E d i t o r i n c h i e f ZHURINOV MuratZhurinovich, doctor of chemistry, professor, academician of NAS RK, president of the National Academy of Sciences of the Republic of Kazakhstan, general director of JSC "Institute of fuel, catalysis and electrochemistry named after D.V. Sokolsky» (Almaty, Kazakhstan) H = 4 E d i t o r i a l b o a r d: ABSAMETOV Malis Kudysovich, (deputy editor-in-chief), doctor of geological and mineralogical sciences, professor, academician of NAS RK, director of the Akhmedsafin Institute of hydrogeology and hydrophysics (Almaty, Kazakhstan) H = 2 ZHOLTAEV Geroy Zholtaevich, (deputy editor-in-chief), doctor of geological and mineralogical sciences, professor, director of the institute of geological sciences named after K.I. Satpayev (Almaty, Kazakhstan) Н=2 SNOW Daniel, Ph.D, associate professor, director of the labotatory of water sciences, Nebraska University (Nebraska, USA) H = 32 Zeltman Reymar, Ph.D, head of research department in petrology and mineral deposits in the Earth sciences section of the museum of natural history (London, England) H = 37 PANFILOV Mikhail Borisovich, doctor of technical sciences, professor at the Nancy University (Nancy, France) Н=15 SHEN Ping, Ph.D, deputy director of the Committee for Mining geology of the China geological Society, Fellow of the American association of economic geologists (Beijing, China) H = 25 FISCHER Axel, Ph.D, associate professor, Dresden University of technology (Dresden, Germany) H = 6 KONTOROVICH Aleksey Emilievich, doctor of geological and mineralogical sciences, professor, academician of RAS, Trofimuk Institute of petroleum geology and geophysics SB RAS (Novosibirsk, Russia) H = 19 ABSADYKOV Bakhyt Narikbaevich, doctor of technical sciences, professor, corresponding member of NAS RK, Bekturov Institute of chemical sciences (Almaty, Kazakhstan) H = 5 AGABEKOV Vladimir Enokovich, doctor of chemistry, academician of NAS of Belarus, honorary director of the Institute of chemistry of new materials (Minsk, Belarus) H = 13 KATALIN Stephan, Ph.D, associate professor, Technical university (Dresden, Berlin) H = 20 SEITMURATOVA Eleonora Yusupovna, doctor of geological and mineralogical sciences, professor, corresponding member of NAS RK, head of the laboratory of the Institute of geological sciences named after K.I. Satpayev (Almaty, Kazakhstan) Н=11 SAGINTAYEV Zhanay, Ph.D, associate professor, Nazarbayev University (Nursultan, Kazakhstan) H = 11 FRATTINI Paolo, Ph.D, associate professor, university of Milano-Bicocca (Milan, Italy) H = 28 News of the National Academy of Sciences of the Republic of Kazakhstan. Series of geology and technology sciences.

Prediction of temperature distribution behavior during thermal recovery processes is necessary for engineering evaluation of field operations. Such a prediction can be used in the case of hot-and cold-water injection into a reservoir and also applies in some other thermal recovery processes, such as in-situ combustion and steamflooding.The mathematical formulas discussed involve the concept of a time-dependent overall heat-transfer coefficient. In the first portion of the paper, we discuss two new analytical solutions that describe the temperature distribution in linear and radial reservoirs in the case of hot-and cold-water injection. A comparison with published laboratory hot-water injection data demonstrates the validity of the solution for linear geometry. Since the new analytical model considers the heat conduction in addition to convection and heat loss, it describes the thermal behavior in a more general form than does Lauwerier's model. These two models are compared also.The application of the time-dependent overall heattransfer coefficient concept to the thermal behavior of the steam plateau portion of the in-situ combustion process is discussed in the second part of the paper. The result is fairly satisfactory.

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Suspended solids in the injection water cause impairment of water injectivity during waterflooding operations. Suspended solids affect reservoir properties and decrease the permeability of reservoir rocks causing an increase of injection pressure and a decrease in water injectivity. Removal of all suspended solids from injection water is an expensive and economically unfeasible process. To minimize the effects of suspended solids to the formation, it is necessary to determine an impairment mechanism of suspended solids on oil displacement and, therefore, optimize the water treatment process. In this paper, an analytical model that describes the relationship between injection water quality and impairment mechanisms on oil displacement is presented. A formation impairment was calculated, introducing the parameter called impairment ratio, which represents the ratio between suspended solids and pore size distribution of reservoir rock. Based on the impairment ratio, decreases in porosity and permeability were calculated with changes in capillary pressure, relative permeability, and displacement efficiency. The model was tested for three different types of injection water. Results indicated the presence of formation impairment even with the smallest particles. Suspended solids had the greatest influence on porosity and permeability impairment. The model could be used as input for reservoir modelling studies for monitoring and controlling displacement efficiency during waterflooding as well as for planning and modification of water treatment units.

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Reliable data on the properties of the porous medium are necessary for the correct description of the process of displacing hydrocarbons from the reservoirs and forecasting reservoir performance. The true permeability of the reservoir is one of the most important parameters which determination is time-consuming, costly and require skilled labor. The paper describes the methodology for determining the permeability of a porous medium, based on machine learning. The results of laboratory experiments, available in the database (terrigenous reservoirs with permeability in the range from 12 to 1132 md), are used to train the neural network, and then to predict the reservoir permeability. Comparison of the predicted and calculated permeability values showed a fairly good match between them with the determination coefficient of 0.92. The last task considered in this paper is to obtain an analytical expression describing a fluid flow in a porous medium using machine learning. This procedure enabled to obtain a resultant equation of fluid flow in a wide range of reservoir parameters and pressure gradients, which can be used in reservoir simulators.

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