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In Search of  The Biggest Bang for the Buck Differential Statistical www.Entrac-Petroleum.com R E (Gene) Ballay SPWLA UAE ...
In Search of The Biggest Bang for the Buck <ul><li>Geoscientists are accustomed to dealing with uncertainty </li></ul><ul>...
<ul><li>In Search of The Biggest Bang for the Buck </li></ul><ul><li>Why quantify the uncertainty? </li></ul><ul><ul><li>T...
<ul><li>In Search of The Biggest Bang for the Buck </li></ul><ul><li>Concepts (and spreadsheet) applicable to many common ...
In Search of The Biggest Bang for the Buck <ul><li>As  carbonate petrophysicists , we are  typically faced with Sw(Archie)...
<ul><li>In Search of The Biggest Bang for the Buck </li></ul><ul><li>As  carbonate petrophysicists , we are  typically fac...
<ul><li>In Search of The Biggest Bang for the Buck </li></ul><ul><li>Formation resistivity </li></ul><ul><ul><li>The 6FF40...
The  Differential Approach Over-view <ul><li>Chen & Fang  have taken an  In Depth Differential Look at Sw(Archie) Uncertai...
The  Differential Approach Over-view <ul><li>Chen & Fang  produced  generic charts to facilitate locale-specific evaluatio...
The  Differential Approach Over-view <ul><li>Chen & Fang ’s results have been  coded to an  Excel spreadsheet , to facilit...
The  Differential Approach Over-view <ul><li>Chen & Fang ’s results have been  coded to an  Excel spreadsheet , to facilit...
The  Differential Approach Over-view <ul><li>At   ~ 10 pu ,  tortuosity in the pore system is far more important than “n...
The  Differential Approach Over-view <ul><li>The differential approach  is  applicable to many common oilfield issues </li...
The  Statistical Approach Over-view <ul><li>Many phenomena in nature are not Gaussian distributed .  </li></ul><ul><li>The...
The  Statistical Approach Over-view <ul><ul><li>An  advantage of Monte Carlo  is that  many types of distributions can be ...
The  Statistical Approach Over-view <ul><li>The  Monte Carlo method relies on repeated random sampling to model results  <...
The  Statistical Approach Over-view <ul><li>A  limitation of Monte Carlo  is that  special software is often utilized  </l...
The  Statistical Approach Over-view <ul><li>The  Monte Carlo method makes use of probability distributions to produce a cu...
The  Statistical Approach Over-view <ul><li>Many Oilfield analyses can be accomplished with Excel  </li></ul><ul><ul><li>T...
The  Statistical Approach Over-view <ul><li>The statistical approach is applicable to many common oilfield issues </li></u...
The  Statistical Approach Over-view <ul><li>Monte Carlo simulation can be used to characterize the uncertainty in Sw , for...
The  Statistical Approach Over-view Sw(Archie) Uncertainty <ul><li>One issue of interest  is the  dependence of Sw upon in...
Statistical vs Best / Worst <ul><li>There is a  95% likelihood   that  Sw is contained within + / - 2   </li></ul><ul><ul...
Derivatives vs Statistics Over-view <ul><li>Chen & Fang  identify the  attribute resulting in the greatest Sw uncertainty,...
<ul><li>Chen & Fang  identify  “m” as the dominant issue </li></ul><ul><li>The  Monte Carlo Base Case  is at  lower left ,...
The Spreadsheet Spreadsheet may be modified for locally specific conditions <ul><li>Set up to illustrate various distribut...
<ul><li>The  Monte Carlo   method is  attractive when it is infeasible or impossible to compute an exact result with a det...
The Differential Approach <ul><li>An  alternative,  deterministic  approach to error analysis is accomplished by taking th...
Monte Carlo Simulation of Sw(Archie) <ul><li>Monte Carlo  simulation can be used to characterize the uncertainty in Sw , f...
Monte Carlo Simulation of Sw(Archie) <ul><li>Porosity  is described by a  Gaussian distribution , centered on  20 pu   wit...
Monte Carlo Simulation of Sw(Archie) <ul><li>Monte Carlo  simulation can be used to characterize the uncertainty in Sw , f...
Monte Carlo Simulation of Sw(Archie) <ul><li>Porosity,  “m” & “n”  are determined with  individual  Random Number  inputs ...
Monte Carlo Simulation of Sw(Archie) <ul><li>Calculations are in the  Sw_Random  worksheet,  live-linked  to the  Sw_Resul...
Monte Carlo Simulation of Sw(Archie) <ul><li>To modify spreadsheet to accommodate a different three parameter simulation, ...
Monte Carlo Simulation of Phi(Rhob) <ul><li>To modify spreadsheet to accommodate a different three parameter simulation,  ...
Monte Carlo Simulation of “m(Dual Porosity)” <ul><li>To modify spreadsheet to accommodate a different three parameter simu...
Monte Carlo Simulation of Sw(Archie) <ul><li>Porosity  is specified as a  Gaussian distribution , centered on  20 pu   wit...
Monte Carlo Simulation of Sw(Archie) <ul><li>Porosity  is specified as a  Gaussian distribution , centered on  20 pu   wit...
Monte Carlo Simulation of Sw(Archie) <ul><li>As a QC device, the  distribution of Excel random numbers  used to drive the ...
Monte Carlo Simulation of Sw(Archie) Sw(Archie) Uncertainty <ul><li>One issue of interest  is the  dependence of Sw upon i...
Monte Carlo Simulation of Sw(Archie) Excel Bins <ul><li>The Freq of a specific Bin is not a centered value </li></ul><ul><...
