that treats the produced oil at wells and batteries as arriving customers that need to be serviced and delivered to markets by a limited number of trucks. Uncertainties in oil production and truck service and travel times are captured using stochastic processes and modeled by underlying prob-ability distributions. The final system allows to simulate an arbitrary num-ber of days starting from a specified initial state and collects information about timing and destination of dispatched trucks as well as expected oil production, delivered amounts, and shut-ins to inform several of Noble En-ergy’s decisions and evaluate their impact on specific operational goals. This group’s report also describes how changes to the initial state or glob-ally to the overall system can be used to simulate and experiment with more substantial business decisions including the opening and closure of wells, effects of weather, or building new infrastructure.

The second group (Megan Morrison, Rebecca Steck Pituch, and Tay-lor Williams) focused more closely on a potential connection to the network modeling group and decided to use queueing to establish a list of battery priorities to determine a dispatch schedule. Based on given data of bat-tery capacities and average flow rates, these students modeled flow rates as random variables and used the mathematical software system Matlab to build an underlying queueing model that computes the expected num-ber of days until each battery would reach a predetermined capacity level, or alternatively, the relationship between expected output and days of pro-duction. By choosing corresponding probability distributions, this approach also accounts for some of the variability in Noble Energy’s operation.

Project 3: Dynamic Modeling Group

Arguably the most challenging project was accepted by this third group of students who combined ideas from both the first and second group to develop a dynamic bi-level bi-objective maximum-flow model to determine optimal inventory levels and a multi-day dispatch schedule that ensures

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guaranteed deliverabilities, maximizes production, and minimizes shut-ins. Similar to the stochastic modeling group, the first of two interdependent phases of this group’s solution methodology uses a probabilistic approach based on a Monte Carlo simulation to obtain insight about the relationship between minimum inventory levels and deliverability, specifically the risk of loss in production and subsequent supply shortfalls. While Noble Energy is reported to keep its operational reserve level at about 50% of overall production, the students’ findings indicate that significantly smaller levels would suffice to control the risk of shortfalls at less than 1%. While the specific results clearly require a careful further verification, the proposed methodology may provide Noble Energy with an interesting strategy to in-crease the efficiency of its current operations and reduce storage require-ments by maintaing a significantly smaller inventory at hand.

In the second part of this project, the predetermined inventory levels are then used to compute the maximum flow of oil that can be hauled from each of the batteries and delivered to market without falling below mini-mum inventory requirements. Similar to the network modeling group, the underlying system is modeled as a linear integer program (IP) that maxi-mizes total haul or profit; consideration of split loads or additional contrac-tual agreements are not included for simplicity but could be incorporated in an analogous manner as already described in the first project. Similarly, this IP model is also implemented using the mathematical optimization software GAMS and solved using the IBM/ILOG solver CPLEX with auto-matic data input from Microsoft Excel, whose macro-capabilities were also used to code the first-phase Monte-Carlo simulation to replace this group’s initial, original implementations in both Ruby and Python.

In addition to their initial max-flow IP, this group’s project also describes two further enhancements to their model: first, the basic IP formulation is embedded within a dynamical system to allow for the optimal control of inventory over a multiple-day planning horizon, and second, the IP is cast as a bi-objective program to further analyze the trade-off between produc-tion or profit maximizaproduc-tion and the avoidance of shut-ins. Specifically, this allow to properly weight the requirement to never shut in, and the prelimi-nary results reports by the students seem to indicate that certain shut-ins may also be profitable, under some circumstances. Of course, these find-ings are again based on imperfect knowledge and several assumptions but may provide Noble Energy with some ideas and new analytical methods to assess their current operations and find ways for further improvement.

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Project 1

A Linear Programming Model to

Optimize Oil Load Dispatch and

Transportation at Noble Energy

Meng Cao, Jennifer L. Diemunsch, Christopher

W. Fricks, Daniel C. Kamis, Meagan M. Power,

and Stephanie R. Tsen

Abstract

This paper is written in response to a question posed by Noble Energy with regard to the current product logistics implementation. Noble Energy is an oil company, which makes daily decisions regarding shipment of oil from various batteries to particular destinations, using different hauling compa-nies. This report presents a basic model to describe this system. We chose to implement a method that is commonly used in modeling to de-scribe a transportation network. We decided to use a Network Flow Linear Program as the basis for building our model. Our purpose in building this model was twofold. First, we wanted to model the transportation network to aid in ensuring that the current dispatch system is operating with effi-ciency. Second, we wanted to be able to leave Noble Energy with a power-ful analysis tool that is able to give them insight into other aspects of their business. Throughout the paper we will detail our approach, execution, and subsequent conclusions as they relate to the model.

