Example 1


Well 3 is interesting as it has a lot of core on the mineralogy. Some of the core is high quality XRD and some is not. There is also FTIR. The problem the analyst faces is what do we believe is correct, since they all give different results? So the well’s data becomes a “science project” to figure it all out.

There are six levels of computation difficulty in computing wells:

  1. Easiest, not difficult as nothing to check results against.
    1. No Nuclear Spectroscopy,
    2. No nuclear magnetic resonance,
    3. No core to check results.
    4. Confidence in results is lowest.
  2. Moderate difficulty
    1. No nuclear spectroscopy
    2. No nuclear magnetic resonance
    3. XRD core
    4. Moderate confidence in results
  3. Good – some more difficulty
    1. No nuclear spectroscopy
    2. No nuclear magnetic resonance
    3. XRD plus XRF core
    4. Good confidence in results
  4. Better – difficult
    1. Nuclear spectroscopy
    2. No nuclear magnetic resonance
    3. XRD plus XRF core; some RHOG, perm, Sw porosity with mixed lab methods and some unreliable data but not known what is reliable.
    4. Better confidence in results
  5. Best – most difficult; Experimental, constant revision.
    1. Nuclear spectroscopy
    2. Nuclear magnetic resonance
    3. XRD plus XRF plus FTIR core, perhaps several labs with mixed quality and different results and unknown reliability.
    4. Most confidence in results
  6. Robust – for multi-wells –difficulty proportional to core quality.
    1. Nuclear spectroscopy
    2. Nuclear magnetic resonance
    3. XRD plus XRF plus FTIR core, perhaps several labs with mixed quality and different results and unknown reliability.
    4. Most confidence in results

Summary: The confidence in the results is inversely proportional to the difficulty of the computation. The difficulty of the computation is proportional to the abundance of core data, as the “degrees of freedom” for the computation results is constrained. Core quality can be checked against itself by comparing the chemistry of the XRD or FTIR vs. the XRF elements. They must agree or the confidence in the core XRD is very limited. In lieu of XRF, the XRD and FTIR chemistry can be checked against the nuclear spectroscopy (such as ECS™ ) elements. If there are no nuclear spectroscopy measurements but there are XRF core elements, the XRF can be used to guide the prediction of nuclear spectroscopy elements when combined with offset well nuclear spectroscopy. In particular, it is critical to have XRF over carbonate zones in a mixed carbonate and clastic environment, unless a measured nuclear spectroscopy log is available.

The objective of computing the well is:

  1. Determine the bottom line: porosity, permeability water saturation.
  2. Validate core to use in the interpretation.
  3. Determine mineralogy, validate with core elements and minerals.

Before we can determine the bottom line we have to do steps 2 and 3. The notes are below. We start with a level four degree of difficulty and escalate to a level five to complete the processing.

Objective Viewed With Plots: Level Four

There are about 6 or 7 plots in the process of interpretation:

  1. Fig. 1, Plot 1 is “RAW LOGS AND CORE XRF ELEMENTS” where raw log quality is checked.
  2. Fig. 2, Plot 2 will be “PRE-INTERPRETATION”, where TOC and Rw are determined.
  3. Fig. 3, Plot 3 will be “COMPUTED INTERPRETATION, Part One”; Part one starts us off to look at details regarding the cluster rock type analysis summary of Sw, Por, and Rw in a program called Hydrocarbon Quick Look (HCQL).
  4. Figs. 4, 5, Plot 3a and 3b are side trips to show how porosity is plotted, and how the quick look hydrocarbon (HCQL) is done.
  5. Fig. 6, Plot 4 will be “COMPUTED INTERPRETATION, Part Two”; Part two looks at the “Bottom line porosity, water saturation and permeability” to see if it is reasonable or needs revision. Part three looks at the evidence that the “Bottom Line”, is valid or not. We check the core minerals, core elements and reconstructed logs to see if everything is OK. If tune-up is needed, we go to the next step. If all OK we end here and make comments about the best zone to drill horizontally in.
  6. Fig. 7, [Plots 5, 6, 7, 8, 9, 10 are not shown but are used to finally end up at Fig. 7, Plot 11 which is shown]. A point to note is the permeability loses significant figures in the LAS APPS transfer process, so it must be “cut and pasted” directly from the spreadsheet to the GAMLS well’s LAS file. This situation was “fixed” by Casey Struyk April 4, 2013, to provide an option for 10 decimal places, from 5 decimals, specifically for permeability. However, unless you update to the new LAS APPS you may encounter this problem.

We have camouflaged any company names in order to use this example. Notations to add:

  1. Depths are changed
  2. Logs recorded are Triple combo with Neutron Spectroscopy. The aluminum curve was not recorded but was subsequently predicted. The nuclear magnetic resonance log was predicted.
  3. Can you find the Pay zones?
  4. Production has not been released. Do you expect it should be oil, gas or water?

The idea of this summary is to intrigue the reader, whom we assume is a Log Analyst or Petrophysicist, into going through the process of interpretation which involves the first three plots.

Since interpretation is complicated by the value of the input data, in terms of incomplete or low quality core and logged data, one cannot usually use a single program for all wells. However, that is exactly what we propose to do: use one program for all wells. One program must have enough flexibility, to fit all the potential applications of siliclastics and carbonate environments, whether conventional or resource plays. Modifications must be made easily to fit the core or pre-conceived ideas and answer all of the “What if…” scenarios you come up with as you interpret the well. In order to use this one program we have to allow for incomplete logging programs, by predicting missing curves. Incomplete core data means we have to rely solely on reconstructed logs to achieve quality control. Confidence in the results becomes lower as there may be no unique solution.