Nonlinear relationship affects probability distributions.  Oilfield Review. Autumn 2002. Ian Bryant, Alberto Malinverno, M...
Monte Carlo vs Best / Worse Case <ul><li>There is a  95% likelihood   that  Sw is contained within + / - 2   </li></ul><u...
In Search of  The Biggest Bang for the Buck <ul><li>One issue of interest  is the  dependence of Sw upon individual attrib...
Uncertainty in Archie’s Equation Where to spend time, and money, in search of an improved Sw estimate Identifying the  Big...
In Search of  The Biggest Bang for the Buck <ul><li>Chen & Fang ’s results have been  coded to an  Excel spreadsheet , to ...
In Search of  The Biggest Bang for the Buck <ul><li>Chen & Fang ’s results have been  coded to an  Excel spreadsheet , to ...
In Search of  The Biggest Bang for the Buck <ul><li>At   ~ 10 pu,  tortuosity in the pore system is far more important t...
In Search of  The Biggest Bang for the Buck <ul><li>Chen & Fang ’s  results should be apparent  in a  similarly specified ...
In Search of  The Biggest Bang for the Buck <ul><li>Derivatives  identify the  attribute resulting in the greatest Sw unce...
In Search of  The Biggest Bang for the Buck <ul><li>Approximate the various relative uncertainties as follows </li></ul><u...
In Search of  The Biggest Bang for the Buck <ul><li>+ / - 1    will encompass ~ 68% of the distribution </li></ul><ul><li...
In Search of  The Biggest Bang for the Buck <ul><li>Derivatives  identify the  attribute resulting in the greatest Sw unce...
In Search of  The Biggest Bang for the Buck <ul><li>Derivatives  identify  “m” as the dominant issue </li></ul><ul><li>The...
Monte Carlo vs Best / Worst Case <ul><li>One  also observes that  the Best / Worst numerical evaluation of Sw(Archie)  is ...
In Search of  The Biggest Bang for the Buck <ul><li>If  porosity  were to be  25 pu , rather than 10,  the focus changes <...
In Search of  The Biggest Bang for the Buck <ul><li>Sw uncertainty is a dynamic issue  </li></ul><ul><ul><li>Should be eva...
Comments by Carlos Torres-Verdin, University of Texas <ul><li>Phillipe Theys  has a book which addresses this issue, as do...
Carlos Torres-Verdin:  Be sure to recognize  that  logs are affected by various issues Ozark Mtns Road Cut, SW Missouri
Quantifying Petrophysical Uncertainties S J Adams 2005 Asia Pacific Oil & Gas Conference, Jakarta, Indonesia, 5 - 7 April ...
We appreciate the unidentified LSU faculty who posted their material (located via Google) to  http://www.enrg.lsu.edu/pttc...
http://www.statsoft.com/textbook/stathome.html Additional Considerations
Useful Links http://en.wikipedia.org/wiki/Monte_carlo_simulation <ul><li>http://www.ipp.mpg.de/de/for/bereiche/stellarator...
Early morning carbonate bluffs along Steel Creek, Arkansas Thank you for your Time and Interest
R. E. (Gene) Ballay’s  32 years in petrophysics  include  research and operations  assignments in Houston (Shell Research)...
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In Search of the Biggest Bang for the Buck

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Presentation on Uncertainty given by Gene Ballay to the SPWLA Abu Dhabi Chapter on 19th Oct 2009

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Transcript of "In Search of the Biggest Bang for the Buck"

  1. 1. In Search of The Biggest Bang for the Buck Differential Statistical www.Entrac-Petroleum.com R E (Gene) Ballay SPWLA UAE October 2009
  2. 2. In Search of The Biggest Bang for the Buck <ul><li>Geoscientists are accustomed to dealing with uncertainty </li></ul><ul><ul><li>While minimum and maximum variations (for example) are common, they may be incomplete, and even non-representative </li></ul></ul><ul><li>There are two basic alternatives </li></ul><ul><ul><li>Partial derivatives </li></ul></ul><ul><ul><li>Statistical simulation </li></ul></ul>The Devil’s Promenade , SW Missouri
  3. 3. <ul><li>In Search of The Biggest Bang for the Buck </li></ul><ul><li>Why quantify the uncertainty? </li></ul><ul><ul><li>Time and Budget </li></ul></ul><ul><ul><ul><li>There is typically a limited timeframe and budget. Where do we focus? </li></ul></ul></ul><ul><ul><li>Lack of data and/or previous experiences </li></ul></ul><ul><ul><ul><li>Even with ‘focus’, we seldom have all the data we would like </li></ul></ul></ul><ul><ul><ul><li>A previous bad experience , by either our self or someone else on the Team, will compound the unease </li></ul></ul></ul>
  4. 4. <ul><li>In Search of The Biggest Bang for the Buck </li></ul><ul><li>Concepts (and spreadsheet) applicable to many common oilfield issues </li></ul><ul><ul><li>The various, discrete measurements that make up routine and special core analyses </li></ul></ul><ul><ul><li>The various attributes that are required to convert lab capillary pressure to a reservoir conditions </li></ul></ul><ul><ul><li>Reservoir volumetrics </li></ul></ul><ul><li>Once we have mastered one calculation, it is straight-forward to address another </li></ul>
