PNG 490 INTRODUCTION TO PETROLEUM DESIGN

FINAL REPORT

Team 1: PENNTROLEUM

JARED BANZHOFNICK CURRANJOHN KANENUR HAFID MOHADICHRISTIAN SMITHTAYLOR VINCENT

Table of ContentsSummary/Abstract3Introduction3Materials and Methods4Results6Discussion7Conclusion9References10Figures and Tables11

Summary/AbstractThe goal of this report is to present all of the work that was done by PennTroleum during the Spring semester of 2015. First, the coalbed methane reservoir was discretized by using MatLab and the structural maps of the top and bottom of the reservoir (Figures 1 and 2) to create three dimensional structural maps for both the top and the bottom of the coalbed (Figures 3 and 4). In the next phase, the well logs for all 11 wells were analyzed to determine the water saturation, porosity, and net pay thickness (Figure 5). For other petrophysical properties with uncertainties, MatLab was used to generate random numbers. The original gas in place of the reservoir was then calculated 15,000 times, and with the use of a Monte Carlo simulation the probability of the reservoir containing different levels of original gas in place was determined. This simulation is represented very well by the histogram in the Figures and Tables section (Figure 6). The next step was to generate a digitization of the water saturation, porosity, and net pay (Figures 7, 8, and 9). Finally, a distribution map of the reserve per unit volume of the reservoir was created to help identify the sweet spot. A Monte Carlo simulation was then implemented in order to conduct an uncertainty analysis on the reserve estimation and sweet spot identification (Figures 10, 11, and 12).Introduction Energy is one of the most important aspects of life as we know it. There are numerous conventional and unconventional sources, but one of the more unique unconventional exists in the coalbed formations where methane is also present. Coalbed Methane is natural gas or methane(CH4) that occurs in coal beds and has been generated during the conversion of plant material to coal. The ultimate goal of this project is spread into 3 semesters with each semester focusing on a few important aspects that is related to petroleum and natural gas engineering. In this semester, the ultimate goal is to evaluate the Original Gas in Place (OGIP) from the provided well logs with the assistance of the Monte Carlo Simulation protocol. This helps by accounting for all the uncertainties of the petrophysical properties. First of all, there are 11 operating wells located in Sweetwater County, Wyoming, USA that are currently being serviced by Halliburton and Schlumberger. Provided with the basic information such as the structural maps, well log data, core sample data, and production history of all 11 wells, the team is expected to work on those information with a few tools and petroleum engineering principles to build a reservoir model equipped with useful data that later on will be use to find the original gas in place (OGIP) and sweet spot to drill. The whole process of reservoir modelling is extremely important to be done as detail as possible in order to ensure an accurate outcome. With some careful planning, our team divided the whole project into a few well organized procedures that will later be explain in methodology section. Each and every graph, table, coding that was made along the way was analyzed properly in the result section so that it can be an easy future reference. The tables and graphs are also further explained in the discussion section where our team interpreted the finding in a manner that public reader can understand and finally the overall finding of the project is gathered and concluded in the conclusion section.Materials and MethodsIncluded in the materials is a series of data that was provided for a reservoir located in Sweetwater, Wyoming. This data included structural maps, well logs, and the production history for the wells drilled. The structural maps contain contour lines that allow us to record the depths of the formation. Two structural maps were provided, a map for the top of the formation along with a map for the bottom of the formation. In order to create an accurate image with Matlab, grid lines were incorporated on the structural maps as seen in figure 1 and 2. These grid lines allow one to record the depth values for each block on the structural maps by using excel. Also, the grid line system made it simple to assign values to specific areas over the entire map. After the depths are recorded, a 3-D figure can be made through programming to show a representation of the formation as seen in figures 3 and 4. The method of using grid lines really made the 3-D figures accurate. The structural maps also contain the locations of the eleven wells that were drilled. These maps were very useful throughout the entirety of this semester. After the 3-D figure is created, the well logs are then utilized. When solving for original gas in place the well logs hold the most informative data such as, API, resistivity, neutron porosity, and density porosity values. These wells logs are observed to determine the amount of net pay zone there is in the formation for each of the eleven wells. To determine the net pay zones a certain set of criteria had to be met. These values include an API value less than 60, resistivity greater than 50 [ohm-m], and a neutron porosity greater than 50%, as well as matrix density less than 2 g/cc. After determining the amount of net pay zones for each well, Matlab was then used to run the Monte Carlo Simulation to find the amount of original gas in place. The Monte Carlo Simulation interprets the data and presents the chance of recovering a certain amount of gas. There is the P90, P50, and P10 as shown in figures 10, 11, and 12. Also, there are graphs provided for the production history that show how prosperous wells six through eleven were over a period of time.

