SPE 166111

Data Driven Analytics in Powder River Basin, WY Mohammad Maysami, Razi Gaskari, Intelligent Solutions, Inc., Shahab D. Mohaghegh, Intelligent Solutions, Inc. & West Virginia University

Copyright 2013, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in New Orleans, Louisiana, USA, 30 September–2 October 2013. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract

In the past few years, we have observed the introduction of smart technologies that adapt themselves to specific needs of

individual users. There are many mobile and web-based services with learning capabilities that play the role of a personal

assistant in our daily life. The foundation of this new class of services is a paradigm shift from intensive computational

modeling and simulation of complicated phenomena toward data driven analytics. The oil and gas industry with the

uncertainties convoluted into our measurements and understanding of the subsurface should not be excluded from this recent

paradigm shift. Data driven analytics have proven to be a powerful alternative to conventional numerical and analytical

solutions. In their advanced form, data driven technologies may be used as comprehensive management tools of oil and gas

assets.

In this paper, we study Hilight field in Powder River Basin, a mature field with large number of wells. Lack of sufficient

dynamic data such as flowing pressure for mature fields is common among these types of fields. Conventional data analyses

impose a challenge in the absence of time-variant field measurements in additional to production history. Acquisition of a

comprehensive data set for oil and gas assets, in general, is a costly luxury that is not financially feasible for all investment

budget ranges. Data-driven approach along with pattern recognition techniques can introduce a potential solution to this

challenging task and extract practical and valuable insights which can be vital to identification, planning and developments of

assets and plays.

In this work, we analyze data from nearly 400 wells with partial completion and workover data. Well logs for only 15 wells is

accessible providing less than 10% petrophysical data attributes over the entire well sets. Available production rate history

for 185 wells starts from June 1969 and extends until April 2012. The information value of this dataset is investigated

through a multi-step workflow. The workflow includes reservoir delineation and geological modeling, volumetric reserve and

recovery factor estimations, production decline curve analysis, fuzzy pattern recognition (FPR) analysis and key performance

indicators (KPI) analysis. FPR analysis provides time-laps spatial patterns, enabling us to qualitatively study the reservoir

depletion and fluid flow in Hilight field. The result of these analyses has been used to identify the depletion distribution over

time and sweet spots for infill locations. KPI analysis identifies relative influence of different parameters on hydrocarbon

production. Top-Down Model is developed and used for field development planning and economic analysis on proposed new

wells. The workflow has a minimal computational footprint compared to conventional methods. It has been demonstrated

how these data driven techniques can be employed as a guide toward an improved reservoir management and planning.

Introduction

Data is the most valuable element in solving scientific problems independent of the nature and complexity of problem itself

or even the approach and techniques used to obtain the solution. The theoretical branch of science appeared when the

generalization and modeling replaced empirical science and made it possible to describe simple phenomena. Data has been

used to develop, propose, verify and validate physical and mathematical theories ever since. With the technology

advancement in the last few decades and improvements in computing power and resources, a paradigm shift from theoretical

to computational problem solving was inevitable. Computational paradigm similar to earlier techniques revolves around data

2 Maysami, Gaskari, Mohaghegh SPE 166111

and modeling and simulation of complex phenomena with a goal of minimizing the difference error between estimated output

results and the observations and measured data from those phenomena (1; 2; 3).

In today’s world, however, the gigantic amount of data from all around the world calls for a new paradigm shift toward data-

intensive and data mining to tackle various problems in different scientific and engineering fields. The need for such problem

solving techniques becomes more visible with a glance at intelligent and targeted marketing and user-specific services and

suggestions. These recent features of our life are made possible based on all the collected historic data from multiple users on

different digital and web-based services.

Data acquisition, especially in oil and gas industry, is a costly and risky investment due to many unknowns associated with

the subsurface. The uncertainties rise in both information content and positive use of data in securing more production. Such

characteristics make the data acquisition a less attractive choice when smaller financial assets and thus less risk capacities are

present. Mature fields, as a particular example, commonly pose a challenge when evaluated for future planning and

revitalization. One of the main obstacles is the lack of any type of data other than production rates. The absence of sufficient

data is a limiting factor for many conventional reservoir simulation and management methods. This disadvantage, however,

does not stop data driven modeling techniques from providing necessary reservoir management tools based on a minimal

requirement for data availability.

