Enhanced Oil Recovery Planning

Transcription

1 University of Wyoming Wyoming Scholars Repository Petroleum Engineering Senior Design Final Reports Chemical & Petroleum Engineering Student Scholarship Spring 2016 Enhanced Oil Recovery Planning Brennan Holowaty University of Wyoming Bryson Jones University of Wyoming Trent Kostenuk University of Wyoming Quentin Stronski University of Wyoming Tyson Trail University of Wyoming See next page for additional authors Follow this and additional works at: Part of the Petroleum Engineering Commons Recommended Citation Holowaty, Brennan; Jones, Bryson; Kostenuk, Trent; Stronski, Quentin; Trail, Tyson; and Zuchetto, Ryan, "Enhanced Oil Recovery Planning" (2016). Petroleum Engineering Senior Design Final Reports This Thesis is brought to you for free and open access by the Chemical & Petroleum Engineering Student Scholarship at Wyoming Scholars Repository. It has been accepted for inclusion in Petroleum Engineering Senior Design Final Reports by an authorized administrator of Wyoming Scholars Repository. For more information, please contact

2 Author Brennan Holowaty, Bryson Jones, Trent Kostenuk, Quentin Stronski, Tyson Trail, and Ryan Zuchetto This thesis is available at Wyoming Scholars Repository:

3 ENHANCED OIL RECOVERY PLANNING Final Report Prepared For: Dr. Brian Toelle Prepared by: Group #14 Bryson Jones Trent Kostenuk Brennan Holowaty Quentin Stronski Tyson Trail Ryan Zuchetto Date Prepared: Original Version: April 18 th, 2016 Current Version: May 6 th, 2016

4 MEMO To: Brian Toelle From: Group #14 Date: May 6, 2016 Subject: Project #5 Enhanced Oil Recovery Planning This document contains final project objectives and recommendations. The project focuses on performing a reservoir characterization and dynamic simulation in an effort to develop the primary objective of the group, an optimum enhanced oil recovery scenario (EOR) for the Candy Draw field located in Campbell County, Wyoming.

5 Executive Summary The team s project was to provide an EOR recommendation to the Candy Draw Field located in Northeastern Wyoming. Initially, a field overview was obtained from the EORI including the associated field information, wells, tops, production history, and reservoir properties. Twelve wells have produced roughly four million stock tank barrels since This total includes the incremental production gained from the current water flood program started in 1990 of which produced approximately two million, three hundred thousand barrels. During this year, 5 wells were converted to water injectors to begin a secondary recovery water flood scheme. This information was obtained from the Wyoming Oil and Gas Conservation Commission website. After researching the applicable EOR methods for this field and consulting with the EOR screening check sheet, the team decided that a polymer injection program would be best suited for this application. With this information, the group performed a field characterization, constructing both a static and dynamic model by utilizing the combination of Schlumberger s Petrel software program and CMG. As this field has been part of a water flood injection program since 1990, part of the analysis was to note the incremental production generated and to evaluate the water flood program and injection rates to the current secondary recovery program. The team then performed a complete evaluation based on the models constructed and has provided a final EOR recommendation based on the results and researched findings. The team s economic feasibility analysis illustrates the benefits of implementing comparable polymer flooding EOR projects to similar reservoirs that are in a more virgin state. The results show a stark contrast in profit in regards to the timing of the EOR project. The team recommends reservoirs with less pressure drawdown and production depletion when considering such an EOR planning project. An undertaking of this magnitude has required significant time, energy and successful collaboration by all team members. Changes have been agreed upon with ongoing challenges in order to better suit a desired project result on schedule. Included in this report are the final project work flow chart, project schedule (Gantt chart) and risk assessment all purposed to provide stakeholders with visual representations of the progress and overall map of the project. The project plan includes the various tasks, sub tasks, decision points and deliverables broken down item by item. This facilitates a quick and complete understanding for stakeholders of the necessary steps that were required to complete the project on time. i

6 Table of Contents Executive Summary... i Table of Contents...ii Table of Figures... iii List of Tables... iv Nomenclature... iv Introduction... 1 Team Strategy... 5 Project Workflow... 6 Project Schedule... 9 Risk Analysis Data Review Stratigraphic Review Powder River Basin Project Results Petrel Static Modeling CMG Dynamic Modelling Enhanced Oil Recovery Planning Enhanced Oil Recovery Economics Summary Conclusions Bibliography Appendices... A Appendix #1: Well Data... A Appendix #2: Risk Matrix... B ii

