CHAPTER TWENTY-NINE: FRACTURED RESERVOIRS 2
Quantitative Models

Table of Contents
29.00 Introduction to this Chapter
29.01 Log Overlays and Crossplots to Quantify Fractures
29.02 Calculating Permeability From Stoneley Attenuation
29.03 Calculating Formation Strength
29.04 Calculating Fracture Intensity (Crain’s Method)
29.05 Calculating Fracture Intensity and Initial Flow Rate (Schafer’s Method)
29.06 Calculating Fracture Porosity and Fracture Permeability from Fracture Aperture
29.07 In Conclusion
29.08 Exercises for Chapter Twenty-Nine
29.09: Bibliography for Chapter Twenty-Nine

Return to Chapter Twenty-Eight: Fracture Identification
Continue to Chapter Thirty: Dual Porosity Model for Fractured Reservoirs

Publication History: This Chapter is an updated version of part of Chapter Nine of The Log Analysis Handbook - Volume Two, originally self-published in 1990 as a seminar handout and workbook.

CHAPTER TWENTY-NINE: FRACTURED RESERVOIRS 2
Quantitative Models

29.00 Introduction to this Chapter
This Chapter covers straight-forward quantitative and semi-quantitative methods for evaluating fractured reservoirs. Fracture Identification is discussed in Chapter Twenty-Eight and the Dual Porosity Model is covered in Chapter Thirty. Rock Stress and Mechanical Properties are the topic for Chapter Twenty. These four Chapters comprise a mini-course in Fractured Reservoir Theory and Practice and should be read as a set.

Quantitative fracture methods include fracture intensity calculations that help to discriminate between lightly fractured and heavily fractured intervals. Fracture porosity and fracture permeability are covered as well as secondary porosity index and Pickett plots for finding the cementation exponent, M.

29.01 Log Overlays and Crossplots to Quantify Fractures
Sonic/density or sonic/neutron porosity overlay presentations help find vugs and caverns in carbonates. Fractures are often associated with these porosity types. Sonic derived porosity is generally considered to be intergranular or intercrystalline (primary) porosity, whereas density or neutron derived porosity measures primary (intergranular or intercrystalline) plus secondary (vuggy, solution, or fracture) porosity. Note that the words primary and secondary porosity are used here in their traditional log analysis sense and not in a strict geological sense. However, much of the log analysis literature, especially with respect to the dual porosity model for fracture analysis, uses the terms as described in this paragraph. As mentioned earlier, fracture porosity is very small and is usually overwhelmed by the vuggy portion.

Density neutron crossplot porosity minus sonic porosity yields a result, traditionally called secondary porosity index or SPI, usually attributed to vugs or caverns, and to a lesser degree, fractures.

Where:
SPI = PHIsec = secondary porosity index
PHIxnd = density neutron crossplot porosity corrected for shale and lithology
PHIsc = sonic porosity corrected for shale and lithology

The calculation rules for PHIxnd and PHIsc are defined in Chapter Seven. Raw logs seldom have the correct scales to make an adequate overlay, so computer processed curves are usually used. Lithology must be known or computed accurately for this comparison to be valid; this is possible in pure limestone sections but not always in mixed lithology.

If fracturing is sufficient enough to increase the total porosity substantially, the porosity comparison method allows fractured zones to be detected. This is usually not the case, but if an increase in porosity of more than 1 or 2% due to fracturing is present, it certainly can be seen.

Figure 29.01 shows an Austin Chalk example. The cross hatched area on the log defines zones where density porosity is greater than sonic porosity. In this case, it looks like the difference is due to rough or large hole, and not entirely to fracture porosity. However, the presence of fractures is almost certain.

FIGURE 29.01: Density - sonic overlay in Austin Chalk

A better plot would use the neutron or density neutron crossplot porosity compared to the sonic porosity, with the sonic porosity computed with a matrix travel time derived from the density neutron or density neutron PE lithology calculation. These methods are described in Chapter Eight. The black shading shows intervals where density porosity is lower than sonic. The example claims this is due to shaliness, but some of it may be due to inadequate lithology compensation in the sonic porosity calculations.

Methods have been developed using the above porosity measurements which lend themselves better to computer analysis. For example, by crossplotting Mlith and Nlith values, points which fall in certain areas of the crossplot could represent secondary porosity, ie. vuggy porosity plus fracture porosity. Secondary porosity raises the Mlith value compared to the same rock with no secondary porosity. See Chapter Nine for details of this calculation.