Regarding missing curves, for example, we want to use the Spontaneous Potential (SP) to aid in the estimation of formation water resistivity, Rw. When the SP is missing, perhaps because the oil content of the mud is too high, we will predict the SP from an offset well that has a water base mud. We have not experienced any problems in making and using this prediction. However, the SP may need to be baseline-straightened before proceeding with calculations. It should also be bed-thickness corrected, hydrocarbon-corrected and converted to a Static SP. There is a company in Denver that specializes in that, called PST INC., (Petrophysical Solutions). We have used them for some important wells and they do a credible job. Most of the time, we will just straighten the SP, and carry on, unless it becomes critical that corrections to the SP and induction logs be made.

The interpretation process has to have “self-checks” built in. That is why, not only is core necessary, but curve reconstruction at the most basic level (i.e. elements), is necessary. As this well will illustrate, the reconstructed elements will point out a problem before you may notice it on the core mineral cross-checks with the logs.

The basics must be right before the bottom line can be relied upon. The method of starting with elements allows one to cross-check the calculated minerals by reconstructing the elements (level four) or constraining the reconstructed elements to the input elements (level five). Otherwise, how does one know if the minerals that are calculated or the core minerals are valid? Perhaps core XRD or FTIR says they are “close enough for country work” but the element reconstruction provides the check of, are they really as close as they could be? Other methods of interpretation in other software packages will provide the “bottom line” values but the advantage of the spectroscopy method is we can cross check the results at the basic level:

  • Does element reconstruction agree with input elements?
  • Does the calculated grain density agree with core grain density, even allowing for the light weight kerogen?
  • Does the calculated porosity agree with core porosity? Core porosity depends on the state of drying that the core had before measurements were made. Core porosity also depends on the size of the small capillaries because displacement of a pore filling fluid may not be able to enter the small pores. Furthermore, was the sample ground up before measurements? The method of deriving core porosity is important in understanding the results. Core porosity is not an absolute standard to calibrate log porosity.
  • Ditto for core water saturation and core permeability.

Fig. 1, Plot1 Raw Logs

Start with Raw logs, Fig. 1, Plot 1. However, we soon see that the original core data is off depth, so we must put it on depth before we compare the raw logs to core.

The next step after looking at raw logs is to do some “Pre-interpretation” processing, to obtain values for missing data such as Rw, free fluid (we use CMRP_3MS_P, you may have other preferences); we also need TCMR, TOC via travel time, density, uranium, Nieto’s work, and any other method you may prefer to stack up against core-generated values.

Now we can make the depth plot 1 of the raw logs:

First, a note about compatible scales: a neutron scale of 0.25 to -0.15 on a limestone matrix, should have a density RHOB scale of 2282 kg/m3 to 2950 kg/m3. However, the scale shown for the density is 1950 kg/m3 to 2950 kg/m3, which is equivalent to 45 to -0.15. Moral: watch out for incompatible porosity scales! Since the gas flag is based on matrix-adjusted porosity scales and not the limestone scale, this incompatible limestone scale was not changed, as it has also has the advantage of separating the density and neutron curves.

Fig. 1, PLOT 1 Raw Logs



Fig. 2, Plot 2 Pre-interpretation Plot

The Raw Logs look OK, so, we can proceed with the “Pre-Interpretation” plot. In the process we have derived KUT (and compared to the existing potassium, thorium and uranium); also we decide on the TOC calculation that best fits core TOC. We selected the delta Rhob-resistivity method.

We also use the SP to derive an Rw. An interesting side note is that this well used Invermul™ mud but the SP was still valid. Note the resistivity plot contains the four foot induction logs. The one foot “AO90” logs would be more appropriate, we think, but the best way to get Rt is to use PST™ to provide their method of correction to obtain Rt. Else use the service provider. Else, use the one foot measurement. [If you send me the logs we can get them done for you at PST as we have a “consultant rate” or contact them yourself.]

Fig. 2, PLOT 2 Pre-interpretation



Fig. 3, Plot 3 First Computation Plot

After inputting the curves to the “Level 4 PROGRAM”, we can obtain the results and make the first plot of the computation.

Now we are ready to look at the computed interpretation.

The big plot will be discussed in smaller plot sections so that we can explain the nuances of the interpretation a step at a time. One is interested not only in the bottom line of how much hydrocarbon is there but also how you arrived at that conclusion. So the first three plots will be:

  1. Fig 3, Plot 3. Summaries of sections (yellow boxes), with so called “Pickett Plots” of porosity and resistivity. The summaries can be made over any interval. We have chosen to show three for illustration purposes. The summary gives the depth interval in metres, porosity (Por), water saturation (Sw) and porosity times metres abbreviated as PorM. If the depths were in feet then it would cumulate Por-Ft. It also shows the formation water resistivity (Rw) that was used for the Sw calculation. To obtain this calculation, the left track showing the cluster of neutron, density, GR, Pe and calcium was used to provide probabilistic lithology by rock type. Then matrix density was assigned to each rock type, to provide probabilistic porosity from the density log (blue coding) we match this result with our detailed calculation of porosity to get an approximation for the summary. Next, using Rw, coefficient “a”, cementation “m”, & saturation index “n”, water saturation is calculated. By variations of the “a”, “m”, “n” we get a match with the detailed Sw, which had been calculated using complete mineralogy.



