Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach

Bassam, A.; Santoyo, E.; Andaverde, Jorge; Hernández, J.A.; Espinoza-Ojeda, Orlando Miguel

doi:10.1016/j.cageo.2010.01.006

Cited by 78 publications

(22 citation statements)

References 39 publications

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“…The uses of these series allow us to compare the results obtained by LSRPM with a true SFT reported. These BHT-time series have been used to validate a wide variety of methods (see for instance: Andaverde et al, 2005;Santoyo et al, 2000;Bassam et al, 2010;Eppelbaum and Kutasov, 2011). Here Kutasov-Eppelbaum (KEM) Generalized version of the constant linear heat source Kutasov and Eppelbaum (2005) these temperature data were used for carrying out the numerical validation procedure of the LSRPM.…”

Section: Lsrpm Validation and Applicationmentioning

confidence: 99%

Improved method for estimating static formation temperatures in geothermal and petroleum wells

2015

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Section: Lsrpm Validation and Applicationmentioning

confidence: 99%

Improved method for estimating static formation temperatures in geothermal and petroleum wells

2015

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“…Then, the measured HT-hole temperatures can significantly differ from the formation equilibrium temperature and should be corrected [Kutasov and Eppelbaum, 2015]. Moreover, the determination of transient temperature under circulation and shut-in conditions is complicated by the occurrence of loss circulation [Fomin et al, 2003[Fomin et al, , 2005Bassam et al, 2010]. If loss circulation occurs, drilling fluid would be flown into surrounding formation so that it becomes hard to precisely define the temperature profile of a well.…”

Section: 1002/2016gc006548mentioning

confidence: 99%

Reconstruction of rocks petrophysical properties as input data for reservoir modeling

Cantucci

Montegrossi

Lucci

et al. 2016

Geochem Geophys Geosyst

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The worldwide increasing energy demand triggered studies focused on defining the underground energy potential even in areas previously discharged or neglected. Nowadays, geological gas storage (CO2 and/or CH4) and geothermal energy are considered strategic for low‐carbon energy development. A widespread and safe application of these technologies needs an accurate characterization of the underground, in terms of geology, hydrogeology, geochemistry, and geomechanics. However, during prefeasibility study‐stage, the limited number of available direct measurements of reservoirs, and the high costs of reopening closed deep wells must be taken into account. The aim of this work is to overcome these limits, proposing a new methodology to reconstruct vertical profiles, from surface to reservoir base, of: (i) thermal capacity, (ii) thermal conductivity, (iii) porosity, and (iv) permeability, through integration of well‐log information, petrographic observations on inland outcropping samples, and flow and heat transport modeling. As case study to test our procedure we selected a deep structure, located in the medium Tyrrhenian Sea (Italy). Obtained results are consistent with measured data, confirming the validity of the proposed model. Notwithstanding intrinsic limitations due to manual calibration of the model with measured data, this methodology represents an useful tool for reservoir and geochemical modelers that need to define petrophysical input data for underground modeling before the well reopening.

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“…Other applications of ANNs in geothermal studies have been presented by Alvarez del Castillo et al [16] for modelling two-phase flows in geothermal wells, by Bassan et al [17] for the estimation of the static formation temperatures in geothermal wells and by Arslan [18] for the optimization of a Kalina cycle power generation system from medium temperature geothermal resources.…”

Section: Introductionmentioning

confidence: 98%

Artificial neural networks for the generation of a conductivity map of the ground

et al. 2015

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a b s t r a c tIn this paper a neural network is used for the generation of a contour map of the ground conductivity in Cyprus. Archived data of thermal conductivity of ground recorded at 41 boreholes are used for training a multiple hidden layer neural network with feedforward architecture. The correlation coefficient obtained between the predicted and training data set is 0.9657, indicating an accurate mapping of the data. The validation of the network was performed using an unknown dataset. The correlation coefficient for the unknown cases was 0.9553. In order to broaden the database, the patterns used for the validation of the technique were embedded into the training data set and a new training of the network was performed. The correlation coefficient value for this case was equal to 0.9718. A 10 Â 10 km grid is then drawn over a detailed topographic map of Cyprus and the various input parameters were recorded for each grid point. This information was then supplied to the trained network and by doing so ground conductivity was predicted at each grid-point. This map will be a helpful tool for engineers in designing geothermal heat pump systems in Cyprus.

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Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach

Cited by 78 publications

References 39 publications

Improved method for estimating static formation temperatures in geothermal and petroleum wells

Improved method for estimating static formation temperatures in geothermal and petroleum wells

Reconstruction of rocks petrophysical properties as input data for reservoir modeling

Artificial neural networks for the generation of a conductivity map of the ground

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