Formation temperature estimation by inversion of borehole measurements

Geophysics ◽  
1988 ◽  
Vol 53 (7) ◽  
pp. 979-988 ◽  
Author(s):  
Song Cao ◽  
Ian Lerche ◽  
Christian Hermanrud
Keyword(s):  
Thermal Conductivity ◽  
Input Data ◽  
Synthetic Data ◽  
Thermal Stabilization ◽  
Efficiency Factor ◽  
Temperature Measurements ◽  
Quality Data ◽  
Formation Temperature ◽  
Drilling Mud ◽  
Specific Heats

We describe a new numerical method that uses inverse methods to model thermal stabilization of a borehole after drilling mud circulation has stopped. The following five geophysical parameters can be estimated from the method: (1) true formation temperature [Formula: see text] (2) mud temperature [Formula: see text] at the time the mud circulation stops; (3) thermal invasion distance (R) into the formation before the formation is at the true formation temperature[Formula: see text]; (4) formation thermal conductivity (K) perpendicular to the borehole; and (5) efficiency factor (F) for heating mud in the borehole after mud circulation has stopped. Crucial input data for the model are the temperature measurements with shut‐in time taken at a fixed depth, more than two measurements being required, and the mud temperature at the surface at the time circulation stops. Other input data include the radius of the borehole, and the densities and specific heats of the drilling mud and of the formation on which the temperature measurements are made. Applications of the new inverse procedure to both synthetic data and field data show that the true formation temperature in many cases can be estimated precisely (to within about 0.4 percent); that the mud temperature can be estimated with acceptable accuracy (5 percent or so); while the thermal conductivity (K), the thermal invasion distance (R), and the efficiency factor (F) can be roughly estimated, provided high‐quality data are available.

Estimation of formation temperature from bottom‐hole temperature measurements: COST ♯1 well, Norton Sound, Alaska

Geophysics ◽  
1988 ◽  
Vol 53 (12) ◽  
pp. 1619-1621 ◽  
Author(s):  
S. Cao ◽  
C. Hermanrud ◽  
I. Lerche
Keyword(s):  
Thermal Conductivity ◽  
Numerical Method ◽  
Time Scale ◽  
Efficiency Factor ◽  
Diffusion Time ◽  
Formation Temperature ◽  
Temperature Estimation ◽  
Bottom Hole ◽  
Free Parameters ◽  
Borehole Temperature

We recently developed a numerical method, the Formation Temperature Estimation (FTE) model, to determine formation temperatures by inversion of borehole temperature (BHT) measurements (Cao et al., 1988a). For more than two BHT measurements, the FTE model can estimate (1) true formation temperature [Formula: see text], (2) mud temperature [Formula: see text] at the time the mud circulation stops, (3) thermal invasion distance R into the formation before the formation is at the true formation temperature, (4) formation thermal conductivity K perpendicular to the borehole, and (5) efficiency factor F for mud heating in the borehole after mud circulation has stopped. The method optimizes three free parameters: τ (diffusion time‐scale), ε (scaling parameter related to the thermal invasion distance R), and [Formula: see text] (normalized efficiency factor for mud heating.

Bottom‐hole temperature stabilization with continued circulation of drilling mud

Geophysics ◽  
1982 ◽  
Vol 47 (12) ◽  
pp. 1716-1723 ◽  
Author(s):  
M. F. Middleton
Keyword(s):  
Thermal Stabilization ◽  
Well Log ◽  
Formation Temperature ◽  
Drilling Mud ◽  
Physical Conditions ◽  
Bottom Hole ◽  
Hydrocarbon Maturation ◽  
Log Interpretation

Determination of true formation temperature from measured bottom‐hole temperature (BHT) is important for well log interpretation and geothermal studies, especially with the current realization of the role of temperature in hydrocarbon maturation. A “bulk” thermal diffusivity of the borehole‐rock system of approximately [Formula: see text], initially suggested by Leblanc et al (1982), is confirmed by comparison with a two‐media borehole model. In general, time‐consecutive BHT measurements exhibit slower stabilization than those predicted by thermal conduction models. A simple model of thermal stabilization of a borehole with continued circulation after cessation of drilling is proposed. By modeling the thermal sink due to continued circulation of drilling mud as an exponentially decaying sink, thermal stabilization curves more consistent with observation are obtained. A good estimate of true formation temperature can be obtained by a curve‐matching technique where the observed BHT data are well behaved and the physical conditions in the borehole closely match the assumed model. However, it is virtually impossible in some cases to obtain a precise estimate of true formation temperature with BHT measurements from well log runs with current BHT stabilization models.