  1. The combined mineralogy is shown in the next track. Core values of quartz are plotted as red dots. The core shows less quartz than the computation. The formation is about 50% quartz, some feldspar (green), muscovite (light grey), illite (dark grey), kaolinite (dark orange), dolomite (purple) and calcite (light blue).
  2. The next track we have seen in the Pre-interpretation includes the spectral GR which was predicted for this well. It turns out that after predicting it, we also noticed it had already been recorded, as “Thor”, “Uran”, and “Pota”. So, here is an opportunity to check the prediction.
  3. Resistivity is next, with Rw, Ro and the induction resistivity curves. The separation of the light blue wet resistivity (Ro) and the other resistivity curves, shows there is indeed a lot of hydrocarbon here.
  4. The saturation track shows the residual oil, which is heavy shown in dark olive green. This bitumen-type oil is derived from the uranium and thorium curves. The “U” and “Th” have a crustal relationship of about 1/3 Thorium ~ uranium, in ppm. So, when there is more uranium than this 1:3 ratio, bitumen is indicated. From previous calibration (a Spearfish well with core), this is displayed as residual oil saturation, Sor. The light blue shaded curve is Sw. The green curve, shaded with grey, is the bound water saturation of the clay water, SWB. When Sw is close to SWB, the formation has to be at irreducible water saturation.
  5. The next track shows the bulk volume of water (BVW) as well as the BVW_CUTOFF. The BVW_CUTOFF is shaded in green and when Sw (blue) is within the green shading, no water will be produced. In the top zone of the well, BVW is often greater than BVW_CUTOFF, and the blue flags at the right, are generated. This top zone will produce water especially after a frac.
  6. So now we have the question, will gas or oil be produced, assuming, a frac will be made? The answer lies in comparing the density and neutron porosities and looking for cross-over, where the neutron reads less than the density. These porosity curves have been “adjusted for mineralogy, or matrix adjusted”. The PHID_MAD is density porosity using a matrix-adjusted density (MAD). The PHIN_MAN is matrix adjusted neutron. We do not see any cross-over where the neutron reads a lower porosity than the density. Hence, the interpretation is that this zone is oil prone.
  7. The last track on the right is the hydrocarbon pore volume (HCPV) adjusted to surface conditions and presented as original oil in place in millions of barrels of oil (MMBO). Scanning down, we see the zone with the least residual oil, is the zone shown by the botom yellow box.

Figs. 4 & 5, Plots 3a, 3b, Plot 3 LEVEL 4 “First Computation Plot: Porosity Plot Explained”

Next, we will explore the rest of the story, porosity, permeability. Then we will move to the quality control where reconstructed logs are compared to input logs.

This is probably a good place to show how the porosity plot has been put together. It has a series of curves from the top of the list to the bottom. Each curve is coded (shaded) from the curve to the right. As each curve is overlain by the next curve, the shading is replaced, except for the difference between the curves, which now represents some part of the porosity.

Curve 1, total porosity, “TPOR”, is shaded grey to represent the bound water. Of course it is not all bound water as when we overlay PHIE the separation between TPOR and PHIE is bound water. PHIE is TPOR – VWB.

However, there may be some TOC that has displaced the bound water, at some part of the life of the rock. So we place a curve called KEROGEN UPPER [boundary] as shown in track 2.

In Track 3, we add PHIE. The darker blue coding shows possible water in the small capillaries. Then add free porosity, in light blue. This shows possible free water. The term CMFF_PRED is just CMRP_3MS that has been clipped so it cannot go above PHIE and cannot go below zero [of course, but predicted values will have a “normal” distribution and some may read less than zero.]

In track 4, add KEROGEN LOWER which displays the space that TPOR-BVW or HCPV will occupy if there are hydrocarbons. It is coded as a rose colour and will represent the hydrocarbon both in the large and small capillaries.

Track 5 add HCPV minus capillary hydrocarbon (HCPV_CAPHC) & code as green

for free oil; now overlay the same curve but code as red if gas (Cross-over of PHIN_MAN & PHID_MAD); note, no gas shows.

Track 6. Add the porosity whose grain density has a correction for kerogen

TPOR_KER_GRI (yellow). This is the porosity that is expected to be measured on the core, [kerogen is part of the rock] if the Gas Research Institute (GRI) method has been used to measure the core porosity.

Below is the plot we were discussing above, Fig. 4:

Fig. 4, Plot 3a Overlay of Porosity Curves



Fig. 5, Plot 3b First Computation Plot: Interpretation Summary Explained

Fig.5, PLOT 3b, Interpretation summaries:


Fig. 5, plot 3b “CWLS WELL 3 (COMPUTED INTERPRETATION SUMMARIES RO SW OIL PLOT 3B).png”. [ Curves will be separated and shown below so they are readable.]

Fig. 6, Plot 4, First Computation Plot: First Cut at Interpretation Bottom Line, Porosity, Perm, & Sw Explained



Some notes to go with Fig. 6, plot 4.

Our plot has kept the mineralogy and resistivity, water saturation and reserves of oil, for reference, from PLOT 3.

  1. Track 6, from the left is permeability. Note the scale is 0.000001 mD to 1 mD. The permeability is so low; we assume that was why there were no measurements taken when this core was analyzed.
  2. The next track needs some explanation. It is a series of porosity curves that are “overlain” to create an interpretation. We previously have shown how these curves are overlain, Fig. 4, PLOT 3A, but for now, just look at the colours:
    • The pink (or purple shading, depending on how your computer interprets this rose colour) is total porosity minus the water filled porosity. The difference is the hydrocarbon filled porosity (HCPV). The green is the free fluid CMRP_3MS. Recall this was a predicted curve, not measured. Assuming the prediction is correct, the green shows a lot of free oil, which is about 45% of the HCPV. For reference, there is a black curve called “Bill_HC_PHI” which is 15% of the HCPV. We use this curve and to check the “normal” CMRP_3MS. This predicted CMRP_3MS is definitely not normal, so we will check the prediction in our quality control section.
    • The yellow curve is the porosity that is equivalent to the Gas Research Institute (GRI) method of measurement. The TOC is taken into account, thereby lowering the grain density, as shown in the next track, and the resulting porosity is much lower. Note that this TPOR_KER_GRI porosity is very close to what was measured on the core, (black dots), especially in the top lobe.
    • The free water is shown in the top lobe by the very light blue colour. Notice the difference in grain density between the top of the top lobe and the free water in the lower part: where there is free water the TOC is lower.
    • The bottom lobes are four in number. The best one, identified before is the 3rd lobe, based on bitumen content.
  3. Summaries of pay, porosity, show the bottom line: lobe 3 is the best. The flow capacity indicates there will be no flow unless a fracture job is performed. All indicators agree that lobe 3 is the best lobe for a horizontal well.

