2.2. Issues that have arisen since last time.Our most challenging task besides figuring out the CMG computer program was to collect the necessary data, as the data that was given to us by the EORI lacks some fundamental properties that are crucial to complete modeling of chemical flooding operations. In particular, section 1.3 in this report notes the paramount importance of raw data showing interfacial tension as a function of alkali and surfactant concentration. There is no substitute for this hard data in chemical flood modeling; unfortunately, we do not have a great deal of this data, and we have found ourselves resorting to using data from other fields reported in the literature. Even though we feel that we have learned the basics of operation of the CMG simulator, we have sill found that CMG can be finicky and will sometimes produce errors in certain simulations for particular fields. This is particularly vexing, as we have run successful simulations on several other fields using consistent data input methods. In the end, it appears that some field simulations simply do not end up working, and we are struggling to understand why. We did manage to get results for few fields, so the various results from these simulations will be focus of the content presented in report. To ensure the accuracy of the results, we want to run simulations on as many fields as we can. So far, the goal is around ten fields. A sample of this size would likely give us more certainty as to the accuracy of our results. At this point, we could come up with some more definitive preliminary conclusions, which is the general goal for this project. Based on the few fields that we have run so far, as discussed in the previous section of this report, the early conclusion is that waterflooding usually results in greatest value, while the ASP and polymer floods have yet to add a significant contribution towards the greater oil recovery and economic enhancement. One of our next tasks should be to evaluate fields with different permeabilities to see how chemicals would help in the lower permeability environments where pore sizes are smaller and capillary trapping may be more extensive, which would result in the reservoir being more responsive to ASP flooding or polymer flooding operations. As oil prices are quite low right now, we also recognize the uncertainty related to any economic analysis using the uncharacteristically low prices seen of late. To this end, it may be ideal for us to come up with a process in which we input data into CMG, get the production data, and then analyze that data with different prices of oil to see the effect on overall economics. We may also consider reporting results in terms of non-financial metrics, as well, such as in terms of total recovery enhancement, for example.Finally, after more detailed study of the simulations done so far, we have come to the conclusion that our producing bottomhole pressure may be too low, because we are seeing water production at very high rates that probably cannot be sustained in the face of reasonable surface facility constraints on liquid throughput. The producer bottomhole pressure was inputted as a typical minimum that can be obtained using a rod pumping, artificial lift system. From casual internet research on this topic, we found typical minimum bottomhole pressures reported for such systems at around , so this was the pressure used in our simulations. After further consideration, however, we have determined that a minimum pressure such as this may not be appropriate for a flooding situation in which continual water injection serves to maintain reservoir pressure; this would likely lead to flow rates that are unrealistically high. It may be contributing to the general biasing of all results so far toward waterflooding. If there is no restriction placed on producing rates, then recovery could be greatly accelerated in the case of a standard water flood over the case of an ASP or polymer flood in which large slugs of viscous fluid must be driven through the reservoir. These more viscous slugs would consume more of the available pressure drop from the injector to the producer and slow production rates and overall recovery. The end result of all this will be a waterflood that quickly produces large quantities of oil at a high water cut versus an ASP or polymer flood that produces oil more slowly at a more favorable water cut. Even with the more favorable water cut, however, it may be that the delay in recovery in the ASP/polymer case may be enough to cause these options to be less attractive than a regular waterflood. Constraining total liquid rates to more normal levels, however, may counteract this and cause reduced water cuts brought about by ASP or polymer flooding to be much more valuable.Thus, from this it appears that we may need to consider including total liquid production rate constraints on the producers in our models. The difficulty we see with this, however, is that the maximum practical liquid rate is likely to be a function of surface facilities constrains, which can vary widely from field to field in terms of overall quality and capacity. Plus, we also envision that detailed information on surface facilities arrangement could be difficult to locate. We may wish to invest further research into this topic going forward in our design implementation.

