Example 2

Objective

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. 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.

WELL 1 is a level four project and is interesting as it has a lot of core XRD & some XRF on the mineralogy. Some of the core is high quality XRD and some is not. The high quality is the data that has muscovite, analyzed recently by Calgary Rock. The “other” quality has muscovite and illite grouped together, analyzed in 2008 by Terra Tek. The problem the analyst faces is what do we believe is correct? How can we use the two different core data sets to validate our log computation?

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.

Objective Viewed With Plots

This computation process will have about 6 or 7 plots, but only a few will be shown here:

1. Plot 1 is “RAW LOGS AND CORE XRF ELEMENTS”.
2. Plot 2 will be “PRE-INTERPRETATION”.
3. Plot 4 will be “COMPUTED INTERPRETATION, Part One”; Part one starts us off to check the reconstructed elemental logs to see if we have provided a plausible computation. A recomputation is made to try to improve the results, and results in Plot 5.
4. Plot 5 will be “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 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.
5. Plots 6, 7, 8, 9, 10 are used [not shown] to finally end up at Plot 11 which is shown and which is consistent and plausible.

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 (ECS) and CMR.
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 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 will 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, 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 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. 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 10 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. We shall see later that the reconstruction after pass 5 is not nearly as good as this is.
• Note that there are minerals containing Fe that have not been included in the reconstruction such as pyrite. Also note the dashed iron curve is recorded Fe minus 14% Al. This is peculiar only to the ECS™ tool. This dashed Fe is what we expect to agree with the core XRF, and it does.
• The same applies to Ti, where rutile has not been included in the reconstruction. The core does agree with the input recorded elements.

Fig. 1 “ELEMENTS CORE AND RECON FROM PASS 10 05-04-2013 9-46-14 PM.jpg”

• 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. The plot is from pass 10 to show what it should look like. There is no core grain density to verify on this well, but there is on example well #3.

Fig. 2 “TOC AND RHOG PASS 10 05-04-2013 10-04-42 PM.jpg”

• 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.
• Ditto for core water saturation and core permeability. Note the core perm is plotted on a scale that is 1000 times the other perms. Perhaps the core perm was not the current status methodology and simply was a conventional method on unconventional rocks. 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 10, so this shows what porosity, perm and Sw should look like when almost finished.

Fig. 3 “CORE SW PERM POR CHECK PASS 10 05-04-2013 10-34-13 PM.jpg”.

Sw core and logs are close. Lower Sw [top, lower perm] zone on core reflects the lower porosity measured by the core (?). High core-perm [10,000 too high] is probably from fractures that were not filled with epoxy before measurement. Ground-up samples measured with pulse decay circumvent this problem. Conventional core porosity does not penetrate small capillaries; hence “total” porosity is not measured, but rather just the larger & medium pores are measured. Ground samples circumvent this problem and are expected to be close to the yellow “GRI” porosity measurement.

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

In The Beginning

Plot1 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, Plot 1. However, we soon see that the original core data may be off depth, so we must put it on depth before we compare the raw logs to core.

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

Fig. 4 “PLOT 1 WELL #1 RAW LOGS AND XRF.png”

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 CMRP_3MS, because it is available and because the late John Kovacs told me it was the best to use; you may have other preferences); we also have TCMR, and we can calculate TOC via TCMR, travel time (Passey), density (Passey), uranium (modified “Passey-Bob”, plus Nieto’s work [valid for Montney but also works elsewhere], and any other method you may prefer to stack up against core-generated values. We used TOC_RHOB for the initial runs and TOC_CMR for the final run, shown in Plot 11. The TOC_CMR seemed to correspond with core more closely than TOC_RHOB, although they give very similar results.

The “Formulas” give you the necessary equations to solve for these values. However, before you can compare to core, the core must be on depth. [Has been depth corrected].