Other crossplots using porosity from sonic, neutron, or density versus each other or gamma ray, and matrix density versus matrix travel time (MID plot) are also used to solve particular cases. Crossplots are not as helpful as depth plot overlays.

A deep resistivity/Rxo overlay log is useful in spotting the shallow resistivity crossover caused by fractures. Rxo is the calculated value of the formation resistivity with the mud filtrate filling all pores. The value is derived from the shallowest resistivity device that was run. This might be a microlog or proximity log, which are often run on linear scales, making it difficult to compare to the deep resistivity on a logarithmic scale. Compatible scales are made in the computer truck or computer center so the analyst can see what is happening. When Rxo is less than the deep resistivity in fresh muds, vertical fractures are indicated. An example is shown in earlier Figure 29.02, along with a dipmeter fracture identification log.

FIGURE 29.02: Shallow resistivity overlay compared to dipmeter FIL

Another approach is to make a crossplot, using logarithmic scales, of the apparent formation factor against porosity. The plot represents the Archie formation factor equation:

Where:
F = formation factor (unitless)
A = tortuosity constant (unitless)
M = cementation exponent (unitless)
PHIe = effective porosity (unitless)
Rxo = resistivity of invaded zone (ohm-m)
RMF@FT = mud filtrate resistivity at formation temperature (ohm-m)
Ro = resistivity of un-invaded zone water zone (ohm-m)
RW@FT = formation water resistivity at formation temperature (ohm-m)

When the cementation exponent, M, is a constant it corresponds to a straight line of constant slope passing through the point F = A and PHIe = 1.0 on this plot. The tortuosity constant, A, is often taken equal to 1 for this analysis.

FIGURE 29.03: Porosity - resistivity crossplot (Pickett plot) identifies fractures

In a non-fractured zone, the apparent M will be slightly too high if hydrocarbons are present. In a fractured zone, M will be much lower. Figure 29.03 is an example of such an effect. Most of the points in this very tight zone (average porosity = 3%) plot above the M = 2.0 line due to residual gas saturation. Many points however plot at much lower values of M and range down to M = 1.1, with the predominate value near M = 1.4. Low values of M are common in fractured reservoirs. The more heavily fractured zones give the lowest M values.

29.02 Calculating Permeability From Stoneley Attenuation
While propagating along the borehole wall, the Stoneley wave is able to exchange energy with the formation fluid in a process called acoustic flow. This communication between the borehole and formation is proportional to the mobility of the fluids, which in turn is proportional to permeability and fluid viscosity. Increases in communication decrease Stoneley amplitude, because energy is used up when acoustic flow is initiated. This is equivalent to increased Stoneley attenuation, which therefore can be calibrated to predict formation permeability.

FIGURE 29.04: Permeability from Stoneley wave attenuation

The attenuation data can be represented as pseudo-permeability and presented on a qualitative scale: low attenuation corresponding to low permeability and high attenuation to high permeability. To facilitate comparisons of pseudo-permeability to actual formation producibility, the attenuation data can be integrated to provide a potential flow profile for comparison to an actual spinner flowmeter log. An example is provided in Figure 29.04.

This curve could be correlated to core permeability to obtain a calibrated permeability curve. Correlation is usually as good as porosity and saturation correlations, which have been used for many years, and often much better than these in fractured and vuggy zones.

29.03 Calculating Formation Strength
There are two other ways the computer can help present a synthesis of fracture indicating logs. One is to calculate formation strength and elastic properties. The theoretical background for rock strength and elastic properties calculations from well logs are shown in Chapter Twenty.

The other is to reduce the indicators to a single curve representing fractures intensity or fracture probability. Fracture intensity is covered below in Sections 29.03 and 29.04

29.04 Calculating Fracture Intensity (Crain’s Method)
The various log derived fracture indicators can be merged through a computer program which allows a wide variety of inputs. The input curves are assigned a threshold value, a median value, and a maximum probability as a fracture detector. In addition, each input is weighted according to its correlation to natural fracturing in the specific area. This form of program lends itself to a small rule based expert system.

In most areas, the major weighting is assigned to the shear attenuation from the sonic waveforms and to dipmeter differential conductivity. Lesser weighting is assigned to compressional attenuation, caliper rugosity, density correction, deep to shallow resistivity ratio, and uranium content. Where oblique fracturing are expected, the shear input may be weighted lower and the compressional input weighted higher. The output of the program is a fracture probability curve.

