Example 3







There are seven levels of computation difficulty in computing wells. The more data that is measured, the fewer degrees of freedom one has in providing a consistent result: hence, difficulty and accuracy increase:

  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. Easy – not difficult to compute but no core to check results.
    1. Nuclear spectroscopy,
    2. No nuclear magnetic resonance,
    3. No core.
    4. Better confidence in results.
  3. Moderate difficulty
    1. No nuclear spectroscopy
    2. No nuclear magnetic resonance
    3. XRD core
    4. Moderate confidence in results
  4. Good – some more difficulty
    1. No nuclear spectroscopy
    2. No nuclear magnetic resonance
    3. XRD plus XRF core
    4. Good confidence in results
  5. 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
  6. 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
  7. 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.

ROCK CREEK ECS™ is a level two project and is interesting as it has some production data but, at first glance, the depth of the perfs are lower than the depth of the logs. However, the logs may be on true vertical depths (TVD) and the core may be on measured depth (MD). We will check this as we go along.


The objective of computing the well is:

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

Before we can determine the bottom line we have to do steps 2 and 3. The notes are below.

Objective Viewed With Plots

This computation process will have about 24 plots:

  1. Fig. 1 is “RAW LOGS AND ECS™ ELEMENTS”. Fig. 1B is the predicted SP.
  2. Fig. 2 is “PRE-INTERPRETATION, TOC from uranium and TCMR”.
  3. Fig. 3 is “COMPUTED INTERPRETATION, Part One”; Part one starts us off to check the reconstructed elemental logs to see if we have provided a plausible computation.
  4. Fig. 4 will be about starting with the raw logs.
  5. Fig. 5 checks the TOC to use and the predicted Rw from the SP vs. the water catalog value.
  6. Fig. 6 shows a condensed, hard to read, view of the bottom line, “COMPUTED INTERPRETATION, Part Two”; we look at the evidence that the “Bottom Line of Por-perm-Sw”, is valid or not. We check the core minerals, core elements (when core is available) 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.
  7. Figs. 7, 8, 9, 10 & 11 are used for easier-to-read plots.
  8. Figs. 12 to 17 are the summaries.
  9. Figs. 18 to 24 are in the Appendices.

Notations to be aware of:

  1. Depths are from the LAS file for the logs. However there is no notation as to whether this is TVD or MD. Depths for core, perfs and fracture treatments are from an “Accucard”. These are assumed to be measured depths. The card also has the TVD and MD for the tops.
  2. Logs recorded are Triple combo with Neutron Spectroscopy (ECS™) but no CMR. The hole was drilled with oil based mud and no spontaneous potential (SP) was recorded. The first reading of the natural gamma spectroscopy logs, potassium, uranium and thorium, is about 10 metres off bottom.
  3. Can you find the Pay zones?
  4. Initial production has been released. Do you expect it should be oil, gas or water from looking at the raw logs?

The idea of this summary is to inform the reader, of the process of interpretation, so they can decide what confidence to assign to the conclusions.

Since interpretation is complicated by the value of the input data, in terms of incomplete or low quality data, one cannot usually use a single program for all wells and depositional environments. However, that is exactly what we propose to do: use one program for all wells. We want to use one program that has 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.

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, as it is on this well, perhaps because the oil content of the mud is too high, we predict the SP from an offset well that has a water base mud. We have not experienced any problems in making this prediction. However, the SP needs 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 process, 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, until it becomes critical that corrections to the SP must be made.

The interpretation process has to have self-checks built in. That is why, not only core is necessary, but curve reconstruction at the most basic level, is also necessary. 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. Otherwise, how does one know if the minerals calculated or the core minerals are valid? Perhaps core XRD or other data says they are “close enough for country work” but the element reconstruction provides the final 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? The example shown below is after pass 2 has been made, so this shows reasonable reconstruction of Al, Ca, Si and K. These are the main components of the mineralogy that we are going to solve for.
    • Note that there may be minor minerals containing Fe or titanium that have not been included in the reconstruction. Also note the shaded iron curve is recorded Fe minus 14% Al. This is peculiar only to the ECS™ tool, and is a result of aluminum contamination in the detectors for iron.



