Thermal Conductivity Of Sedimentary Rocks Research Articles

Analyzing and Estimating Thermal Conductivity of Sedimentary Rocks from Mineral Composition and Pore Property

In this study, thermal conductivities of 128 rock samples located in the Xiong’an New Area and Tarim Basin were measured using the optical scanning and transient plane source methods. The thermal conductivities of the Xiong’an New Area samples range from 1.14 to 6.69 W/(m·K), in which the mean thermal conductivities of dolomite and sandstone are 4.95 ± 1.19 and 1.80 ± 0.44 W / m · K , respectively. In the Tarim Basin, sandstone samples have thermal conductivities ranging from 1.21 to 3.56 W/(m·K) with a mean value of 2.51 ± 0.66 W / m · K . The results can provide helpful reference data for studies of geothermics and petroleum geology. Calculation correction and water-saturated measurements were conducted to acquire in situ rock thermal conductivity, and good consistency was found between both. Compaction diagenesis enhances bulk thermal conductivity of sedimentary rocks, particularly sandstones, by decreasing the rock porosity and mineral particle size. Finally, correction factors with respect to mineral grains were proposed to correct the thermal resistance of intergrain contacts and degree of intactness of crystals, and an optimized formula was adopted to calculate the thermal conductivity of sedimentary rock based on rock structure and mineral constituents.

Теплопроводность осадочных горных пород Лено-Вилюйской нефтегазоносной провинции

Теплопроводность осадочных горных пород Лено-Вилюйской нефтегазоносной провинции

Thermal conductivity of sedimentary rocks in the Sichuan basin, Southwest China

The optical scanning method was adopted to measure the thermal conductivities of 418 drill-core samples from 30 boreholes in Sichuan basin. All the measured thermal conductivities mainly range from 2.00 to 4.00 W/m K with a mean of 2.85 W/m K. For clastic rocks, the mean thermal conductivities of sandstone, mudstone, and shale are 3.06 ± 0.73, 2.57 ± 0.42, and 2.48 ± 0.33 W/m K, respectively; for carbonate rocks, the mean thermal conductivities of limestone and dolomite are 2.53 ± 0.44 and 3.55 ± 0.71 W/m K, respectively; for gypsum rocks, the mean thermal conductivity is 3.60 ± 0.64 W/m K. The thermal conductivities of sandstone and mudstone increase with burial depth and stratigraphic age, but this trend is not obvious for limestone and dolomite. Compared with other basins, the thermal conductivities of sandstone and mudstone in Sichuan basin are distinctly higher, while the thermal conductivities of limestone are close to Tarim basin. The content of mineral composition such as quartz is the principal factor that results in thermal conductivity of rocks differing from normal value. In situ thermal conductivity of sandstones was corrected with the consideration of water saturation. Finally, a thermal conductivity column of sedimentary formation of the Sichuan basin was given out, which can provide thermal conductivity references for the research of deep geothermal field and lithospheric thermal structure in the basin.

Теплопроводность осадочных горных пород Лено-Вилюйской нефтегазоносной провинции.

Теплопроводность осадочных горных пород Лено-Вилюйской нефтегазоносной провинции.

Evaluation of common mixing models for calculating bulk thermal conductivity of sedimentary rocks: Correction charts and new conversion equations

Different numerical models can be deployed to calculate the matrix thermal conductivity of a rock from the bulk thermal conductivity (BTC), if the effective porosity of the rock is known. Vice versa, using these parameters, the BTC can be determined for saturation fluids of different thermal conductivity (TC). In this paper, the goodness-of-fit between measured and calculated BTC values of sedimentary rocks has been evaluated for two-component (rock matrix and pores) models that are used widely in geothermics: arithmetic mean, geometric mean, harmonic mean, Hashin and Shtrikman mean, and effective-medium theory mean. The examined set of samples consisted of 1147 TC data in the interval 1.0–6.5Wm−1K−1. The quality of fit was studied separately for the influence of lithotype (sandstone, mudstone, limestone, dolomite), saturation fluid (water and isooctane), and rock anisotropy (parallel and perpendicular to bedding). From the studied models, the geometric mean displays the best, however not satisfying correspondence between calculated and measured BTC. To improve the fit of all models, respective correction equations are calculated. The “corrected” geometric mean provides the most satisfying results and constitutes a universally applicable model for sedimentary rocks. In addition, the application of the herein presented correction equations allows a significant improvement of the accuracy of existing BTC data calculated on the basis of the other mean models. Finally, lithotype-specific conversion equations are provided permitting a calculation of the water-saturated BTC from data of dry-measured BTC and porosity (e.g., well log derived porosity) with no use of any mixing model. For all studied lithotypes, these correction and conversion equations usually reproduce the BTC with an uncertainty<10%.