Data driven analytics, unlike the conventional reservoir simulation and management techniques, focuses on hard measured

data from the asset and attempts to create a logical insight on the subsurface fluid flow in the reservoir free of any predefined

functional form. Complimentary data in addition to production rates and well logs only adds to accuracy of the model. Many

studies have shown the effectiveness and accuracy of artificial intelligence and data mining (AI&DM) techniques in

modeling and managing oil and gas assets around the world (4; 5; 6; 7). The benefits of data driven modeling become more

visible when reservoir management and future planning is the final goal. Data driven models with their minimal

computational footprint in comparison to conventional modeling methods, make it feasible to analyze multiple scenarios

within a practical time frame (2).

In this article, we study the Powder River basin in Wyoming using data driven techniques for modeling, evaluation and

analysis. The available data from the field is qualitatively analyzed by various techniques such as fuzzy pattern recognition

and the fluid drainage over the years is probed. Next, the economic impact of new wells in future is studied using the

developed data driven model for the field.

Hilight Field in Powder River Basin

This study was performed on Hilight Field in the Powder River Basin about 20 miles south of Gillette, Wyoming. Hilight

Field is a large stratigraphic trap in the Muddy sandstone that was discovered in 1969. Waterflooding was initiated in the

early 1970s and expanded through the late 1990s. Raw data was acquired from IHS Energy’s database. Initial review of the

data revealed that there are a total of 396 wells in the Hilight field and production rate data for 185 wells is available. Well

logs for only 15 wells are available providing limited petrophysical data over entire reservoir.

A geographical distribution of wells, color-coded based on their type and availability of data attributes is presented in Figure

1. Wells with production rate data are shown as filled orange circles and the wells with well log data are marked by red rings.

Green circles represent the wells producing from different formations. Non-vertical producing wells and geographically

outlier producing wells are shown by blue and purple circles, respectively. Note that most of provided well logs fall into west

side of the field. In order to increase certainty and integrity of the analysis and extracted information content, we have picked

160 wells for our analysis excluding wells completed in different formations or remote locations comparing to majority of

wells in addition to non-vertical producers. Figure 2 depict a field development timeline where the wells are clustered based

on their date of first production. This map summarizes the location of initial production wells in the field and how new wells

added up over time until 2012. Circles with darker fill colors represent older production wells with earlier date of first

production (DOFP).

Data Driven Analytics

The techniques used in our analysis and workflows are based on integration of reservoir engineering with Artificial

Intelligence and Data Mining (AI&DM) technology. Following is a list of processes that were implemented in the study of

Hilight field as steps of data driven analytics workflow.

Hilight field dataset review and preparation:

Reservoir Characteristics (Petrophysical Properties)

Well Logs for certain wells

SPE 166111 Data Driven Analytics in Powder River Basin, WY 3

Production history.

Completion details.

Workovers and Stimulations

Delineation and Volumetric Calculations (on a per-well basis)

Performed Key Performance Indicators Analysis (KPI)

Cluster Analysis based on KPI results to study relative influence of different reservoir properties as well as

operational constraints.

Fuzzy Pattern Recognition to qualitatively identify field development strategies (4; 5; 6):

Well Performance during fixed interval from date of first production

Remaining reserves as a function of time

Recovery factor

Reservoir depletion and sweet spots

Top-Down Model Development (2; 5; 6; 7; 8; 9)

Forecast the production of proposed wells

Economic Analysis of Proposed New Wells

The first and most fundamental step in a data driven approach is data compilation, cleansing, quality control. Preparation of a

comprehensive spatio-temporal dataset which can represent the fluid flow in the field is the basis of the analysis (3). In this

step, production data along with average reservoir properties for each well based on provided well logs is compiled into the

dataset. Well specifications such as coordinates, perforation depths, and workover details are also integrated to secure more

information in the dataset.