Plot 2 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; also have decided on the TOC calculation that best fits core TOC. We initially selected the delta Rhob-resistivity method that we modified from Roy Benteau’s “Passey” method. We also use the SP to derive an Rw using the equations in “Formulas”. This well used Invermul™ mud so the SP was not recorded but rather, predicted from an offset well. Note the resistivity plot contains only AF30 and AF60 induction logs so the rest of the measurements did not get transferred, since this is a multi-used, released LAS file. The best way to get Rt is to use PST™ to provide their method of correction to obtain Rt. Else, use the service provider. Normally we get them done at PST but in this case there was insufficient data. They need a full induction suite as well as the service company name to do deconvolution. They use the shallowest reading for bed thickness but our clustering method also provides bed boundaries. We don’t have the full induction suite of curves.

PLOT 2 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.

Fig. 5 “CWLS WELL 1 PLOT 2 PRE-INTERPRETATION LOGS.png”.

The top potassium [and other elements above the yellow “top”] from core does not look correct. The core does not have XRF so the elements were derived from the chemistry of the XRD. [If one has a choice, XRF should always be run with XRD. Else, there is no way to check the core against core measurements.] Note that aluminum from the XRD chemistry is less than Al from the log. When this happens it can mean the core [bulk] clays are not correct. Kaolinite from the core may be slightly low, for example.

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.

Plot 4, 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 4, Shown Below, is for reference, not readability. We will re-plot in smaller sections for readability.

Fig. 6 “CWLS WELL 1 BOTTOM LINE PLOT 4.png”

Fig. 7 “PLOT 4 FIRST PASS C8 MINERALS, RESIS, SW, OOIP TRACKS.png”

1. The combined mineralogy is shown in the 2nd track. Core values of quartz are plotted as red dots and the dots are much lower than the computed quartz. The formation is about 50% to 70% quartz, some feldspar (light green), muscovite (light grey), illite (dark grey), kaolinite (dark orange), dolomite (purple) and calcite (light blue). So as stated above, we see that the core quartz is significantly lower than the computed quartz. So we will constrain the quartz for the next pass (we tried 0.5, 0.6, 0.8, & 0.85 and ended with 0.8, of-the-value-shown-in-Plot-4.)
2. 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 some hydrocarbon here. However, there is not as much as expected below the slave point, so we calculate an “Rw_Nieto” over the Slave Point and save that for the next pass. (Archie Rw_Nieto = Sw_Nieto^m_zero Rt/F). Calculating an Rw in this manner will result in an Rw that is “too low” as it converts to salinity that is super-saturated. However, we will try it to see what it looks like in terms of Sw.
3. Sw is next and compares favourably with core. However, since quartz is not right yet, how much credibility can we give to Sw? Ditto for original oil in place.

Fig. 8 “PLOT 4 FIRST PASS C8 MINERALS PERM POR RHOG TRACKS.png”

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.

• Note the permeability scale for the core is 10,000 times the log derived value, just to get it on the page. It appears that Routine Core Analysis (RCA) was used rather than methods proposed by the Gas Research Institute, and the core were probably fractured.

The Timur perm (red) and the SDR perm (blue) have been truncated in the LAS file.

• The PERM_KER_GRI (yellow, shaded) is much lower than expected, given that the POR_KER_GRI (yellow) is close to effective porosity, in the next track. It is expected that this perm would be close to the Timur and SDR perm. The suggested reason deals with not having the minerals correct yet but we will wait and see.
• 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 though the minerals are not correct. So the perm we want to focus on getting correct is the yellow curve, PERM_KER_GRI.

The next 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 example 3, 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 (HCPV). The green shading is the free fluid CMRP_3MS. The green shading indicates this free fluid is oil. Recall this was a measured curve on this well, so is expected to be valid. The green shows a lot of free oil, which is about 33% of the HCPV. The yellow curve is the porosity that is equivalent [calibrated to] to the Gas Research Institute (GRI) method of measurement that includes kerogen. In the GRI method, the [TOC] kerogen is taken into account, thereby lowering the grain density, as shown in the next track, and the resulting porosity is much lower. On this well, the TOC is low enough so that the yellow porosity curve is about the same magnitude as the effective porosity.
• The free water is shown uphole by the very light blue colour.
• The Muskwa lobes are four in number. The best one(s) will be identified later as we are surer of the interpretation model.

