Presentation is loading. Please wait.

Presentation is loading. Please wait.

Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) Chad E. Kronkosky, P.E President, CEK Engineering LLC.

Similar presentations


Presentation on theme: "Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) Chad E. Kronkosky, P.E President, CEK Engineering LLC."— Presentation transcript:

1 Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) Chad E. Kronkosky, P.E President, CEK Engineering LLC Pursuing Ph.D. “Part Time”, TTU Petroleum Engineering Anticipated Candidacy Fall 2014 SPEE Midland Chapter September 10, 2014

2 Copyright © 2014 CEK Engineering LLC Presenter Biography Introduction To CEK Engineering LLC and its President Chad E. Kronkosky, P.E.  Education B.S. in Petroleum Engineering (TTU 2006) M.S. in Petroleum Engineering (TTU 2009) Ph.D. in Petroleum Engineering (TTU ~ Anticipated Candidacy Fall 2014)  Industry Experience CEK Engineering LLC (2012 to Present) President of CEK Engineering LLC (CEK). CEK was formed to provide Professional Engineering Services to the Oil and Gas Industry. Our clients range from Large Private Equity Management Teams to Small Independent Oil and Gas Operators. We have project experience in Texas, Louisiana, New Mexico, Kansas, Colorado, Montana, and North Dakota, but our primary focus is the Permian Basin of West Texas. For more information on CEK Engineering LLC, please visit Bold Operating LLC (2010 – 2012) Reservoir Engineer for small Private Equity Management Team (EnCap). Solely focused in the Permian Basin; Grow By The Bit Company. Gained valuable financial experience; worked with lending institutions, private equity analysts/managers, etc. A.C.T. Operating Company (2006 – 2010) Graduate Petroleum Engineer working under Marshall Watson, Ph.D., P.E. Current Chair of the Petroleum Engineering Department At TTU. Experience encompassed: Secondary Recovery Projects, CBM, Corporate Management, Prospect Development, etc. (almost anything you can imagine, especially for a two man company). The man in red above is a lot of the reason I am giving this presentation today; Thank You Marshall for all you have done for me.

3 Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) This Presentation Is Not About Statistical Theory! “I’m Sorry To Disappoint The Academics In The Audience” This Presentation Is Going To Discuss Observations Of Improper Use/Analysis Of Data! We Will Also Discuss “Tools” (Namely “R”) That Can Be Used To Help Us Understand Our Data.

4 Copyright © 2014 CEK Engineering LLC Presentation Outline “ Conceptually” Statistical Data Analysis & Visualization  What’s The Problem With Our Current Methodology? Is There A Problem?  Is There A Solution To This Supposed Problem?  Brief Discussion On My Research And How “CEK” Looks At Resource Plays. Wolfberry Infill Drilling Example  Aggregation Of Production Data.  Why Is My Unconventional Reservoir Behaving Conventionally? We need to understand well performance and completion data to answer this! ( Especially For The Small Operators! ) (Why It’s Important To Get Right!) ( Just Frac The Out Of It… Right? )

5 Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) Statistical Data Analysis & Visualization What’s The Problem With Our Current Methodology? Is There A Problem? “Damn Excel! How the ‘Most Important Software Of All Time’ Is Ruining the World!” 1

6 Copyright © 2014 CEK Engineering LLC What’s The Problem With Our Current Methodology? Is There A Problem? – “Damn Excel!” Current Methodology/Practices  Gather Data/Generate Data We take historical records and store them! –In Spreadsheets! –In Word Processing Documents –In Databases  Perform (for the most part ad hoc) Analyses We take disparate data stores and generate masters data files. In most instances, this master data file “starts/ends” as an Excel file. We Seldom QA/QC our Data! We perform analyses (write in-line formulas, etc.) on these master data files to generate some form of visual communication (plots, tables, etc.). We typically solve most of our problems with a single spreadsheet. (if need be we can export this data to a professional software package).  We Then Repeat The Process At A Later Date! This means updating our ad hoc data files (typically manually) (+80% of the time) (This is where the headache starts!) (companies actually populate databases?) (extremely important but who has the time)

7 Copyright © 2014 CEK Engineering LLC What’s The Problem With Our Current Methodology? Is There A Problem? – “Damn Excel!” So What’s The Big Problem?  Who Here Has Actually Ever Back Check A Spreadsheet? Is this even possible once we start building linked formulas spanning multiple sheets/workbooks? Is there a method to our madness that can be followed by someone a lot less technically inclined ? Better yet, could you defend yourself in a Court of Law with your analysis? (I’m sure there is a spreadsheet Guru In here, We Know Who You Are!)