The next step is to compare core to log interpretation. Then, to compare to core, input values of the elements and reconstructed values of the elements. An important reconstruction to consider is the modeled reconstructed neutron. This indicator is developed from the mineral effects on the neutron log. When reconstruction is close to the measured neutron, the minerals all have to be pretty close to being reasonable.

The core and log minerals look close. Are we finished yet? Check out Fig. 7, PLOT 5 specifically looking at the element reconstruction, level 4.



The gold curves are reconstructed curves, from the computed minerals. They do not look great over the shale zone of interest, except Ca, Fe, and Ti. Aluminum is high, Silicon is high, and potassium is high. What does this mean? Look at the large plot 5 (Fig. 8) and subsequently some expanded plots to figure out what is happening.




We have seen the bottom line, where porosity Sw and permeability have been displayed. Now our question is what confidence can we put in the core and log results?

  1. How does the neutron reconstruction compare to the input neutron?
  2. We saw above that, “How do the reconstructed elements compare to the input elements?” was “not great”.
  3. Finally, what is an overall opinion of the validity of the computation?

OK, let’s look at these questions one at a time.



  1. How does the neutron reconstruction compare to the input neutron? Our opinion is it is pretty good, with a correlation coefficient of 85%. How is the calculation made?
  • The reconstructed neutron is based on two calculations:
    • Calculate the matrix for the neutron curve:
      • TNPH_matrix = (Sumproduct(minerals ,TNPH of Minerals)
    • Calculate the fluid portion of the neutron response:
      • TNPH_fluid = Sum(fluids in invaded zone *TNPH of fluids)
    • Combine the fluid and matrix (Total porosity*TNPH_fluid)+((1-total porosity)*TNPH_matrix)
  • We can see that it is important to have the mineral fractions, especially the clays, and the fluid fractions in the total-porosity zone of the neutron as well as the shallower invaded-zone and an estimate of the depth of the neutron investigation into the invaded zone, to be as close as possible. So, the reconstructed neutron provides a check on these calculations and assumptions. If the modeled neutron, M_NPHI is close to the recorded neutron, then all is “in the ball park”. The correlation coefficient in this computed zone is 85%, which is about as good as it gets, given bed boundaries and assumptions involved in the calculation.

Remember, however, that the reconstructed elements were not great. So, we have a pretty good mathematical fit of elements to the neutron but the reconstructed elements say the minerals could be better.

  1. How do the core minerals compare to the log minerals? We have depth corrected the core but it still may not be exactly correct. So now is the time to be critical.


Fig. 10, LEVEL 4 QFM, Carbonate and Clay, “PLOT 5 CMP Q CAR CLA FOR QC.png”

  • First, we compare the high quality red dots of core XRD quartz to the light orange, calculated quartz. The high quality XRD is very close to the log calculation. This is not always the case, as the accuracy of the XRD often depends on which lab, which person and which model and cross-check procedure is used. To do a “Reynold’s Cup” quality job, it is time consuming and expensive. To illustrate, some green XRD data points of quartz plus Kspar are plotted. One can see they are generally less than the log calculated value, and they are lower quality XRD work. So, if one settles for faster, lower quality, less expensive XRD data, then one has to put a higher error bar on the results. In this case the log calculation may be more accurate than the XRD, provided, of course those high quality XRF elemental measurements have been made. In the case where no elemental log measurements were made, but predicted log elements were used (not the case in this well), then the log elements will have a higher error bar. Assessing these error bars is pretty much an “eyeball” method, unless one uses statistical techniques that are available in the “Robust Elm” GAMLS program.
  • The “Robust Elm” is a totally new technique that will be announced in June 2013 at the SPWLA conference. The huge advantage of the Robust Elm (elements) program is the model can be subsequently applied to wells without core or neutron spectroscopy (NS), once the model has been established on wells with neutron spectroscopy that have good quality core analysis. The core analysis is merged with the NS elements to provide a “core-driven” mineral computation. Initial results look great on this well.
  • Second, compare the calcite and dolomite to the core. The high quality XRD of calcite is shown in light blue dots and compares very well with the logs. The lower quality XRD of total carbonate in blue square dots is in the ballpark. The conclusion is the log calculation of calcite is acceptable and, perhaps, better than the lower quality XRD.
  • Third, compare the clay fractions. Here we do have the high quality XRD for the Muscovite. The core Muscovite is higher than the log calculation of Muscovite at the upper end of the core data. Maybe we could do better? The XRD of kaolinite compares well with the log calculation. The total clay is in the ball park with core XRD [but considerably less than the FTIR, not shown on this view]. What is right?
  1. How do the reconstructed elements compare to core and the input elements? We looked at this earlier, Fig. 7, but just to remind ourselves that we may not be finished, review it again. On the other hand, we may elect to be finished here, depending on the time constraints we have.


Fig. 11, Examine Elements vs. Reconstruction Level Four “PLOT 5 CMP ELEM.png”

Review again, just to be sure we are satisfied, or not, as we are getting pretty close.