7 Table of Figures Figure 2: Candy Draw Field Area and Boundaries [1]... 2 Figure 3: Powder River Basin Location [5]... 2 Figure 4: Annual Oil and Water Production... 3 Figure 5: Annual Oil and Water Production... 4 Figure 6: Phase 1 Project Planning... 6 Figure 7: Phase 1 Project Planning (Continued)... 7 Figure 8: Phase 2 Project Execution... 8 Figure 9: Candy Draw Structural Map [4] Figure 10: Powder River Basin Geological Timeline [5] Figure 10: Petrel Top of Minnelusa Structural Map Figure 11: Petrel 3 D Base of Minnelusa Structure Figure 12: Petrel 2 D Base of Minnelusa Structural Map Figure 13: Petrel 2 D Porosity Map Figure 14: Petrel Permeability Model Figure 15: Petrel 2 D Water Saturation Map Figure 15: CMEX IMEX Simulation to Date Figure 16: CMG IMEX Polymer Model Simulation Figure 17: Time Step #1(1985) Oil Saturation Figure 18: Time Step #2(1988) Oil Saturation Figure 19: Time Step#3(2000) Oil Saturation Figure 20: Time Step#4(2026) Oil Saturation Figure 21: Time Step#1(1985) Water Saturation Figure 22: Time Step#2(1991) Water Saturation Figure 23: Time Step#3(2006) Water Saturation Figure 24: Time Step#4(2026) Water Saturation Figure 25: EOR Screening Criteria Figure 27: Polymer EOR Flood [6] Figure 28: Annual Polymer EOR Production 2016 Startup Figure 29: Return on Investment 2016 Startup Figure 30: Annual Oil and Water Production Figure 31: Annual Polymer EOR Production Startup Figure 32: Return on Investment 1994 Startup Figure 33: Well Data [1]... A Figure 34: Risk Matrix... B iii

8 List of Tables Table 1: Well Count and Production Overview... 1 Table 2: Project Schedule Table 3: Risk Assessment Table 4: Data Review Table 5: 8 TH Reservoir, Austria Polymer Costs (Euros) SPE, Sieberer, Jamek, Clemens Table 6: 8 TH Reservoir, Austria Polymer Costs Applied to Candy Draw, Wyoming Table 7: Cumulative Polymer EOR Production 2016 Startup Table 8: Cumulative Polymer EOR Production 1994 Startup Nomenclature UWYO EORI WOGCC EOR EIC LAS CMG University of Wyoming Enhanced Oil Recovery Institute Wyoming Oil and Gas Conservation Commission Enhanced Oil Recovery Energy Innovation Center Log ASCII Standard Computer Modelling Group Ltd. iv

9 Introduction A team of six petroleum engineering students were selected to perform Enhanced Oil Recovery (EOR) research and plan a development project. The group selected the Candy Draw field located in Northeastern Wyoming (see figure 1). The field was discovered in June of 1985 by Marlin Oil Corporation. Over the duration of field activity, twelve wells have reported production while only seven are currently active producers. Five are active water injectors. There are also two abandoned wells that were not included in the analysis. The field produces from a stratigraphic trap structure in the lower B sandstone of the Permian Minnelusa formation located within the Powder River Basin. The first discovery well was drilled in July of The field was discovered by utilizing seismic stratigraphic data. Unfortunately, the seismic data was not available for this project. The field contains nine million barrels of proven reserves and has reported a cumulative production of over four million stock tank barrels to date. Table 1: Well Count and Production Overview 1 Page

10 PETE 4735 Final Report Group #14 Figure 1: Candy Draw Field Area and Boundaries [1] The Powder River Basin is a geologic structural basin located primarily in Northeastern Wyoming and Southeastern Montana. The basin is responsible for supplying upwards of forty three percent of the nation s coal. Hydrocarbons are produced from rocks predominantly from the thick section of Cretaceous rock. The Candy Draw field s producing Minnelusa formation is located at an average depth of seventy four hundred feet into the basin. The field has been utilizing a secondary recovery water flood treatment since the early 1990 s in an effort to increase recovery rate. The ultimate project objective is to research and analyze various Figure 2: Powder River Basin Location [5] enhanced oil recovery options and make an educated recommendation for future production scenarios. 2 Page

11 Figure 3: Annual Oil and Water Production Shortly after the field was subjected to a waterflood secondary recovery plan, production rates drastically declined. Referring to figure 3 above, you can conclude injection breakthrough occurs approximately in the year This breakthrough is defined as when a field first begins to produce the injected water. In 1992, water production rates jumped from an average of twenty thousand barrels per year between 1985 and 1991 to an average of nearly threehundred twenty five thousand barrels annually between 1993 and A distinct drop off in production can be seen just as injection breakthrough occurs. This phenomenon raises questions regarding the waterflood injection program and further validates the viability of a tertiary recovery program for this field. This will be explored in detail during the project. Cumulative oil and water production is reflected in the graph below (figure 4). The plateau of oil production indicates the waterflood injection program benefit may be limited or less effective than originally expected. However, according to SciTech Connect, two million barrels of additional production were expected from the waterflood injection program and that total has been exceeded. The scope of this project will address these questions in detail. 3 Page

12 Figure 4: Annual Oil and Water Production An evaluation of the field was completed based on the results of a reservoir characterization and dynamic simulation developed by utilizing petroleum engineering software such as Schlumberger s Petrel and Computer Modelling Group s (CMG) IMEX. IMEX is a threephase, black oil reservoir simulator, also capable of simulating polymer injection rate production effects. These results ultimately allowed the team to create a detailed EOR plan. The final optimum EOR scenario specifically tailored to the Candy Draw field was determined from the inputted field, as well as reservoir and production data obtained from the EORI and WOGC. The ultimate project objective was to research and analyze various enhanced oil recovery options and make an educated, economic recommendation for future production scenarios. 4 Page