Porosity looks reasonable. The core PHIE is in the expected range for RCA analysis.

• The yellow POR_KER_GRI is usually closer to core when core porosity uses a GRI method of analysis.
• 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].

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 PLOT 4 FIRST PASS C8 MINERALS PHID PHIN M_NPHI VS. NPHI TRACKS.png

1. Note that PHID_MAD and PHIN_MAN show no gas, since there is no “cross-over”. 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 . 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, so far, the modeled neutron is only slightly less than the recorded neutron, indicating that the clay minerals may be close.
• We have seen the quartz vs. core, so 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)
3. We can see that it is important to have the mineral fractions, especially the clays, as well as the fluid fractions in the total porosity and 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 all is “in the ball park”. The correlation coefficient in this computed zone is 75%, which is not as good as it gets, but not bad. [Our final calculation has an 85% correlation].

Fig. 10 “PLOT 4 FIRST PASS C8 MINERALS M_NPHI ILL T_CLAY CHL SMEC TRACKS.png”

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

• Total clay is reasonable, compared to Calgary Rock values of core [red squares are Calgary Rock XRD data; “other” company grouped illite with muscovite so their values are too high for clay but OK for clay plus muscovite].
• Kaolinite is high, compensating for chlorite being too low.
• The reason that the M_NPHI is a little low compared to the recorded neutron is not that chlorite TNPH (48) is greater than kaolinite TNPH (45) but rather that the compensation of too much kaolinite and too little chlorite is not quite enough. Ideally, we would like to separate chlorite and kaolinite better than we have but are unable, so far.
• 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 OK 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 ~ OK with core.
• Chlorite (CEC =15 ) and Kaolinite (CEC =6 ) have relatively lower CEC contributions, compared to illite (CEC=25), and the abundance of chlorite+kaolinite+smectite is about ½ that of illite, so their separation is not critical to overall CEC.
• Our conclusion so far is that our single current problem is to lower quartz. We expect that will increase plagioclase, so let’s look at where plagioclase is now. [“Interpretation challenge” so far: We also should decrease carbonate to increase muscovite, but at this stage we did not understand that well enough to do it. More on this logic later.]

Fig. 11 “PLOT 4 FIRST PASS C8 QUARTZ PLAG KSP MUSC.png”

• Question, “Has Quartz being too high resulted in muscovite being too low?” [We thought so, but it turned out to be an “interpretation challenge”]. Muscovite has a CEC somewhere between zero and 10, but we have chosen to give it a CEC of zero. Hence, there is no effect on Sw, from muscovite. There will be an effect on muscovite being too low as the potassium will be too low.
• The combined feldspar is ~ OK but plag is too low above the Muskwa. In the Muskwa zone, Plag is OK.

Next, we will review the reconstructed elements. Recall that the elements we looked at in the beginning of this story were the “final” elements. What we are looking at next are the “beginning” elements, which will lead us to the constraints required for the next pass.

Fig. 12 “PLOT 4 FIRST PASS C8 RECON ELEMENT.png”

Previously we said, “The main effect of quartz being too high is that muscovite is too low.” Then we plotted muscovite and found that it was too low. So, how do we explain that potassium is too high, above the Muskwa zone? The sources of potassium are K_spar, illite and muscovite. Illite is OK. That leaves Kspar being too high? But we have seen that it is not. Makes one scratch their head, eh? Look at illite again. There is no good core above the Muskwa except for one point by Calgary Rock and that one point is OK. So it is possible that illite is too high above the Muskwa? [“Interpretation insight”: the trump card is that if carbonate decreases then clay increases and, therefore, muscovite increases, as muscovite = total clay – sum (ill + kao + chl + smec). In retrospect, we should have decreased carbonate by setting carbonate = Ca/0.4 instead of Ca (mixture of 0.4 and 0.22, depending on the PEF to determine the amount of calcite (0.4) and dolomite (0.22))]. When we increase muscovite, potassium will increase but decreasing illite will cause potassium to decrease.