8 Copyright © 2014 CEK Engineering LLC What’s The Problem With Our Current Methodology? Is There A Problem? – “Damn Excel!” London Whale  2012 – JP Morgan Chase 6.2 Billion Dollar Trading Loss! “In the wake of last year’s $6.2 billion JPMorgan Chase JPM trading loss, traders have been fired, top executives have been hauled in front of Congress, and the FBI, among other regulators, is investigating. But you know who really needs to be questioned? Bill Gates. According to an internal report on the trading loss released in February, the model that was supposed to monitor and limit the amount of risk the bank’s London traders were taking was “operated through a series of Excel spreadsheets, which had to be completed manually, by a process of copying and pasting data from one spreadsheet to another.” One key measure was added when it should have been averaged. The result: Risk officers at JPMorgan believed the credit derivatives bets were half as risky as they actually were. So, I guess, CEO Jamie Dimon can pass $3.1 billion off on Excel. The rest is still on him.

9 Copyright © 2014 CEK Engineering LLC What’s The Problem With Our Current Methodology? Is There A Problem? – “Damn Excel!” MF Global  October 2011: MF Global Transfers Client Account Funds To Its Own Account! “About a year before MF Global went bust, consultants hired by the firm determined it needed to improve the “end user computer tools such as Excel spreadsheets” that the commodities broker used to monitor risk and how much money it had in its customers accounts, and to make sure that some of that money didn’t end up in the account being used by CEO Jon Corzine to bet on whether or not Europe was about to implode. Those upgrades were never made.” 1

10 Copyright © 2014 CEK Engineering LLC What’s The Problem With Our Current Methodology? Is There A Problem? – “Damn Excel!” Barclays – Lehman Bros. Purchase  September 2008 Collapse – Purchase Offer “When Barclays sent over its offer to buy up Lehman Brothers in the immediate wake of the firm’s September 2008 collapse, it did so with an Excel spreadsheet. The makers of the spreadsheet, which detailed Lehman’s assets and what Barclays was willing to buy, hid, rather than deleted, nearly 200 cells. But when a junior law associate at Cleary Gottlieb Steen & Hamilton converted the Excel file to a PDF and ed it over to the bankruptcy court, the hidden parts of the spreadsheet reappeared. The result: Along with the parts of Lehman Barclays wanted, the British bank was also forced to swallow losses on an additional 179 toxic deals it never intended to buy.” 1

11 Copyright © 2014 CEK Engineering LLC What’s The Problem With Our Current Methodology? Is There A Problem? – “Damn Excel!” Important Takeaway  Humans Are Prone To Errors!  Our World Has Gotten More And More Complex! Our industry is adapting to Probabilistic Methods and workflows; Oracle “Crystal Ball” and Palisade Five years ago only a small group of people were using these tools. The current activity in the industry (predominantly Unconventional Reservoirs; 1925 Total Rigs ~ 70% Horizontal as of 9/5/2014) is stressing our ability to make “Good Decisions” in my opinion. Tremendous Amounts of Capital are being wasted in part due to our workflows being behind the curve.  We Live In A Button Click Society! Engineering Judgment is slowly being eroded/replaced by the use of software. All you do is put straight lines through data points, right! Young engineers (and non-engineers) are exposed to these “Black Box” solutions and immediately start “Button Clicking” their way to the “answer”.  As we now know “Button Clicks” do have consequences !

12 Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) Statistical Data Analysis & Visualization Is There A Solution To This Supposed Problem? “We Live In A Complex World That Requires Simplified Solutions!”