  5. 5. In Search of The Biggest Bang for the Buck <ul><li>As carbonate petrophysicists , we are typically faced with Sw(Archie) estimates, in the face of uncertainty </li></ul><ul><ul><li>Mineralogy </li></ul></ul><ul><ul><li>Porosity </li></ul></ul><ul><ul><li>Formation brine resistivity </li></ul></ul><ul><ul><li>Formation resistivity </li></ul></ul><ul><ul><li>“ m” and “n” exponents </li></ul></ul><ul><li>Exhibit following </li></ul>
  6. 6. <ul><li>In Search of The Biggest Bang for the Buck </li></ul><ul><li>As carbonate petrophysicists , we are typically faced with …… </li></ul><ul><li>Mineralogy </li></ul><ul><ul><li>In some (KSA Shuaiba, for example) reservoirs, mineralogy is nearly uniform, and hence well known. </li></ul></ul><ul><ul><li>In other (probably most) locales, uncertainty exists in this basic information </li></ul></ul><ul><ul><li>In West Texas one may be faced with three minerals (limestone – dolostone – anhydrite) , and only two logging tools (density-neutron) </li></ul></ul><ul><ul><ul><li>Anhydrite may be present as obvious nodules, or as (more subtle) cement </li></ul></ul></ul><ul><li>Porosity </li></ul><ul><ul><li>Porosity is often thought of as +/- “x” pu of uncertainty, but in actual fact the uncertainty may be a function of the amount of porosity present </li></ul></ul><ul><ul><ul><li>Tool measurement techniques and statistical noise </li></ul></ul></ul><ul><li>Formation brine resistivity </li></ul><ul><ul><li>We’ve all worked Fields for which we had high confidence in Rw, but that is not always the case </li></ul></ul><ul><li>Exhibit following </li></ul>
  7. 7. <ul><li>In Search of The Biggest Bang for the Buck </li></ul><ul><li>Formation resistivity </li></ul><ul><ul><li>The 6FF40, as an example, has a Skin Effect limitation at the low resistivity end , and a signal-to-noise issue at high resistivity </li></ul></ul><ul><ul><li>The mud resistivity (and hence borehole & invasion effects ) may change from one well to the next, indeed one logging interval to the next </li></ul></ul><ul><li>“ m” and “n” exponents </li></ul><ul><ul><li>How many of us have ever been “really sure” of our exponents? </li></ul></ul><ul><ul><li>In carbonates, wettability (and hence “n”) may vary with Sw, and present a significant challenge in the transition zone </li></ul></ul><ul><li>Sw(Archie) involves the combination of all these attributes, and their respective uncertainties </li></ul><ul><li>Is it any wonder that “one size may not fit all feet”? </li></ul>
  8. 8. The Differential Approach Over-view <ul><li>Chen & Fang have taken an In Depth Differential Look at Sw(Archie) Uncertainty , in terms of input attribute values and respective uncertainties </li></ul>Sensitivity Analysis of the Parameters in Archie‘s Water Saturation Equation. The Log Analyst. Sept – Oct 1986
  9. 9. The Differential Approach Over-view <ul><li>Chen & Fang produced generic charts to facilitate locale-specific evaluation </li></ul><ul><li>At  ~ 20 pu, for stated conditions, the saturation exponent is a relatively minor issue </li></ul><ul><li>Sw(Archie) attributes with associated Best Estimate and Uncertainties </li></ul><ul><ul><li>More on this later </li></ul></ul><ul><li>For attributes specified above, and in the case of  ~ 20 pu , “n” is a relatively minor issue </li></ul>
  10. 10. The Differential Approach Over-view <ul><li>Chen & Fang ’s results have been coded to an Excel spreadsheet , to facilitate locale specific, digital evaluation </li></ul>
  11. 11. The Differential Approach Over-view <ul><li>Chen & Fang ’s results have been coded to an Excel spreadsheet , to facilitate locale specific, digital evaluation </li></ul><ul><li>Sw(Archie) attributes with associated Best Estimate and Uncertainties </li></ul><ul><li>For attributes specified above, and in the case of  ~ 30 pu, the priorities change . </li></ul><ul><li>Now ‘m’ & ‘n’ are of about equal importance </li></ul>
  12. 12. The Differential Approach Over-view <ul><li>At  ~ 10 pu , tortuosity in the pore system is far more important than “n” variations </li></ul>These relations may be coded into nearly any petrophysical s/w package, to facilitate foot-by-foot evaluation
  13. 13. The Differential Approach Over-view <ul><li>The differential approach is applicable to many common oilfield issues </li></ul><ul><li>ArchieUncertainty.pdf for derivation & additional considerations </li></ul><ul><ul><li>How to calculate the derivatives </li></ul></ul><ul><ul><li>How to calculate the propagation of error </li></ul></ul>Additional Details
  14. 14. The Statistical Approach Over-view <ul><li>Many phenomena in nature are not Gaussian distributed . </li></ul><ul><li>The probabilistic method allows one to model non-Gaussian distributions </li></ul><ul><li>More difficult to set up </li></ul><ul><li>Not included in the petrophysical s/w that is in use </li></ul>