We also see that reconstructed aluminum is a bit too high above the Muskwa. Kaolinite has twice the Al compared to Chlorite (Kao Al = 20; Chl Al = 10; Musc Al = 19, Ill Al = 10). So Kao being too high accounts for Al being too high.

We see that reconstructed silicon is too high and we expected that, since quartz is too high.

There is an anomaly in the calcium track for Calgary Rock. The Calcium computed from the carbonate XRD does not match the Ca from the XRF. This probably is a result of [our] including siderite in the carbonates and [our] incorrectly assigning Ca instead of iron to the siderite. [“Interpretation challenge”: that anomaly problem led us down the garden path: we thought that since the reconstruction was OK, the carbonate was OK and we could not believe the core that was showing lower XRF and a higher XRD-Ca, than our measured elements showed.]

Note that for all other elements, except Titanium (no rutile in the back-calculation), the elements back-calculated from the XRD are extremely close to the XRF. In fact, they are so close that one data point almost sits on top of the other. That means Calgary Rock is doing a fantastic job in their XRD. Iron is a little different but that results from no pyrite in the back-calculation. So the variances are a result of our abbreviated mineral model, not Calgary Rock’s XRD work. Very Impressive!

We have looked at everything so now it is time to recompute, lowering quartz. We tried quartz at a constraint of 60% of the original but we saw little if any change. In the computation flow, carbonate is computed first. Then clay+muscovite is computed. The quartz is what is left over.

[We should have decreased carbonate too but did not. Consequently, we saw little change as shown in plot 5. If we had decreased carbonate then clay+muscovite would increase and quartz would decrease. Since clay+muscovite would increase, and clay was constrained to remain about the same, muscovite would increase and we would have been closer to solving the problem. We are including our “interpretation challenges and insights” in this analysis to try to help the reader with the logical reasoning for their next well. Hindsight helps!]

Plot 5: Second Cut At Interpretation, QC With Reconstructed Elements Explained.

Plot 5 is shown as a big plot but the scales are not intended to be read; smaller sections of the plot that are readable are shown later.

Fig. 13 “CWLS WELL 1 PLOT 5 SECOND COMPUTATION).png”

COMMENTS ON PLOT 5

Overall, just looking at the reconstructed elements (gold colour), Plot 5 does not look any better than Plot 4. However, we will go through the plot in a standardized way.