13 Copyright © 2014 CEK Engineering LLC Is There A Solution To This Supposed Problem? Solutions Are Only As Limited As We Limit Ourselves There’s Lots Of Solutions!  Microsoft Office Solutions Visual Basic for Applications –User Defined Function (UDF’s) instead of complex in-line functions. MS Access is our friend! –Once datasets grow beyond a few spreadsheets we really need to start thinking about generating a database. MS Access is about the easiest one to learn and use. P.S. A lot of industry software use MS Access databases as their data store.  Third Party Applications SLB’s Oil Field Manager, Merrick’s RIO, etc. –These are great off the shelf data stores & management tools. Oracle’s “Crystal Ball Suite” and Palisade’s “Decision Suite” –Monte Carlo simulation, Decision Trees, Sensitivity Analysis, Statistical Analysis, Neural Networks, Optimization. Integration with VBA for automation.  Open Source Software – “R”

14 Copyright © 2014 CEK Engineering LLC Is There A Solution To This Supposed Problem? Solutions Are Only As Limited As We Limit “R”selves There’s Lots Of Solutions!  Microsoft Office Solutions Visual Basic for Applications –User Defined Function (UDF’s) instead of complex in-line functions. MS Access is our friend! –Once datasets grow beyond a few spreadsheets we really need to start thinking about generating a database. MS Access is about the easiest one to learn and use. P.S. A lot of industry software use MS Access databases as their data store.  Third Party Applications SLB’s Oil Field Manager, Merrick’s RIO, etc. –These are great off the shelf data stores & management tools. Oracle’s “Crystal Ball Suite” and Palisade’s “Decision Suite” –Monte Carlo simulation, Decision Trees, Sensitivity Analysis, Statistical Analysis, Neural Networks, Optimization. Integration with VBA for automation.  Open Source Software – “R” During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science. 2

15 Copyright © 2014 CEK Engineering LLC Is There A Solution To This Supposed Problem? Data Analysis Software When Excel Just Doesn’t Cut It!  Excel As A Data Analysis Software Excel is a poor choice for statistical analysis beyond textbook examples, the simplest descriptive statistics, or a few columns of data. Was never designed to fulfill the Data Analysis role, add-on packages are required (Crystal Ball, Decision Suite)!  Commercial Data Analysis Software SAS, SPSS, Stata, Minitab, Tibco Spotfire, Tableau etc.  Open Source “R” Pros –Arguably one of the most popular Data Analysis Software out there…Free! Also, most commercial packages support “R” –Open Source! Huge user base! And 1000’s of Libraries of source code available to download. Cons –Command Line Interface (Sorry Button Clickers). RStudio is an elegant IDE for programing. –Support is limited to user blogs, stackoverflow.com

16 Copyright © 2014 CEK Engineering LLC Is There A Solution To This Supposed Problem? Data Analysis Software – “R” What Does “R” Look Like? Scripting Editor Output Console Global Variables Plots, Workspace

17 Copyright © 2014 CEK Engineering LLC Is There A Solution To This Supposed Problem? There Are Lots Of Solutions…We Need To Choose The Right Ones Our Industry Is Generating Tremendous Amounts Of Data… The Problem Now Is Not Having Data… But How Do I Interpret This Data ! Efficiently!!! We Now Entering The I-Field !

18 Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) Statistical Data Analysis & Visualization Brief Discussion On My Research And How “CEK” Looks At Resource Plays “The Goal Is To Work Smarter…Not Harder!”

19 Copyright © 2014 CEK Engineering LLC Brief Discussion On My Research Data Analysis Tools For Engineers By Engineers We Need Better Tools!  Excel Is A Great Software, But Is Not The Right Tool For Everything! Excel is a wonderful data organizer / cleanup tool. In-line formulas are difficult to debug to say the least. VBA can help organize our thought process. Third Party Applications can help extend Excel’s capabilities.  Data Analysis Software Is Great, But For The Most Part Was Developed For Data Scientist Data Analysis Software can solve problems in a few lines of code that would take 100’s of lines of code in Excel VBA with Third Party Applications. We need a better way to document our work. “knitR” package can allows the integration of “R Code” with LaTex, HTML, Markdown; this allows us to have reproducible research through means of literate programing. We need Data Analysis Software Build for Engineers by Engineers “rShiny” package can allow us to build web apps for interactivity.