1.6. Step 5 of Design Implementation: Evaluation of Economic Potential of Fields and Field RankingOnce reliable forecasts are obtained for different fields from CMG for the overall oil production, we can use these results from the simulation to perform our economic analysis. The basis of our economic assessment will be a discounted cash flows (DCF) model where estimated revenues from the forecasted oil and gas production over time will be discounted back to the present, as well as other anticipated costs. We intend to use a discount rate of 8% for this analysis, which is roughly representative of typical discount rates used in many different investment valuation applications.For this analysis, we have identified a number of key costs to be considered. The first important costs will be related to severance taxes and royalty, as these are flat-rate costs that are applied directly to the gross value of the produced products and so are easily incorporated into the assessment. Currently, the severance tax rate in Wyoming is 6% (Wyoming Taxpayers Association, n.d.). Jordan has extensive personal experience as a land professional in Wyomingincluding experience in analysis of federal title. From his general knowledge, he knows that most productive land in Wyoming is located on BLM land. BLM leases will either have a royalty rate of on older leases or on newer ones. Taking an average of these royalty rates, one arrives at a rate of , so this will be the royalty rate assumed for most of our field analyses, unless specific data is obtained to the contrary. One other important production-related cost is related to the produced water. Fields subjected to waterflooding and chemical flooding often have high, attendant water production rates. This excess water production often carries with it significant costs related to disposal of the water, as set forth in great detail by Bailey, et al. (2000). Their comprehensive work on this important topic also includes estimations of these water disposal costs. These cost estimates include costs related to lifting, separation, de-oiling, filtering, pumping, and injecting. Based on their general industry experience, they provide estimates of average disposal costs per barrel of water produced for a variety of flow rates. We intend to use the cost estimates provided by this paper to determine the relevant water disposal costs in our economic analysis.Obviously, product pricing is an important factor in the economic analysis, and these prices can certainly vary greatly on day-to-day basis. We have determined to use pricing data obtained from the U.S. Energy Information Administration (2015a; 2015b, which reports typical crude and natural gas prices for Wyoming producers. The prices used represent the most recent prices reported in these databases (November 2014). These prices were and for oil and gas, respectively. The basic procedure followed in the DCF model involves determining the revenue from production in each compounding period (determined from forecasted production and assumed product prices), as well as the associated costs. These cash flows, then, will be discounted back to present value using the assumed discount rate, as noted before. Appendix E provides a more thorough example of the computations associated with this process, while Appendix F shows comprehensive results for one of the fields analyzed thus far.The final important cost to consider is the cost of the injected chemicals. Calculation of these costs can be somewhat involved, as these costs come into play only at a certain points in time when the different chemical slugs are injected. An example of the complications related to this would be in one of our flood simulations for the Deadman Creek field. In this particular simulation, the introduction of the chemicals was delayed until the year 2022, and chemical injection continued until 2027. There were also periods when only type and concnetrations of the injected chemicals were varied. These situations musts be dealt with carefully; the exact process is more fully described in Appendix E. Prices for chemicals will likely be determined from informal internet searches. These prices can vary depending on the exact type of akali, surfactants, and polymer used.In addition to all of these costs, we would also potentially like to research costs related to operations charged by service companies and other similar entities. Once these are known, they could be implemented into the analysis; these would have the function of bringing estimated field values down to more realistic levels. However, we believe that the assumptions we have made so far in relation to costs should be sufficient for the purposes of field relative rankings, at the very leastif not for completely accurate field valuation.The end goal of our economic evaluation is to rank fields in order of economic potential for chemical flooding. As discussed in the previous section of this report, we intend to perform flood simulations on as many of the most attractive fields, as determined by our screening work in the previous semester, as possible. These simulations will be performed on a hypothetical 5-spot pattern in each field, as discussed in the previous section, and, so the economic analysis described in this section will apply to a flood of such a 5-spot. Thus, this strategy will require translation of results obtained through forecasting and economic assessment of such a 5-spot to an estimated value for the entire field. As will be shown in Appendix E in this report, the economic analysis on the 5-spots will be split into two parts: an estimation of the net present revenue from production (less applicable royalties, taxes, and water disposal costs) and an estimation of the net present cost of injected chemicals. The total discounted chemical cost will then be subtracted from the discounted net revenue to determine the overall net present value of the 5-spot pattern. To translate these results to what could be expected from a full-field implementation, we will simply divide the total net present value of the 5-spot by the assumed pattern area to get an estimate of the net present value per acre for the particular flooding operation. This may then be multiplied by the total field acreage (which we have available from the EORI) to arrive at an estimate of the full-field net present value of the particular flood analyzed. We intend to create forecasts and corresponding economic assessments of an ASP flood, a polymer flood, and a simple water flood for all the fields analyzed. The flood type producing the best expected economic results will be selected for each field, and the results from these optimum flood types will be compared against each other for determining field rankings.

APPENDIX C. STAKEHOLDERS Since the end of last semester nothing much has changed in terms of who we would be dealing with, or the place of the operations. What did change was the overall economic environment, where the price of oil dipped even since the last time we were preparing this report, which was about 3 months ago. Since then, the oil dropped from about $70 to $50. That negative oil price change perhaps could lead to a weaker oil operations activity in the areas, and it would change the dynamics of all stakeholders involved. Now the investors would invest less into oil, and service companies will buy fewer chemicals. Maybe due to this the price of chemicals would go down due to a weaker demand. And having the potential lower cost of chemicals could partially offset the dropping oil price when calculating the overall cost and benefit for the ChEOR. If there are communities that were opposed to the oil boom or fracking, now with the fewer activities they may be more satisfied with that. 1. Investors they are starting to invest less money into it as they see less returns on it. Investors could be anyone, completely unrelated to oil or technical fields. They only care about return and dont care about the oil or petroleum technology. For them if they see fewer returns they are not going to invest into it anymore. In a future it will hurt the oil industry as it is always in need of new technologies to keep producing new oil from a developed fields2. Economic market as mentioned before, the price dipped even more, and energy industry needs to be prepared for more decline. However, some believe that the price of oil may have hit the bottom, so itll either stay at the bottom or rise just a little above it. 3. Chemicals as was suggested above, the price of chemicals may drop due to lower demand. With that its availability may increase as bigger supply would predict. 4. Operations team or servicing company as these companies hitting recession, theyre reducing the workforce and the budget. In those conditions the pace of operations may slow down. If the price will go down even lower they may completely refuse to do the work, and therefore they will drop from our stakeholders list.5. Government and regulatory agencies No changes there. We may speculate that perhaps some of the tougher rules could be eased to encourage the oil development during the hard times. Other than that itll stay the same as per last semesters report. 6. Local municipalities, towns, public they may notice a slowed down activity in the region. Those who opposed the Big Oil may be satisfied, but those were for the oil development, may want those companies to come back, and perhaps encourage local governments to implement new rules to ease the operations rules or life some restrictions that were in the past preventing the oil companies of conducting the operations easily.

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