1. How does the neutron reconstruction compare to the input neutron?
2. How do the core minerals compare to log minerals
3. How do the reconstructed elements compare to the input elements?
4. 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 not so good [no better than last pass], with a correlation coefficient of 75%.
2. How do the core minerals compare to the log minerals? We have depth corrected the core but it still may not be exactly correct.
• First, we compare the high quality red square dots of core XRD quartz to the light orange, calculated quartz. The high quality XRD is still not very close to the log calculation. 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. The lower quality red dots show the computed quartz is still higher than the core quartz, so we will make a correction for the next pass. Until we get quartz right, it is not worthwhile to look much further, as the minerals are all interdependent.
• So our question is, “What is right?”
3. How do the reconstructed elements compare to core and the input elements?
• First, compare Aluminum
• The core XRF compares well with the input, but not OK with the output gold reconstructed curve. The output gold, reconstructed aluminum is much higher than the input curve. Why? The minerals containing large amounts of aluminum are the feldspars and the clays Kaolinite, for example] as well as muscovite. Since it appears the feldpars and clays compare OK to the low quality XRD, we are left with the muscovite. Here are some possible reasons:
• The muscovite is too high, but our plot shows it is not.
• 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 blue curve. In addition the XRF compares well, too. The XRD calcium is odd and perhaps has a scale problem?
• 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. The reconstructed iron compares well with core XRF and with the input DWFE_WALK2. In general, the reconstructed iron is a lot less than the measured iron, indicating that the iron minerals have not been included in the model in the correct amount. One that we know of is pyrite. We usually do not include pyrite as it perturbs the analysis too much when pyrite is calculated from a predicted sulphur, so we will just make a mental note to check out pyrite.
• Fourth, compare silicon
• The reconstructed silicon is 20% higher than the input silicon… (Whenever muscovite is present?) Let us continue the analysis of the reconstructed curves.
• Fifth, compare Titanium
• The reconstructed titanium is not close to both the input Titanium and the core XRF Titanium. Muscovite has zero titanium in our model. We do not have rutile in our model so that would create this type of problem. Another mental note to check rutile.
• Sixth, compare potassium
• The reconstructed potassium is higher than the input potassium everywhere. Is too much muscovite or other potassium-bearing minerals such as kspar and illite, the problem?
4. Finally, what is the validity of the computation? Our investigation above, says we should have less muscovite but we do not have enough muscovite. Confusing.
5. What have we learned?
• Quality control of the computation is important and may elucidate where problems in the computation, may occur. However, one can still come to incorrect conclusions as this story illustrates.
• Measured elements allow us to use comparisons to core elements and core minerals effectively.
• 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. 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. 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. All sources of potassium must also be in balance, when the “best” result is computed.
6. 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. 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. Managers will hopefully be left with the impression that this method is a good way to “milk” 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 resource plays. When we went through the first interpretation pass, we thought we were going to be finished at the fifth plot. However, the analysis method worked so well that it indicated we were not at the correct result yet.

Eventually, we think the result will be 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, 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.

We move on to PLOT 11, which is the result of several runs. 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.

Plot 11 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. Consequently, core plus elements and 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.

Plot 11 is for reference, not readability. Plots that have sections of plot 11 will be shown for readability

Fig. 14 “CWLS WELL 1 PLOT 11 PASS 9.png”

This big plot is impossible to read when compressed to fit the page, so we will look at sections of it.

PLOT 11, Final Pass, Notes:

1. The quartz matched the core when a constraint of 0.8 was applied.

Fig. 15 “QUARTZ PLAG KSP MUSC AND CORE 05-04-2013 5-27-18 PM.jpg”

2. However, even though the constraint on Plag was set at 0.2, which is pretty low, the plag was higher than core. This is the opposite problem we had at the beginning, so there may be a better result if we just kept iterating. However, plag is not going to affect the final result of porosity, perm and Sw, so we stopped.
3. he K-Spar matched core very well.
4. The muscovite matched core.

Below are the clays

Fig. 16 “CLAY KAO CHL SME TOTAL PASS 10 05-04-2013 5-42-01 PM.jpg”

5. The total clay plus muscovite is (purple curve, track 2, WCLAY_MUSC_CM, which is WCLAY plus muscovite using the Herron Clay-mica (CM) model) is close to the black square core dots from Terra Tek, 2008, which grouped illite with muscovite.
6. Smectite matched core.
7. Chlorite “sort of” matched core but was lower than core at the top of the core.
8. The kaolinite compensated for chlorite by being slightly higher than core. The combination of kaolinite and chlorite provides a relatively high TNPH which helps the neutron modelling [M_NPHI] to fit the recorded neutron.
9. Kaolinite matched core at the top and was slightly higher than core below in the rest of the zone.
10. The total clay matches the Calgary Rock core, which separated illite from muscovite. The total clay from Terra Tek core, which did not separate illite from muscovite, was of course, high. The Terra Tek analysis was made in 2008 and most companies did not separate illite from muscovite back then.

Next we will look at the expanded views of TOC and porosity.