  • First, compare Aluminum
    • The core XRF compares well with the input, (a calculation, not a measurement, for the ECS™ tool) aluminum (blue curve) and the output gold reconstructed curve. However, where there is no XRF, the output gold, reconstructed aluminum, is much higher than the input curve, especially in the lower lobes 3 & 4. Why? The minerals containing large amounts of aluminum are the feldspars and the clays as well as muscovite. Since it appears the feldpars and clays compare OK to the low quality XRD [is it right or not?], we are left with the muscovite. Here are some possible reasons:
      • The muscovite is too high or we have assigned too much aluminum to muscovite (0.191 vs. illite of 0.105. kaolinite of 0.204, chlorite of 0.099, smectite of 0.10); let’s look at the other elements and maybe we will have a correlation.
  • Second, compare calcium
    • The reconstructed calcium is almost a perfect overlay of the input curve. In addition the XRF compares well, too.
  • Third, compare iron
    • Since this is an ECS™ measurement of iron, the “measured” iron has 14% aluminum in it. This is not the case, if we had the more recent LithoScanner™, the GEM™ or the FLEx™ measurements. So, we first must subtract the 14% aluminum from the Fe signal. An alternative is to add 14% DWAL to the reconstructed Fe, which is what we have shown. The reconstructed iron compares well with core XRF and with the input DWFE_WALK2.
  • Fourth, compare silicon
    • The reconstructed silicon is ~20% higher than the input silicon, whenever muscovite is present. A reconstructed silicon scale of 0 to 0.7 fits almost perfectly, over the input scale of 0 to 0.5. If we compare the mineral value of SiO2’s Si of 0.467 to Muscovite’s Si of 0.21 we see that if we did not have muscovite, then the fit to Si would be better. Consequently, “it would be nice” to have more high quality XRD to confirm the muscovite. This reconstructed Si has confirmed the reconstructed Al question that we had above. Probably too much muscovite in the model. But before placing the limit on muscovite, let us continue the analysis of the reconstructed curves.
  • Fifth, compare Titanium
    • The reconstructed titanium is pretty close to both the input Titanium and the core XRF Titanium. Muscovite has zero titanium in our model. The titanium comes from the Rutile [TiO2], primarily.
  • Sixth, compare potassium
    • The reconstructed potassium is higher than the input potassium when there is muscovite. A rescale of the reconstructed potassium to a 0 to 0.06 scale fits the input nicely. Muscovite has 9.4% K2O, so the potassium indicates there should be less muscovite.
  1. Finally, what is the validity of the computation? Our investigation above, says we should have less muscovite.
  2. What have we learned?
  • Quality control of the computation is important and can elucidate where problems in the computation, may occur. Keep in mind, however, that we can still come to the wrong conclusions.
  • Measured elements allow us to use comparisons to core elements and core minerals effectively. Therefore, run neutron spectroscopy logs to give you a chance to interpret as best as possible. If one does not have measured elements to back check the computation with, one may be wrong and not know it.
  • Errors in calculated values of porosity, permeability and water saturation could well be hidden by comparing only to core porosity, permeability and water saturation
  • When the model gets the basic building blocks right (the elements) one can have more confidence in the derived results at the higher levels of porosity water saturation and permeability.
  • So, a recomputation is necessary, and that is what Quality Control is all about. A good friend of mine in the “olden days” of field work mused, “Why is it that we always find the time to do it right the second time, but we are in too much of a hurry to do it right the first time?” My reply is, “We don’t always know if it is right or wrong until we try it and test the result.” So this example illustrates that concept.
  • This software is designed to accommodate changes so that core can be honoured but science is always behind modifications to accommodate core, not just arbitrary shifts that cannot be justified. For example, we saw that reconstructed potassium was too high. Therefore, muscovite was too high or illite was too high or potassium feldspar was too high. Limiting muscovite is fine but the other two sources of potassium must also be in balance, when the “best” result is computed.
  1. Final interpretation: takes into account reconstruction of elements, particularly potassium, to provide the proper balance of input and reconstructed elemental logs. In addition core and log calculations agree closely. Hence, one can have confidence in the bottom line of porosity, permeability and water saturation, to best define the hydrocarbon pore volume. Considering the huge cost of the next step, to drill horizontally, one needs the best interpretation possible.

As you can see there is a lot of detail here. The point is that one does not push a button and out pops the correct result.

When we went through the first interpretation pass, we thought the process was going to be finished at the fifth plot. However, the analysis method worked so well that it indicated the muscovite or illite was too high, resulting in too high potassium and too high silicon (or quartz is too high) and too high aluminum. Consequently, rework the illite, muscovite and kaolinite and k_spar because these are all affected by changing the illite and muscovite.

Eventually, the process must show a good balance of all the input elements and core. If one did not have core, the process would be more difficult but could be done by paying attention to the reconstruction processes. Furthermore, the analyst would have to be very experienced in the area they were working in to know what was likely from a core point of view. For example, I showed John Nieto (“Mr. Montney”) a sample of a Montney zone I’d interpreted, without core. John said, “There is not much Muscovite in the Montney” and he showed me a thin section that indicated there was some, but not nearly as much as I had “put in”. Knowledge like this is priceless. People that have it have looked at a lot of core. John also said, “Log interpretation is a zero sum process, that is why I have XRD to tell you what the answer is…” Of course, I pointed out that XRD is a zero sum process too as they find the minerals and sum to 100, thereby hiding the inconsistencies. But, John is right: one needs core mineralogy and elements that are quality-measured in order to do a geologically-correct log computation, rather than just a mathematically-correct one.

Through a series of iterations where we keep checking the results on aluminum, silicon and potassium as well as core muscovite, core plag, core k-spar, we will finally come up with a good balance. Since there is twice as much Si in Quartz as there is Si in clay, perhaps our illite is too low? We can only raise it a little until Swb ~ Sw.

We have a huge range in core XRD to core FTIR. Increasing illite resulted in an approximate match of SWB and Sw. According to the clay core measurements of FTIR illite and FTIR total clay; we could increase the clay about 4 times. When we do that then SWB and Sw no longer match, as SWB >> Sw. So, by testing to maintain SWB ~ Sw, we end up with increasing the clay very slightly as shown below in PLOT 11.

