Effective Porosity etc..

Total or Effective Porosity? etc..

Think for yourself with questions like these on Dr M’s  Petrophysics Courses

Public Courses annually in Vienna (May) and Dubai (October). In-House by request

Logic provides a value for n in non-strongly water wet reservoirs

A value for mixed wettability n: Many oil reservoirs have mixed wettabilities. These will have Sw’s below those implied from conventionally cleaned, strongly water wet, capillary pressure Sw-Ht data. If we reverse calculate n from these high capillary pressure Sw’s using log data, n will be high: we shall be assuming a higher Sw for the logged resistivity. Conversely, the conventionally cleaned and hence strongly water wet special core analysis electrical n values on these same plugs will be low: water wet systems have low n’s, n will be low. These two observations provide a means to constrain n and largely overcome your worries in mixed wet reservoirs. This information is probably sitting quietly behind you on your shelf, right now.  Can you hear it?

Cementation Exponent, m.  Function and Calibration

The objective of m is to convert the log evaluation’s Ø, with a*Rw, into the log evaluation’s estimate of Ro (water saturated formation resistivity), not the core analysis Ro or the “true” Ro!  The laboratory is an inadequate analogy of in-situ conditions – witness the 100’s of learned papers telling us so, the latest tranche informing us that electrical anisotropy further invalidates our core data a and m’s. With certain water zones (Sw100 zones from side-walls, cuttings and chromatograph), spanning a range of Ø in rocks of the same pore-type (similar Ø-k xplots) we have the actual log data’s value for Ro and m – the actual data we need.  Why do we choose to ignore Nature’s own reservoir conditions m laboratory and worry instead about reservoir to laboratory inequalities? This particular faith in core data is misconceived.. m must predict the log data’s Ro not the lab data’s Ro which is often irrelevant and misleading.  To repeat.. the lab data’s Ro can be misleading. Think for yourself – consider the objective and focus on the data that will actually be used in the evaluation.

Saturation Exponent, n.  Function and Calibration

The objective of n is to convert the log evaluation’s value of Ro/Rt into the actual reservoir Sw. The log evaluations metre scale estimate of Rt/Ro departs from the lab’s cm scale measurement of Rt/Ro as heterogeneity increases.  The value of n is determined by the n definition plot, commonly seen in core analysis reports, such that n is the negative slope of Log(Sw) vs. Log(Rt/Ro). This plot is conventionally populated by core data, with its numerous shortcomings, notably that Rt/Ro is not measured at the scale of the log data’s Rt/Ro which will actually be used in the evaluation. This n definition plot can be used more effectively if we ..  consider the objective and focus on the data that will actually be used in the evaluation.

Net Pay Cutoff SCAL Calibration

At last, a cut-off you can justify! Have you or your friendly petrophysicist ever used relative permeability data to determine a value for your oil reservoir’s netpay cut-off? No? But relative permeability data defines the conditions at which oil becomes mobile … at which an oil saturated rock becomes PAY.

Pore Throat Size controls Sw & k.. so use Sw for k!

Think “Pore throat size distribution controls Sw AND k, so therefore..” k and Sw are both controlled by pore throat size. Porosity and NMR T2 distributions are not (surface area). Does your company use Sw or Ø*Sh’s stronger correlation with k to predict k? We pay for NMR logging, core analysis “flow zones” and “rock types” in attempts to address k variations with porosity.. but we often don’t need to. Sometimes we just need to look at what we already have for a cheaper, more powerful correlation of log analysis results with k.. (Reason: Service companies can’t make money out of data that already exists.. that is the task of the Interested Petrophysicist)

Data Hierarchy is the correct basis for Petrophysical Data Integration

A Passive “Comparison with” or an Active “Calibrate to”? What does the experienced petrophysicist intuitively use as criteria for the reservoir Data Hierarchy, that is ranking data for its usefulness? This petrophysicist suggests Directness, Accuracy and Spatial Definition are the root criteria of experienced staff. How do we navigate through competing data sets and methods? Experienced petrophysicists, geologists and engineers: What do you use? Analyse That!

Why Pay to log for Free Fluid Volume? Think Data Hierarchy

Do we really need FFV for Permeability ? The PPL method does not require a log of Free Fluid Volume (FFV) or Log Mean T2 to solve for permeability. This is advantageous during logging operations because it avoids the need to log the far more problematic FFV with an NMR tool – for example FFV cannot easily be obtained in gas zones and logging is often slowed dramatically to an expensive crawl in the belief that the full T2 spectrum must be logged to achieve useful NMR results. Not so. Bound Fluid Volume (BFV) is the easiest and fastest log to obtain with an NMR tool. It is 100% water, rapidly polarizes and is rapidly counted in early T2 time. With this, a robust and accurate permeability is achieved by the PPL approach, in clastics and non-vuggy carbonates. Whilst service companies may be aware of how this can be achieved it usually is not in their interests to advance such an approach. Why, because service companies make money from running logs, not from evaluating your reservoir. Indeed, many wells can be accurately evaluated for permeability without even acquiring NMR data, set your “Interested Petrophysicist” to work on that one..

Petrophysicists : Think First, Pay Later

The message of many of these asides and of 25 years experience in petrophysics is that service companies have excellent staff but are not motivated to evaluate your reservoir.  They are motivated to create data, that’s their job. It is the chosen niche of PETROPHYSICS, from PhD to present, not to create data but to extract and use the maximum from the data we have.

See also Controversy

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