The reservoir development plan is a complex process and should be analysed from different points of view. The process was analysed in terms of geology, petrophysics, modelling, production technology and economics. Therefore, different methods should be used for the project

Milke, A. A.

Tsivelev, K. V.

IOP Conference Series : Earth and Environmental Science

Electronic archive of Tomsk Polytechnic University

      Integral analysis of geological and field data for selection of oilfield development strategy A A Milke1 and K V Tsivelev2 National Research Tomsk Polytechnic University, 30 Lenin Avenue, Tomsk, 634050, Russia E-mail: 1aleks_milke@mail.ru, 2tsivelevkirill@mail.ru  Abstract. The reservoir development plan is a complex process and should be analysed from different points of view. The process was analysed in terms of geology, petrophysics, modelling, production technology and economics. Therefore, different methods should be used for the project.  1.  Introduction The oilfield A is located in Kaymysovky oil region next to Dvurechenskoe and Krapivenskoe oilfields. There are 9 exploration wells drilled in field A, five of which have core data. The base properties, such as porosity, permeability and water saturation are determined on the basis of core data. As the main material the following logs data are used: gamma ray log (GR), spontaneous potential log (SP), resistivity logs, neutron logs, potential logs, and so on. There were 270 samples taken from core of different production intervals. All data were analyzed separately for two production intervals: U11-2 and U13-4. The properties distribution was constructed on the basis of core data.  2.  Analysis of geological and field data First of all, the intervals, which could be potential sources of hydrocarbons, were determined on the basis of SP and GR logs for all wells [1]. The shaliness should be calculated for the effective porosity analysis. Shale normally contains radioactive bearing minerals and gamma ray log could be used for shale identification. Shaliness was analyzed using different models, the best result was achieved by the Larionov model. On the basis of this method the effective porosity curve was calculated for each well. The comparison of core porosity and log porosity for U11-2 and U13-4 was made separately. Then on the basis of the «base well» concept, the effective porosity curves were built for wells without core data. The «base well» was determined using the following criteria: wells lithology similarity and the lateral distance between the wells (well A2 was chosen). The permeability was measured with the use of nitrogen gas, so the core permeability data were corrected on the Klinkinberg effect, provided that there was a slippage effect of gas molecules along the grain surface. Then the dependency between porosity and permeability was calculated based on core data. The effective porosity curves were used for this reason. It was decided to use unique dependency for each development object. Despite the fact that the exponential type of correlation was obtained, the determination coefficient was high, due to log and core porosity similarity. The average log derived parameters for U11-2: φ=15%, k=7.7 mD, Sw=0.48; for U13-4: φ=17.2%, k=176 mD, PGON2016 IOP PublishingIOP Conf. Series: Earth and Environmental Science 43 (2016) 012057 doi:10.1088/1755-1315/43/1/012057Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distributionof this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.Published under licence by IOP Publishing Ltd 1      Sw=0.43. The average core derived parameters for U11-2: φ=13.2%, k=8.8 mD, Sw=0.56; for U13-4: φ=18%, k=162 mD, Sw=0.37.  One of the main objectives of property estimation is correct calculation of Stock Tank Oil Initially in Place (STOIIP). However, additional step should be made in order to eliminate the values which are of minor importance for reservoir field development. The criteria for this elimination include several steps: water saturation, porosity, permeability and shaliness cut-off criteria [2]. The cut-off criteria are estimated separately for each productive formation. The cut-off criteria are defined for U11-2: Sw=0.76, φ=0.116, k=0.88 mD, Vsh=0.24 and for U13-4: Sw=0.73, φ=0.1202, k=1.06 mD, Vsh=0.27. The interpreted parameters such as porosity, lithology logs and well picks of formation boundaries were used as an input data. The structures of geological model were constructed by offsetting Bazhen bottom structure.  The next step was to build up of 3D structural grid. Using modeling software, separate grids were constructed for U11-2 and U13-4. Geological model was build using cell size of 100 m by 100 m for U13-4 and U11-2. The size was chosen to optimize calculating time and obtain accurate model. The number of layers were selected so that the model fully describes vertical heterogeneity typical for regional depositional environment. The STOIIP (Stock Tank Oil Initially In Place) was U13-4=7.92 mln m3 and U13-4=34.81 mln m3. The lithological characteristics were distributed by means of indicator modeling. In terms of lateral trends variogram from similar rocks was used and vertical lithology was distributed by vertical proportional curves for each layer separately [3]. Variogram parameters for lateral distribution were: azimuth – assumed direction of sediment deposition, long section rank – 4000 m, cross-section rank – 2000 m. Vertical variogram parameters: 1 m for U11-2 and up to 3 m for U13-4. In terms of input data the pointwise interpretation of porosity log was used and then it was scaled into cells. Then the porosity parameter was distributed by kriging interpolation method. The same azimuth of variogram was used for lithology distribution. The estimation of hydrocarbons volume is based on statistic data correlation results of petrophysics and core analysis [4]. The STOIIP estimation is conducted by three primary methods: deterministic, stochastic or probabilistic, and geo modeling [5]. Using the deterministic method, the STOIIP for U11-2 is 7.49*106 m3 and for the U13-4 is 33.9*106 m3. Using the stochastic method, the STOIIP for U11-2 is 7.6*106 m3 and for the U13-4 is 34.2*106 m3. Using the geological model, the STOIIP estimation is 7.92*106 m3 and 34.81*106 m3, respectively.  