13 Team Strategy Upon selecting the candidate field to be optimized, a specific strategy/plan was put in place to ensure that all deliverables as well as the final product would be completed on time. Firstly, the large group of six was sub divided into three groups of two to divide out the tasks equally. This would allow the team members to work in smaller groups enabling a more specific focus which has proven to be more effective through the duration of the project. To ensure that the group as a whole remains collaborative, aligned, and working toward the same objectives, the team scheduled frequent meetings to keep each other informed on each sub group s progress. Additionally, the group had implemented a messaging application which helped to keep the group updated. Although the sub tasks were divided up, the major deliverables were completed as a large group to ensure that everyone had input and agreed with the content included in the submitted deliverables. Upon discovering the geophysical log format was incompatible with the software, the group was faced with additional tasks. As a result, the team had to alter the schedule and strategy accordingly. The group s GANTT and Work flow deliverables were revised accordingly. Otherwise the original strategy was proven effective as challenges were overcome. 5 Page

14 Project Workflow In developing the group s project workflow chart, the group considered all projects tasks, decision points, dependencies and deliverables. The project was separated into two main phases. Phase one focused on project planning initiatives specifically, while the second phase concentrated on project execution. The planning initiative phase began with utilizing the UWYO EORI database in making an informed selection of an appropriate field. Ultimately, the group decided on the Candy Draw field located in Northeastern Wyoming. After completing the first decision point, the first task was to acquire all available reservoir and well data from the UWYO EORI database, as well as the WOGCC. The acquired data was then used to develop the first deliverables; the draft interim design, as well as team assignments, project workflow and scheduling. Figure 5: Phase 1 Project Planning 6 P age

15 In order to complete the objectives of a reservoir characterization and simulation, thorough research of any and all software available was executed. Group members were tasked with this research and ultimately the most relevant software suite was selected. Concurrently, group members were tasked with conducting EOR research with the intent of identifying all methods that were applicable to the reservoir. Upon completing the research and making a decision on a software suite, group members were tasked with the development of a risk assessment. This entailed identifying potential project risks, controlling them, and mitigating them as required. This concluded the project planning initiatives phase. Figure 6: Phase 1 Project Planning (Continued) The second phase (figure 8 below) of the project began with organizing, correlating and most importantly, verifying all the raw reservoir and well data. This was a crucial task because through the development of the risk assessment, data integrity was identified as the biggest risk based on impact and likeliness. Once the data was deemed complete and accurate, next began the task of loading the data into the selected software suite. Upon the completion of this task, the reservoir characterization objective was completed. The reservoir characterization was then used to develop the second project objective, the reservoir simulation. The reservoir characterization and simulation models, in addition to the EOR research conducted earlier, allowed the group to make an informed decision on the selection and development of the optimum EOR scenario. 7 P age

16 Figure 7: Phase 2 Project Execution 8 Page

17 Project Schedule The project schedule (table 25) was broken down into two phases. The first phase consisted of project planning which included design review, scheduling, planning deliverables, data review, and research. All team members were accountable for deliverables while the research and data review were broken into groups of two. This provided high productivity levels during phase one of project planning. Team meetings provided sufficient updates to ensure all members were informed of current project status. Phase one was completed the week of December 7, Phase two was a continuation of data review as well as data input needed to complete the project execution. Once the reservoir characterization and simulation were completed, an optimum EOR method was selected. An EOR planning scheme was planned, taking into account economics, environmental safety, and development strategies; thus concluding phase two. A significant change to the project schedule involved making the EOR research concurrent with the reservoir characterization research. This change was made because the deliverables did not depend on one another. This allowed deliverables to be completed at the same time. Other changes made to the project schedule included adjusting the time allocated for converting log data to a compatible format using Neuralog. Converting logs from image files to las files was not in the original scope of the project but was a necessary change in order for the characterization software to accept log data. This task increased the project work load. The final addition to the project schedule was the EOR planning and economics tasks. These tasks were pushed back in the timeline due to complications with the simulation software. Most of the research was completed earlier in the schedule but the selection of an optimal EOR plan was not able to be completed until the week of April 18, Page

18 Table 2: Project Schedule 10 Page

19 Risk Analysis There were four primary potential risks identified with this project. The first risk involved issues with project scheduling. The impact rating of this risk was deemed minor because the Gantt chart and project scheduling could be adjusted accordingly throughout duration of project. The risk was rated unlikely because group members were notified of any scheduling changes well in advance and the syllabus was clear and specific. Overall risk level before safeguards as determined by the risk level chart was low. Mitigations and controls included regular project updates and scheduling reviews to ensure timely coordination of project tasks and deliverables. Risk level after safeguards was low. Table 3: Risk Assessment 11 Page