  15. 15. The Statistical Approach Over-view <ul><ul><li>An advantage of Monte Carlo is that many types of distributions can be used to characterize the uncertainty of a parameter </li></ul></ul><ul><ul><li>Normal, log normal, rectangular, triangular, etc </li></ul></ul>
  16. 16. The Statistical Approach Over-view <ul><li>The Monte Carlo method relies on repeated random sampling to model results </li></ul><ul><li>This approach is also attractive when it is infeasible or impossible to compute an exact result with a deterministic algorithm. </li></ul>
  17. 17. The Statistical Approach Over-view <ul><li>A limitation of Monte Carlo is that special software is often utilized </li></ul><ul><ul><li>Not included with many commercially available petrophysics s/w packages </li></ul></ul><ul><li>When available , the implementation of a MC Model over a large section of log data can be very time consuming </li></ul>
  18. 18. The Statistical Approach Over-view <ul><li>The Monte Carlo method makes use of probability distributions to produce a cumulative probability result </li></ul><ul><ul><li>The determination of a probability distribution is superior to the single deterministic value </li></ul></ul><ul><ul><li>Insight is gained into the “upside” and “downside” </li></ul></ul><ul><ul><ul><li>+ / - 1  will encompass ~ 68% of the distribution </li></ul></ul></ul><ul><ul><ul><li>+ / - 2  ~ 95 % of the distribution </li></ul></ul></ul>http://en.wikipedia.org/wiki/Standard_deviation
  19. 19. The Statistical Approach Over-view <ul><li>Many Oilfield analyses can be accomplished with Excel </li></ul><ul><ul><li>This eliminates both the expense of, and need to learn, commercial programs </li></ul></ul><ul><ul><li>@Risk, Crystal Ball, etc </li></ul></ul><ul><ul><ul><li>If you have the time, and budget, to use @Risk, etc…Great! </li></ul></ul></ul><ul><ul><ul><li>If not, and you want to leverage your Excel skill set, consider that option </li></ul></ul></ul><ul><li>Oilfield applications typically use “ normal ”, “ log normal ”, rectangular and “ triangular ” statistical distributions . </li></ul><ul><ul><li>Relevant Excel functions </li></ul></ul><ul><ul><ul><li>Rand, NormDist, NormInv, LogNormDist, LogInv, TriangleInv </li></ul></ul></ul>
  20. 20. The Statistical Approach Over-view <ul><li>The statistical approach is applicable to many common oilfield issues </li></ul><ul><li>If a “reference” spreadsheet is available, Monte Carlo may be easier to implement than derivatives </li></ul><ul><li>MonteCarloModeling.pdf and MonteCarloIllustrations.pdf for additional details and considerations </li></ul>Additional Details Additional Details
  21. 21. The Statistical Approach Over-view <ul><li>Monte Carlo simulation can be used to characterize the uncertainty in Sw , for the attributes (& associated uncertainties) tabulated at right </li></ul><ul><li>“ a”, Rw and Rt are assumed to be well-known, reflected here by no STD specification </li></ul><ul><li>This simulation is approximating an Sw interpretation for which the porosity, “m” & “n” estimates are each subject to individually specified uncertainty </li></ul><ul><li>Porosity (for example) is described by a Gaussian distribution , centered on 20 pu with a standard deviation of 1 pu </li></ul>Illustrative Monte Carlo Application
  22. 22. The Statistical Approach Over-view Sw(Archie) Uncertainty <ul><li>One issue of interest is the dependence of Sw upon individual attribute values / uncertainties </li></ul><ul><li>With the specifications at right </li></ul><ul><li>Sw(mean) = 0.357 </li></ul><ul><li> (Sw) = 0.038 </li></ul><ul><li>There is a 95% likelihood that Sw is contained within + / - 2  </li></ul><ul><ul><li>(0.357 – 0.076) < Sw < (0.357 + 0.076) </li></ul></ul><ul><ul><li>0.28 < Sw < 0.433 </li></ul></ul><ul><li>Be aware of how Excel ‘bins’ data and ‘non-linear effects’ </li></ul><ul><li>Supplemental material for details </li></ul>
  23. 23. Statistical vs Best / Worst <ul><li>There is a 95% likelihood that Sw is contained within + / - 2  </li></ul><ul><ul><li>0.28 < Sw < 0.433 </li></ul></ul><ul><li>The Best / Worst case would yield considerably more uncertainty </li></ul><ul><ul><li>0.239 < Sw < 0.50 </li></ul></ul><ul><li>In practice, it’s unlikely (but not impossible) that Best / Worst of all attributes would occur simultaneously, so that Sw(Monte Carlo) provides a better representation </li></ul>
  24. 24. Derivatives vs Statistics Over-view <ul><li>Chen & Fang identify the attribute resulting in the greatest Sw uncertainty, with a derivate approach </li></ul><ul><li>In the case at right , “m” is the dominant issue </li></ul><ul><li>This same issue can be addressed with Monte Carlo simulation (below) </li></ul><ul><li>The Base Case is at lower left, with each simulation towards the right reflecting an individual improvement in “ Phi ”, “ m ” and “ n ” precision by 10 %. </li></ul><ul><li>Exhibit following </li></ul>