Determination of thermal properties and formation temperature from borehole thermal recovery data

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1835-1846 ◽  
Author(s):  
Tien‐Chang Lee ◽  
A. D. Duchkov ◽  
S. G. Morozov
Keyword(s):  
Synthetic Data ◽  
Thermal Recovery ◽  
Model Parameters ◽  
Formation Temperature ◽  
Drilling Mud ◽  
Borehole Data ◽  
Linear Inversion ◽  
Element Simulation ◽  
Model Formation ◽  
Parameter Values

Thermal recovery in boreholes cooled by circulation of drilling mud has been modeled for estimating formation temperature and thermal conductivity. Coupled with a finite‐element simulation of heat conduction, inverse modeling for the desired parameters starts with a genetic algorithm that feeds initial estimates of model parameters to an iterative quasi‐linear inversion scheme. In addition to using the rms misfit between the computed and observed borehole temperatures, the results are assessed by comparing or constraining the model formation temperature with a value obtained conventionally from an asymptotic temperature–time relation for a steady line source. The model conductivity is further evaluated for equality with a conductivity value, which is estimated through simulation of heat exchange between the formation and circulating mud. Test results on synthetic data and two sets of highly noisy borehole data from Lake Baikal in Russia indicate that the two equality criteria in temperature and conductivity are achievable. Multiple runs of GA‐IM are used to find mean parameter values and their uncertainties. The resultant model conductivity values are consistent with those measured in cores with a needle‐probe method.

scholarly journals Thermal Conductivity Modeling of Aqueous CuO Nanofluids by Adaptive Neuro-Fuzzy Inference System (ANFIS) Using Experimental Data

2017 ◽  
Vol 62 (2) ◽  
pp. 202 ◽  
Author(s):  
Mohammad Hemmat Esfe
Keyword(s):  
Thermal Conductivity ◽  
Input Data ◽  
Fuzzy Inference ◽  
Regression Coefficient ◽  
Good Precision ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Triangular Function ◽  
Best Responses ◽  
Conductivity Modeling

In this article, thermal conductivity data of aqueous nanofluids of CuO have been modeled through one of the instruments of empirical data modeling. The input data of 5 different volume fractions of nanofluid obtained in four temperatures through experiments have been considered as network inputs. Also, triangular function, due to providing the best responses, has been used as membership function in ANFIS structure. The modeling results show that fuzzy networks are able to model thermal conductivity results of nanofluids with good precision. Regression coefficient of this modeling has been 0.99.

scholarly journals Estimating Efforts and Success of Symmetry-Seeing Machines by Use of Synthetic Data

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 227
Author(s):  
Eckart Michaelsen ◽  
Stéphane Vujasinovic
Keyword(s):  
Extraction Method ◽  
Input Data ◽  
State Of The Art ◽  
Recognition Performance ◽  
Human Subjects ◽  
Synthetic Data ◽  
Ground Truth ◽  
Comparative Test ◽  
Real Imagery ◽  
Dominant Part

Representative input data are a necessary requirement for the assessment of machine-vision systems. For symmetry-seeing machines in particular, such imagery should provide symmetries as well as asymmetric clutter. Moreover, there must be reliable ground truth with the data. It should be possible to estimate the recognition performance and the computational efforts by providing different grades of difficulty and complexity. Recent competitions used real imagery labeled by human subjects with appropriate ground truth. The paper at hand proposes to use synthetic data instead. Such data contain symmetry, clutter, and nothing else. This is preferable because interference with other perceptive capabilities, such as object recognition, or prior knowledge, can be avoided. The data are given sparsely, i.e., as sets of primitive objects. However, images can be generated from them, so that the same data can also be fed into machines requiring dense input, such as multilayered perceptrons. Sparse representations are preferred, because the author’s own system requires such data, and in this way, any influence of the primitive extraction method is excluded. The presented format allows hierarchies of symmetries. This is important because hierarchy constitutes a natural and dominant part in symmetry-seeing. The paper reports some experiments using the author’s Gestalt algebra system as symmetry-seeing machine. Additionally included is a comparative test run with the state-of-the-art symmetry-seeing deep learning convolutional perceptron of the PSU. The computational efforts and recognition performance are assessed.

scholarly journals Evaluation of methods for gravity wave extraction from middle-atmospheric lidar temperature measurements

2015 ◽  
Vol 8 (11) ◽  
pp. 4645-4655 ◽  
Author(s):  
B. Ehard ◽  
B. Kaifler ◽  
N. Kaifler ◽  
M. Rapp
Keyword(s):  
Gravity Wave ◽  
Gravity Waves ◽  
Spectral Response ◽  
Synthetic Data ◽  
Temperature Measurements ◽  
Data Set ◽  
Wide Range ◽  
Lidar Measurements ◽  
Background Temperature ◽  
Wave Induced