20 The second risk was identified as issues with stakeholder relations. The impact rating was determined to be moderate because healthy team dynamics were critical to project success and progression. The risk was rated as unlikely as team members value inter team relationships and are respectful to all involved stakeholders. The overall risk level before safeguards was determined by the risk level chart as moderate. Mitigations and controls included professional treatment of all involved stakeholders, as well as regularly implemented team building exercises. Risk level after safeguards is low. The third identified risk was ineffective communication. The impact rating of this risk was deemed as major because in cases of ineffective communication, quality and efficiency of project tasks, decisions, and deliverables would be compromised. The risk was rated as unlikely because group members possess communication tools required to effectively schedule and attend group meetings. The overall risk level before appropriate safe guards as determined by the risk level chart was high. Current mitigations and controls included utilizing effective communication tools and attending all team meetings to minimize misunderstandings and promote constructive team discussion. Risk level after safeguards was low. The final identified risk was compromised data integrity. The risk impact rating was determined to be major because insufficient data quality or lack of data would have negative implications on modelling and decision points. The likelihood rating was moderate because data acquired by the group members was relatively incomplete. The overall risk level as determined by the risk level chart was extreme, making this the projects most critical risk. Mitigations and controls included review of data and correlations to ensure no discrepancies. Any outcropping data was reviewed and eliminated as required. The risks that were identified above and mitigated included data integrity and project scheduling. With data integrity the group identified that the log file format was incompatible with the two software programs chosen for modelling. The risk was mitigated by finding a trial version of a software that converted the log files to the compatible file format. The project scheduling risk included major changes to the workflow because of a better understanding of the scope and direction of the project. The group met and decided upon proper workflow changes and discussed new decision points and task paths. New risks identified included the learning curve with the new software chosen for the dynamic modelling of the reservoir, CMG. The risk mitigation began immediately as the group contacted professionals at the school for direction and tutorial recommendations to make the new software more familiar. 12 Page

21 Data Review The amount of data review required has been extensive. Reservoir characterization and simulation needed to include all field, well, formation, reservoir, and production data. Unfortunately, seismic data could not be acquired for this project. Shortcomings with the data reside mostly with well log file format. All of the log files obtained were in.tif format, which is more or less a scan of the hardcopy log, and not compatible with the selected characterization and simulation software Petrel and CMG. Multiple attempts were made to contact the operating company with no word back to see if the team could obtain.las log files, which are compatible. Mitigation strategy was followed and upon researching possible solutions the team contacted a software company called Neuralog and obtained a trial for a digital log converting software. The free trial presented challenges in the form of learning how to function the software. Also, the free trial granted was for a relatively short duration, 15 days. The team was able to overcome this challenge and got the project back on schedule within two weeks. Field Information Well Information Discovery year Well identification (APINO numbers) Well counts Well location (UWI and latitude/longitude) Field status Well elevation Primary/Secondary reservoir Producing reservoir Avg. Reservoir depth Well status Avg. Reservoir thickness Completion date Avg. Elevation Total depth Cumm. Production Geophysical log data Reservoir Properties Geographic basin Lithology Net pay Permeability Depth Temperature Viscosity Formation Information Formation names Formation tops Kelly bushing elevations Perforation Intervals Natural Fractures Faulting Depositional Enviroment Production Information Complete annual and monthly oil production (bbls) Complete annual and monthly gas production (Mcf) Complete annual and monthly water (bbls) Table 4: Data Review 13 Page

22 Stratigraphic Review Powder River Basin In Figure 9 below, a structural map of the Candy draw field is displayed. More specifically, the contoured lines depicted are those of the Minnelusa formation in a sub sea depth scale. This map shows the structurally high and low points of the reservoir which was helpful in inputting contour lines into the Petrel modelling software. This generated more accurate water and oil saturation values (area specific) as well as a better understanding the current water injection scheme/format. Figure 8: Candy Draw Structural Map [4] 14 Page

23 Figure 9: Powder River Basin Geological Timeline [5] Demonstrated in Figure 10, is a cross section of the Powder River Basin in which the Candy Draw is located. The Minnelusa is Permian in age (Later years of the Paleozoic Era) and had a deposition similar to Sabkha in the Persian Gulf (sand dunes). Main types of trapping mechanisms of the Minnelusa formation include Opeche (cap rock) truncation and sealing, loss of porosity by anhydrite cementation, and encasement by dolomite. Knowing how the formation was trapped was a huge aid in determining the optimal EOR polymer injection scheme for the Candy Draw field as well as understanding the current water flooding scheme. 15 Page

24 Project Results Petrel Static Modeling As phase two of the project was underway, an unforeseen circumstance was encountered and addressed by the project s data integrity risk mitigation strategy. Unfortunately, the well log data the team acquired was an incompatible file format. Several attempts were made to contact the operating company, Marlin Oil Corporation, to try and retrieve.las format log files but the group was unsuccessful in its attempts. The team s solution to this roadblock was to contact a software company called Neuralog and receive a license for a fifteen day trial of their software which has the ability to digitize the physically scanned log files into the desired.las format. The roadblock put the project two weeks behind schedule, but the project schedule recovered to a timely completion. Figure 10: Petrel Top of Minnelusa Structural Map In figure 10, a 2 D display of the top of the Minnelusa formation is shown (arrow pointing North) along with its respective wells. This map is comparable to Figure 9 in the pages above which, is a good indicator of validity with respect to the input data. In the picture above, solid circles indicate oil producers, open circles with arrows through them indicate injectors, and circles with cross hares indicate that they are abandoned. The structural map represents the 16 Page