  25. 25. <ul><li>Chen & Fang identify “m” as the dominant issue </li></ul><ul><li>The Monte Carlo Base Case is at lower left , with each simulation towards the right reflecting an improvement in “ Phi ”, “ m ” and “ n ” individual precision by 10 %. </li></ul><ul><li>Improved definition of cementation exponent results in the most efficient improvement in Monte Carlo Sw estimation ( lowest standard deviation in Sw ) per Monte Carlo simulation </li></ul><ul><li>Monte Carlo is consistent (as expected) with Derivatives </li></ul>Derivatives vs Statistics Over-view
  26. 26. The Spreadsheet Spreadsheet may be modified for locally specific conditions <ul><li>Set up to illustrate various distributions, and to model Phi(Rhob), “m” & Sw(Archie) </li></ul><ul><li>Be aware that illustrations are from Excel 2007, in the backwards compatible mode, and Excel 2003 screens may be different </li></ul><ul><li>Illustrative application follows </li></ul>
  27. 27. <ul><li>The Monte Carlo method is attractive when it is infeasible or impossible to compute an exact result with a deterministic algorithm. </li></ul><ul><li>Excel can handle common probability distributions, and can thus serve as a Monte Carlo simulator. </li></ul><ul><li>Quantitative estimation of uncertainty allows one to determine where time / money is most effectively spent, and to further avoid the trap of being misled as a result of a previous bad experience with a poorly defined parameter </li></ul><ul><li>The importance of the various input parameters will change, according to the various magnitudes. There is a linkage in that one parameter becomes more or less important as another parameter value is changed, so the simulation must be executed for locally specific conditions. </li></ul>Additional Details as a Text Document The Statistical Approach
  28. 28. The Differential Approach <ul><li>An alternative, deterministic approach to error analysis is accomplished by taking the derivative of Sw(Archie) with respect to each attribute </li></ul><ul><li>S w n = a R w / (  m R t ) </li></ul><ul><li>The same approach will suffice for a shaly sand equation </li></ul><ul><li>The various terms in the derivative expression quantify the individual impact of uncertainty in each term, upon the result </li></ul><ul><li>The relative magnitude then allows one to recognize where the biggest bang for the buck, in terms of a core analyses program, suite of potential logs, etc is to be found (Figure 1). </li></ul>Additional Details as a Text Document
  29. 29. Monte Carlo Simulation of Sw(Archie) <ul><li>Monte Carlo simulation can be used to characterize the uncertainty in Sw , for the attributes (and associated uncertainties) tabulated at right </li></ul><ul><li>“ a”, Rw and Rt are assumed to be well-known, reflected here by no STD specification </li></ul><ul><li>This simulation is approximating a foot-by-foot Sw interpretation, for which the porosity, “m” & “n” estimates are subject to uncertainty </li></ul><ul><li>Porosity (for example) is described by a Gaussian distribution , centered on 20 pu with a standard deviation of 1 pu </li></ul><ul><li>Exhibit following </li></ul>Monte Carlo Simulation of Sw(Archie)
  30. 30. Monte Carlo Simulation of Sw(Archie) <ul><li>Porosity is described by a Gaussian distribution , centered on 20 pu with a standard deviation of 1 pu </li></ul><ul><li>Two Thousand calculations are performed and the results are accumulated </li></ul><ul><li>Exhibit following </li></ul>
  31. 31. Monte Carlo Simulation of Sw(Archie) <ul><li>Monte Carlo simulation can be used to characterize the uncertainty in Sw , for the attributes (and associated uncertainties) tabulated at right </li></ul><ul><li>“ a”, Rw and Rt are assumed to be well-known, reflected here by no STD specification </li></ul><ul><li>“ m” and “n” are also Gaussian distributed , and centered on 2.0 , with standard deviations of 0.1 </li></ul><ul><li>“ m” & “n” may be varied independently by simply modifying the above Table </li></ul><ul><ul><li>Calculations are live-linked </li></ul></ul><ul><li>“ a”, Rw and Rt may also be varied in the MC model by a straight-forward extension </li></ul><ul><li>Exhibit following </li></ul>Monte Carlo Simulation of Sw(Archie)
  32. 32. Monte Carlo Simulation of Sw(Archie) <ul><li>Porosity, “m” & “n” are determined with individual Random Number inputs to NormInv , and are not driven by a single, common, random number source </li></ul><ul><li>Exhibit following </li></ul>Excel Details
  33. 33. Monte Carlo Simulation of Sw(Archie) <ul><li>Calculations are in the Sw_Random worksheet, live-linked to the Sw_Results worksheet </li></ul>Be Careful to enter values appropriately, and not over-write live links
  34. 34. Monte Carlo Simulation of Sw(Archie) <ul><li>To modify spreadsheet to accommodate a different three parameter simulation, simply re-title the various attributes and adjust the individual calculations as required </li></ul>
  35. 35. Monte Carlo Simulation of Phi(Rhob) <ul><li>To modify spreadsheet to accommodate a different three parameter simulation, simply re-title the various attributes and adjust the individual calculations as required. This worksheet models Phi(Rhob). </li></ul>