We have divided the Rock Creek into units A to F, based on the cluster analysis of the different rock types within the Rock Creek zone. You can see the types of rock are quite different, from three types of sand / silt to calcitic sands. Also, note the high PEF (track 2) in units C & E. We do not know what causes this high PEF but assume it has something to do with barite invasion and may indicate a zone with low pressure or fractures. The resistivity signature on these two zones is quite different. The elements are also different, so it does not appear to be based on unique mineralogy. Perhaps, then, it is a fractured zone (?), resulting in barite invasion. The mud weight is 1260 k/m3, so there is barite in the inverted oil emulsion mud.

The value of the ECS™ log is in making decisions based on elements, and, eventually, mineralogy, when we complete the interpretation.

The next Fig. 1b, shows the predicted SP.


The SP is not recorded due to the invert mud. Consequently, an SP was predicted and used to calculate the Rw from the SP. The Rw_SP was “calibrated” using the CWLS water catalog value for the Rock Creek of 0.39@25C. The value is from the Rock Creek at 2766m in 8-10-49-114W5. This catalog value is shown on the plot as a green line, when corrected for temperature. Note this catalog value appears to be near a positive SP deflection. As the interpretation proceeds, we can check Ro and Rt in several zones to ensure the Rw is reasonable.

Our next step in pre-interpretation is to calculate a TOC and grain density.

Does the calculated grain density agree with core grain density, even allowing for the light weight kerogen? The plot below shows TOC and the computed grain density with and without kerogen; they are almost identical. The plot is from pass 2 to show what it should look like. There is neither core grain density nor TOC to verify computations on this well.

Fig. 2 “TOC AND RHOG.jpg”

The two TOC’s are different. Since we do not have core TOC to confirm, we will choose the TOC_URAN, since “usually” organic carbon is present when uranium is present. What difference does TOC make? The porosity from the grain density is lower, resulting in higher Sw. In this case, using the uranium TOC results in almost no effect on grain density and porosity.

  • 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? So the method of deriving core porosity is important in understanding the results. Core porosity is not an absolute standard to calibrate log porosity. Furthermore, we do not have access to the porosity from the sidewall cores for this well.
  • Ditto for core water saturation and core permeability. If fractures in the core are not sealed before the measurement of perm with an epoxy, then core perm will be too high, relative to matrix perm.

The plot shown below is from pass 2, so this shows what porosity, perm and Sw look like when finished.


Fig. 3 shows the Sw, porosity and permeability. The best zone for a horizontal well is zone C. This zone has the highest porosity. However, it is mainly gas, with little oil. This might explain the position of the perfs in zones B and F, where there is some oil, as well as gas.

My conclusion is that someone did a nice job in completing this well. I assume that since the whole section was fractured, the position of the perfs was set to try to obtain the most oil. One can see the oil on the total porosity track, coloured green (the porosity track has the first listed curve as “TPOR”, 10th track from left or 6th track from right). The red is the gas. The curve that bounds the red and green is the free fluid from the predicted CMRP. The red is “turned on” by the gas flag.

Re depth:

The core, frac and perf depths in the “Accucard” must be measured depths. I converted them to TVD depths and find the perf and frac depths are now right in the Rock Creek section where they should be, instead of apparently being below the recorded log depths.

Now that we have seen what “final” results look like, let’s start at the beginning to see how we got to “final results”.

In The Beginning

Fig. 4 Raw Logs

We will walk through the process, including our challenges in order to give you a road map on the logic in doing the interpretation process.

Start with Raw logs, Fig. 4.

The program that we use for clustering uses imperial units, rather than SI, for rock type identification; so we convert to imperial units prior to running the cluster.


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 (in this well we have to predict CMRP_3MS, because it is not available); we also have to predict TCMR, and we can then calculate TOC via TCMR, as well as uranium (modified “Passey-Bob”), and any other method you may prefer to stack up against core-generated values. We used TOC_URAN for the porosity calculations.

Pre-Interpretation Plot

We use the word “Interpretation” and “Computation” interchangeably. However, real “Interpretation” starts when the “Computation” is finished and is at the end of this summary. There are decisions to make in the computation process so the analyst usually refers to these decisions as interpretation.

The raw logs are OK, so we can proceed with the “Pre-Interpretation plot”. In the process we have checked KUT and compare to the values in GAPI units of potassium, thorium and uranium vs. the recorded total GR; we also have decided on the TOC calculation that best fits our preconceived notion as we have no core TOC. We used the predicted SP to derive an Rw. This well used Invermul™ mud so the SP was not recorded but rather, predicted from an offset well.