Fig. 12, Big Plot 11 Final Computation Plot: Last Cut At Interpretation Bottom Line, QC Reconstructed Elements


Fig. 12, LEVEL 4 Large Plot 11 Final Computation Plot: Last Cut At Interpretation Bottom Line, QC Reconstructed Elements

Here are some more readable plots, taking 4-6 tracks at a time.


Fig. 13, LEVEL 4 Plot 11, TOC, Sw and Original Oil in Place, “Depth 11 (APRIL 9 FINAL CMP MIN RT TOC_CMR SW OOIP).png”

  • The quartz core fits OK with the computation.
  • TOC fits well with the TOC derived from the density and resistivity (blue, TOC_DRHOB_BOB) as well as the orange TOC_CMR, which was used for this final pass. The TOC_CMR fits the higher CORE_TOC data points slightly better than the TOC from the density & resistivity.
  • Sw is very close to Swb indicating irreducible water. The center lobe 3 has the least bitumen.
  • Excellent oil in place.
  • The real formation pressure was unknown, so 0.43 psi/ft was used. A more likely pressure might be > 0.8 psi/ft.
  • Lobe 3 has the best TOC and high Swb.


Fig. 14, LEVEL 4 Plot 11, Perm, porosity, grain density and Pe-Rhob cluster, “Depth 11 APRIL 9 LEVEL 4 CMP MIN PERM TPOR RHOG CMP_PE_RHOB.png”

  • The perm is very low as expected. Lobe 3 perm is lower, perhaps due to higher TOC, resulting in lower porosity.
  • The core porosity is close to the yellow POR_KER_GRI, with some very low values.
    • The predicted free (green) oil is quite high at 3%.
    • Note the core grain density lies in the envelope of the RHOG_KER_GRI_METRIC (green) and the RHOG_METRIC (brown). Everything fits where it should.
  • The cluster plot on the right, uses PEF and RHOB only, with 4 rock types, and the green shading is the TOC where density is low with PEF low.
  • Lobe 2 has the best porosity and perm.


Fig. 15, LEVEL 4 Plot 11 QFM, Carb and Clay “Depth 11 (APRIL 9 LEVEL 4 CMP MIN WQFM CAR CLA VS CORE).png”

  • The good XRD data is shown in light green dots in each track.
  • The FTIR (bright red squares) is low for quartz; however, it does correlate with the darker green XRD dots at about 2310. Perhaps the calculated quartz must be lower (?)
  • The FTIR is high for clay. Our assumption is that both illite and muscovite are lumped together.
  • The good XRD shows muscovite at about 10% and shows little variation. From the computed muscovite the 10% seems like a good average, from an eyeball point of view for some of the core section, light green dots. There was no muscovite measured with the FTIR (red squares) nor the BIG_D_LAB (blue dots), probably due to old technology.


Fig. 16, LEVEL 4 Reconstructed Elements, Level Four, “Depth 11 (CMP MIN elements).png”

  • The reconstructed-element curves (Recon, gold colour) for aluminum and calcium are very close to the input blue curves.
  • All reconstructed curves are “spikey” or active, possibly (?) due to the influence of the TCMR on the TOC_CMR input. Previous computations used the Delta RHOB_RT for TOC, with less vertical resolution.
  • Iron is active for the same reason(?).
  • Silicon is high, as the calculated quartz is high to core.
  • Titanium is high as the main source is rutile and the scale is very sensitive.
  • Potassium is high, so the potassium sources of Kspar, illite and muscovite must be slightly high.
    • Plotting the minerals in the track results in a correlation of illite with most of the difference between the input and reconstructed potassium (see zone 2276 to 2288, where the brackets and arrow are). Consequently, perhaps illite should be lower (?). We do not have any core illite due to the “contamination” with muscovite in the measurement.
  • Conclusion is the calculated minerals are in the ballpark but not exactly right. Some vertical averaging and additional passes could perhaps (?) improve the fit of input and reconstructed curves.


Fig. 17 LEVEL 4 Plot 11, CEC, Quartz, Feldspars, Muscovite, QFM; CWLS WELL 3 PLOT 11 “Depth 11(CMP MIN CEC Q F M).png”

  • The CEC and WMIN are on a compatible scale to show how they relate.
  • Core quartz from the good XRD (light green dots) agrees fairly closely with the computed quartz, however, the computed quartz is higher. Perhaps another pass will bring it into line.
  • The core plag from the good XRD is similar to the core FTIR. The computed log shows sporadic plag in the lower lobes. The computed and core plag in the upper lobe agree closely.
  • Computed and core K-Spar are similar.
  • Computed and core muscovite are close.


Fig. 18, LEVEL 4 Plot 11, CEC, Clay, Illite, Kao, Chlorite, Smectite, “Depth 11(CMP MIN CEC CLAY)”

  • Computed clay and “good” core bulk clay XRD are very close.
    • There is no “good” core illite.
    • Only the bulk clay was measured with the “good” core XRD.
    • The FTIR illite correlates with illite plus muscovite, indicating the measurement was contaminated by both being measured as one.
  • There is no “good” core Kaolinite. The FTIR shows approximately the same kaolinite that was computed.
  • There is no “good” core chlorite. The FTIR shows less than was computed.
  • There is no “good” core smectite. The FTIR shows about the same as what was computed.
  • In summary, the computation shows the same total clay as the “good” core . Hence, the effect on CEC is about right.
  • The computation iteration was stopped at this point.
  • Summaries were made as below.