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Project 1: Linear Programming Model of Oil Dispatch and Transportation

1.1

Introduction

The oil industry is a multibillion dollar business that includes exploration, extraction, refining, transporting and marketing petroleum products. These products are used throughout the world as a source of fuel, and the world’s population is growing ever more dependent on this source of energy. It is vital to the economy that we continue to explore and extract oil reserves efficiently throughout the world.

Noble Energy is an S&P 500 company with wells operating in the deep-water Gulf of Mexico, offshore Eastern Mediterranean and offshore West Africa and specializing in the extraction and transportation of oil to refin-ery operations. In this paper, we focus on Noble Energy operations in the Wattenberg oil field in northeastern Colorado. This field is the home for many wells producing oil at varying grades and varying rates. Throughout the field there are batteries with tanks, which store the oil, until it can be pumped from the tanks and transported.

In business, it is important to reevaluate day to day operations as a company grows in order to maintain and improve efficiency. Also, in today’s market, with the evidence of the ecological impact a business makes on the planet, the global footprint of a business is also becoming a focal point. A more efficient dispatch system for Noble Energy could help the business as a whole prepare for future growth and improve daily operation efficiency. A system that determines where the oil is hauled based on a cost analysis of which batteries need to have oil pumped would help inform the decision of where to send oil and which trucking company should haul the oil could substantially improve Noble Energy’s operations.

The remainder of the paper begins with background information re-garding research into linear programming, network flows, and other mod-els. We then detail our specific mathematical model of the transportation network we use. Next we discuss the specific details as it relates to our implementation and validation for Noble Energy. Finally, we discuss future work for this project.

1.2

Background

Optimization is a field of mathematics which aims to fulfill various con-straints, and within that framework optimize an objective function. Linear

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M. Cao, J. Diemunsch, C. Fricks, D. Kamis, M. Power, and S. Tsen

programming and network flow theory are well-studied subfields of opti-mization, and are widely used for mathematical modeling. These are also used in a variety of ways from business and economics to engineering. With its wide variety of uses, linear programming is often used in indus-tries that use it for optimization of transportation and telecommunication networks. Other information regarding linear programming and network flows can be found in [1] and [5].

One of the main decisions that Noble Energy makes on a daily basis is where to transport oil. We focused on the decision of dispatching trucks to batteries, and where those trucks should haul the load they pick up. We use network flow theory as a base to build a mathematical model of Noble Energy’s dispatch system. Our proposal offers a linear model to gain insight for a more efficient dispatch system. This optimization of a dispatching system may provide Noble Energy with more information to aide their decision-making process, with the goal of increasing profit and minimizing dispatches that could cause shut-ins.

1.2.1

Literature Review

In 2008, Liong et al. [2] discussed the Vehicle Routing Problem (VRP) which is a problem seeking to service a number of customers with a fleet of vehicles at minimal total cost. The VRP can be grouped in four types:

• Classical;

• Capacitated;

• Time Windows;

• Pick-Up and Delivery.

In Classical VRP, the customers, driving time between customers, and the service times at each customer are known in advance. The goal of Clas-sical VRP is designing vehicle routes that would be the most cost efficient so that

1. each city in the network is visited exactly once by exactly one vehicle, 2. the start and end of the vehicle routes are all at the depot, and 3. some side constraints are satisfied.

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In VRP with Pick-Up and Delivery, the problem can be divided into two Classical VRPs, one for Pick-Up and the second for Delivery, since real-life situations are usually more complicated than the Classical VRP.

This article is relevant to our model because there are similar condi-tions, such as designing vehicle routes that would be most cost efficient, that we took into consideration for our solution and our solution uses a sim-ilar Pick-Up and Delivery VRP where there are two paths. The differences between the problem we considered and the VRP is that our starting and ending destinations are not the same, changing the model formulation.

The problem considered in [4] is a multimodal transportation problem with both scheduled and flexible-time services. Overall, the problem is de-scribed to be a multimodal network where given a set of origin-destination transport requests, one must optimally route these requests through a min-imum cost network that satisfies each customer’s demand.