WHERE:
CFI = calculated fracture index (fractional)
RESS = shallow resistivity
RESD = deep resistivity
PHID = density porosity
PHIN = neutron porosity
DELT= sonic travel time
GR = gamma ray
PE = photo electric effect
CAL = caliper
DCOR = density correction
DELTA_CAL = differential caliper
NTEST = number of thresholds tested

This equation is written for a specific case; curves can be added or deleted and thresholds adjusted to suit the circumstances. Note that the thresholds are in Metric units, all curves have equal weight, and the amount of excursion of a curve beyond its threshold is not considered. The result is normalized between 0.0 and 1.0 by the value of NTEST. More elaborate fracture intensity indicators are common.

FIGURE 29.05: Open hole fracture indicator (CFI) compared to FMI results

An example is shown in Figure 29.05. Both the CFI curve and micro-scanner fracture intensity (frequency or fractures per meter, labeled FREQ). There is a strong correlation between the FMI data, which represents ‘ground truth’, and the CFI curve. This does not always happen and the CFI must be calibrated to each specific case.

Note the Fracture Aperture (APER) and Fracture Porosity (PHIf) curve values are very small, but typical of most fractured reservoirs. Matrix porosity (PHIe) is significant and overwhelms the fracture pore volume.

It is sometimes possible to relate the sum of CFI over an interval to drill stem test flow capacity (KH) or to well productivity (IPR or AOF). CFI can also be compared and calibrated to fracture intensity (fractures per meter) from formation micro-scanner processing. This is useful when only a few wells have FMS or FMI data while others do not.

29.05 Calculating Fracture Intensity and Initial Flow Rate (Schafer’s Method)
Since the productive potential of fractured wells cannot be determined easily with conventional logs and Archie's saturation equation, an alternative technique of open hole analysis was proposed by Schafer to identify and eliminate poor wells which would not pay out.

A quantitative expression of fracture intensity (SFI) was derived from the dipmeter log and empirically related to established production histories (see Figure 28.35, lower half and top left). The SFI of 16 wells was then plotted against second month average daily production for each and an equation was selected to fit the observable relationship. Finally, economics of well pay out were applied in order to assign a commercial cut off SFI value.

FIGURE 29.06: Shafer’s fracture intensity (SFI) analysis based on dipmeter anomalies

Where:
A = total opposite pad fracture length on FIL in perforated intervals (ft or m)
B = total length of borehole width elongation greater than 25% of hole diameter (ft or m)
C = total single pad fracture length on FIL in perforated intervals (ft or m)
D = maximum borehole ellipticity (short / long diameters)
SFI = fracture intensity index (unitless)
Qi = initial flow rate (bbl or m3)
Bo = oil formation volume factor (vol per vol)
KF1 = 1.00 for English units
KF1 = 0.3048 for Metric units
KF2 = 1.00 for English units
KF2 = 0.159 for Metric units

Payout is expected when SFI > 2.0 for oil wells with no gas sales, and when SFI > 1.6 for oil wells with gas sales. The constants in equation 1 should be calibrated for each area and will vary with average gas/oil ratio.

Large borehole elongations in fractured reservoirs indicate the intersection of major fractures, which pass completely through the borehole. Flow capacity (millidarcy feet) will increase as A and B footage values increase. These two parameters quantify the fracture indicators that contribute most significantly to production.

Parameter C indicates small scale fractures limited in extent. Single pad fracture footage on the FIL generally has little or no corresponding hole washout. Fractures of this nature will contribute hydrocarbons initially, especially after artificial stimulation, but due to the small potential reservoir production will rapidly drop off.

The degree of borehole ellipticity, D, was chosen to be an indicator of fracture width or intensity of fracture spacing. Fracture width or spacing intensity will determine permeability and therefore affect well capacity. Assuming this, then borehole ellipticity will be inversely proportional to well productivity.

The dipmeter log parameters described above were determined for the Austin Chalk in Texas and Louisiana. They are used as quantitative indicators of well capacity and correlate reasonably with initial well flow rate. However, other variables which complicate the relationship must be considered, such as gas/oil ratio, fracture treatments, well mechanical problems, partial reservoir depletion, and reservoir changes external to the borehole. Gas/oil ratio, which can vary greatly even between offsetting wells, will affect flow rate. Well stimulations, such as acid wash or hydraulic fracturing , will normally increase initial production beyond that predicted by the above correlations.