Figure 1 - Map of all wells in Highlight field dataset, divided into categories. Note the location of wells with production (orange circles) as well as provided well logs (red rings).

One of the first processes of the workflow on the dataset is performing delineation and geostatic modeling. Delineation

process is a specific implementation of Voronoi graph theory (5; 10) to generate so called Estimated Ultimate Drainage Area

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HILIGHT Field, WY - All Wells

All Wells Wells with Production Well Logs Non-Vertical Producers Producing From Other Formations Outlier Producers

4 Maysami, Gaskari, Mohaghegh SPE 166111

(EUDA). Volumetric reserve calculation as a common reservoir engineering practice is the next step after delineation. The

geological model, also known as geo-cellular model, static model or earth model, includes the major reservoir characteristics

that are used as the basis of all hydro-dynamic models including the reservoir models (2; 3). A major advantageous

characteristic of data driven modeling and analytics is that it incorporates field measurements and tries to avoid

interpretations as much as possible. As such, our data driven also uses a geostatical model using only measured reservoir

properties for its analysis. Figure 3 shows an example of such isolation of the production wells as well as the map of depth

for Hilight field as example of geostatistical modeling.

Figure 2 - Map of all wells in Highlight field dataset, color-coded based on their date of first production. Darker fill colors represent wells with earlier dates of first production.

Decline Curve Analysis (DCA) is a classic Production Data Analysis (PDA) technique based on fitting the recorded

production rates by a mathematical function (4; 11; 12). These fitted curves for each individual well represent the general

decreasing trend of production rate and can be used to predict the future production performance of the well by extrapolation.

Examples of DCA for two individual wells in Hilight field are depicted in Figure 4. DCA provides a starting point in

processing production data and also a basis for calculating Estimated Ultimate Recovery (EUR) (11; 12; 13). Decline curve

analysis, however, falls short in providing meaningful link between production rates and reservoir properties. Therefore,

sensitivity or uncertainty analysis based on measured properties of the field is a missing feature when simplistic techniques

such as DCA or regression (14; 15) are used.

Fuzzy Pattern Recognition Analysis

Conventional statistical analysis such as cross-plots and regressions mostly fails to find hidden trends and patterns in data (14

pp. 7-10). Fuzzy pattern recognition, as a more complex and modern technology, compensates for shortcomings of traditional

methods in revealing non-trivial patterns in large datasets. Fuzzy pattern recognition is a class of multi-dimensional

descriptive classification methods based on fuzzy set theory.

Identification of key performance indicators is one of the most important tasks in understanding any system’s behavior. Most

of the traditional methods evaluate the parameters, one parameter at a time, and mostly use simple regression analysis. The

collective interaction between parameters must be taken into account, for a comprehensive analysis and modeling of any

complex, non-linear and dynamic process. Therefore, one should identify the key performance drivers in a process by

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Date of First Production

All Wells DOF Before 1970 DOF 1970-80 DOF 1980-90 DOF 1990-2000 DOF 2000-12

SPE 166111 Data Driven Analytics in Powder River Basin, WY 5

analyzing the parameters in a combinatorial fashion. Key Performance Indicator (KPI) analysis based on fuzzy cluster

analysis has been tested and validated on known problems. This technique outperforms all other techniques, especially when

applied to complex, dynamic and non-linear problems.

Figure 3 - Delineation of Highlight field (Left) and geological models for (Right) depth [ft].

Figure 4 – Decline Curve Analysis (DCA) for two individual wells in Hilight field.

While KPI analyses extract valuable information regarding behavior and relationship in data, it allows for more detailed

probing of dataset. Relative influence of different values of certain data attributes is an example of such possibilities. Figure 5

shows an example of such detailed analysis studying effect of different workover injection fluid types on production rate in

Hilight field. KPI analysis can be paired with cluster analysis to go further and look a frequency distribution of best 9 months

Cumulative Production for specific value of workover injection fluid (see Figure 5).