PRE-INTERPRETATION: Note, when SLB measures nuclear spectroscopy, they provide QFM, CAR, CLA, RHOG, and PERM at the wellsite. When using old LAS files, this information may be missing, or for permeability, truncated. In this well they were present, except permeability. They are useful as a QC check on future computations.


The predicted CMR_3MS_P is shaded in red on the neutron-density track 4. It appears to be too high since it is higher than the density porosity and shows free fluid all the way into the POKER CHIP SHALE. When we did the final computation and compared it to the total porosity, we found that subtracting 3 PU from the above predicted value made sense.

We are now ready to do a computation.

After inputting the curves to the “Program” we obtain the results and make the first of the computation plots.

First Computation Plot: First Cut At Interpretation Bottom Line, Porosity, Perm, & Sw Examined

The big plot below will be discussed in smaller plots 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.

“Bottom Line” Plot,

Shown Below, is for reference, not readability. We will re-plot in smaller sections for readability.



  1. The combined mineralogy is shown in the 5TH track (far right). The formation is about 50% to 80% quartz, little feldspar (light green), muscovite (light grey), illite (dark grey), kaolinite (dark orange), dolomite (purple) and calcite (light blue).
  2. Resistivity is in the second track, 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 some nice hydrocarbon here.
  3. Sw is next. The olive colour is residual oil, SOR, and is calculated from a crustal relationship of uranium and thorium. When uranium is greater than 3* Thorium, there is “excess” uranium; the excess is assumed to be in bitumen or kerogen. The grey is bound water saturation. An important “quality control” on the Sw, is that the Sw must be greater or equal to the bound water saturation. If the bound water saturation is higher than the total Sw, then the CEC is too high, resulting from clays being too high. Since, the illite is usually the most abundant clay, then often illite is too high.


Now, we will explore the rest of the story, permeability and porosity, with the idea that if there are obvious problems we could fix them before running the next pass.

  • The PERM_KER_GRI (yellow) is about the same as the shaded orange perm, as TOC is low; hence TOC has no effect on the grain density.
  • The PERM_NIETO is close to the PERM_ECS. Both are calculated independent of minerals. Perm Nieto is from an equation developed by John Nieto for the Montney and is presented as a QC check on PERM_ECS, since they are usually very close. PERM_ECS is developed from the elements and porosity, not minerals, so should be close to the correct value even if the minerals are not correct.

The porosity track needs some explanation. It is a series of porosity curves that are “overlain” to create an interpretation. We will show how these curves are overlain in the appendix, 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. This difference is the hydrocarbon-filled porosity volume (HCPV).
  • The green or red shading is the free fluid CMRP_3MS_P. The green shading indicates this free fluid is oil and the red indicates gas. Recall this was a predicted curve on this well, so it not necessarily expected to be valid. Recall that a pre-interpretation plot indicated the predicted CMRP_3MS_p looked too high. We subtracted 3 PU to move this curve to the plotted value. The red shows a lot of free gas and a little free oil, which is about 33% to 50% of the HCPV.
  • The free water is shown uphole (not shown as there is no free water in this zone) by the very light blue colour.
  • The Rock Creek lobes are six in number (A to F). The best one(s) will be identified later as we are surer of the interpretation model.

Porosity looks reasonable. The rose colour shading shows the oil in the small capillaries and the green shading shows the oil in the larger pores [in the pore fraction that is identified by CMRP_3MS_P].

Next we will move to the quality control where reconstructed logs are compared to input logs and see what information we can glean before running the next pass.

Fig. 9 M_NPHI & NPHI

  1. Note that PHID_MAD and PHIN_MAN (track 2) shows gas, since there is “cross-over”, illustrated by the red gas flag. The “MAD” stands for matrix-adjusted density and the “MAN” stands for matrix adjusted neutron. These calculations are made directly from elements for the grain density and neutron matrix, so these curves are valid whether the minerals are correct or not.
  2. An important reconstruction to consider is the modeled reconstructed neutron, M_NPHI (heavy black curve in right-most track). This indicator is developed from the mineral effects on the neutron log. When reconstruction is close to the measured neutron, the clay minerals [highest TNPH] all have to be pretty close to reality. In this well, in the Poker Chip Shale, the modeled neutron is slightly less than the recorded neutron, indicating that the clay minerals are slightly less than they should be. Above the shale, the red indicates a gas effect on the recorded neutron. Since the PHID_MAD and PHIN_MAN also showed a gas effect, then we can only say the minerals in the Rock Creek are probably correct.