25 changes in subsea elevation across the producing reservoir formation. As in the scale above, the red area to the north are the highest, while the south is the lowest in the Minnelusa formation. The structural map above is critical in determining the oil water contact. From this information, interpolations can be made as to how the injection wells will react throughout the formation. North is into the page Figure 11: Petrel 3 D Base of Minnelusa Structure Upwards Direction is North Figure 12: Petrel 2 D Base of Minnelusa Structural Map 17 Page

26 Shown in Figure 11 and 12, is a 3 D and a 2 D base subsea elevation structural map of the Minnelusa formation. The base is used in the following images to really emphasize on the structures within the Minnelusa formation. Again, red being the highest elevation showing a prominent anticlinal structure, and purple showing structurally low points in the formation. Displayed on the 3D map is the total vertical depth shown on the y axis. This paints a good picture to how deep the wells are with respect to sea level and ground elevation. Upwards Direction is North Figure 13: Petrel 2 D Porosity Map Figure 13 above, is a 2 D porosity map of the Minnelusa formation. The map is an indicator demonstrating the porosity variations throughout the reservoir. The porosity values were created from the neutron density logs imported into petrel. Creating a porosity map was necessary for interpretation of the producing Minnelusa zone, and well as future simulations in CMG. The porosity map demonstrates and average porosity was in the range of 19 to 20 percent, depicting a good sandstone reservoir. 18 Page

27 Figure 14 below, is a 2 D permeability map of the Minnelusa formation. The map is an indicator demonstrating the permeability variance throughout the reservoir. Using the average permeability of 107mD, permeability map was generated. In order to create permeability variance within the reservoir, 107mD was set as a mean permeability. An assumption of 25mD standard deviation was then applied. Appling this assumption the modeled reservoir should demonstrated characteristics closer to true values. The permeability map along with the created data created in the static model is necessary to accurately perform reservoir simulations. Upwards Direction is North Figure 14: Petrel Permeability Model 19 Page

28 Upwards Direction is North Figure 15: Petrel 2 D Water Saturation Map Above in Figure 15, is a 2 D water saturation map representing the water wet areas of the Minnelusa formation. As depicted in the picture, a mean water saturation 70 percent is shown indicating an oil saturation of about 30 percent. This is useful in knowing how our dynamic model will react to more oil saturated areas and how the team could potentially target the red areas in the reservoir as shown above. 20 Page

29 CMG Dynamic Modelling A major deviation from the original project plan was to use CMG software for dynamic modeling rather than Petrel. Multiple software suites were researched and the decision was made as a result of feedback received from key project stakeholders during the symposium in December The team followed up on these recommendations in multiple meetings with Dr. Alvarado, Associate Professor of the University of Wyoming s Petroleum Engineering department. Dr. Alvarado works closely with the University s Enhanced Oil Recovery Institute (EORI). Petrel was utilized for the static model as the software had the capability of exporting static models from Petrel to CMG for dynamic modeling. In the project simulation there were too many assumption required to run the STARS polymer flooding simulation. The group decided to use IMEX for both simulations. The black oil model in IMEX was used for secondary water injection simulation, and the Polymer model in IMEX was used for polymer flooding simulation CMG requires various inputs and assumptions to model flow between dates through the reservoir. The file had to be saved as a rescue file (.bin) from Petrel and then uploaded into CMG in a bin format. Upscaling the grid blocks was necessary as CMG has a maximum allowable input of 10,000. The original model that the group tried to upload was in the range of 66,500. Once the grid was up scaled to the acceptable grid block cap, all of the geophysical properties had to be up scaled as well. Having the geophysical properties up scaled allowed us to import properties into the CMG model for dynamic simulation. Without up scaling the geophysical properties individually the dynamic model cannot interpolate the individual properties. The geophysical properties imported from petrel were porosity, I J K permeability and water and oil saturations. All of the individual property maps are displayed above in the static model section. With the exception of permeability, only an average permeability map is included. 21 Page

30 Creating a dynamic model simulation that displayed accurate results was important to the project. History matching was an important component needed in achieving an acceptable accuracy percentage. Another component that was significant to achieving accurate results was having the correct start up/shut in dates input correctly into CMG. As some of the wells in the field changed from producers to injectors during their life span history so matching was significant in achieving a realistic model. Figure 15 is a simulation of the Candy draw field ran until January 1 st, As seen in the figure below, the cumulative production from the model to date is 744,490 m 3 (4.68 MMbbls) of oil. As stated above, the actual cumulative production for the Candy Draw field is 4.1 MMbbls of oil. These numbers demonstrated an 11.5% overestimation in cumulative production. Although the history matching was not a hundred percent accurate, the group felt it was close enough to proceed forward with the EOR polymer flooding simulation. Figure 16: CMEX IMEX Simulation to Date 22 Page