20 Copyright © 2014 CEK Engineering LLC How “CEK” Looks At Resource Plays We Do A Poor Job Aggregating Our Datasets Aggregating Correctly Is Tough!  We Aggregate To Much & Don’t Explore Our Data We need to subset our data into subpopulations before we blindly start making sample distributions to simulate! –This is difficult to do. In Excel this is typically a tedious ad hoc process; even with Third Party Applications. –To do this correctly we perform: “Independent Two-Sample T-Test” for normal distributions, ANOVA, and “Likelihood-Based” or “Bootstrap” methods for log-normal distributions. Combining Sample Subpopulations smears the mean of our data, invariably in the wrong direction! (i.e. we increase EUR’s)  Our Data Is Typically Non-Normal This presents a major challenge as most of the statistical techniques engineers are taught assume normality! –We use linear models (i.e. least squares) on data that is non-normal!  Lies, damned lies, and statistics! Do we do this? We should use generalized linear models or non-linear models. Is the engineering community informed with these techniques?

21 Copyright © 2014 CEK Engineering LLC Brief Discussion On My Research & How “CEK” Looks At Resource Plays I Intend To Generate Software For “Engineers” Which Will Allow Them To Explore Their Data Efficiently My Firm Currently Uses This Software In Workflow For Our Clients In Workflows “Engineers” Are Accustom To!

22 Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) WolfBerry Infill Drilling Example Aggregation Of Production Data “Or Improper Aggregation Of Production Data”

23 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Aggregated By Start Date Year (Rate Time Semi-Log Plot)

24 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Aggregated By Start Date Year (Rate Time Semi-Log Plot)

25 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Aggregated By Start Date Year (Rate Time Log-Log Plot) half-slope – linear flow period unit-slope – boundary dominated period? Note: Horizontal axis is exaggerated, thereby deceiving actual slopes

26 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Aggregated By Start Date Year (Rate Time Log-Log Plot) Note: Horizontal axis is exaggerated, thereby deceiving actual slopes quarter-slope – bilinear flow period half-slope – linear flow period

27 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Aggregated By Start Date Year (Rate Cumulative Oil Production Cartesian Plot) Decreasing w/ Aggregated Start Date Time

28 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Aggregated By Start Date Year (Rate Cumulative Oil Production Cartesian Plot) Decreasing w/ Aggregated Start Date Time

29 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Aggregated By Start Date Year (Rate Cumulative Oil Production Semi-Log Plot) Straight Lines On This Plot = Harmonic Decline (b = 1) Note: Sudden Change In Decline Rate (2010 – 2011). This coincides to the infill drilling campaign! 106 MSTB 78 MSTB ~ 25% Decrease In EUR Due To Decline Rate Increase (Boundary Dominated Flow?)

30 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Aggregated By Start Date Year (Rate Cumulative Oil Production Semi-Log Plot) Straight Lines On This Plot = Harmonic Decline (b = 1) MSTB MSTB MSTB MSTB MSTB 2008 – 20 Wellbores 2009 – 34 Wellbores 2010 – 87 Wellbores 2011 – 190 Wellbores 2012 – 275 Wellbores Cumulative Wellbore Counts ~ As Infill Drilling Continued On This Project; There Was An Precipitous Decline In EUR’s EUR’s Decreasing

31 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Aggregated By Start Date Year (Rate Cumulative Oil Production Semi-Log Plot) Straight Lines On This Plot = Harmonic Decline (b = 1) Average 74 MSTB MSTB MSTB MSTB MSTB MSTB Note: Average EUR Decline Curve Approaches The 2008 Aggregate Projection

32 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Implications Of Improper Aggregation Start Date Year # of WellsEUR / Well MSTB / Well EUR Total MSTB , , , ,250 Total27515,885 Average Oil EUR / Well = 15,885 MSTB / 275 Wells ~ 58 MSTB / Well Aggregate Wells By Start Date Year And Summarize Why Not Use The Average Normalized Decline Curve?