29.06 Calculating Fracture Porosity and Fracture Permeability From Fracture Aperture
Quantitative analysis of fracture aperture is possible by further processing of formation micro-imager conductivity data. The algorithm is based on the concept that higher conductivity means a larger open fracture. The fracture aperture and fracture frequency can be combined to obtain fracture porosity and fracture permeability.

Where:
KF1 = number of main fracture directions
= 1 for sub-horizontal or sub-vertical
= 2 for orthogonal sub-vertical
= 3 for chaotic or brecciated
PHIfrac = fracture porosity (fractional)
Df = fracture frequency (fractures per meter)
Wf = fracture aperture (millimeters)
Kfrac = fracture permeability (millidarcies)

Note: Equations 2, 3, and 4 give identical results.

Example 1:
Df = 1 fracture per meter
Wf = 1.0 millimeters
PHIfrac = 0.001 * 1 * 1 = 0.001 fractional (0.1%)
Kfrac = 833 * 100 * 1^3 * 1 * 1 = 83300 millidarcies

Example 2:
Df = 10 fractures per meter
Wf = 0.1 millimeters
PHIfrac = 0.001 * 10 * 0.1 = 0.001 fractional (0.1%)
Kfrac = 833 * 100 * 0.1^3 * 10 * 1 = 833 millidarcies

These examples represent well fractured reservoirs. You can see that the volume of hydrocarbon is very small but the permeability is very high. Chapter Thirty contains a discussion of the dual porosity model as proposed by Dr Roberto Aquilera in the mid 1970’s, before the invention of the formation micro-scanner. His approach combines “fracture-related” porosity (solution and vuggy porosity associated with the fractures) with the “true” fracture porosity. This means his “fracture porosity” is much higher than the FMI derived fracture porosity.

If you believe that the phrase “fracture porosity” is a literal definition, then this porosity will usually be pretty small - in the order of 0.0001 to 0.01 fractional porosity (0.01 to 1.0%). If you believe that the phrase includes vuggy and solution porosity related to the presence of fractures, then the value could be much higher. The important thing is to recognize that there are two definitions for “fracture porosity”.

An example of a fracture aperture log from a program called Frac-View is shown in Figure 29.07. The permeability calculation was not available in this program.

FIGURE 29.07: Fracture frequency, aperture, and porosity log

Rose diagrams, polar plots, and stereonet plots of dip azimuth and/or dip angle are helpful tools to track the high permeability fractures. Figure 29.08 shows one such illustration - a rose diagram showing fracture direction.

FIGURE 29.08: Fracture direction on a Rose Diagram.

29.07 In Conclusion
Fractures are an important economic component in many reservoirs, and may be the only socially redeeming factor in most tight reservoirs. Fractures can be found in nearly all sedimentary basins and in nearly all types of traps. Many are unsuspected until an anomalous production test is run or a production history match fails on a reservoir simulator. Quantifying fracture intensity will help in production prediction and reduce the history-match problem in a reservoir simulation.

Most open hole logs show some indication of the fractures, although the effect may be subtle, and hard to quantify. Some logs show fractures better than others, and these should be run if fractures form a significant fraction of the reservoir permeability.

The mere presence of fractures, however, is usually a good sign, unless the fractures also penetrate the water leg of the pool. In this case it is difficult to stop water from being produced with the oil or gas. A good quantitative analysis of the porosity and water saturation will help prevent completions too close to the water contact.

29.08: Exercises for Chapter Twenty-Nine
Exercise 29.01: Quantitative Fracture Concepts
1. Define a fracture and its porosity components. Why are fractures important? (5 marks)

2. Explain how to find M from a Pickett Plot. What range of M values indicate fractures? (5 marks)

3. Why can we estimate permeability from Stoneley wave attenuation? (5 marks)

4. What formation strength properties can be calculated from sonic and density logs? Which one is most useful in fracture analysis? (5 marks)

5. How is fracture intensity calculated from open hole logs? (5 marks)

6. How is initial flow rate estimated in the Austin Chalk reservoir? (5 marks)

7. Which log can be processed to provide fracture porosity? (5 marks)

8. Choose 3 different and realistic fracture apertures and calculate fracture porosity and fracture permeability. (15 marks)

Exercise 29.02: Fracture Intensity Exercise (50 marks)
1. Evaluate the presence of fractures on the three log segments shown below.