Abstract. This study evaluates commonly used methods of extracting gravity-wave-induced temperature perturbations from lidar measurements. The spectral response of these methods is characterized with the help of a synthetic data set with known temperature perturbations added to a realistic background temperature profile. The simulations are carried out with the background temperature being either constant or varying in time to evaluate the sensitivity to temperature perturbations not caused by gravity waves. The different methods are applied to lidar measurements over New Zealand, and the performance of the algorithms is evaluated. We find that the Butterworth filter performs best if gravity waves over a wide range of periods are to be extracted from lidar temperature measurements. The running mean method gives good results if only gravity waves with short periods are to be analyzed.

scholarly journals Thermal Conductivity of High Performance Concrete in Wide Temperature and Moisture Ranges

10.14311/174 ◽  
2001 ◽  
Vol 41 (1) ◽  
Author(s):  
J. Toman ◽  
R. Černý
Keyword(s):  
Thermal Conductivity ◽  
High Performance ◽  
Room Temperature ◽  
High Performance Concrete ◽  
Measuring Method ◽  
Point Of View ◽  
Temperature Measurements ◽  
Temperature Fields ◽  
Measured Temperature ◽  
Hot Wire

The thermal conductivity of two types of high performance concrete was measured in the temperature range from 100 °C to 800 °C and in the moisture range from dry material to saturation water content. A transient measuring method based on analysis of the measured temperature fields was chosen for the high temperature measurements, and a commercial hot wire device was employed in room temperature measurements of the effect of moisture on thermal conductivity. The measured results reveal that both temperature and moisture exhibit significant effects on the values of thermal conductivity, and these effects are quite comparable from the point of view of the magnitude of the observed variations.

Inversion of bottom‐hole temperature data: The Pineview field, Utah‐Wyoming thrust belt

Geophysics ◽  
1988 ◽  
Vol 53 (5) ◽  
pp. 707-720 ◽  
Author(s):  
Dave Deming ◽  
David S. Chapman
Keyword(s):  
Thermal Conductivity ◽  
Temperature Field ◽  
Heat Flow ◽  
Random Noise ◽  
Synthetic Data ◽  
Element Model ◽  
Geothermal Gradient ◽  
Oil Field ◽  
Conductive Heat ◽  
Bottom Hole

The present day temperature field in a sedimentary basin is a constraint on the maturation of hydro‐carbons; this temperature field may be estimated by inverting corrected bottom‐hole temperature (BHT) data. Thirty‐two BHTs from the Pineview oil field are corrected for drilling disturbances by a Horner plot and inverted for the geothermal gradient in nine formations. Both least‐squares [Formula: see text] norm and uniform [Formula: see text] norm inversions are used; the [Formula: see text] norm is found to be more robust for the Pineview data. The inversion removes random error from the corrected BHT data by partitioning scatter between noise associated with the BHT measurement and correction processes and local variations in the geothermal gradient. Three‐hundred thermal‐conductivity and density measurements on drill cuttings are used, together with formation density logs, to estimate the in situ thermal conductivity of six of the nine formations. The thermal‐conductivity estimates are used in a finite‐element model to evaluate 2-D conductive heat refraction and, for a series of inversions of synthetic data, to assess the influence of systematic and random noise on the inversion results. A temperature‐anomaly map illustrates that a temperature field calculated by a forward application of the inversion results has less error than any single corrected BHT. Mean background heat flow at Pineview is found to be [Formula: see text] (±13 percent), but is locally higher [Formula: see text] due to heat refraction. The BHT inversion (1) is limited by systematic noise or model error, (2) achieves excellent resolution of a temperature field although resolution of individual formation gradients may be poor, and (3) generally cannot detect lateral variations in heat flow unless thermal‐conductivity structure is constrained.

Thermal Conductivity and Specific Heat Evaluation of Tissue Using Laser

2020 ◽  
Vol 1002 ◽  
pp. 303-310
Author(s):  
Sudad Issam Younis ◽  
Haqi I. Qatta ◽  
Mohammed Jalal Abdul Razzaq ◽  
Khalid S. Shibib
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Thermal Properties ◽  
Specific Heat ◽  
Iterative Procedure ◽  
Input Data ◽  
Maximum Error ◽  
Heat Transfer Analysis ◽  
Long Wavelength ◽  
Inverse Heat Transfer

In this work, an inverse heat transfer analysis was used to determine thermal conductivity and specific heat of tissue using special iteration. A laser with a long wavelength was utilized to impose heat to the tissue. The heat that induced in the sample causes an increase in the temperature of a tissue which is measured by a thermocouple. The readings were used together with that analytically obtained from the solution of the heat equation in an iterative procedure to obtain the thermal properties of tissue. By using this method, accurate thermal conductivity and specific heat of tissue could be obtained. It was found that the maximum error in output result and the error in input data were in the same order and that there was a linear relationship between output and input errors.

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