33 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Implications Of Improper Aggregation 74 MSTB Straight Lines On This Plot = Harmonic Decline (b = 1) If We Include Tail End Data… We Might Be Tempted To Use A Hyperbolic Exponent b > 1 ( i.e. concave up ) 120 MSTB Tail End Effects Are Dominated By Early Time Well Completions Note The Jaggedness Of Curve Caused By Relatively Few Wells. Note Increase In Serration Caused By Decreasing Aggregation Counts

34 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Implications Of Improper Aggregation Start Date Year # of WellsEUR / Well MSTB / Well EUR Total MSTB , , , ,250 Total27515,885 Average Oil EUR / Well = 15,885 MSTB / 275 Wells ~ 58 MSTB / Well Average Normalized “Type Curve” = 74 MSTB / Well Aggregate Wells By Start Date Year And Summarize Reserves / Resources Overstated By 25% Using Average Normalized “Type Curve” 20,350 MSTB instead of 16,110 MSTB Or Worse…If We Were Tempted To Used A Hyperbolic Exponent > 1 Based On Our Average Normalized Decline Curve… We Could Be Overstating Reserves / Resources By 110% (120 MSTB vs. 58 MSTB).

35 Copyright © 2014 CEK Engineering LLC Normalized Production Type Curves Implications Of Improper Aggregation Start Date Year # of WellsEUR / Well MSTB / Well EUR Total MSTB , , , ,250 Total27515,885 Average Oil EUR / Well = 15,885 MSTB / 275 Wells ~ 58 MSTB / Well Average Normalized “Type Curve” = 74 MSTB / Well Aggregate Wells By Start Date Year And Summarize Undeveloped Reserves / Resources Will Also Be Overstated Using The Average Normalized “Type Curve” Versus Most Current EUR Projection (2012) Overstated By ~ 50% (74 MSTB vs. 50 MSTB) For This Project Area, This Equates To 960 MSTB or 2,800 MSTB (40 - Undeveloped 40 ac. Locations x 21 or 70 MSTB) Of Reserves / Resources Overstated. or Worse, Overstated By ~ 140% (120MSTB vs. 50MSTB).

36 Copyright © 2014 CEK Engineering LLC Aggregation Of Production Data Implications Of Improper Aggregation Aggregation Errors Such As These Have Serious Economic Implications!

37 Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) WolfBerry Infill Drilling Example Why Is My Unconventional Reservoir Behaving Conventionally? “A Tale Of Two Reservoirs” – Literally!

38 Copyright © 2014 CEK Engineering LLC 12 Month Cumulative Oil Production Aggregated By Start Date Qtr. & Lease Note: Box Plots Represent (min, max values – end point of black line tails; base of box = 25 th percentile; black line in box = mean; top of box = 75 th percentile). Black Dots are potential outlier data points. Colored By Lease. Note: In all instances, initial wells outperform subsequent wells. Statistically this is impossible; also this violates our definition of a resource play at least initially. Note: Late time wells exhibit narrower distributions; and more consistent results. At this point our “resource play” rock is dominating production.

39 Copyright © 2014 CEK Engineering LLC 12 Month Proppant Normalized Cumulative Oil Production Aggregated By Start Date Qtr. & Lease Note: Box Plots Represent (min, max values – end point of black line tails; base of box = 25 th percentile; black line in box = mean; top of box = 75 th percentile). Black Dots are potential outlier data points. Colored By Lease. Note: In all instances, initial wells outperform subsequent wells. Statistically this is impossible; also this violates our definition of a resource play at least initially. Note: Late time wells exhibit narrower distributions; and more consistent results. At this point our “resource play” rock is dominating production.

40 Copyright © 2014 CEK Engineering LLC 24 Month Cumulative Oil Production Aggregated By Start Date Qtr. & Lease Note: Box Plots Represent (min, max values – end point of black line tails; base of box = 25 th percentile; black line in box = mean; top of box = 75 th percentile). Black Dots are potential outlier data points. Colored By Lease. Note: In all instances, initial wells outperform subsequent wells. Statistically this is impossible; also this violates our definition of a resource play at least initially. Note: Late time wells exhibit narrower distributions; and more consistent results. At this point our “resource play” rock is dominating production.

41 Copyright © 2014 CEK Engineering LLC 24 Month Proppant Normalized Cumulative Oil Production Aggregated By Start Date Quarter and Lease Note: Box Plots Represent (min, max values – end point of black line tails; base of box = 25 th percentile; black line in box = mean; top of box = 75 th percentile). Black Dots are potential outlier data points. Colored By Lease. Note: In all instances, initial wells outperform subsequent wells. Statistically this is impossible; also this violates our definition of a resource play at least initially. Note: Late time wells exhibit narrower distributions; and more consistent results. At this point our “resource play” rock is dominating production.