2. Determine fracture footage and orientation.

3. Which intervals have predominately vertical and which predominately horizontal fractures?

4. Use Schafer’s fracture intensity method to estimate initial production rate.

FIGURE 29X.02: FIL Log segments for Exercise 29.02

Exercise 29.03: Fracture Intensity - Shaly Carbonate (50 marks)
1. Review logs on next 5 illustrations.

a. Shade limestone, dolomite, and shale in appropriate different colors.

b. Shade the porosity. Estimate porosity in the best zones.

c. Is there an obvious water zone?

d. Shade fracture indications on resistivity, density, caliper, and dipmeter.

e. Do the shales influence the extent of fractures?

f. Estimate total fracture length from each fracture indicator. Which is most accurate? Why?

FIGURE 29X03A: Dipmeter log segment for Exercise 29.03

FIGURE 29X03B: Induction log segment for Exercise 29.03

FIGURE 29X03C: Density neutron log segment for Exercise 29.03

FIGURE 29X03D: Expanded scale dipmeter log segment for Exercise 29.03

29.09 Bibliography for Chapter Twenty-Nine
Open Hole Logs and Fractures

1: Fracture intensity mapping from well logs and structure maps; Pirson,S.J., Trunz,J.P.,Jr., Gomez,P.; Society of Professional Well Log Analysts, 23 p., 1967

2: Evaluation of fractured reservoirs; Pickett,G.R., Reynolds,E.B.; Society of Petroleum Engineers Journal, p. 28-38, 1969

3: Analysis of naturally fractured reservoirs from conventional well logs; Aguilera,R.; The Journal of Canadian Petroleum Technology, p. 764-772, 1976

4: Reservoir evaluation of fractured cretaceous carbonates in south Texas; Beck,J., Schlutz,A., Fitzgerald,D.; SPWAL 18th Annual Logging Symposium, 25 p., 1977

5: Computer caliper, finger prints of the hole, from Austin Chalk to Ellenburger; Kading,H.W.; Society of Professional Well Log Analysts 18th Annual Logging Symposium, 12 p., 1977

6: Well log analysis in the Austin Chalk trend; Bishop,W.D., DeVries,M.R., Fertl,W.H.; Society of Professional Well Log Analysts 18th Annual Logging Symposium, 12 p., 1977

7: Combined log analyses and material balance help to explain performance of naturally fractured reservoirs below the bubble point; Aguilera,R.; Society of Professional Well Log Analysts: The Log Analyst, p. 17-26, 1977

8: Buchan field: evaluation of a fractured sandstone reservoir; Butler,M., Phelan,M.J., Wight,A.W.R.; Society of Professional Well Log Analysts: The Log Analyst, p. 23-31, 1977

9: Current status on the study of naturally fractured reservoirs; Aguilera,R., van Poollen,H.K.; Society of Professional Well Log Analysts: The Log Analyst, p. 3-23, 1977

10: Application of fracture identification logs in the cretaceous of north Louisiana and Mississippi; Brown,R.O.; Transactions Gulf Coast Association of Geological Societies, v. 28, p. 75-91, 1978

11: Geologic aspects of naturally fractured reservoirs explained; Aguilera,R., van Poollen,H.K.; The Oil and Gas Journal, 13 sections, 1978

12: Fracture detection in west coast reservoirs using well logs; Hefin,J.D.; Society of Petroleum Engineers , 14 p., 1979

13: Log evaluation of a fractured reservoir Monterey shale; Cannon,D.E.; Society of Professional Well Log Analysts 20th Annual Logging Symposium, 14 p., 1979

14: Fracture identification log use in Cretateous of N. Louisiana, Mississippi; Brown,R.O.; The Oil and Gas Journal, p. 350-355, 1979

15: Predicting the orientation of hydraulically created fractures in the Cotton Valley formation of east Texas; Brown,R.O., Forgotson,J.M., Forgotson,J.M.,Jr.; 55th Annual Technical Conference of Society of Petroleum Engineers of American Institute of Mining Metallurgical Engineers, 11 p., 1980

16: Fracture detection from well logs; Suau,J., Gartner,J.; Society of Professional Well Log Analysts: The Log Analyst, p. 3-13, 1980

