AAPG Meeting, Dallas, Texas, April 2004

SMU Geothermal Lab Poster on Bottom Hole Temperature (BHT) Calibration  

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Extended Abstract  #87616

BLACKWELL, DAVID, and MARIA RICHARDS, SMU  Dept of Geological Sciences, Dallas, TX 75275-0395, 214-768-2745, blackwel@smu.edu

 Calibration of the AAPG Geothermal Survey of North America BHT Data Base

The AAPG Geothermal Survey of North America (GSNA) was a large effort carried out in the early 1970s that culminated in the production of a massive data base (over 20,000 Bottom Hole Temperature (BHT) points from over 10,000 wells in the US, Canada, and Mexico).  The publication of continent scale maps by the USGS resulted from the project based on the collated data (Kehle, 1970, DeFord and Kehle, 1976).  AAPG made the data base available on a CD-ROM in 1994. The data set has received some use, but that use has been limited, in part because of the uncertain error associated with the raw measurements and the lack of calibration comparisons to accurate equilibrium temperature measurements.  There were several corrections proposed, but only two simple ones were finally applied, one for Louisiana and another for west Texas (the description of these in the final project is ambiguously stated). 

 

Harrison et al. (1983) based their calibration of BHT data to measured temperatures in Oklahoma, using mostly drill stem temperature (DST) measurements. SMU also had success comparing BHT data corrected using their equation to thermal reconstructions for the Anadarko Basin in Oklahoma, based on heat flow and conductivity modeling (Gallardo and Blackwell, 1999).  So in this study of the GSNA data base we started from that correction.  The exact BHT Correction Equation used, from Harrison et al. (1983), is:

 

Tcf = -16.51213476 + 0.01826842109*Z - 2.344936959E-006*Z2

 

The Tcf values are subtracted or added to the original BHT values.  Z is the depth in meters.  This equation was only calibrated to a depth of about 3 km.  The equation is similar to ones originally proposed by Kehle et al. (1970), but not actually used in the final GSNA project. 

 

We calibrated the Midcontinent and Gulf Coast portion of the GSNA data base for the 0.6 to 3km (4 km in the Gulf of Mexico region) depth range, using accurate equilibrium temperature measurements in over 30 wells, (Figure 1), that SMU collected over many years (see for examples Blackwell and Steele (1989) and Blackwell et al. (1999)) and a few logs that others have published.  Limiting the GSNA data to well measurements within 50 km (0.5) of each equilibrium log we had, resulted on average 50 BHT measurements for each area comparison.  These were considered our test case.  We applied the Harrison et al. (1983) correction to these BHT values.  After application of that correction there was still a bias related to depth/gradient/temperature difference, so we applied a secondary correction that was a function of the BHT well gradient.  In general the higher the gradient the more negative the difference between the corrected BHT and the measured temperature.  A gradient of 26 C/km was the zero (no correction or crossover point for gradients).  Using the final (2nd) correction, the test BHT gradient data set had a standard deviation (SD) of 2C/km as opposed to a SD of 5C/km for the first (Harrison) correction.  For a well in the 2-3 km depth range the smaller SD corresponds to a gradient variation of only 1-3C/km, or about 5 to 15% of the measured value in a normal heat flow environment.

 

The shape of the Harrison correction is such that it is negative above about 1000 m and positive below.  Because measured bottom hole temperatures at shallower depths are noisier- due to the effects of a smaller temperature difference, less regularity of the drilling effect (winter versus summer drilling), and some small uncertainty in the surface temperature, the corrected gradients for these also tend to be very noisy.  Therefore we initially considered only BHTs below 600 m depth.  When applying the secondary correction based on gradient, it was applied generally below 750 m.  The differences were systematic below the 0 correction crossover point of the Harrison curve. 

 

The only significant correlation found was between gradient and the temperature correction error (Figure 2).  The mean temperature differences between the measured (equilibrium wells) and the two calculated corrected temperatures are shown in Figure 2.   These represent about 20 sites in the Midcontinent and Gulf Coast with depth ranges of 2500 500 m.  The differences are plotted as a function of gradient.  In of the sites the error on the 2nd corrected temperatures is less than 5C and averages near 0. Thus it is the best correlation between the error and the gradient.  There are clearly anomalies.  The Lackland well in Texas is visibly colder than the surrounding BHTs.  Yet the log is linear and there is no obvious effect of cold downflow in the well.  Several of the deeper wells are geopressured, suggesting that the recovery may have been faster than in normally pressured wells.  On the other hand several wells with high gradients have very large errors.  As a result the tendency for using the BHT data is to miss the highest and lowest gradients in an area. 

 

The secondary correction was applied to all of the AAPG BHT data in the 1000 to 3000 m depth range as a function of gradient based on the least squares line shown in Figure 2 below.  This corrected gradient was used with thermal conductivity estimates to obtain heat flow estimates for Western Great Plains and Gulf of Mexico (including Mexico) data shown below in Figure 1 (Blackwell and Richards, 2004a). 

 

The correction from below 3 km is still not established.  The correction should decrease with depth below 3 to 4 km, but there are not enough data to easily quantify the form of the decrease. 

 

References Cited

 

AAPG, American Association of Petroleum Geologists, CSDE, COSUNA, and Geothermal Survey Data_Rom, 1994.

Blackwell, D.D. and Maria Richards, Calibration of the AAPG Geothermal Survey of North America BHT Data Base, AAPG Annual Meeting, Dallas, TX, Poster session, paper 87616, 2004a. (see poster above)

Blackwell, D. D., and M. Richards, 2004 Geothermal Map of North America, AAPG, scale 1:6,500,000, 2004b.

Blackwell, D.D., and J.L. Steele, Thermal conductivity of sedimentary rock-measurement and significance, pp 13-36, in Thermal History of Sedimentary Basins: Methods and Case Histories, ed. N.D. Naeser, and T.H. McCulloh, Springer-Verlag, New York, 320 pp., 1989.

Blackwell, D. D., G. R. Beardsmore, R. K. Nishimori, and M. J. McMullen, Jr., High Resolution temperature logs in a petroleum setting, examples and applications, p. 1-34, in Geothermics in Basin Analysis, ed. D. Merriam and A. Forster, Plenum Press, New York, 241 pp., 1999.

DeFord, R.K., and Kehle, R.O., Chairmen, Geothermal gradient map of North America:  American Association of Petroleum Geologist and U.S. Geol. Survey, scale 1:5,000,000, 1976.

Gallardo, J. and D.D. Blackwell, Thermal structure of the Anadarko Basin, Oklahoma, Bull. Amer. Assoc. Petrol. Geol., 83, 333-361, 1999.

Harrison, W.E., K.V. Luza, M.L Prater, and P.K. Chueng, Geothermal resource assessment of Oklahoma, Special Publication 83-1, Oklahoma Geological Survey, 1983.

Kehle, R.O., R.J. Schoppel, and R.K. DeFord, The AAPG geothermal survey of North America, U.N. Symposium on the Development and Utilization of Geothermal Resources, Pisa 1970, Geothermics Special Issue 2(1), 358-368, 1970.

Kehle, R.O., Geothermal survey of North America, 1972 Annual Progress Report for the AAPG, 23 p., 1973.

 

 

Figure 1  Location of equilibrium logs compared to AAPG GSNA data points. 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2.  BHT error as a function of the gradient in the well.  The data with only the Harrison et al (1983) correction are shown as the dark diamonds (Series 1).  The linear (Series 1) least squares fit equation is shown as the black line.  The change in value from the 2nd calculated temperature (Series 2) is shown in pink.  Based on the least squares line (Series 2 red line) the data shows no gradient bias.

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