  36. 36. Monte Carlo Simulation of “m(Dual Porosity)” <ul><li>To modify spreadsheet to accommodate a different three parameter simulation, simply re-title the various attributes and adjust the individual calculations as required. This worksheet models the Wang & Lucia Dual Porosity “m” Exponent </li></ul>
  37. 37. Monte Carlo Simulation of Sw(Archie) <ul><li>Porosity is specified as a Gaussian distribution , centered on 20 pu with a standard deviation of 1 pu </li></ul><ul><li>2,000 calculations are done, and the result “checked’ by means of histograming the resulting porosity distribution and calculating the resulting statistics </li></ul><ul><li>Exhibit following </li></ul>Cross Check
  38. 38. Monte Carlo Simulation of Sw(Archie) <ul><li>Porosity is specified as a Gaussian distribution , centered on 20 pu with a standard deviation of 1 pu </li></ul><ul><li>2,000 calculations are done, and the result “checked’ by means of histograming the resulting porosity distribution and calculating the resulting statistics </li></ul><ul><li>The MC simulation is observed to reproduce the specified inputs, digitally and graphically </li></ul><ul><li>Random number “mean” & “Std” converge to input values </li></ul><ul><li>Exhibit following </li></ul>Cross Check
  39. 39. Monte Carlo Simulation of Sw(Archie) <ul><li>As a QC device, the distribution of Excel random numbers used to drive the Monte Carlo simulation, are ‘binned’ from zero to one </li></ul><ul><ul><li>With 2000 simulation performed, we expect to find Frequency ~ 200 in each of the ten bins </li></ul></ul><ul><ul><li>The Excel Random() function has approached the ideal distribution </li></ul></ul>QC the Monte Carlo
  40. 40. Monte Carlo Simulation of Sw(Archie) Sw(Archie) Uncertainty <ul><li>One issue of interest is the dependence of Sw upon individual attribute values / uncertainties </li></ul><ul><li>With the specifications at right </li></ul><ul><li>Sw(mean) = 0.357 </li></ul><ul><li> (Sw) = 0.038 </li></ul><ul><li>There is a 95% likelihood that Sw is contained within + / - 2  </li></ul><ul><ul><li>(0.357 – 0.076) < Sw < (0.357 + 0.076) </li></ul></ul><ul><ul><li>0.28 < Sw < 0.433 </li></ul></ul><ul><li>Be aware of how Excel ‘bins’ data </li></ul><ul><li>Exhibit following </li></ul>
  41. 41. Monte Carlo Simulation of Sw(Archie) Excel Bins <ul><li>The Freq of a specific Bin is not a centered value </li></ul><ul><li>Sw_Bin=0.25  0.225 .LT. Sw .LE. .25 </li></ul><ul><li>Sw_Bin=0.275  0.25 .LT. Sw .LE. .275 </li></ul><ul><li>Sw_Bin=0.30  0.275 .LT. Sw .LE. .30 </li></ul><ul><li>Sw_Bin=0.325  0.30 .LT. Sw .LE. .325 </li></ul><ul><li>Sw(Mean)=0.357 but Bin Freq, and associated graphic, peaks to the high side of this value </li></ul><ul><li>Bin Values are shifted with respect to actual distribution </li></ul><ul><li>Be aware of how Excel ‘bins’ data </li></ul>
  42. 42. Nonlinear relationship affects probability distributions. Oilfield Review. Autumn 2002. Ian Bryant, Alberto Malinverno, Michael Prange, Mauro Gonfalini, James Moffat, Dennis Swager, Philippe Theys, Francesca Verga. <ul><li>The Archie relationship for a given formation, with constant “a”, “m”, “n” and Rt results in a hyperbolic relationship of Sw to porosity (blue) </li></ul><ul><li>This relationship distorts the frequency distributions , which are shown along the axes </li></ul><ul><li>A normal uncertainty distribution about a given porosity ( green ) becomes a log-normal distribution for the resulting Sw uncertainty (red) </li></ul><ul><li>The mean value ( dashed yellow ) and three sigma points ( dashed purple ) show the skewed Sw distribution </li></ul><ul><li>Sw distributions determined with Monte Carlo modeling are distorted at high values, since Sw cannot exceed 100%. </li></ul>Nonlinear Relation Is An Additional Issue
  43. 43. Monte Carlo vs Best / Worse Case <ul><li>There is a 95% likelihood that Sw is contained within + / - 2  </li></ul><ul><ul><li>0.28 < Sw < 0.433 </li></ul></ul><ul><li>The Best / Worst case corresponding to + / - 2  , would yield considerably more uncertainty </li></ul><ul><ul><li>0.239 < Sw < 0.50 </li></ul></ul><ul><li>In practice, it’s unlikely (but not impossible) that Best / Worst of all attributes would occur simultaneously, so that Sw(Monte Carlo) provides a better representation </li></ul>
  44. 44. In Search of The Biggest Bang for the Buck <ul><li>One issue of interest is the dependence of Sw upon individual attribute values / uncertainties </li></ul><ul><li>With the values specified at right </li></ul><ul><li>Sw(mean) = 0.357 </li></ul><ul><li> (Sw) = 0.038 </li></ul><ul><li>Monte Carlo simulation also allows one to determine  for any possible (improved) input distribution, and to therefore identify where the ‘biggest bang for the buck” in reducing Sw uncertainty is at </li></ul><ul><li>Exhibit following </li></ul>Where Is The Biggest Bang For The Buck?