This article is similar to our model because of the usage of conditions like a contracted vehicle fleet, a pick-up and delivery constraint, and usage of time restrictions. However, this article discusses multimodal transporta-tion, meaning it discusses using multiple types of transportatransporta-tion, whereas our problem only considers using the trucking companies to haul the oil. We also avoid split-loads, but this article is not concerned about avoiding these, and finds them to be cost-efficient.

This article is also relevant because there are constraints, such as fixed and variable costs, time restraints, and pick-up and delivery constraints, that are similar to constraints our problem considers, and so this article was influential for our model. However, the multimodal nature of their problem causes their costs to be much more variable than ours, when determining shipping costs and transportation requirements.

In [3], Neralic et al. considered the Production-Transportation Prob-lem (PTP) in the petroleum industry. The PTP is a generalization of the transportation problem, with several plants placed at different locations and many customers where products are delivered. Some distinguishing considerations in the PTP are that

1. each producer can operate in several modes of production, and 2. each mode is characterized by quantities of products and variable

production costs.

In these problems, minimizing transportation costs along with minimizing production costs are considered. This particular article considered the

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Bilevel PTP, which are problems containing a hierarchical decision struc-ture where each level has independent and often conflicting objectives. There are two levels, an upper level (the leader) and a lower level (the fol-lower). The leader organizes the production so as to meet the demand at minimal production costs. The follower organizes the transportation of the products to the customers so as to achieve minimal transportation costs.

The ideas seen here are useful, since some of the constraints, namely, ensuring that each of the customer demands were met and being sure to not exceed the available products when determining where to transport them, relate to the ones that we will consider.

1.3

Our Approach

Linear programming is one tool that allows us to mathematically describe real life constraints while considering an objective function. Thus we chose these to study the dispatch process. The set of requirements that Noble currently has is very specific to their internal operations, therefore we de-veloped a mathematical model to describe their requirements, so we could gain insight into the truck dispatching decisions for any given day.

For our model, we consider the batteries as source nodes and the deliv-ery locations as our destinations, as seen in Figure 1.1. The arcs between the batteries and the destinations display the possible trucking companies that can be used to haul. We weight the edges with the revenue that is gen-erated if that particular path is chosen. In order to describe this network, we created equations to describe real life constraints while maximizing the profit.

1.3.1

Mathematical Formulation

For each load we must consider the battery, the trucking company that will haul, and the delivery destination. Thus we consider the following sets:

B − Batteries,

C − Trucking Companies, and

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Figure 1.1: Oil Transportation Network

Destinations

Batteries

1.3.2

Variables

Our model uses the following variables:

xi,j,k − Number of loads sent from batteryito destinationkusing company j,

pi,j,k − Profit for sale at destinationk when taken from batteryiby companyj,

si − Supply of oil available at batteryi,

tj − Number of loads needed to meet monthly contract with companyj,

hj − Maximum number of hauls company j can take per day, and

mk − Minimum number of loads to send to destination k.

1.3.3

Model

maximize X i∈B X j∈C X k∈D pi,j,kxi,j,k (1.1) subject to X i∈B X k∈D

xi,j,k ≥tj for eachj ∈C (1.2)

X

i∈B

X

k∈D

xi,j,k ≤hj for each j ∈C (1.3)

X

i∈B

X

j∈C

xi,j,k ≥mk for eachk ∈D (1.4)

X

j∈C

X

k∈D

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Objective function

Equation (1.1) is our objective function. Our goal is to maximize the total

profit based on the price to send a load from any batteryito a destination

k using a trucking companyj.

Fulfilling Trucking Contracts

Equation (1.2) ensures that our model will assign a minimum number of

hauls to each trucking companyj so that the monthly contracts are met.

Maximum Hauls

Equation (1.3) ensures that our model does not assign more hauls to each

trucking companyj than the company can take on a given day.

Minimum Loads to a Destination

Equation (1.4) ensures that a minimum number of loads are sent to each

destinationkon a given day.

Maximum Loads Taken

Equation (1.5) ensures that our model will not take more supply from any batteryithan it currently holds.

1.3.4

Other Considerations

In addition to the above constraints, we considered other constraints that we factored into the way in which our profits are calculated. Noble Energy currently has contracts with companies who hold leases for batteries that stipulate which trucking companies can haul for them. In order to account for this, if some battery cannot be assigned to a specific trucking company, then the profit along the corresponding arc is zero. Thus Noble Energy has a zero profit if the “incorrect” (based on contracts) trucking company is assigned to pick-up loads at a battery. Using the same method we also factor in the contracts that stipulate if oil from a specific battery must be delivered to a specific location.