ResultsMany of the final results simply exist as the structural maps themselves, leaving little to no quantitative analysis possible. However, qualitative analysis can still be performed. Regarding the structural and 3-D representation maps (Figures 1-4), it became fair to assign certain structural designs to the field. Where the cluster of wells were existed a structural trough, whereas wells 1 and 2 were in an area of an anticlinal dome. Next, upon reading the well logs at 10-foot intervals, conclusions were made based on the minimum, maximum, and average values over the interval of interest. These conclusions are in the form of the table, as shown in Figure 5. Additional interesting information provided from the well logs was the location of the wells. The range, township, and sections were provided thereby giving us the opportunity to find satellite imagery of the well sites. This imagery correlated extremely well with the provided structural maps, thereby strengthening the quality of the dataset.The ultimate goal of this project was to report the gas in place for the field and reservoir characterized. There was an initial Monte Carlo Simulation that provided a broad range of the possible gas for the area. The least likely, but also most appetizing, estimate proved a P10 value of 194 BCF. The P50 estimate, one that is a general average, ended up being 40 BCF while the P90 estimate was slightly less than a BCF. Once petrophysical interpolations were accounted for using Matlab functions (and presented in Figures 7-9), original gas in place calculations were able to computed on a per unit area basis. The procedure, known as the Regional Monte Carlo Simulation, produced good results where a sweet spot was identified. This sweet spot was evaluated to be the area with the highest gas content per acre-ft, which ended up being in the Southeast corner of the provided structural maps, also near where the cluster of wells already existed. The P10 high estimate of the sweet spot ended up being about 1.69MMSCF per the unit block created.Finally, some production data was provided in which qualitative analysis was performed. The data was for wells 6 through 11, all operated by Barrett Resources, Inc. Production of oil, gas, and water was plotted and presented in Figures 13-18. These plots proved that these 6 wells performed as a typical coalbed methane reservoir would, showing stages of dewatering, positive production, and decline.DiscussionThe results of the coal bed study yielded fair results. The first part of the experiment yielded excellent results. The field was plotted well and the top and bottom depths that were calculated generated 3-D surface plots that were what was expected and compare pretty well with the results obtained by the other studies done on these wells. As it can be seen, the top and the bottom structure maps resemble one another in the fact that both have the same deep areas and the same shallow areas as to make the reservoir somewhat of the same thickness across the board, which it should not be exact, but is usually similar. The wells were then studied for their petrophysical properties in relation to calculating the original gas in place. The most important properties are represented in Figure 5. Here, the average water porosity and average water saturation that was calculated for each well is shown along with how many feet of net pay that was estimated to be present in each well. These numbers are shown because they represent the most important factors in calculating the amount of gas that can be recovered or is available to be recovered. As can be witnessed, wells 1,3,4,9,10, and 11 have the highest net pays as found from reading the well logs, this is the most important area for obtaining data on OGIP because it shows how many feet of space.After running the Monte Carlo simulation on all of the data, figure 6 was generated representing the different chances of estimating how much gas is in place in this reservoir. The values obtained are slightly lower than that of which was obtained from other analyses done on these same wells, but it is unknown what is the actual correct estimation of what was in these wells.As the water saturation 3-D representation shows (Figure 7), the water saturation tended to be lowest in the upper right corner of the normal orientation given for the wells, what is also known as the northeast. It can also then be seen in the digitization of porosity and net pay that they are also higher in the northeast of the original well orientation. Except, the net pay of the two wells on the left was actually pretty high, so it altered the data slightly. Whether this huge plateau to the left affected the final calculations is unknown, but it could not have helped. If one then looks at the P90, P50, and P10 unit OGIP maps (Figures 10, 11, and 12 respectively), it can be seen that the highest unit OGIP occurs also in the northeast side of the map. This corresponds with what was expected after analyzing the well logs and constructing the digitization of the petrophysical properties. This also corresponds with where the wells have been drilled. The wells were drilled mostly on the eastern side, but there were two wells on the left side where there was a large possible area for net pay. It just might be another hydrocarbon present more than just methane in that area producing the high gamma ray reading, but low OGIP estimation.ConclusionAfter viewing the data and the graphs they generated we conclude that drilling in the South-East corner, the designated sweet spot, would yield a profitable return on investment. Through the use of Matlab we were able to take the average data from each of the 11 well logs and piece together a 3-D image of what the surrounding area looks like. From the porosity, net pay, and water saturation graphs rendered by Matlab we saw that the South-East corner of the map has the highest porosity, lowest water saturation, and has good net pay zones all of which contribute to our determination of this region being the sweet spot. The Monte Carlo simulation that we ran through Matlab yielded OGIP values that we believe to be sufficiently high enough to return a profit if more wells were to be drilled in this area. To improve our analysis we believe that a closer look at the well logs is needed. Some of the logs were pretty difficult to read so having more time to carefully review the logs could yield better petrophysical properties and therefore better OGIP calculations. The next step in this process would be to start thinking about drilling new wells within this area or possible even using a preexisting well to start producing.

References1. Applied Drilling Engineering by Adam Bourgoyne, Martin Chenevert, Keith Millheim, and F.S. Young, Jr. ISBN 1-55563-001-0, published by SPE.2. Bjorlykke, K., 2010, Petroleum Geoscience: From sedimentary environments to rock physics: Springer, Heidelberg, 508 p.3. Hunt, J.M., 1996, Petroleum Geochemistry and Geology: W.H. Freeman and Co., New York, p. 743.4. Selley, R.C., 1998, Elements in Petroleum Geology. 2nd ed.: Academic Press, New York, 470 p.

Figures and Tables

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Figure 1 Top of Structural map with major and minor gridlines

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Figure 2 Bottom of Structural map with major and minor gridlines

Figure 3 Digitization of Top of Structural Map

Figure 4 Digitization of Bottom of Structural Map

Figure 5 Petrophysical well data

Figure 6 Histogram of OGIP for entire reservoir

Figure 7 Water Saturation Digitization

Figure 8 Digitization of Porosity

Figure 9 Digitization of NetPay

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Figure 10 Digitization of P90 Unit OGIP #VALUE#

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Figure 11 Digitization of P50 Unit OGIP #VALUE#

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Figure 12 Digitization of P10 Unit OGIP #VALUE#

Figure 13: Well 6 Production for 2001

Figure 14: Well 7 Production for 2001

Figure 15: Well 8 Production for 2001

Figure 16: Well 9 Production for 2001

Figure 17: Well 10 Production for 2001

Figure 18: Well 11 Production for 2001

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Figure 18 Satellite Imagery of Well Sites and Sections 33 & 35