31 Figure 16 below is a simulation of the Candy draw field to January 1 st These production numbers account for the field being under secondary water flood until January 1 st 2016, then being converted to tertiary polymer flooding for the duration of the simulation. As demonstrated in the figure, the cumulative oil production in the Candy Draw field after 10 years of polymer flooding is estimated to be 1,145,300 m 3 (7.2 MMbbls) of oil. When compared to a water injection simulation running until the year 2026, this is a significant increase in cumulative production. This will be later discussed throughout the economics portion of the report. Figure 17: CMG IMEX Polymer Model Simulation As described later in the economics portion of the report, numerous simulations were run for two different economic cases. Case 1 is the example shown above where a water flood was run until 2016, then the reservoir was converted to a polymer flooding operation. Case 2 is a theoretical simulation converting to polymer flooding in the year This date was deemed the best time to switch to EOR recovery. The group deemed it was the best time to switch to polymer flooding as production from waterflood was declining. Cumulative production results were simplified in tables rather that showing all of the simulation data directly from CMG. This data is shown in the economics section. 23 Page

32 One of the important aspects of the dynamic model is to show the time steps of dynamic properties that changed throughout the simulation. As described earlier, having accurate geophysical property models in the static model is necessary to run the dynamic model. These properties needed to be imported directly from the static model. Without these petrel properties, CMG was unable to interpret and run a dynamic model simulation. The time step figures shown below for both oil and water saturation are from 1985 to These depict the Case 1 scenario where the candy draw field was under secondary recovery water injection until 2016, then switched to tertiary polymer flooding EOR recovery. The group felt it was necessary to include the time lapse for oil and water saturations. The water saturation figures showed better visuals of the injection program in the Candy Draw field. To incorporate the best images of the property changes during the simulation, the time step for water and oil saturations are not taken in the same years. Figures 17 through 20 show time step images of oil saturation, and figures 21 through 24, show the time step images of water saturation. Injection Wells Production Wells Figure 18: Time Step #1(1985) Oil Saturation 24 Page

33 Injection Wells Production Wells Figure 19: Time Step #2(1988) Oil Saturation Injection Wells Production Wells Figure 20: Time Step#3(2000) Oil Saturation 25 Page

34 Injection Wells Production Wells Figure 21: Time Step#4(2026) Oil Saturation Injection Wells Production Wells Figure 22: Time Step#1(1985) Water Saturation 26 Page

35 Injection Wells Production Wells Figure 23: Time Step#2(1991) Water Saturation Injection Wells Production Wells Figure 24: Time Step#3(2006) Water Saturation 27 Page

36 Injection Wells Production Wells Figure 25: Time Step#4(2026) Water Saturation 28 Page

37 Enhanced Oil Recovery Planning When a virgin oil field is first produced, driving forces such as water drive, gas cap expansion or solution gas drive mechanisms will naturally produce the reservoir for some time. However, once this driving mechanism is exhausted, other means are necessary to maximize the project s economics. At this juncture, secondary recovery and enhanced oil recovery (EOR) methods are considered. As this project is focused on exploring various possibilities for secondary recovery and EOR scenarios, the project team has devoted considerable time to EOR research. Ultimately, the team recommends polymer flooding scenario for the subject, Candy Draw field. EOR projects now account for approximately three percent of the world s total oil production. With the drastic drop in conventional hydrocarbon production, the concept of EOR planning has become more important than ever. There are three main mechanisms which may improve oil displacement and/or production; solvent extraction for miscibility improvement, interfacialtension reduction, and viscosity change. The polymer flooding strategy utilizes viscosity change properties to improve oil sweeping effectiveness. Oil prices are the primary factor in determining if and how EOR projects are funded and implemented. Illustrated below in Figure 25 are screening criteria for the eight most popular EOR methods. Additional screening criteria included reservoir clay content and injection brine salinity; both should be low for desirable results. An EOR method is selected based on the screening criterion of oil properties such as gravity, viscosity, and composition. Reservoir properties such as formation permeability, depth and temperature are also important. The most suitable reservoir properties for polymer injection flooding are high permeability and low temperature. 29 Page

38 Figure 26: EOR Screening Criteria When the Candy Draw field and its producing formation are compared with the aforementioned criteria, eliminations of potential methods can be immediately made. Nitrogen and flue gas can be eliminated as it requires a much higher oil gravity (>35⁰API) than the Minnelusa formation offers (~25⁰API). Hydrocarbon gas injection could potentially be a suitable method, but the team had concerns over dipping stratigraphy which was not recommended. Carbon dioxide and other immiscible gas injection methods were eliminated due to unsuitable oil viscosity and saturation. Thermal and mechanical EOR methods were dismissed due to temperature and depth constraints, leaving polymer flooding as the best recommendation. High temperature reservoirs break down the polymer structure and garner them useless to the task at hand. Higher quality polymers are available, but cost effectiveness is paramount for polymer flooding applications. The primary goal in polymer water flooding is to enhance the viscosity of the flood water when waterflood mobility is too high. Polymers, among other additives are mixed with water to decrease mobility of the flood water. The EOR method effectively improves the sweep 30 Page