Furthermore, qualitative and quantitative patterns are extracted over entire field using fuzzy pattern recognition techniques

(4; 5; 6; 7; 11). The field-wide pattern recognition analysis allows dividing the reservoir into regions with different quality

indices in order to indentify hidden and non-trivial distribution patterns of different attributes throughout the reservoir. The

idea behind this type of analysis is to be able to integrate and put all information in perspective and thus draw conclusions

and design field development strategies. Figure 6 shows an example of such analysis for best 3 months of cumulative

production in Hilight field. Performance of the different zones of the field through time can be investigated in this type of

analysis. Figure 7summarizes time-lapse variations of the field for certain time interval past first date of production for all the

wells in the field. Darker shades represent more productive regions in the field. Note the consistency of the regions over time.

Fuzzy Pattern Recognition analysis can be used to assist the decision making process and designing field development

strategies such as identifying sweet spots (See Figure 10).

6 Maysami, Gaskari, Mohaghegh SPE 166111

Figure 5 – KPI Analysis: (Left) Relative influence of different workover injection fluid on cumulative production of best 9 months. (Right) Frequency distribution of best 9 months of cumulative production for wells with acid as workover injection fluid.

Figure 6 - Fuzzy Pattern Recognition (FPR) analysis based on cumulative production of best 3 months. (Top-Left) Top map of field with fuzzy patterns of production index (Cumulative production of best 3 months) along latitude and longitude. Field is divided to partition with different quality indices ranging from excellent to poor. (Top-Right) 3D view of the field partitioning based on quality

indices. (Bottom) Table summarizing the average value of production index (PI) for each partition.

Well Quality analysis (WQA) is another type of fuzzy pattern recognition where fuzzy sets are used to classify wells based

on production index parameters (see Figure 8) and impact of single or multiple attributes, such as reservoir properties, on

these defined well classes are demonstrated in step form (see Figure 9). In Figure 8, three classes of poor, average and good

are defined for cumulative production in the Hilight field. It can be concluded from the analysis shown in Figure 9 that

cumulative production is monotonically increasing with an increase in completion footage or decrease in shot density.

Similar behavior patterns can be easily extracted using this type of analysis for various production indices and independent

attributes.

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KPI Analysis - Relative Influence of Workover Fluid Type on Cumulative Production

SPE 166111 Data Driven Analytics in Powder River Basin, WY 7

Figure 7 - Analyzing time-lapse field performance. Field is divided to partition with different quality indices ranging from excellent

(darker shades) to poor (lighter shades).

Top-Down Model (TDM)

Top-Down Model (TDM) is a class of AI-based models based on actual field data (2; 5; 6; 7). The physical laws governing

the fluid flow in the reservoir is deduced from the spatio-temporal dataset rather than being imposed directly. Classic

reservoir engineering practices are used in preparation of the comprehensive dataset to be used in development of top-down

models. TDM has an advantage to be operational with minimal interpreted data and has been tested and verified in numerous

cases(2; 5; 6; 7; 16; 8). Inputs to TDM are commonly divided into two types of statics that are single values and constant over

time and dynamics that are varying over time. Figure 11 lists the inputs for developed TDM for the Barrel of Oil Equivalent

(BOE) in Hilight field. Field properties such as well coordinates, top depth, pay thickness and completion fall into static

class. Note that the only dynamic property available beside the production history is dates and types of workovers on each

individual well. Figure 11 shows the history matched result of developed TDM for all the included wells in the Hilight field.

Gray dots and shades represent annual production history and actual cumulative production in the Hilight field, respectively.

The green line represents the matching result obtained from TDM model developed. Estimated cumulative values from TDM

are depicted in green shades.

Top-Down models with the minimal computational footprint and strong dependency on measured data rather than interpreted

data are a preferred candidate for data driven analytics and modeling. Furthermore, Top-Down Models are known to be able

to provide a solution in the absence of sufficient data which is common in mature field. Figure 12, similarly, provides history

matched results for individual wells in Hilight field. Sensitivity of the output (production) in TDM can also be probed versus

single or multiple input variables (reservoir properties) using Monte-Carlo simulation (Figure 13). This is a considerable

advantage over classic techniques such as DCA or simplistic or complex regression analysis (14; 15) which is possible

because of the negligible computation footprint and the systematic nature (input-output relationships) of TDM.