Objective Obtained With Interpretation Process Final Summaries for Level Four

The final summaries are:


Fig. 19, Level Four Pay Summary, “WELL 3 FINAL PAY SUMMARIES 09-04-2013 1-29-35 PM.jpg”


Fig. 20, Level Four Flow Capacity, “WELL 3 LEVEL 4 FLOW CAPACITIES 09-04-2013 1-27-01 PM.jpg”


Fig. 21, Level Four HCQL yellow Box Summary of Flow Capacity, CWLS WELL 3 PLOT 11 “Depth 11 (IMPORTED TRACKS CMP MIN Q YELLOW BOXES).png”

The summary in the yellow box has been adjusted to be close to the summary in the detailed method, by adjusting input probabilistic grain densities of carb and siltstone as well as “a”, “m” and “n”. The fact that they match is because we forced them to match, so it just a gross check on the math.


Fig. 22, Level Four OOIP, Plot label is “WELL 3 LEVEL 4 OOIP NO CUTOFFS 09-04-2013 1-23-06 PM.jpg


Fig. 23 Level Four HCPV, “WELL 3 LEVEL 4 HCPV AT RESERVOIR CONDITIONS 09-04-2013 1-19-53 PM.jpg”


Fig. 24, Level Four Fractional Flow, “WELL 3 LEVEL 4 FRACTIONAL FLOWS 09-04-2013 1-12-00 PM.jpg

Lobe 3 has 3% water and 22% oil fractional flow. The relative perms assume any permeability to water will flow. Probably an incorrect assumption, since Swb is very close to Sw, the formation is more likely to be at irreducible water conditions.

Lobe 4 now looks good with only 2% water, 16% oil fractional flow. However, since lobe 4 is near the carbonate water, 21% water flow, lobe 3 would be the best to penetrate with the horizontal.

Furthermore, lobe 3 has the highest OOIP MMBBL/SECT at 33.

Lobe 3 has the highest effective-porosity-metres storage capacity of 3.3 Por-M

Lobe 3 total porosity is 8%; PHIE is 7%; Sw is 20%; and SWB IS 18%.

Lobe 3 has 23 M of PAY 1 [Phie>6%, no water flags].


Fig. 25, Level Four Input Constraint Summary, “LAST LEVEL 4 PASS 34 WELL 3 TOC_CMR 09-04-2013 12-39-09 PM.jpg”

Will one more pass, constraining qtz to 30%, ill to 10%, be any better? Result is less muscovite but similar recon potassium and quartz. So, to improve, we must move to Level Five.



This pass is called “experimental”, as it is a Herron spreadsheet that will forever be updated to resolve new challenges as they come along. This method is our “work bench”, designed to innovate new “what if…?” What you see today is the best we know how to do, today. Hopefully, tomorrow will be better. The “Robust Elm” is designed to be an improvement, for example.

What is new today?

Level Five “Normalize” tab has been added to:

    • A series of constraints are added so that abundance of the elements in a mineral cannot be greater than the input elements. Seems to make sense.
      • Oxides are summed from RECONSTRUCTED elements for
        • SO2
        • CAO
        • FE2O3
        • SIO2
        • K2O
        • TIO2
        • AL2O3
        • SUM OF ABOVE
      • Oxides are summed from elements input from NS (i.e. ECS™ )
      • Ratio taken of input elements converted to oxides / reconstructed oxides. [Why not just use elements? Because we are dealing with a sum and are not convinced that the sum of the elements gives a geologically correct value, whereas the sum of the oxides does.]
      • Normalized oxides = Ratio* Level 4 reconstructed elements converted to oxides.
      • The normalized oxide becomes the new constraint (limit) for the elements to be used in the calculation of minerals, since it is related to the input elements. The input element gets “used up” as we build minerals in a prioritized manner.
    • Priority established for the calculation of minerals from new oxide constraints:
    • SO2
      • Anhydrite first if > 3.6% anhydrite (> 0.00847 S)
      • Pyrite second with remaining S.
    • CaO
      • Select zone for Carbonate = Ca/0.4, when Pe ~ 5 (calcite).
      • Else, CaCo3 % = (Pe_%Ca*Ca/0.4 + Pe_%DOL*Ca/0.22 + %Anh*0.236/0.236)
    • Fe2O3
      • Illite is first, using Fe from Fe2O3 constraint.
      • Chlorite is second using remaining Fe2O3.
      • Muscovite is third using remaining Fe2O3.
      • Reconstruct Fe part one from:
        • Pyrite
        • Quartz
        • Kspar
        • Plagioclase
        • New muscovite
        • New illite
        • Kaolinite
        • Smectite
        • New chlorite
        • Dolomite
        • Calcite
        • Sum Fe
        • Miscellaneous Fe bearing minerals, 1.99*sum Fe (Herron model)
        • Sum Fe = Fe Recon part one
    • K2O
      • Illite is first, using K from K2O constraint.
      • Muscovite is second, from remaining K2O
      • Reconstruct K part one from:
        • Quartz (trace amounts)
        • Kspar
        • Plagioclase (trace amounts)
        • Newest muscovite
        • Newest illite
        • Kaolinite (trace amounts)
        • Smectite (trace amounts)
        • Chlorite (trace amounts)
        • Dolomite (trace amounts)
        • Calcite (trace amounts)
        • Sum K = K Recon part one
    • Al2O3
      • Illite is first, using Al from Al2O3 constraint.
      • Muscovite is second, using remaining Al.
      • Chlorite is third, using remaining Al.
      • Smectite is fourth using remaining Al; limit Smectite from both the previous level 4 smectite and the water in smectite (WMIN_SMEC) where,
        • WMIN_SMEC
          • Smectite has TNPH of 23
          • Kaolinite has TNPH of 45
          • WMIN_SMEC = 23/68*(PHIN_MAN-PHID_MAD)*1/TPOR*1/RHOG)
      • Kaolinite is fifth, using remaining aluminum. Kaolinite is previously determined in level four.
      • Kspar is sixth, using remaining aluminum, with limit of Kspar* core calibration, (Y$3 constraint).
      • Plagioclase is seventh, using remaining aluminum, with limit of Plag* core calibration, (Y$2 constraint).
      • Reconstruct Al part one, from:
        • Quartz level 4 (trace amounts)
        • Newest Kspar
        • Newest Plagioclase
        • Newest Muscovite
        • Newest illite
        • Kaolinite level 4
        • Newest Smectite
        • Newest Chlorite
        • Dolomite level 4 (trace amounts)
        • Calcite level 4 (trace amounts)
      • Sum Al = Al Recon part one