39 efficiency of waterflood, but can sometimes contribute to permeability reduction as a result. Chemical flood EOR strategies have been less common, however, polymer flooding continues to be successful in improving oil sweeping efficiency. Polymer flooding techniques contribute little to the world s EOR production in contrast with thermal and gas injection applications. It is believed reservoir wettability could have significant implications on polymer flooding success, however the research to confirm such relationships is not yet fully developed. The main constraint for polymer flooding EOR methods is economic feasibility and profitability. Figure 27: Polymer EOR Flood [6] Enhanced Oil Recovery Economics According to Taber, Martin and Seright EOR Screening Criteria revisited, current highmolecular weight polymers often experience high retention and low propagation rates for rock permeabilities less than 100mD. As the subject reservoir holds an average permeability of 95mD, this is a potential concern. Lower molecular weighted polymers could provide relief to this constraint. However, reducing the molecular weight of the polymer requires more polymer, which will negatively impact the economics of the project. Another limitation this project may encounter also concerns the subject reservoirs permeability limitations. The added polymers 31 Page

40 make it increasingly difficult to maintain target injection rates. This problem can be mitigated by hydraulically fracturing the injection wells, but this strategy would all but kill the project s economics. As mentioned above, the most common project killer for polymer injection EOR methods is economics. Before the specifics of the proposed polymer EOR plan are detailed, it is crucial the reader understands the scope of the economic feasibility study. The goal was to ultimately determine if implementing a polymer injection EOR project would be a profitable venture. If so, at what oil price, and when the project would return on its investment. To achieve this goal, the study investigated the required capital, operating cost, and revenue at various oil prices that would be incurred on top of existing figures. That is, existing costs and revenues were subtracted from the expected costs and revenues once the polymer flood is initiated. In order to perform such an economic analysis, certain assumptions had to be made. Firstly, no additional injection or production wells would be drilled. Secondly, because the Candy Draw field is currently producing under a secondary waterflood mechanism, existing surface equipment, facilities, utilities and other infrastructure can be utilized. Also, for simplicity, taxes and petroleum revenue royalties were neglected. The primary source that provided the project with approximations for what to expect in terms of incremental capital and operating costs was a report published by the Society of Petroleum Engineers (SPE), Polymer Flooding Economics, Sieberer, Jamek, Clemens, This source was particularly reliable because it was published recently in April of The subject of study was a reservoir located in Austria called the 8 TH Reservoir. The team had confidence utilizing this source as the 8 TH reservoir is very comparable in terms of well count, permeability and area. Listed below in table 5 are the baseline costs they presented in Euros. Table 5: 8 TH Reservoir, Austria Polymer Costs (Euros) SPE, Sieberer, Jamek, Clemens These polymer costs (euros) were converted to American dollar figures using the April 29 th, 2016 exchange rate of $1.14USD per Euro. The table below illustrated the incremental costs expected for the Candy Draw field by utilizing the currency conversion and applying the assumptions introduced above. 32 Page

41 Table 6: 8 TH Reservoir, Austria Polymer Costs Applied to Candy Draw, Wyoming As illustrated in the table above the group estimated a capital cost of six million dollars in capital expenditures to expand the existing facilities to accommodate the polymer flooding project. This was acquired by estimating the facility expansion costs to be roughly twenty percent of building the 8 TH reservoir facility from the ground up. Secondly, the team arrived at an estimated increase of just over five hundred fifty thousand dollars in operating cost. Most of this incremental operating cost is attributed to the cost of the polymer raw material as well as water treatment and polymer separation of produced fluid. A cost of just under four hundred thousand dollars per year was calculated based on the injection rate of the five injector wells, multiplied by the converted polymer cost from the 8 TH Reservoir study. The polymer, after currency conversion was calculated to be approximately $3.50 per kilogram. A concentration of one kilogram of polymer per cubic meter of injected water was acquired from our CMG dynamic modelling software simply by trial and error. When developing the dynamic model the group tried various concentrations in an effort to determine the concentration that would yield the most productive results. Additionally, an estimated increase of two hundred thousand dollars was expected for the produced fluid treatment required. Return on investment (ROI) was calculated utilizing a relatively elementary economics formula: ROI = Revenue Capital Cost Operating Cost Inserting the values discussed above: Equation 1: Return on Investment ROI = Incremental Revenue $6,000,000 $568,941/yr It is important to remember this incremental revenue is the production of the proposed polymer flooding project with the production expected from the existing waterflood deducted. This incremental production was multiplied by various oil prices to calculate the project ROI. These oil prices are futuristic averages. 33 Page