    1. Let’s look at the clays, since these are the biggest contributors to the modeled neutron, M_NPHI. How is the calculation made?
      • The reconstructed neutron is based on two calculations:
        • Calculate the matrix for the neutron curve:
          • TNPH_matrix = (SUMPRODUCT((pyrite+1.99*DWFE),TNPH of pyrite))+(SUMPRODUCT((Quartz+Orthoclase+Plagioclase),TNPH of (60% NaSpar-40% CaSpar Plagioclase)))+(SUMPRODUCT((Dolomite + Calcite),TNPH of Dolomite))+(SUMPRODUCT(Illite,TNPH of Illite))+(SUMPRODUCT(Chlorite,TNPH of Chlorite))+(SUMPRODUCT(Kaolinite,TNPH of Kaolinite))+(SUMPRODUCT(Smectite,TNPH of Smectite))+(SUMPRODUCT(Muscovite,TNPH of Muscovite))
            • Note that quartz plus feldspar (QF) are grouped as these are small TNPH and the partitioning of quartz, Kspar and Plagioclase depends on aluminum and potassium and silicon and may have a small error.
            • Dolomite + Calcite (carbonate) are grouped, as these are small TNPH, and the partitioning of dolomite and calcite is dependent on the PEF, which may be in error.
            • But the clays are not grouped, as the TNPH of each clay is large and very different: Kaolinite TNPH is 45; Chlorite TNPH is 48.2, Smectite TNPH is 23 (we assume mixed layer with high illite in mixed layer); Illite TNPH is 24.7. Consequently, it is important to be able to separate the clay types in the clay family.
        • Calculate the fluid portion of the neutron response:
          • TNPH_fluid = (Sw*TNPH of formation water, usually set at 0.90)*(1-Invasion fraction, usually set at 85%)+((1-Sw)*$AP5*(TNPH of gas, usually set at 0.52))*(1-invasion fraction) + ((1-$CS5)*(1-$AP5)*(TNPH of oil, usually set at 1.056))*(1-invasion fraction) + (TNPH of filtrate, usually set at 1.5)*(invasion fraction)
        • Combine the fluid and matrix (Total porosity (TPOR)*TNPH_fluid)+((1-total porosity (TPOR))*TNPH_matrix)
    2. We can see that it is important to have the mineral fractions, especially the clays, as well as the fluid fractions, both in the total porosity zone and the invaded zone. Furthermore, an estimate of the depth of the neutron investigation into the invaded zone has to be made [we use 85%, normally]. 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 minerals and fluids are all “in the ball park”. The correlation coefficient in this computed zone is 50%, which is not as good as it gets in an oil zone, but is expected low due to the gas effect on the recorded neutron.

Fig. 10 “ROCK CREEK CLAYS.jpg”

We look at the clays, since these are the biggest contributors to the modeled neutron, M_NPHI, and note that:

      • Total clay in the Poker Chip Shale is reasonable, but in low in the bottom five metres (4 m per large depth line on this scale, 1:100). Overall, this clay delineation may be the best we can do and it does not affect the interpretation of water saturation very much because:
        • Illite is reasonable and that this is the largest contributor to CEC, which is the critical input to Sw correction for clays. Note that it is critical to separate illite from muscovite, as muscovite has no CEC and illite does.
        • Smectite is also a high contributor (CEC=~50 for mixed layer) but the abundance is low.
        • Chlorite (CEC =15) and Kaolinite (CEC =6) have relatively lower CEC contributions, compared to illite (CEC=25), and the abundance of the sum, chlorite+kaolinite+smectite is similar to that of illite, so their separation is not critical to overall CEC.
      • Our conclusion so far is our single current problem in the validation of the computed result is to obtain core to cross-check the individual minerals.


