Uncertainty analysis in creating production forecast: Difference between revisions

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[http://petrowiki.org/Sandbox:Uncertainty_range_in_production_forecasting Uncertainty range in production forecasting] gives an introduction to uncertainty analysis in production forecasting, including a PRMS based definition of low, best and high production forecasts. This page topic builds on this with more details of how to approach uncertainty analysis as part of creating production forecasts.
[http://petrowiki.org/Sandbox:Uncertainty_range_in_production_forecasting Uncertainty range in production forecasting] gives an introduction to uncertainty analysis in production forecasting, including a PRMS based definition of low, best and high production forecasts. This page topic builds on this with more details of how to approach uncertainty analysis as part of creating production forecasts.


Probabilistic subsurface assessments are the norm within the exploration side of the oil and gas industry, both in majors and independents<ref>Rose, P. 2007. Measuring what we think we have found: Advantages of probabilistic over deterministic methods for estimating oil and gas reserves and resources in exploration and production. AAPG Bulletin 91 (1): 21–29. http://dx.doi.org/10.1306/08030606016.</ref>. However, in many companies, the production side is still in transition from single-valued deterministic assessments, sometimes carried out with ad-hoc sensitivity studies, to more-rigorous probabilistic assessments with an auditable trail of assumptions and a statistical underpinning. Reflecting these changes in practices and technology, recently SEC rules for reserves reporting (effective 1 January 2010) were revised, in line with PRMS, to allow for the use of both probabilistic and deterministic methods in addition to allowing reporting of reserves categories other than “proved.” This section attempts to present some of the challenges facing probabilistic assessments and present some practical considerations to carry out the assessments effectively.
Probabilistic subsurface assessments are the norm within the exploration side of the oil and gas industry, both in majors and independents<ref>Rose, P. 2007. Measuring what we think we have found: Advantages of probabilistic over deterministic methods for estimating oil and gas reserves and resources in exploration and production. AAPG Bulletin 91 (1): 21–29. http://dx.doi.org/10.1306/08030606016.</ref>. However, in many companies, the production side is still in transition from single-valued deterministic assessments, sometimes carried out with ad-hoc sensitivity studies, to more-rigorous probabilistic assessments with an auditable trail of assumptions and a statistical underpinning. Reflecting these changes in practices and technology, recently SEC rules for reserves reporting (effective 1 January 2010) were revised, in line with PRMS, to allow for the use of both probabilistic and deterministic methods in addition to allowing reporting of reserves categories other than “proved.” This section attempts to present some of the challenges facing probabilistic assessments and present some practical considerations to carry out the assessments effectively. Add content


It should be noted that for simplicity the examples referred to in this section are about calculating OOIP rather than generating probabilistic production forecasts directly. Clearly OOIP/GOIP is the starting point of any production forecast and gives a firm basis from which to build production forecasts. However, &nbsp;the outcome of probabilistic assessments are usually a set of deterministic models tied to distributions of not only OOIP but also recovery factor, initial production rate, and/or other scalar descriptions of a reservoir. These deterministic models can then be used to generate a set of low, best, and high production forecasts.
It should be noted that for simplicity the examples referred to in this section are about calculating OOIP rather than generating probabilistic production forecasts directly. Clearly OOIP/GOIP is the starting point of any production forecast and gives a firm basis from which to build production forecasts. However, &nbsp;the outcome of probabilistic assessments are usually a set of deterministic models tied to distributions of not only OOIP but also recovery factor, initial production rate, and/or other scalar descriptions of a reservoir. These deterministic models can then be used to generate a set of low, best, and high production forecasts.
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| style="width: 93px; text-align: center;" | 0.35
| style="width: 93px; text-align: center;" | 0.35
|-
|-
| style="width: 118px; text-align: center;" | '''Boi [rb/stb]'''<br/>
| style="width: 118px; text-align: center;" | '''Boi [rb/stb]'''<br />
| style="width: 81px; text-align: center;" | 1.05
| style="width: 81px; text-align: center;" | 1.05
| style="width: 91px; text-align: center;" | 1.10
| style="width: 91px; text-align: center;" | 1.10
| style="width: 93px; text-align: center;" | 1.15
| style="width: 93px; text-align: center;" | 1.15
|-
|-
| style="width: 398px; text-align: right;" colspan="4" | '''Table 1—OOIP Monte-Carlo test uncertainty ranges'''<br/>
| colspan="4" style="width: 398px; text-align: right;" | '''Table 1—OOIP Monte-Carlo test uncertainty ranges'''<br />
|}
|}
<p style="text-align: center;">'''OOIP [stb] = 7758 * area * hnet * phie * (1-Sw) / Boi'''</p>
<p style="text-align: center;">'''OOIP [stb] = 7758 * area * hnet * phie * (1-Sw) / Boi'''</p>
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<span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 9.5pt;"></span>[[File:OOIP Cumulative frequency distributions-Wolff.jpg|Figure 1 OOIP Cumulative frequency distributions]]
<span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 9.5pt;"></span>[[File:OOIP Cumulative frequency distributions-Wolff.jpg|Figure 1 OOIP Cumulative frequency distributions]]




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== Noteworthy papers in OnePetro ==
== Noteworthy papers in OnePetro ==


Vogel, L.C. 1956. A Method for Analyzing Multiple Factor Experiments—Its Application to a Study of Gun Perforating Methods. Paper SPE 727-G presented at the Fall Meeting of the Petroleum Branch of AIME, Los Angeles, 14–17 October. [http://dx.doi.org/10.2118/727-G http://dx.doi.org/10.2118/727-G].
Vogel, L.C. 1956. A Method for Analyzing Multiple Factor Experiments—Its Application to a Study of Gun Perforating Methods. Paper SPE 727-G presented at the Fall Meeting of the Petroleum Branch of AIME, Los Angeles, 14–17 October. http://dx.doi.org/10.2118/727-G.


== Noteworthy books ==
== Noteworthy books ==
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== External links ==
== External links ==


Production forecasts and reserves estimates in unconventional resources. Society of Petroleum Engineers. [http://www.spe.org/training/courses/FPE.php http://www.spe.org/training/courses/FPE.php]
Production forecasts and reserves estimates in unconventional resources. Society of Petroleum Engineers. http://www.spe.org/training/courses/FPE.php


Production Forecasts and Reserves Estimates in Unconventional Resources. Society of Petroleum Engineers. [http://www.spe.org/training/courses/FPE1.php http://www.spe.org/training/courses/FPE1.php]
Production Forecasts and Reserves Estimates in Unconventional Resources. Society of Petroleum Engineers. http://www.spe.org/training/courses/FPE1.php


[[#Top|Back to top]]
[[#Top|Back to top]]
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== Category ==
== Category ==
[[Category:5.6 Formation evaluation and management]] [[Category:5.6.9 Production forecasting]] [[Category:GIWS-PF]] [[Category:Chapter 4]] [[Category:Martin Wolff]] [[Category:DW]] [[Category:DW Complete]] [[Category:DW All Pages]]
[[Category:5.6 Formation evaluation and management]]  
[[Category:5.6.9 Production forecasting]]  
[[Category:GIWS-PF]]  
[[Category:Chapter 4]]  
[[Category:Martin Wolff]]  
[[Category:DW]]  
[[Category:DW Complete]]  
[[Category:DW All Pages]]
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