Baric and temperature dependences for the thermal conductivity of sedimentary rocks

The effective thermal conductivity of secondary rocks (sandstone, dolomite, aleurolite) is studied under high pressures of up to 400 MPa and temperatures of 273–523 K. It is established that the temperature and baric functions of thermal conductivity depend substantially on the degree of crystallization and the mosaicity of rock-forming compounds.

Borehole Temperature Logging and Characteristics of Subsurface Temperature in the Sichuan Basin

AbstractBased on temperature logging of 9 boreholes and thermal conductivity measurement of 297 samples in Sichuan basin, 9 terrestrial heat‐flows of high quality are reported and thermal conductivity stratigraphic column is suggested as well. The contour maps of geothermal gradient and heat flow are presented after combining with previously available data. The variation of thermal conductivity of sedimentary rocks in Sichuan basin is mainly controlled by the lithology, rather than the current burial depths. At present, the geothermal gradient ranges from 17.7 to 33.3 °C/km with an average value of 22.8 °C/km in the basin, and the heat flow ranges from 35.4 to 68.8 mW/m2 with an average of 53.2 mW/m2, which are typical of cratonic basins with medium‐low heat flows. In regard to the areal distribution of heat flows, they are relatively higher in the southwestern and central part and lower in the northern part of the Sichuan basin. The heat flows are apparently dependent upon tectonic settings of the basement.

On the determination of thermal conductivity of sedimentary rocks and the significance for basin temperature history

ABSTRACT Thermal history is an important control on generation of gas and oil in source rocks. Therefore, a realistic treatment of thermal conductivity in rocks is essential for hydrocarbon generation modelling in sedimentary basins. We present two different techniques for thermal conductivity estimation: (1) upscaling, based on arithmetic, geometrical and harmonic mean values of averaged conductivity from anticipated mineral composition; (2) infometric regression of thermal conductivity and anisotropy from measurements in rocks of known porosity and mineral composition. The accuracy of the estimate depends on the technique and we expect that the most precise way to predict thermal conductivity of sedimentary rocks leads to considerable improvement in the accuracy of basin modelling and hydrocarbon prospect identification. Various thermal conductivity models based on porosity and lithology are used for modelling temperature history for sensitivity purposes. Models where matrix conductivity is determined from measured thermal conductivities based on the mineralogy give considerably lower strata temperatures than models where geometrically averaged matrix conductivity are determined from mineralogy. This is assumed to be caused by deficiency in the mixing laws not taking into account the texture effect of the sediments. The temperature histories estimated by the three mixing law models – arithmetic, geometrical and harmonic mean – are considerably different. We show that the mixing law models give a temperature history and predicted hydrocarbon maturation far off even if we have temperature measurement at the same location. For example the harmonic model predicts the onset of the maturation of the Jurassic formations 20 million years later than does the model based on measured conductivities.

Using neural networks to predict thermal conductivity from geophysical well logs

SUMMARY We present a new approach, based on neural networks, to predict the thermal conductivity of sedimentary rocks from a set of geophysical well logs. This method is calibrated on Ocean Drilling Program (ODP) data, which provide several thousands of conductivity measurements combined with five geophysical well logs (sonic, density, neutron porosity, resistivity and gamma ray). This data set is used to train multilayer perceptrons (MLP) and to find an empirical relationship between well logs (MLP inputs) and thermal conductivity (MLP output). Validation tests suggest that MLP provide better estimates of thermal conductivity (within ∼15 per cent confidence level) than classical linear models, and still give satisfying results with sets of only four well logs if neutron porosity is included. In two ODP sites (863B and 1109D), MLP’s predictions are compared to conventional ‘mixing’ methods. Although this latter technique gives reliable results provided that rocks description is precise enough, the MLP is more straightforward, does not need any extra parameter and makes predictions in good agreement with the experimental trends. This method will be useful in the estimation of heat flow from data acquired in scientific and industrial boreholes.