      • Question, “Is quartz correct?” Only core can tell. When quartz is correct, then often everything else is correct, since quartz is the last mineral to be calculated.
      • Quality control of the computation is important and may elucidate where problems in the computation, may occur. One can still come to incorrect conclusions. However, not using elements gives one no cross-checks on computation validity.
      • Measured elements allow us to use comparisons to core elements and core minerals effectively when core is available.
      • 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, when recomputation is necessary, this software is designed to accommodate changes so that core can be honoured but we make sure that science is always behind modifications to accommodate core, not just arbitrary shifts that cannot be justified [common in the pre-1980’s obsolete volume of shale (Vsh) methods].
  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 must agree closely when core is available. 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. This modeling process does that.

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.

We think log interpreters will appreciate the detail to “milk” (i.e. Doing more with Data) as much as possible from the logs to provide the best possible petrophysical interpretation. Furthermore, we need good up-to-date log measurements [nuclear spectroscopy, nuclear magnetic resonance, image logs as well as shear, compressional, density with Pe, neutron and natural gamma] as well as core XRD & XRF in order to correctly interpret these complicated rocks in resource plays.

Eventually, when core is available, we think the result will be a good balance of all the input elements and core. If one does not have core, the process is more difficult to validate but is 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, if they did not have nuclear spectroscopy logs, as they would have to guess whether carbonate was present or not, and whether it was likely cementing or not. Fortunately, on this well, we have an ECS™ log to tell us what calcium is present.

Through a series of iterations where we keep checking the reconstructed results on aluminum, silicon and potassium as well the modeled neutron, we have come up with a reasonable balance. The production results concur with our calculations in a qualitative sense.

Final Computation Plot: Last Pass at the Interpretation Bottom Line, QC of Reconstructed Elements Checked.

We have learned from this exercise that there is no direct way to go from elements to minerals, as opposed to mineral groups, which Dr. Michael Herron et al figured out how to do in the 1990’s. Consequently, core plus elements and modeled neutron reconstruction as well as a comparison of SWB and Sw are very important, to constrain this normalization process. This comment is being [overly?] candid. It not only takes some experience to do a professional job but it also takes malleable software that can get the analyst to the best result.

What about Brittleness?

Brittleness can be calculated if the shear and compressional logs are available. They involve the equations for Poisson’s Ratio and Young’s Modulus. They are not available for this well.

There is often a correlation of brittleness with the quartz curve. Zone C has high quartz so is expected to be brittle. It also has an unusually high Pe so that could tie in with natural fractures and zone weakness. See second Appendix for more on this conversation. Brittleness is important when a frac job is planned. It is also important to give a target for a horizontal well.

Objective Obtained With Interpretation Process: Final Summaries

The final summaries are:


One can see that Lobes B, C and E are significantly better than the rest, in terms of [net] PAY1.


Storage capacity is best in lobes B, C & E. Flow capacity is best in C and E. Considering C & E, Sw is best in C at 24% vs. 52% in E.


Note that the yellow summary boxes [quick look method] on Fig. 14 ~ confirm the above Final Summary storage capacity, Porosity*metres (PorM) and gives 0.43 for Lobe C vs. 0.47 for the “Final Summary” [the colourful summaries above]. Lobe E for porosity-m is 0.36 which is very close to the above Final Summary of 0.43 for PHIE-h. It would seem that one could just use the quick look method and get similar results as the detailed method. However, the quick look method required input grain density of sandstone of 2.68, siltstone of 2.70, limestone 2.70 and dolostone of 2.87, as well as a=1, m=n=1.85, in order to achieve the close similar values. While these are reasonable values, there is no standard way to predict them, since the simple clean formation Archie relationship is used and the shaliness will change their values. On this well, shaliness is low, so the Archie method works adequately. The detailed method provides a procedure to obtain the relevant computation inputs. The way we circumvented the selection of the variables was to pick the grain densities so that a match was obtained with the detailed porosity calculations in the “Program”. Then a, m, and n were selected to match the detailed Sw from the “Program”. So we used the “yellow box” summary to approximately QC cross-check our detailed summary information.


The original gas in place (OGIP) shows that lobes B, C and E dominate the pay picture.


Of course lobes B, C and E dominate this pay picture too, as this is a “reservoir conditions” compared to “surface conditions of STP” for the OGIP.


The fractional flow is a simplified method depending on relative permeability’s, and says all lobes B TO E are important, but Lobes C & E are clearly the best lobes, based on delivery speed. Lobe E has the best permeability and therefore the best deliverability. Hence, Lobe C, with the highest Fractional Flow of hydrocarbons, is the best target for the horizontal.


