Quality Assuring the Petrophysical Evaluation of Thin Beds

Transcription

1 Quality Assuring the Petrophysical Evaluation of Thin Beds Paul F. Worthington Park Royd P&P (England) Ltd Ascot, UK London Petrophysical Society 21 September 2017

2 Structure Introduction Scenario Approach Volumetric Analysis Field Examples Conclusions

3 Introduction Thin beds are one of the major causes of by-passed pay in the world today A thin bed is one that is not sufficiently thick to be fully resolved by a logging tool

4 Tool Resolution

5 Introduction Historically, thin bed evaluation has drawn upon forward modelling or the signal enhancement of standard logs An alternative philosophy uses a coarse-resolution three-dimensional induction tool plus an electrical micro-imager to calculate the bed properties Simplifying assumptions Resistivity only Porosity problem remains

6 Scenarios for Thin-bed Evaluation Scenario Bed range (cm) Nature of Constituent Beds Resolvable Detectable Pluggable Identifiable A B 3-10 C 1-3 D E Yes No Some

7 Scenario Approach Separate the problem into parts Each scenario relates to a range of bed thickness Bed thickness has to be considered relative to Tool resolution Tool availability We focus on the porosity problem in cases where Core data exist in a key well Thin beds are not pluggable Layer boundaries cannot be discerned through downhole measurement

8 Volumetric Analysis Three references Thomas & Stieber (1975) Total porosity system Ruhovets & Fertl (1982) Effective porosity system Juhasz (1986) Total and effective porosity systems

9 Volumetric Analysis Juhasz - total porosity system - laminated sand/shale sequence tsd = ( t - (V shl tsh )) / (1 - V shl ) V shl = ( t - max + V sh (1 - tsh )) / (1 - max )

10 Volumetric Analysis Avoid over-prediction of interstitial clay-mineral volume fraction Correction factors from shale volume fraction (V sh ) to clay-mineral volume fraction (V cm ) Any shale indicator Specifically for the estimation of porosity Do not take max as the highest porosity seen in a database It is a mathematically-derived quantity required for closure

11 Example 1 Scenario B D Bed thicknesses: cm All beds are assumed to be isotropic Laminated shale fraction from electrical microimaging log BVH increases by 65% on application of thin-bed analysis A Scenario D approach (ignoring core and microimaging data) does not produce matching results Disparity is attributed to requirement to estimate maximum attainable porosity max

12 Example 1 Scenario B D No electrical micro-imager, no multicomponent induction, no whole core, est. max = 0.43 Input Parameter Output Parameter Scenario B Scenario D Bed thicknesses (cm) t c V cm / V sh V shl R shl ( m) 1.25 V cm R v ( m) 7.52 V cmd R h ( m) 3.00 R tsd ( m) 10.8 R w ( m) 0.08 tsd m 1.83 S wsd n 1.64 EHT sd (m) 0.113

13 Example 2 Scenario D Bed thicknesses: cm All beds are assumed to be isotropic Laminated shale fraction from multicomponent induction log BVH increases fourfold on application of thin-bed analysis An indirect approach (ignoring multicomponent induction data) does not produce matching results Disparity is attributed to requirement to estimate maximum attainable porosity max

14 Example 2 Scenario D No electrical micro-imager, no multicomponent induction, no whole core, max = 0.40 Input Parameter Output Parameter Direct method Indirect method Bed thicknesses (cm) t b (g/cc) 2.35 V sh V cm / V sh V shl R shl ( m) 1.05 V cm R v ( m) 3.33 V cmd R h ( m) 1.90 R tsd ( m) 5.09 R w ( m) tsd M 1.90 S wsd N 1.81 EHT sd (m) 0.093

15 Conclusions We have assumed thin isotropic beds within a sandshale sequence We have partitioned the thin-bed problem using discrete scenarios based on specified ranges of bed thickness further distinguished by available data For each scenario a workflow has evolved for formation evaluation The scenarios become more challenged as beds get thinner

16 Conclusions Where requisite data do not exist, a lower-level scenario has to be adopted with greater uncertainty The use of compositional equations in the evaluation of porosity needs to be calibrated otherwise closure is not achievable The overestimation of clay-mineral content by shale indicators has been corrected but this requires some core porosity data from the same depositional system

17 Conclusions The approach has been set in terms of optimal data acquisition sufficient to do the job More complex layerings can be accommodated carbonate stringers anisotropic sand and shale But these will require additional data e.g. nuclear magnetic resonance Benefits are improved evaluations of hydrocarbons in place and thence petroleum reserves

18 Epithet 80% of the subject matter of this presentation is available in OnePetro as: Majid, A.A. & Worthington, P.F Definitive petrophysical evaluation of thin hydrocarbon reservoir sequences. SPE Reservoir Evaluation & Engineering 15(5), [October 2012] This is the peer-reviewed version and not the 2011 OMC conference preprint, which is also in OnePetro