Interrelations Between Thermal Conductivity and Other Physical Properties of Rocks: Experimental Data

— A new non-contact and non-destructive optical scanning instrument provided a large number of high-precision measurements of thermal conductivity tensor components in samples of sedimentary and impact rocks, as well as new insights into interrelations between thermal conductivity and other physical properties. More than 800 core samples (dry and fluid-saturated) of sedimentary rocks from different Russian oil-gas deposits and impact rocks from the well “Nordlingen 1973” drilled in the Ries impact structure (Germany) were studied using optical scanning technology. It was established that the thermal conductivity parallel to the stratification is more informative for petrophysical investigations than the thermal conductivity perpendicular to the layering. Different approaches were developed to estimate porosity, permeability, pore space geometry, and matrix thermal conductivity with a combination of thermal conductivity measurements in dry and fluid-saturated samples and mathematical modelling. These approaches allow prediction of the rock porosity and permeability and their spatial distribution along a well using thermal conductivity measurements performed with the optical scanning instrument directly applied to cores. Conditions and constraints for using Lichtenecker-Asaad's theoretical model for the estimation of porosity and thermal conductivity of sedimentary rocks were determined. A correlation between thermal conductivity and acoustic velocity, porosity, density, and electric resistivity of impact rocks was found for different rock types. New relationships between permeability, electrical and thermal conductivity found for sedimentary rocks are described.

Thermal conductivity of sedimentary rocks: uncertainties in measurement and modelling

Abstract In spite of the fact that thermal conductivity is a key factor in basin modelling, knowledge regarding the thermal conductivity of sedimentary rocks is scarce. In particular, hardly any information exists about claystones and mudstones which can make up 70–80% of a sedimentary basin. For the Upper Jurassic sediments of a section across the northern North Sea, basin modelling programs, using the geometric mean model, gave a deviation of 50°C for the present-day temperature, simply because the matrix conductivity was estimated by two different models. These uncertainties in the determination of thermal conductivities are partly due to problems in measuring, and partly to difficulties in modelling the thermal conductivity. Future work should concentrate on comparative studies and tests on all types of sedimentary rocks, directed towards improved measurement methods. This is of particular importance for clays, mudstones and shales, as measurements on these are associated with the greatest uncertainties. Standardized procedures that include detailed descriptions of sampling, sample preparation and measurement techniques are important to ensure that the thermal conductivity measured is unaffected by factors related to the measurement method. In thermal conductivity modelling it is our experience that the influence of sediment texture is underestimated by most models currently applied. Estimates based on the geometric mean model seem to be most reliable when the determination of matrix conductivity is restricted to the sediment type, mineralogy and texture of the sediments.

Thermal conductivity estimation in sedimentary basins

Thermal conductivity is a very important parameter for understanding the present and past thermal regimes of sedimentary basins. The bulk thermal conductivity of sedimentary rocks depends mainly on three factors: (1) their mineralogic and fluid composition, (2) their temperature, and (3) their structure. As shown by a series of measurements on small-scale isotropic samples, the effects of mineralogy and fluid content can be estimated quite accurately using the geometric mean model. However, this estimate does not take into account a possible anisotropy related to various structural effects such as small-scale heterogeneity or crystalline orientation. This anisotropy effect seems especially important in the case of clayey formations: various observations using equilibrium temperature logging, together with classical well logs, can be interpreted as indicating such an effect. The effect appears to depend on depth as would be expected from the major changes occurring in clays during their burial history: compaction, mineralogical transformation and structural reorganization.

Mineralogy, porosity and fluid control on thermal conductivity of sedimentary rocks : Brigaud, F; Vasseur, G Geophys JV98, N3, Sept 1989, P525–542

Mineralogy, porosity and fluid control on thermal conductivity of sedimentary rocks : Brigaud, F; Vasseur, G Geophys JV98, N3, Sept 1989, P525–542

Mineralogy, porosity and fluid control on thermal conductivity of sedimentary rocks

SUMMARY Estimates of thermal conductivity of the main non-clay and clay sedimentary minerals are presented, especially for the case of argillaceous ones. These estimates are averaged estimates based on the interpretation of laboratory conductivity, porosity and mineralogy measurements performed on small volumes (of the order of 100cm3) of water-saturated, moist and air-saturated samples. The sampling is representative of the main sedimentary rocks (sandstones, carbonates, evaporites and shales), and it is composed of samples considered as structurally isotropic material. First, we experimentally verify the first-order control of mineralogy, porosity and fluid content on bulk conductivity, and we demonstrate that such influences may be predicted accurately using a geometric mean model, as long as isotropic samples are used. Then we interpret the data using an inverse method, in order to estimate the average mineral conductivities of the main non-clay and clay minerals which give the best fit to the individual laboratory measurements through the geometric mean model. The analysis is based on measurements on 82 non-clay samples and 29 clay samples taken on oil-well cores or cuttings, on outcrops and on artificially recompacted samples. Estimates of average mineral conductivities returned by the inversion process, for the main non-clay minerals, are similar to generally admitted values: 7.7 W m-' K-' for quartz, 3.3 W m-I K-' for calcite, 5.3 W m-l K-' for dolomite and 6.3 W m-' K-' for anhydrite. Values obtained for the clay minerals are systematically lower than those obtained for non-clay ones, they are of the order of 1.9 W m-' K-' for illite and smectite, 2.6 W m-' K-' for kaolinite and 3.3 W m-l K-' for chlorite. These estimates, combined with porosity and mineralogy data, are used with the geometric mean model in order to verify its first-order validity for predicting thermal conductivity of small volumes of porous isotropic sedimentary rocks. The bulk conductivity is predicted with an accuracy of the order of f10 per cent for moist or water-saturated samples, and of the order of f20 per cent for air-saturated samples.