Lobe B has OGIP BCF/SECT at 4.7 and Lobe C has 6.7 with lobe E at 4.3.

Lobe B has effective-porosity-metres storage capacity of 0.38 Por-m for 6 m (N/G = 2); Lobe C is higher at 0.47 Por-M but is thinner at a gross of 5 m (N/G = 2.63). Lobe E is 0.43 for 4 m (N/G=3).

Lobe B’s porosity is 5.4% and Sw is 37%; Lobe C porosity is 8.5% and Sw is 24%; Lobe E porosity is 8.8% with Sw of 52%; so Lobe C is better.

Lobe B has net 2 M of PAY 1, out of gross (Pay3) of 5 m (N/G = 0.4). Lobe C has 2.63 out of 4.25 (N/G = 0.62). Lobe E has 3 out of 3.88 gross (N/G = 0.77). So, Lobe E is better.

[Pay1 = Phie>6%, no water flags; Pay3 = Phie > 3%, no water flags].

Lobes A, B, C, D and E have no fractional water flow.

Overall, the perf placement near zones B and F with the entire interval from B to F treated with a frac job is a good completion.

Appendix on Porosity Curve Overlay Method

The porosity curves are stacked on top of each other so that differences between them can be shaded to identify the fluid content of the porosity.

The first curve to be plotted is total porosity. This curve is shaded from the curve to the right track edge with a grey colour that represents bound water. When Phie is next plotted, and shaded as blue to the right track, the grey shading stands out as representing bound water. Note that TPOR minus VWB is PHIE [TPOR is total porosity; VWB is volume of bound water; PHIE is effective porosity].

Fig. 18 TPOR minus PHIE: grey shading is bound water.

The next curve to plot is the free fluid curve, which in this well, was predicted. The light blue shading is the potential free water.

Fig. 19 TPOR, PHIE & CMRP_3MS_P.

The light blue shading is potential free water. Dark blue shading is potential capillary water. When the HCPV is overlain, the “potential” capillary and free water will either be real or the pores will be partially or fully filled with hydrocarbons, yielding capillary and free hydrocarbons and partial or no free water.


The rose colour HCPV covers the potential free water, indicating there is no free water, except for a tiny bit just below zone A, in the Poker Chip Shale.

The next curve to overlay is CMRP again, to divide the HCPV into free and capillary HC. There are two curves involve, the first being HCPV_CAP_HC. This is coloured green, for potential free oil.


Potential free oil is shown as green. This will be modified to free gas when the gas flag is “on”.


The distinction of gas and oil, free and capillary, is made with the addition of the CAP_HC and CAP_GAS curves. These curves are derived from the predicted CMRP_3MS_P curve. They show that some oil and some gas will be produced, in line with actual initial production test.

Using all the data available, the suggested green bars indicate the preferred zones to perforate. Actual perfs are shown in black and the fracture treatment interval is shown in gold.

Appendix on Brittleness from Predicted Curves Method

So, what about Brittleness? We could predict a shear and compressional velocity curve and see what it looks like.

The Poisson’s ratio (PR) can be used to validate the gas effect. When PR is < 0.25, gas is probably present. We already know we have gas from the Gas Flag and from production. Does PR work with predicted velocity curves? The PR seems to work OK.


Since PR correlates approximately with the gas flag, then Brittleness can also be calculated. Brittleness is expected to follow the quartz content, so this can be used as a check.


A surprizing result: the Brittleness does not follow quartz in C and E. These are weak zones. The barite invasion is high in these zones. These two observations correlate indicating zones C and E are probably fractured zones, resulting in weakness. So some completions engineers may have figured this out and placed their perfs above and below, but not in the weak zones. Wow! I am impressed.


  • Predicted SP used to calculate Rw.
  • Predicted GR, K, U, TH extended the recorded data over the Poker Chip Shale.
  • Predicted Dt4p and Dt4s allowed calculations of Poisson’s Ratio and brittleness; result was to confirm barite invaded zone is weak, suggesting fractures are present.
  • Predicted CMRP allowed distinction between free and capillary porosity.
  • Predicted TCMR allowed another prediction of TOC.
  • Elements were used to calculate grain density in the presence of kerogen.
  • Elements were used to calculate minerals.
  • Clay minerals were used to calculate CEC for water saturation corrections.
  • Elements were used to define a gas flag via reconstruction of matrix-adjusted neutron and density porosity.

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