  • SiO2
    • Illite is first, using Si from SiO2 constraint.
    • Muscovite is second, using remaining Si.
    • Chlorite is third, using remaining Si.
    • Smectite is fourth, using remaining Si.
    • Kaolinite is fifth, using remaining Si.
    • Kspar is sixth, using remaining Si.
    • Plagioclase is seventh, using remaining Si.
    • Quartz is last with minimum of (previous quartz from level four & remaining Si).
    • Reconstruct Si part one from:
      • New Quartz
      • Newest Kspar
      • Newest Plagioclase
      • Newest Muscovite
      • Newest illite
      • Kaolinite level 4
      • Newest Smectite
      • Newest Chlorite
      • Dolomite level 4 (trace amounts)
      • Calcite level 4 (trace amounts)
    • Sum Si = Si Recon part one

Reconstruct elements from newest, limited minerals to provide Si_Recon_Part_Two, etc. Output as DWSI_RECON, DWCA_RECON, etc., which will replace the recons from level 4, if you are using the same plot.

Fig. 26, Level Five, Big Plot, is below:


Fig. 26 Level Five Plot 11 Big Plot

Of course, this plot is not readable at this scale so we shall break it down, starting from the left.


Fig. 27 Level Five TOC Sw OOIP

Note the critical water saturation Sw_Crit (red, dotted), is equal to the Sw in the bottom lobes but not in the top lobes. Hence, the bottom lobes will produce water-free, but the top will produce water.

Also, the TOC is derived from the difference between the density porosity and the TCMR porosity and is coded green on the 3rd plot from the right. The fourth plot from the right is the TOC computed from the TCMR and RHOB.



Note the modeled neutron in the last track on the right, shows fairly good correlation with the measured neutron, indicating the computed minerals are OK in the shale section. The carbonate sections have the modeled neutron matching the density porosity as one would expect. Confusing on why the recorded neutron is high in the carbonate sections. The correlation coefficient is 83%, which is similar to Level 4 of 87%. Hence, the modeled neutron is insensitive to the changes we made in the mineralogy.

No muscovite shows in the dolomitic carbonate, nor should it. Remember this when we check the potassium reconstruction.



There seems to be muscovite as part of the quartz fraction in the lower calcitic carbonate. The dolomitic lower carbonate has no muscovite by constraint: we set the lower dolomite’s muscovite to 0.0 from 2408, down. On level 4, we set it to a max of 0.4*computed muscovite from carbonate top, 2367, down.

The core muscovite is about ½ the computed muscovite. Remember this when we check the reconstructed elements. If our computed muscovite is correct, then potassium will reconstruct properly. If not, our reconstructed potassium will be high to the input potassium. Furthermore, in Level 5, we programmed so that we constrained potassium to be no higher than the input. So a quality control check is coming up.



The potassium reconstruction shows that the muscovite is very good. In the pay section we have the mineralogy reconstructing well. Note that silicon is reconstructing at a value slightly less than the input. Recall that in level 4, Si_Recon was higher than input.

Note the aluminum in the dolomite reconstructs at less than the input. This may be due to our input being predicted rather than measured. The Schlumberger calculated value for aluminum had been lost in this LAS data set.

The calcium also reconstructs at less than the input. The inference might be that the Pe is incorrectly nearer to 3 than to 5 (?).

Titanium reconstructs right on top of the input, so the input blue curve is hidden. The only source of titanium is rutile.

Iron does not reconstruct as well as level 4.



Core minerals reconstruct well with computed minerals, except muscovite does not. The reconstructed potassium indicates the computed potassium-bearing minerals are OK. Furthermore, the illite + muscovite and total clay plus muscovite, agree with the FTIR.



Clays + Muscovite fit core FTIR fairly well.


Fig. 33 PAY SUMMARY WELL 3 FINAL APR 13 13-04-2013 7-17-31 AM


Fig. 34 FLOW CAPACITY WELL 3 FINAL APR 13 13-04-2013 7-15-43 AM


Fig. 35 OOIP WELL 3 FINAL APR 13 13-04-2013 7-21-48 AM


Fig. 36 HCPV WELL 3 FINAL APR 13 13-04-2013 7-20-31 AM


Fig. 37 FRACTIONAL FLOW WELL 3 FINAL APR 13 13-04-2013 7-23-10 AM.jpg


Fig. 38 INPUT SUMMARY WELL 3 LEVEL 5 APRIL 13 13-04-2013 7-13-06 AM



The storage capacity, Por-M, is similar to the table summary, Fig. 34. The cluster was re-run, including this pass 40 and showed some ankerite rocks this time (grain density on cross plot, 2930 kg/m3). They occur where the quartz is lowest, so may be an indicator of barriers or the rock is different and some thin sections may help to see if there are any real problems.

Summary and Conclusions.

Constraints are necessary and useful to convert elements to minerals. The constraints applied are based on

  • Core elements and minerals.
  • Reconstructed elements, where the elements used to build the minerals are not allowed to exceed the input neutron spectroscopy measured elements.
  • Bound water saturation less than or equal to the computed water saturation.

When computed minerals echo core, confidence in the computations of Sw, Porosity and permeability increase.

The value of converting neutron spectroscopy logs into minerals is the continuous nature of the result of mineral sampling.

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