42 By implementing a polymer waterflood EOR immediately (2016), our dynamic model was able to provide us with the following production: Table 7: Cumulative Polymer EOR Production 2016 Startup Figure 28: Annual Polymer EOR Production 2016 Startup As the field has already produced a total just shy of four and a half million barrels to date, a cumulative production of 4.7 million barrels isn t all that attractive. One can conclude the reservoir pressure and production is too far depleted to be a good candidate for an EOR project. After applying this increase of approximately a quarter million barrels of incremental production to our economic calculations, the following figures were produced. 34 Page

43 Figure 29: Return on Investment 2016 Startup As seen in figure 29 above, by implementing a polymer flooding EOR in 2016, the project will not obtain any return on investment at a futuristic average oil price of forty dollar per barrel, nor sixty dollars per barrel. With a futuristic average oil price of eighty dollars per barrel, a return on investment can be expected in the year 2023, approximately seven years after startup. Furthermore, a net profit of approximately two and a quarter million dollars could be achieved after a ten year period. After accomplishing the economic feasibility study of a polymer flooding EOR for a 2016 startup, the team decided to take it one step further. A new goal was set to determine what kind of profits could be had if the polymer flooding EOR was implemented at an ideal time. Looking back at the field decline analysis introduced earlier, a drawdown can be identified merely a few short years after the secondary waterflood was implemented just before Page

44 Figure 30: Annual Oil and Water Production The team concluded that the first signs of that drawdown (~1994) might be an opportunistic time to implement the proposed polymer flooding EOR project. In doing so, the team s dynamic model provided the production shown below. Table 8: Cumulative Polymer EOR Production 1994 Startup If the proposed polymer flooding program was initiated in 1994, the field would see an increase of approximately nine hundred thousand barrels of oil. Furthermore, this scenario would reach this production ten years sooner than the 2016 startup. 36 Page

45 Figure 31: Annual Polymer EOR Production Startup Figure 32: Return on Investment 1994 Startup By implementing the EOR program at the time the team concludes to be an ideal startup, the incremental oil production would yield a return on investment almost immediately. Depending on futuristic commodity prices, the project could yield profits of up to fifty million dollars. 37 Page

46 Based on the teams economic feasibility research detailed above, a few conclusions can be made. Firstly, the 2016 startup scenario would not experience a return on investment unless futuristic oil prices were greater than approximately seventy five dollars per barrel. However, this return on investment would not be acquired until about seven to ten years into the EOR project. A company considering such a project would be exposing itself to a great degree of risk associated with commodity price. 38 Page

47 Summary An important quality of any successful project team is the ability to work through issues as they arise. The issues that have arisen with this project have been addressed and dealt with accordingly by utilizing the risk mitigation strategies planned. A strategic plan was established to ensure the team worked effectively, in collaboration, and that the project deliverables were completed on time. Phase one of the project was completed last year and phase two is now complete. Through the team s EOR research and screening, a polymer waterflood method was selected as the most appropriate scenario for the Candy Draw field. Clear hydrocarbon and formation properties were key in screening potential EOR methods. The polymer and required surface infrastructure make balancing the project economics very challenging. The year 2016 has seen oil prices stabilize around forty US dollars, and would need to recover substantially for the 2016 startup venture to garner serious consideration. If the EOR planning project were to be applied to a similar yet more virgin reservoir, results would be substantially improved at a wider range of commodity price. 39 Page

48 Conclusions According to EOR Screening Criteria revisited, Taber, Martin and Seright, the most appropriate EOR method for the Candy Draw field is a polymer waterflood injection program. For the ideal scenario, a polymer flood EOR startup in 1994 would supply incremental production such that a return on investment could be achieved within the first couple years of the project, depending on commodity price. Of course, the 1994 startup is not an option today, but the results of this project can be applied to other ventures. One final conclusion can be derived from the data. The team s economic feasibility analysis illustrates the benefits of implementing comparable polymer flooding EOR projects to similar reservoirs that are in a more virgin state. The results show a stark contrast in profit in regards to the timing of the EOR project. The team recommends reservoirs with less pressure drawdown and production depletion when considering such an EOR planning project. 40 Page

49 Bibliography [1] "Wyoming Oil and Gas Commission." N.p., n.d. Web. 07 Dec [2] "Crisis Mapping and Cybersecurity Part II: Risk Assessment." Diary of a Crisis Mapper. N.p., 14 Dec Web. 07 Dec [3] "Petrel Software." Schlumberger Software. Schlumberger, n.d. Web. [4] "AAPG Datapages/Archives." Candy Draw. N.p., n.d. Web. 18 Apr [5] "The Powder River Basin A Root Contributor to Global Warming." WildEarth Guardians. N.p., n.d. Web. 18 Apr [6] "Polymer Waterflooding." Petrowiki. N.p., n.d. Web. 18 Apr [7] Taber, J. J., F. D. Martin, and R. S. Seright. "EOR Screening Criteria Revisited." New Mexico Petroleum Recovery Research Center (2015): n. pag. Web. 41 Page

50 Appendices Appendix #1: Well Data Figure 33: Well Data [1] A

51 Appendix #2: Risk Matrix Figure 34: Risk Matrix B