42 Copyright © 2014 CEK Engineering LLC Total Proppant Amount Aggregated By Frac Style, Maximum Monthly Rate, and Lease Note: Average Completion used ~ 1000 Mlb of proppant with a maximum variance of ± 200 Mlb of proppant from 2008 to Note: Average Completion in 2013 used ~ 1400 Mlb of proppant ~ +40% increase in historic use amount.

43 Copyright © 2014 CEK Engineering LLC 24 Month Cumulative Offset Oil Production (640 ac.) - Prior To Start Date Aggregated By Lease or Section Note: Color Band Represent 95% Confidence Interval Note: Inverse relationship of 24 Month Cumulative Production vs. 640 ac. Total Offset Production At Start Date.

44 Copyright © 2014 CEK Engineering LLC 24 Month Proppant Normalized Cumulative Offset Oil Production (640 ac.) - Prior To Start Date Aggregated By Lease or Section Note: Color Band Represent 95% Confidence Interval Note: Inverse relationship of 24 Month Cumulative Production vs. 640 ac. Total Offset Production At Start Date.

45 Copyright © 2014 CEK Engineering LLC 24 Month Cumulative Water Production Size Of Attribute Relative To Amount Of Produced Water Note: Larger data points clustered in the eastern portion of this plot indicate that this area produces Significantly more water then the western portion of this plot. Only one well is in the 10 th percentile in this area; whereas the western portion of this plot has Zero wells in the 90 th percentile. Transition Zone? Unconventional Reservoirs (PRMS) Exist in petroleum accumulations that are pervasive throughout a large area and that are not significantly affected by hydrodynamic influences (also Called “continuous –type deposits”) (Emphasis Mine) Conventional Reservoirs (PRMS) Exist in discrete petroleum accumulations related to a localized geological structural feature and/or stratigraphic condition, typically with each accumulation bounded by a downdip contact with an aquifer, and which is significantly affected by hydrodynamic influences such as buoyancy of petroleum in water. (Emphasis Mine) It Appears That Some…If Not All Of The Completed Reservoirs Are Behaving Conventionally?

46 Copyright © 2014 CEK Engineering LLC Why Is My Unconventional Reservoir Behaving Conventionally You Might Not Know This Unless You Explore Your Dataset! Not All Resource Plays Are Unconventional Reservoirs!  I’m Sorry, I Mean…Not All Areas/Zones Of A Resource Play Are Unconventional Reservoirs!!  When Analyzing These Plays, We Must Always Remain Cognizant Of This Fact! Tight basin center Shales eventually grade to High Permeability Platform Carbonates! Just because an Operator filed a Completion Reports testifying to the fact that the wellbore’s completed reservoir is in a “Resource Play / Unconventional Reservoir”… doesn’t necessary make it so.  The Only Way We Can Perform Accurate Estimates Is To Explore Our Datasets As Deeply As Possible! Hopefully With “R” Operators Are known to make errors from time to time; not always unintentionally. Don't Blindly Start Aggregating Wells Based Solely On Field Names! Outlier Analysis can generally find these wells.

47 Copyright © 2014 CEK Engineering LLC Questions? Oh…Please Have At Least One! Questions? Thank you for the opportunity to present this to you

48

49 Copyright © 2014 CEK Engineering LLC References 1.)Damn Excel! How the ‘most important software of all time’ is ruining the world. 2.)What is R Images were gathered from various internet sources. Our intention is to not infringe upon any copyrighted work. If we have done so, I deeply regret this error! Please except out apology and we will be happy to credit, or remove your work. Respectfully, Chad E. Kronkosky, P.E. President CEK Engineering LLC th Street Lubbock, TX (806)


Download ppt "Statistical Analysis of the Wolfberry Using “R” Infill Drilling Study (80 ac. to 20 ac. Spacing) Chad E. Kronkosky, P.E President, CEK Engineering LLC."

Similar presentations


Ads by Google