Experiment on estimating thermal conductivity of sedimentary rocks from oil well logging. Geological note : Vacquier, V; Mathieu, Y; Legendre, E; Blondin, E Bull Am Assoc Petrol GeolV72, N6, June 1988, P758–764

Experiment on estimating thermal conductivity of sedimentary rocks from oil well logging. Geological note : Vacquier, V; Mathieu, Y; Legendre, E; Blondin, E Bull Am Assoc Petrol GeolV72, N6, June 1988, P758–764

Experiment on Estimating Thermal Conductivity of Sedimentary Rocks from Oil Well Logging: GEOLOGIC NOTE

Oil well are an important source of geothermal data for studying regional tectonics, reconstructing the evolution of sedimentary basins, and theorizing petroleum generation, migration, and accumulation. A more satisfactory, less laborious method of estimating the thermal conductivity of sedimentary rocks is badly needed. The thermal conductivity of cores from two gas wells in France was measured and correlated with neutron porosity index, sonic interval travel time, bulk density, and gamma-ray logs. To obtain reasonable predictions of conductivity, data were segregated into lithologic groups such as sand-shale, carbonate-shale, and carbonate-sand. A set of regression coefficients in the equation that predicts the conductivity from the logs was calculated for each group. T e correlation coefficients between the measured and predicted conductivities of the core samples were 0.81, 0.75, and 0.61, respectively, for the three lithologic groups. The average percent difference between the measured and the calculated conductivities for the lithologies likely to be encountered in practice is 13.4%. We expect this figure can be reduced to 10% by enlarging the data base for calculating the regression coefficients. In the same basin or oil field, the relative errors from well to well probably will be 6% or smaller because the lithology will be nearly homogeneous.

Oil fields—A source of heat flow data

A method of calculating terrestrial heat flow from oil fields is evaluated by reviewing its application to twelve petroliferous basins, three of them in Brazil and nine in Indonesia. In each well, terrestrial heat flow was calculated by multiplying the temperature gradient (obtained by commonly used procedures) by the effective thermal conductivity. The latter was calculated from quick thermal conductivity measurements on core samples by a transient hot-wire method and from the lithologic log. Typically, in each basin the conductivity of about 300 core specimens was measured and logs of 50–150 wells were examined. In each formation penetrated by a well, the total thickness of each rock type such as shale, sandstone, etc., was determined from the composite lithologic log. The harmonic mean of the thermal conductivity of a formation in the well was computed from these thicknesses and the average values of the conductivities of the rock types measured on cores collected from that formation. The conductivity of the well is the harmonic mean of the conductivities of the formations. The average precision of the determination of formation conductivity can be expressed as the α 95 limits divided by the average value. For twelve basins this measure of precision for formation conductivities averages 3.6% and the average precision of the conductivity of a basin is 2.0%. On the same basis, the precision of the determination of the temperature gradient in a basin averages 4.7% and that of the heat flow 5.8%. Expressing the variability of the geothermal quantities in a basin determined at discrete locations as the standard deviation divided by the average value, we get for the mean variability in the twelve basins: 6.7% for conductivity, 15.0% for the gradient and 16.4% for the heat flow. The variability must come in part from natural causes. In general, at the present level of accuracy no correlation between anomalous heat flow and the occurrence of oil in a basin was found. Values of thermal conductivity of sedimentary rocks and their variation with depth are useful in calculating paleotemperatures. The magnitude of the mean heat flows of the basins is normal (63 mW/m 2) for the Jurassic basins of the stable continental margin of Brazil and somewhat elevated in the Tertiary basins of Indonesia, north of the Sunda trench subduction zone, the values decreasing from 130 mW/m 2 in Central Sumatra to 70 mW/m 2 in eastern Kalimantan. Future improvements in the procedures for determining conductivity might possibly bring out local anomalies in heat flow that might be caused by vertical seepage of water. This would support the hydraulic theory of accumulation of oil and gas.