17: A practical method of well evaluation and acreage development for the naturally fractured Austin Chalk formation; Schafer,J.N.; Society of Professional Well Log Analysts: The Log Analyst, p. 10-23, 1980

18: Fracture identification in the Panoma field Council Grove formation; Etnyre,L.; Society of Professional Well Log Analysts: The Log Analyst, p. 3-6, 1981

19: FCL: a computerized well log interpretation process for the evaluation of naturally fractured reservoirs; Aguilera,R., Acevedo,L.A.; The Journal of Canadian Petroleum Technology, p. 31-36, 1982

20: Formation evaluation in the Texas cretaceous chalk trend; Frost,E.,Jr., Stedman,D., Fertl,W.H.; World Oil, p. 213-236, 1982

21: A variable cementation exponent, M, for fractured carbonates; Rasmus,J.C.; Society of Professional Well Log Analysts: The Log Analyst, p. 13-23, 1983

22: Formation evaluation of fractured carbonate rocks; Millard,F.S.; Course notes , 18 p., 1985

23: The dual laterolog response in fractured rocks; Sibbit,A.M., Faivre,O.; Society of Professional Well Log Analysts 26th Annual Logging Symposium, 34 p., 1985

24: Evaluation of fractured carbonates in the midcontinent region; Brevetti,J.C., Greer,G.K., Weis,B.R.; Society of Professional Well Log Analysts 26th Annual Logging Symposium, 19 p., 1985

25: Evaluation the contributions of fractures to reservoir performance; Lamb,C., Haig,P.; Canadian Well Logging Society, 9 p., 1985

26: Petrophysical detection of microfissures in granites; Pape,H., Riepe,L., Schopper,J.R.; Society of Professional Well Log Analysts 26th Annual Logging Symposium, 17 p., 1985

27: Guides for the interpretation of dipmeter fracture logs; Georgi,D.T.; Society of Professional Well Log Analysts 27th Annual Logging Symposium, 17 p., 1986

28: Determination of high angle fracture plane orientation from SHDT dipmeter; Bateman,R.M.; Canadian Well Logging Society Journal, v. 15, no. 1, p. 85-99, 1986

29: A perspective look at fracture porosity; Hensel,W.M.,Jr.; 62nd Annual Technical Conference of Society of Petroleum Engineers, p. 571-578, 1987

30: New developments in the analysis of cores from naturally fractured reservoirs; Bergosh,J.L., Lord,G.D.; 62nd Annual Technical Conference Society of Petroleum Engineers, p. 563-570, 1987

31: Coring-induced fractures: indicators of hydraulic fracture propagation in a naturally fractured reservoir; Laubach,S.E., Monson,E.R.; 63rd Annual Technical Conference of Society of Petroleum Engineers, p. 587-596, 1988

32: Differences in fracture characteristics and related production: Mesaverde formation, northwestern Colorado; Lorenz,J.C., Finley,S.J.; Society of Petroleum Engineers Formation Evaluation, p. 11-16, 1989

33: Oil detection in fractured carbonates of Chapayal Basin, Guatemala; Lau,M.N., Bassiouni,Z.; Society of Professional Well Log Analysts: The Log Analyst, p. 261-269, 1989

34: Detection and characterization of fractures from generation of tube waves; Hardin,E., Toksoz,M.N.; Society of Professional Well Log Analysts 26th Annual Logging Symposium, 21 p., 1985

35: Using the Stoneley normalized differential energies for fractured reservoir evaluation; Brie,A., Hsu,K., Eckersley,C.; Society of Professional Well Log Analysts 29th Annual Logging Symposium, 25 p., 1988

36: Theoretical models relating acoustic tube wave attenuation to fracture permeability: reconciling model results with field data; Paillet,F.L., Cheng,C.H., Tang,X.M.; Society of Professional Well Log Analysts 13th Annual Logging Symposium, 24 p., 1989

Micro-Scanners and Fractures

1: Applications of digital borehole televiewer logging; Pasternack,E.S., Goodwill,W.P; Society of Professional Well Log Analysts Annual Logging Symposium, p. 427-438, 1983

2: Formation microscanner service; Schlumberger; Manual, 37 p., 1986

3: Formation microscanner; Standen,E.; Schlumberger, 16 p., 1986

4: Formation imaging with microelectrical scanning arrays; Ekstrom,M.P., Dahan,C.A., Chen,M.Y., Lloyd,P.M., Rossi,D.J.; Society of Professional Well Log Analysts: The Log Analyst, p. 294-306, 1987