  45. 45. Uncertainty in Archie’s Equation Where to spend time, and money, in search of an improved Sw estimate Identifying the Biggest Bang for the Buck <ul><li>Both options have attractive features </li></ul><ul><li>The analytical approach can be easily coded into a foot-by-foot petrophysical evaluation </li></ul><ul><li>Monte Carlo simulation can address non-Gaussian distributions. </li></ul><ul><li>Both options are adaptable to the spreadsheet environment </li></ul><ul><li>In addition to quantifying the uncertainty in a single Sw(Archie) estimate, one will likely want to prioritize time and budget, in a manner that most efficiently reduces net Sw uncertainty </li></ul><ul><li>This can be done with either derivatives or MC simulation </li></ul>Derivatives vs Monte Carlo
  46. 46. In Search of The Biggest Bang for the Buck <ul><li>Chen & Fang ’s results have been coded to an Excel spreadsheet , to facilitate locale specific, digital evaluation </li></ul><ul><li>As porosity increases , “m” or “pore system tortuosity” becomes less important , and the tortuosity of the conductive phase, represented by “n”, deserves increased attention, relative to “m” </li></ul><ul><li>Exhibit following </li></ul>The Derivative Approach <ul><li>At  ~ 30 pu, for stated conditions, the three issues approach one another, in importance </li></ul>
  47. 47. In Search of The Biggest Bang for the Buck <ul><li>Chen & Fang ’s results have been coded to an Excel spreadsheet , to facilitate locale specific, digital evaluation </li></ul>Spreadsheet may be modified for locally specific conditions
  48. 48. In Search of The Biggest Bang for the Buck <ul><li>At  ~ 10 pu, tortuosity in the pore system is far more important than “n” variations </li></ul><ul><li>As porosity decreases , “m” or “pore system tortuosity” becomes the dominant issue </li></ul><ul><li>In these circumstances the tortuosity of the conductive phase, represented by “n”, can be nearly neglected (in a relative sense) </li></ul>The Derivative Approach
  49. 49. In Search of The Biggest Bang for the Buck <ul><li>Chen & Fang ’s results should be apparent in a similarly specified Monte Carlo simulation </li></ul><ul><li>Consider the below three situations , with corresponding Chen & Fang results at right </li></ul><ul><li>“ a” = 1: Uncertain(a) = 0% </li></ul><ul><li>Rw = 0.02 : Uncertain (Rw) = 0% </li></ul><ul><li>Rt = 40 : Uncertain (Rt) = 0% </li></ul><ul><li>Phi = 10 , 20 & 30 pu : Uncertain (Phi) = 15% </li></ul><ul><li>“ m” = 2.0 : Uncertain (m) = 10% </li></ul><ul><li>“ n” = 2 : Uncertain (n) = 5% </li></ul><ul><li>In each case, the dominant parameter is identified with the red arrow (at right), per derivatives </li></ul><ul><li>Exhibit following </li></ul>Derivatives vs Monte Carlo
  50. 50. In Search of The Biggest Bang for the Buck <ul><li>Derivatives identify the attribute resulting in the greatest Sw uncertainty </li></ul><ul><ul><li>In the case at right (  ~10 pu) , “m” is the dominant issue </li></ul></ul><ul><li>This same issue can be addressed with Monte Carlo simulation </li></ul><ul><li>Recalling that + / - 1  will encompass ~ 68% of the distribution , and 2  ~ 95 % of the distribution , we approximate the various relative uncertainties per the following exhibit </li></ul>http://en.wikipedia.org/wiki/Standard_deviation
  51. 51. In Search of The Biggest Bang for the Buck <ul><li>Approximate the various relative uncertainties as follows </li></ul><ul><li>+ / - 1  will encompass ~ 68% of the distribution </li></ul><ul><li>+/- 2  ~ 95 % of the distribution, </li></ul><ul><li>Represent the “Phi” relative uncertainty of 15% @ 10 pu (1.5 pu) as 2  ~ 1.5 pu =>  ~ 0.75 pu for Monte Carlo Simulation purposes </li></ul><ul><li>That is, 2  encompasses 95 % of the spread in the distribution , and we’ll set it equal to the 15 % uncertainty in the Chen & Fang analyses </li></ul><ul><li>Exhibit following </li></ul>Uncertainty Specification Derivative  Monte Carlo Total Uncertainty vs Standard Deviations
  52. 52. In Search of The Biggest Bang for the Buck <ul><li>+ / - 1  will encompass ~ 68% of the distribution </li></ul><ul><li>+/- 2  ~ 95 % of the distribution, </li></ul><ul><li>Approximate the “Phi” relative uncertainty of 15% @ 10 pu (1.5 pu) as 2  ~ 1.5 pu =>  ~ 0.75 pu for Monte Carlo Simulation purposes </li></ul><ul><li>Approximate the “m” relative uncertainty of 10% @ 2.00 (0.2) as 2  ~ 0.2 =>  ~ 0.1 for Monte Carlo Simulation purposes </li></ul><ul><li>Approximate the “n” relative uncertainty of 5% @ 2.00 (0.1) as 2  ~ 0.1 =>  ~ 0.05 for Monte Carlo Simulation purposes </li></ul><ul><li>These mean values and standard deviations form the Base Case, against which one compares the MC results </li></ul><ul><li>Exhibit following </li></ul>“ m” & “n” Uncertainty
  53. 53. In Search of The Biggest Bang for the Buck <ul><li>Derivatives identify the attribute resulting in the greatest Sw uncertainty </li></ul><ul><li>In the case at right , “m” is the dominant issue </li></ul><ul><li>This same issue can be addressed with Monte Carlo simulation (below) </li></ul><ul><li>The Base Case is at lower left , with each simulation towards the right reflecting an improvement in “ Phi ”, “ m ” and “ n ” individual precision by 10 %. </li></ul><ul><li>Exhibit following </li></ul>Derivatives vs Monte Carlo
  54. 54. In Search of The Biggest Bang for the Buck <ul><li>Derivatives identify “m” as the dominant issue </li></ul><ul><li>The Monte Carlo Base Case is at lower left , with each simulation towards the right reflecting an improvement in “ Phi ”, “ m ” and “ n ” individual precision by 10 %. </li></ul><ul><li>Improved definition of cementation exponent results in the most efficient improvement in Monte Carlo Sw estimation ( lowest standard deviation in Sw ) per Monte Carlo simulation </li></ul><ul><li>Monte Carlo is consistent (as expected) with Derivatives </li></ul>Derivatives vs Monte Carlo