Figure 8 – Schematic and numeric presentation of fuzzy cluster set for cumulative production

First 3 Months

of Production

First Year of

Production

First 10 Years

of Production

First 5 Years of

Production

8 Maysami, Gaskari, Mohaghegh SPE 166111

Figure 9 – Well Quality Analysis (WQA) for (Left) completion and (Right) shot density in cumulative production

Economic Analysis

One of the main advantages of data driven analytics is the independence of interpreted data as well as flexibility with lack of

dynamic production data (time-lapse bottom-hole flowing pressure and etc.). As discussed earlier, these advantages along

with the negligible computational requirement to traditional reservoir management techniques, allows for a reliable and

practical reservoir management and development tool.

We have integrated the insights gained through the field-wide pattern recognition analysis (see Figure 6, Figure 7, and Figure

10) and the forecasting capability of the developed Top-Down Model (see Figure 11 and Figure 12) to showcase

development strategies in Hilight field. As shown in Figure 14, eight new wells in various locations of the field were planned

to start producing in 2013. The location of these wells was inspired by the field-wide analysis performed on cumulative

production, recovery factor, and remaining reserve. In order to be able to compare the performance of the new wells, they

were distributed in regions of the field with relatively more and less productivity expectation. Excellent quality index for

remaining reserve in north east parts of the field, suggest higher production values for new wells in that region (Well 1, 2,

and 3) in comparison to the rest of new wells. Similarly from average quality indices in Figure 10, it can be argued that

central wells (Well 4 and 5) stand next after new wells in north east in term of production values. Finally, southern new wells

are expected to have lowest production performance among the new wells given the higher recovery factors. Table 1

represents the predicted cumulative production of new wells from 2013 through 2017 and confirms the expected

performances explained above. Note that cumulative production for new wells in north east regions of the field is about twice

this value for the rest of new wells.

Figure 10 - Decision making process (identifying sweet spots) using multiple fuzzy pattern recognition analysis. Warmer and colder

color shades represent better and worse quality indices, respectively.

In order to evaluate the proposed development we have performed economic analysis and calculated net present value (NPV)

using cash flow model (17). The average cost of drilling and completion of new wells in Powder River basin can range

between $400,000 and $600,000 (18; 19; 20). For the practical purposes, we will assume an average value of $500,000 as the

capital expenditure for a new well in the Highlight field. For the analysis purposes, we consider the price of crude oil from

Wyoming to fluctuate around (18; 21). Cost of production for each barrel of oil is estimated to be and

Discount rate and tax in cash flow model is assumed to be , respectively.

Remaining Reserve as of

2015

Recovery Factor Cumulative Production Best 12 Months of

Cumulative Production

SPE 166111 Data Driven Analytics in Powder River Basin, WY 9

Figure 11 – Inputs for Top-Down Model developed for Hilight field, WY (Left). TDM estimates for entire field from 1975 to 2017 (Right). Gray dots represent actual annual oil productions and the green line shows the model estimates and prediction. Grey and

green shaded area, represent the actual and model cumulative production, respectively. The red bars at bottom show the number of active wells in each year.

Figure 12 – TDM estimates for selected individual wells from 1975 to 2017. Gray dots represent actual annual oil productions and the green line shows the model estimates and prediction. Grey and green shaded area, represent the actual and model cumulative production, respectively.

Static Data Dynamic Data

Location - X Q-Oil (t-1)

Location - Y Q-Gas (t-1)

Depth Workover (t-1) Date

Pay Thickness Workover (t-1) Type

Completion

Porosity

Top-Down Model Inputs

10 Maysami, Gaskari, Mohaghegh SPE 166111

The economic analysis is presented in Table 1 in details and the annual Net Present Values for new wells are depicted in

Figure 14. Note the better economic value of new wells in north east regions (Wells 1, 2, 3) that pay back the capital

expenditure (drilling and completion). New well 1, for example, turns enough revenue in first 9 months to pay back the

capital expenditure (Break even time). This time period for wells with lower performance can be extended up to 21 months.