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1.4

Validation of Our Solution Approach

We chose to use the modeling language GAMS to code our model and test data sets and outputs of our model. GAMS is a widely used tool for writing linear programming models. Our program uses the solver CPLEX which interfaces with GAMS. We also found GAMS to be useful for reading in large data sets, such as from csv files. The full program code is given in Appendix 1.A.

To ensure that our model was working, we tested the output on small data sets to ensure that the code would execute. We began by creating a set that consisted of three batteries, three trucking companies and three product destinations. After a few successful tests on small data sets, we incrementally increased the size of the data set. This allowed us to make appropriate changes to our model to correct the results to be more consis-tent with real-world situations. One of the things we noticed early on was that the software was outputting that a large number of split loads should be taken. This led us to program in a constraint that would force only full loads to be taken. We used synthetic data to assign costs of using a truck-ing company to send one load from a battery to a destination as well as the income generated when selling oil to each destination. This data can be modified to reflect current sales prices and costs. We also considered an approximate distance from batteries to destinations to account for the increased cost for sending a load across longer distances. The data tables are shown in Appendix 1.B.

Our goal with designing and implementing our model was to provide Noble Energy with a way to inform their dispatch decisions. Though our model does make some assumptions (such as ignoring uncontrollable shut-ins due to weather or random events), it gives a way to guide decision-making. Our intention is that Noble Energy can use our model to gain insight into the way in which their dispatch network works. We believe our model can be used to help Noble run many different scenarios to help make decisions when generating a dispatch list. One example for such a list is included in Appendix 1.C and may provide a good starting point for its dispatch and transportation planning.

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1.5

Conclusion

This project considered Noble Energy’s transportation problem of deciding which batteries need to be serviced, where to send the loads of oil, and which trucking company to use. The various constraints that are required by Noble Energy, the trucking company, and other contractual obligations were considered when modeling this problem with a linear programming perspective. Our solution used GAMS, with data inputs through Excel, so that new and updated data could be added as frequently as needed.

Our model outputs the number of loads each battery would have picked up, where those loads should be sent, and which hauler should take those loads. While the model was written in terms of loads, it could easily be put in terms of barrels of oil. Also, by removing some constraints, Noble En-ergy could use this model to consider where to expand. For example, not requiring a minimum number of loads to be sent to the destinations could offer support for sending more oil to certain destinations if the model sends most of the loads to a particular place. Thus, we feel this model is flexible enough to become a useful tool to give Noble Energy more information as to which batteries are in need of pick-ups, where oil needs to be sent, and which trucking companies would be available to haul loads.

As with any model, there are certainly areas that do not address all of the real-world needs of Noble Energy. We did not consider uncertainties that could change data or cause problems, and there are other assump-tions that may be inaccurate in certain cases, such as the way our model predicts how much oil is available at a particular battery.

Second, when Noble Energy dispatches haulers to batteries, for those batteries with a higher pumping priority (faster oil flow rates, closer to shut-in range, etc.) Noble Energy can request that haulers pick up the loads within specific time constraints. These time windows are not considered in our model and would be the next step to improve our model so that it more accurately describes the real-life needs of Noble Energy. As it stands, the dispatcher can take into consideration the suggestions provided by our model and use additional work experience and insight to also determine what time windows would be appropriate.

In summary, our model can offer valuable insight into the current dis-patch and transportation at Noble Energy. It may also also be used and further adjusted to inform Noble Energy’s future decisions for expansion.

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1.6

References

[1] R. K. Ahuja, T. L. Magnanti, and J. B. Orlin. Network flows. Prentice

Hall Inc., Englewood Cliffs, NJ, 1993.

[2] Y. Liong, I. W. Rosmanira, O. Khairuddin, and M. Zirour. Vehicle routing

problem: Models and solutions. Journal of Quality Measurement and

Analysis, 4(1):205–218, 2008.

[3] Z. Lukac, D. Hunjet, and L. Neralic. Solving the

production-transportation problem in the petroleum industry.Revista Investigacion

Operacional, 29(1):63–70, 2008.

[4] L. Moccia, J.-F. Cordeau, G. Laporte, S. Ropke, and M. P. Valentini. Modeling and solving a multimodal transportation problem with

flexible-time and scheduled services. Networks, 57(1):53–68, 2011.

Wattenberg Oil Load Dispatch and Hauling Optimization