Fig. 17 “CLUSTER PE RHOB POROSITY HCPV 05-04-2013 5-59-43 PM.jpg”

11. A cluster (left hand cluster above) of PEF and RHOB identifies the kerogen as green in the clastic rocks of the Muskwa, (light RHOB and Low PEF).
12. The 4 lobes in the Muskwa appear to be separated by lower porosity (appear to be thin purple streaks of carbonates on the right hand cluster plot above). The highest porosity is in Lobe 2 so that seems to be the best Lobe for the horizontal. Also, Lobe 1 is a good pay section, so the combined lobes 1 and 2 are clearly the sweet spots.

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. The results are shown in the last plot, Fig. 18, below.

Fig. 18a Brittleness.

The red curve on the second track from the left shows brittleness increasing to the left. Note the correlation with the orange quartz curve. 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:

Fig. 18b “WELL 1 FINAL PAY SUMMARY 04-04-2013 4-39-49 PM.jpg”

One can see that both Lobe 1 and Lobe 2 are significantly better than the rest, in terms of [net] PAY1.

Fig. 19 “CWLS WELL 1 FINAL STORAGE CAPACITY 04-04-2013 4-39-49 PM.jpg”

Storage capacity is best in lobes 1 & 2.

Fig. 20 “PLOT 11 WELL 1 FINAL YELLOW SUMMARY BOXES.png”

Note that the yellow summary boxes [quick look method] on Plot 11 ~ confirm the above Final Summary storage capacity, Porosity*metres (PorM) and gives 2.53 for Lobe 1 vs. 2.55 for the “Final Summary” [the colourful summaries above]. Lobe 2 for porosity-m is 1.9 which is very close to the above Final Summary of 1.86 for PHIE. Lobe 3 porosity-m is 0.87 vs. 0.88 above; Lobe 4 is 0.94 vs. 0.99 above. 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 changes to input grain density of siltstone from 2.71 to 2.75 in order to achieve the close similar values. The detailed method provides a procedure to obtain the relevant computation inputs.

Total porosity and PHIE are very close. Changing the cluster’s siltstone/sh input from a grain density of 2.68 to 2.7 changes the PHIE-metres from 1.74 to 1.86. Hence, the HCQL computation is very sensitive to the user-input to each rock type of the cluster’s probabilistic grain density.

Fig. 21 “CWLS WELL 1 FINAL OOIP 04-04-2013 4-38-01 PM.jpg”

The original oil in place (OOIP) shows that lobes 1 and 2 dominate the pay picture.

Fig. 22 “CWLS WELL 1 FINAL HCPV 04-04-2013 4-32-58 PM.jpg”

Of course lobes 1 and 2 dominate this pay picture too, as this is a “reservoir conditions” compared to “surface conditions of STP” for the OOIP.

Fig. 23 “CWLS WELL 1 FINAL FRACTIONAL FLOW 04-04-2013 4-27-58 PM.jpg”

The fractional flow is a simplified method depending on relative permeability’s, and says all 4 lobes are important, but Lobes 1 & 2 are clearly the best lobes. Lobe 2 has the best permeability and therefore the best deliverability. Hence, Lobe 2 is the best target for the horizontal.

Summary

Lobe 2 has OOIP MMBBL/SECT at 22.4 and Lobe 1 has 26.2.

Lobe 2 has effective-porosity-metres storage capacity of 1.86 Por-m for 14 m; Lobe 1 is higher at 2.55 Por-M but is thicker at a gross of 21.3 m vs. 13.9.

Lobe 2’s porosity is 14.6% and Sw is 33% and is not limited by SWB to 9%; Lobe 1 porosity is 13.4% and Sw is 44%; so Lobe 2 is better.

Lobe 2 has net 14 M of PAY 1, out of gross 14 m. Lobe 1 has 21 out of 21. [Pay1 = Phie>6%, no water flags].

Lobe 2 has no fractional water flow compared to Lobe 1 with 1%. This difference is reflected by the water saturation difference. None of the top 4 Lobes will produce water, but the Beaverhill Lake may produce water.

Lobe 4 has a tight streak at the base which will help to isolate water from below if it has lateral extension. If it does not have lateral extension, what look like it might be a “barrier” may not be a barrier.

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