Figure 13 – Uncertainty Analysis using Monte-Carlo Simulation and TDM. Estimated value of TDM is presented with green line

versus actual annual production values in gray dots. Low and High bounding values are shown in highlighted shade and P50 is shown as dashed line. (Top) Effect of uncertainty of porosity on production of Well-262360000 (Bottom) Effect of uncertainty of pay

thickness and completion on production of Well-25327000.

Figure 14 – Drilling new wells: Location of new wells marked with green circles and numbers (Left). Annual Net Present Value (NPV)

is compared for new wells (Right). Details of Predicted production for New Wells in Highlight field are presented in Table 1.

Conclusion

When we realize that developing a cohesive and reasonably accurate predictive model for an asset, even when professionals

have access to all the data that is internally available in a company, is a complex and time consuming task, it becomes evident

that evaluating prospects using readily avaialbe (public) data is not a trivial matter. Just like any oter disciplines, addressing

complex problems usually requires development of sophisticated tools. Expecting to get good results in evaluating complex

prospects in the oil and gas industry with simple tools (such as Decline Curve Analysis) will prove to be, to put it mildly,

wishful thinking.

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SPE 166111 Data Driven Analytics in Powder River Basin, WY 11

In this paper we demonstrated sophisticated technologies such as Fuzzy Pattern Recognition and Top-Down Modeling that

are categorized as advanced data driven analytics in order to evaluate a given prospect in Bowder River Basin. Comparing

advanced data-driven analytics traditional techniques such as DCA, regression and etc. clearly demonstrate the value added

by these tehnologies.

Large quantity of existing wells coupled with limited data (parameters) availability in the mature fields imposes serious

challenges with traditional simulation and modeling techniques. However, advanced data driven analytics techniques do not

suffer from similar limitation. They have completely open data architecture and can use “any type” of data that is avaiolalbe

in order to accomplish their task. These tools with practical deployment time allows for integrated and versatile investigation

of the assets with reasonable financial resources while extracting valuable information.

12 Maysami, Gaskari, Mohaghegh SPE 166111

Table 1 – Economic Analysis for Proposed Field Developments from 2013 to 2017. Drilling Cost and Price of oil Barrel is assumed to be fixed. Note the decrease in production from good group (New Wells 1, 2, 3) to the rest of new wells.

Drilling and

Completion

Drilling

End Date

Oil Price

(per bbl)

Production

Cost ( bbl)

Prod. Cost

Inflation

Discount

RateTax

500,000$ 2012 70$ 5$ 0% 8% 20%

ProductionCumulative

Production

Production

Cost Revenue OpEx CapEx

Present

Value (PV)NPV (Σ PV)