  55. 55. Monte Carlo vs Best / Worst Case <ul><li>One also observes that the Best / Worst numerical evaluation of Sw(Archie) is considerably more pessimistic than is the +/- 2  Monte Carlo simulation </li></ul><ul><ul><li>The Best and Worst of all parameters are unlikely to occur simultaneously </li></ul></ul>Best Case Worst Case
  56. 56. In Search of The Biggest Bang for the Buck <ul><li>If porosity were to be 25 pu , rather than 10, the focus changes </li></ul><ul><ul><li>“ m” and “n” uncertainties have been set equal in this simulation </li></ul></ul><ul><li>The Base Case is at lower left, with each simulation towards the right reflecting an improvement in “ Phi ”, “ m ” and “ n ” precision by 10 %. </li></ul><ul><li>With the improved porosity, focus shifts to “n”, the tortuosity of the conductive (brine) phase in the presence of a non-conductive (hydrocarbon) phase, as offering the Biggest Bang For The Buck. </li></ul><ul><li>Exhibit following </li></ul>
  57. 57. In Search of The Biggest Bang for the Buck <ul><li>Sw uncertainty is a dynamic issue </li></ul><ul><ul><li>Should be evaluated for each set of circumstances </li></ul></ul><ul><li>Attributes are ‘linked’ </li></ul><ul><ul><li>A change in one can cause another to become more, or less, important </li></ul></ul><ul><li>Concepts (and spreadsheet) applicable to many common oilfield issues </li></ul><ul><ul><li>Allow us to focus time and budget in the most efficient manner </li></ul></ul>
  58. 58. Comments by Carlos Torres-Verdin, University of Texas <ul><li>Phillipe Theys has a book which addresses this issue, as does Darwin. </li></ul><ul><ul><li>The book by Darwin especially considers the effects on porosity (also of the shaly-sand case). He was the first to perform a similar analysis . </li></ul></ul><ul><li>One should be sure to recognize that resistivity logs are affected by invasion and shoulder-bed effects </li></ul><ul><ul><li>This can cause significant errors in the estimation of Sw because Rt is not representative . The latter effects can be more significant that the ones you have explicitly addressed. </li></ul></ul><ul><ul><li>Electrical anisotropy effects could have a significant impact on Rt in deviated and horizontal wells . </li></ul></ul><ul><ul><li>The most significant example is that of thinly-bedded sequences , where nuclear and resistivity logs are significantly biased (in addition to the clay effect) because of shoulder-bed and invasion effects. </li></ul></ul>Additional Considerations
  59. 59. Carlos Torres-Verdin: Be sure to recognize that logs are affected by various issues Ozark Mtns Road Cut, SW Missouri
  60. 60. Quantifying Petrophysical Uncertainties S J Adams 2005 Asia Pacific Oil & Gas Conference, Jakarta, Indonesia, 5 - 7 April 2005 <ul><li>Quantitative uncertainty definition is more than just using Monte Carlo simulation to vary the inputs to the interpretation model </li></ul><ul><li>The largest source of uncertainty may be the interpretation model itself </li></ul><ul><li>In graphic at right,  (Rhob) is calculated with , and without , light hydrocarbon corrections </li></ul><ul><li> (D-N) and  (Dt) are not LHC corrected </li></ul><ul><li>Accounting for LHC effects shifts  (D) to over-lay the core distribution </li></ul>Additional Considerations
  61. 61. We appreciate the unidentified LSU faculty who posted their material (located via Google) to http://www.enrg.lsu.edu/pttc/ Additional Considerations
  62. 62. http://www.statsoft.com/textbook/stathome.html Additional Considerations
  63. 63. Useful Links http://en.wikipedia.org/wiki/Monte_carlo_simulation <ul><li>http://www.ipp.mpg.de/de/for/bereiche/stellarator/Comp_sci/CompScience/csep/csep1.phy.ornl.gov/mc/mc.html </li></ul><ul><li>http://www.sitmo.com/eqcat/15 </li></ul><ul><li>http://www.riskglossary.com/link/monte_carlo_method.htm </li></ul><ul><li>http://www.chem.unl.edu/zeng/joy/mclab/mcintro.html </li></ul><ul><li>http://office.microsoft.com/en-us/ excel /HA011118931033.aspx </li></ul><ul><ul><li>What happens when I enter =Rand() in a cell? </li></ul></ul><ul><ul><li>How can I simulate values of a discrete random variable ? </li></ul></ul><ul><ul><li>How can I simulate values of a normal random variable ? </li></ul></ul><ul><ul><li>Problems </li></ul></ul><ul><ul><ul><li>You can download the sample files that relate to excerpts from Microsoft Excel Data Analysis and Business Modeling from Microsoft Office Online. This article uses the files RandDemo.xls, Discretesim.xls, NormalSim.xls, and Valentine.xls. </li></ul></ul></ul>
  64. 64. Early morning carbonate bluffs along Steel Creek, Arkansas Thank you for your Time and Interest
  65. 65. R. E. (Gene) Ballay’s 32 years in petrophysics include research and operations assignments in Houston (Shell Research), Texas; Anchorage (ARCO), Alaska; Dallas (Arco Research), Texas; Jakarta (Huffco), Indonesia; Bakersfield (ARCO), California; and Dhahran, Saudi Arabia. His carbonate experience ranges from individual Niagaran reefs in Michigan to the Lisburne in Alaska to Ghawar, Saudi Arabia (the largest oilfield in the world). He holds a PhD in Theoretical Physics with double minors in Electrical Engineering & Mathematics , has taught physics in two universities , mentored Nationals in Indonesia and Saudi Arabia, published numerous technical articles and been designated co-inventor on both American and European patents . At retirement from the Saudi Arabian Oil Company he was the senior technical petrophysicist in the Reservoir Description Division and had represented petrophysics in three multi-discipline teams bringing on-line three (one clastic, two carbonate) multi-billion barrel increments. Subsequent to retirement from Saudi Aramco he established Robert E Ballay LLC, which provides physics - petrophysics training & consulting services. He served in the U.S. Army as a Microwave Repairman and in the U.S. Navy as an Electronics Technician, and he is a USPA Parachutist and a PADI Dive Master. Chattanooga shale Mississippian limestone
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