5: Fracture identification and productivity predictions in a carbonate reef complex; Dennis,B., Standen,E., Georgi,D.T., Callow,G.O.; 62nd Annual Technical Conference Society of Petroleum Engineers, p. 579-588, 1987

6: Fracture detection with logs; Schlumberger; The Technical Review, v. 35, no. 1, p. 21-35, 1987

7: Fracture detection in low permeability reservoir sandstone: a comparison of BHTV and FMS logs to core; Laubach,S.E., Baumgardner,R.W.,Jr., Monson,E.R., Hunt,E., Meador,K.J.; 63rd Annual Technical Conference of Society of Petroleum Engineers, p. 265-276, 1988

8: Application of borehole images to three dimensional geometric modeling of aeolian sandstone reservoirs, Permian Rotligende, North Sea; Luthi,S.M., Banavar,J.R.; American Association of Petroleum Geologists, p. 317-p332, 1988

9: Formation microscanner: new developments; Boyeldieu,C., Jeffreys,P.; Society of Professional Well Log Analysts 11th Formation Evaluation Symposium, p. 175-190, 1988

10: Examination of BHTV, FMS, and SHDT images in very thinly bedded sands and shales; Hackbarth,C.J., Tepper,B.J.; 63rd Annual Technical Conference of Society of Petroleum Engineers, p. 119-127, 1988

11: Enhancing borehole image data on a high resolution personal computer; Wong,S.A., Startzman,R.A., Kuo,T.B.; Society of Petroleum Engineers , p. 443-454, 1989

12: The analysis of fracture anomalies on electrical wellbore images; Standen,E.; Schlumberger, 20 p., 1989

13: Study of a complex carbonate reservoir using the formation microscanner tool; Badr,A.R., Ayoub,M.R.; Society of Petroleum Engineers Middle East Oil Technical Conference, p. 345-354, 1989

14: Application of borehole images to geologic modeling of an aeolian reservoir; Plumb,R.A., Luthi,S.M.; Society of Petroleum Engineers Annual Technical Conference, p. 333-344, 1989

15: Recognizing artifact images of the formation microscanner; Bourke,L.T.; Society of Professional Well Log Analysts 30th Annual Logging Symposium, p. 191-216, 1989

16: Thin bed reservoir analysis from borehole electrical images; Trouiller,J.C., Delhomme,J.R., Carlin,S., Anxionnaz,H.; 64th Annual Technical Conference of Society of Petroleum Engineers, p. 217-228, 1989

17: Formation microscanner image interpretation; Serra,O.; Manual, 117 p., 1989

18: Gulf Coast fault orientation determined by formation imaging techniques; Koepsell,R.J., Jenson,F.E., Langley,R.L.; Society of Professional Well Log Analysts 30th Annual Logging Symposium, 24 p., 1989

19: A complete use of structural information from borehole imaging techniques: a case history for a deep carbonate reservoir; Gonfalini,M., Anxionnaz,H.; Society of Professional Well Log Analysts 31st Annual Logging Symposium, 25 p., 1990

20: Comparison of fracture apertures computed from electrical borehole scans and reflected Stoneley waves: an integrated interpretation; Hornby,B.E., Luthi,S.M., Plumb,R.A.; Society of Professional Well Log Analysts 31st Annual Logging Symposium, 26 p., 1990

21: Borehole imaging and its application in well logging: an overview; Pailet,F., Barton,C., Luthi,S., Rambow,F., Zemanek,J.; Society of Professional Well Log Analysts, p. 3-24, 1990

ABOUT THE AUTHOR

E. R. (Ross) Crain, P.Eng. is a Consulting Petrophysicist and a Professional Engineer with over 35 years of experience in reservoir description, petrophysical analysis, and management. He has been a specialist in the integration of well log analysis and petrophysics with geophysical, geological, engineering, and simulation phases of oil and gas exploration and exploitation, with widespread Canadian and Overseas experience.

His textbook, "Crain's Petrophysical Handbook on CD-ROM" is widely used as a reference to practical log analysis. Mr. Crain is an Honourary Member and Past President of the Canadian Well Logging Society (CWLS), a Member of Society of Petrophysicists and Well Log Analysts (SPWLA), and a Registered Professional Engineer with Alberta Professional Engineers, Geologists and Geophysicists (APEGGA)