Break

Even

Well Name Year [bbl/Year] [bbl/Year] [bbl] $ $ $ $ $ Months

2012 - -$ -$ -$ 500,000$ (500,000)$ (500,000)$ 9

2013 13,675 13,675 5.00$ 957,271$ 68,377$ -$ 658,440$ 158,440$

2014 14,548 28,224 5.00$ 1,018,374$ 72,741$ -$ 648,582$ 807,023$

2015 14,367 42,590 5.00$ 1,005,662$ 71,833$ -$ 593,043$ 1,400,066$

2016 14,120 56,711 5.00$ 988,428$ 70,602$ -$ 539,704$ 1,939,769$

2017 13,892 70,603 5.00$ 972,461$ 69,462$ -$ 491,653$ 2,431,422$

2012 - -$ -$ -$ 500,000$ (500,000)$ (500,000)$ 9

2013 13,306 13,306 5.00$ 931,420$ 66,530$ -$ 640,659$ 140,659$

2014 14,120 27,426 5.00$ 988,407$ 70,601$ -$ 629,497$ 770,156$

2015 13,956 41,382 5.00$ 976,927$ 69,781$ -$ 576,098$ 1,346,254$

2016 13,736 55,118 5.00$ 961,520$ 68,680$ -$ 525,011$ 1,871,265$

2017 13,533 68,652 5.00$ 947,338$ 67,667$ -$ 478,951$ 2,350,217$

2012 - -$ -$ -$ 500,000$ (500,000)$ (500,000)$ 9

2013 13,237 13,237 5.00$ 926,604$ 66,186$ -$ 637,347$ 137,347$

2014 13,932 27,169 5.00$ 975,226$ 69,659$ -$ 621,102$ 758,449$

2015 13,857 41,026 5.00$ 969,997$ 69,286$ -$ 572,011$ 1,330,460$

2016 13,748 54,774 5.00$ 962,381$ 68,742$ -$ 525,481$ 1,855,941$

2017 13,651 68,425 5.00$ 955,542$ 68,253$ -$ 483,099$ 2,339,040$

2012 - -$ -$ -$ 500,000$ (500,000)$ (500,000)$ 17

2013 8,007 8,007 5.00$ 560,497$ 40,036$ -$ 385,527$ (114,473)$

2014 8,118 16,125 5.00$ 568,281$ 40,592$ -$ 361,927$ 247,454$

2015 7,878 24,004 5.00$ 551,467$ 39,391$ -$ 325,202$ 572,656$

2016 7,643 31,647 5.00$ 535,024$ 38,216$ -$ 292,135$ 864,791$

2017 7,430 39,076 5.00$ 520,065$ 37,148$ -$ 262,932$ 1,127,724$

2012 - -$ -$ -$ 500,000$ (500,000)$ (500,000)$ 16

2013 8,379 8,379 5.00$ 586,530$ 41,895$ -$ 403,433$ (96,567)$

2014 8,472 16,851 5.00$ 593,040$ 42,360$ -$ 377,695$ 281,129$

2015 8,143 24,994 5.00$ 570,038$ 40,717$ -$ 336,154$ 617,282$

2016 7,821 32,815 5.00$ 547,470$ 39,105$ -$ 298,931$ 916,213$

2017 7,527 40,342 5.00$ 526,862$ 37,633$ -$ 266,369$ 1,182,582$

2012 - -$ -$ -$ 500,000$ (500,000)$ (500,000)$ 19

2013 6,975 6,975 5.00$ 488,215$ 34,873$ -$ 335,809$ (164,191)$

2014 7,051 14,025 5.00$ 493,542$ 35,253$ -$ 314,327$ 150,136$

2015 6,830 20,855 5.00$ 478,079$ 34,149$ -$ 281,925$ 432,061$

2016 6,615 27,470 5.00$ 463,078$ 33,077$ -$ 252,851$ 684,912$

2017 6,421 33,891 5.00$ 449,456$ 32,104$ -$ 227,234$ 912,147$

2012 - -$ -$ -$ 500,000$ (500,000)$ (500,000)$ 21

2013 6,159 6,159 5.00$ 431,102$ 30,793$ -$ 296,525$ (203,475)$

2014 6,284 12,443 5.00$ 439,873$ 31,420$ -$ 280,146$ 76,672$

2015 6,175 18,618 5.00$ 432,257$ 30,876$ -$ 254,904$ 331,575$

2016 6,071 24,689 5.00$ 424,970$ 30,355$ -$ 232,043$ 563,618$

2017 5,981 30,670 5.00$ 418,663$ 29,905$ -$ 211,666$ 775,284$

2012 - -$ -$ -$ 500,000$ (500,000)$ (500,000)$ 21

2013 6,429 6,429 5.00$ 450,044$ 32,146$ -$ 309,554$ (190,446)$

2014 6,544 12,973 5.00$ 458,073$ 32,720$ -$ 291,738$ 101,292$

2015 6,395 19,369 5.00$ 447,678$ 31,977$ -$ 263,997$ 365,289$

2016 6,251 25,620 5.00$ 437,591$ 31,257$ -$ 238,934$ 604,224$

2017 6,123 31,743 5.00$ 428,596$ 30,614$ -$ 216,688$ 820,911$

Ecomonic InformationN

ew

Well

5

New

Well

6

New

Well

7

New

Well

8

New

Well

1

New

Well

2

New

Well

3

New

Well

4

Economic Analysis (Cash Flow Model)

SPE 166111 Data Driven Analytics in Powder River Basin, WY 13

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