The simulation model was based on geological model [6]. Upscaling process was implemented to reduce the number of cells and optimize calculating timing. The lateral dimension of a cell remained unchanged; however, vertical cell thickness was scaled up from 0.8 m to 2 m. The reservoir properties were scaled up to a coarser cell. The STOIIP of geological and simulation model were 42.73 mln m3 and 41.39 mln m3, respectively, for both layers. The dynamic processes were defined by single relative permeability result provided with the core data [7].  The simulation model was produced by Tempest «Roxar» software. Static parameters, such as geological model porosity, permeability, and saturation were used as initial parameters and also PVT properties (Pressure, Volume, Temperature) were used being approximated by specific correlations [8].  Economic analysis of the project was based on evaluation of several potential scenarios of field development. The main variation parameters were drilling pattern and distances between wells, rate of fluid extraction and water injection, changing pattern orientation, hydraulic fracturing, horizontal wells, separate and unified development of both production intervals [9]. All these scenarios were evaluated by the economic model and the most profitable scenario was 5-point pattern with 500*500 in low permeability-thickness product (kh) zones and 1000*1000 in high kh zones.  The choice of formation pressure maintenance was defined by type of formation, the size of the formation and its oil-bearing zone, the presence of gas cap, formation oil viscosity, type of reservoir rock and its permeability, the level of formation heterogeneity, the presence of tectonic failure, and PGON2016 IOP PublishingIOP Conf. Series: Earth and Environmental Science 43 (2016) 012057 doi:10.1088/1755-1315/43/1/0120572      others. The presence of two objects of development with similar properties resulted in evaluating two distinct variants of development: joint and separate.  Two zones of different kh values were defined during reservoir properties evaluation. The kh varies from 1000 to 5000 mD*m at north-west block, whereas at the south-east block the kh values are less than 1000 mD*m. The largest oil recovery index of 51.1% was demonstrated by 5-spot with the 500 meter spacing between production and injection wells. However, comparing economic interpretations, it was shown that, according to kh maps, the most efficient approach was to develop two production zones separately: 500*500 m between production and injection wells within zones of low kh and less concentrated pattern of 1000*1000 m in zones of higher kh values. The case was considered to be the most economically viable with recovery factor 50.6%, which was less than previously mentioned pattern (with recovery factor 51.1%) by 0.5%.  Also, the variant with natural depletion mechanism was simulated. Initially, the case had shown recovery factor 2%, whereas after simulation modeling it revealed recovery factor 9%, which indicated that the aquifer was not included in calculations. As one of the potential pattern of development, horizontal well pattern was simulated. The main challenge in this case was to justify the bottomhole pressure on production wells [10].  It is assumed that the construction is started in 2015 and it is to be continued to first quarter of 2018 when the production commences. The estimate economic life of the field is 8 years with a payback occurring between year 3 and 4. The total production of oil recovered during the project life is 21.2 mln tons of oil and 712.1 mln m3 of gas (used for power generation). The economic oil recovery index (0.45) achieves in 8 years and technical oil recovery index (0.50) achieves in 25 years. Depreciation of assets was performed using Declining Balance method with 25% rate. There were additional funds accounted for miscellaneous (5% from total Capital Expenditures (CAPEX), excluding drilling cost) and for environmental reclamation (5% from total CAPEX). Also, to account for uncertainties, the contingency fund of 25% from total CAPEX was established. There was some exploration cost included in Well development section of capital expenditure.  The revenue will be generated from sales of oil. Contained gas volumes are not in sufficient marketable quantities. Taxation represents 78%, a significant portion of total expenses on the project. Tax model consists of various federal and regional, labor taxes and royalties.  Sensitivity analysis was carried out on the following parameters by changing one parameter at a time between ±30% at 10% intervals while maintaining the rest of the following parameters constant. The Net Present Value (NPV) of field A is the most sensitive to the taxes and exchange rate and less sensitive to Operating Expenditures (OPEX). The Internal Rate of Return (IRR) period of project was the most sensitive to taxes and CAPEX and, secondly, to oil price and less sensitive to OPEX.  The project will produce marketable Urals brand oil which will be sold to local transfer oil pipeline located 30 km from field A. The oil will be treated and analyzed on site before releasing for sale.  3.  Conclusions As a result of the study the development model was constructed and the final variant was chosen. The best variant has no technological limits and the NPV is much higher than in other variants. References [1] Merkulov V P 2008 Production well logging. – Tomsk Polytechnic University, 139 [2] Lalanne B, Massonnat G 2004 Impacts of Petrophysical Cut-Offs in Reservoir Models//SPE Annual Technical Conference and Exhibition. – Houston, Texas [3] Guirillot D 2002 Integration of geological models and reservoir simulators//17th World Petroleum Congress. – Rio de Janeiro, Brazil [4] Ross J G 1997 The Philosophy of Reserve Estimation//SPE Hydrocarbon Economics and Evaluation Symposium. – Dallas, Texas PGON2016 IOP PublishingIOP Conf. Series: Earth and Environmental Science 43 (2016) 012057 doi:10.1088/1755-1315/43/1/0120573      [5] Denney D 1997 Integrated Deterministic/Probabilistic Reserve Estimation//SPE Annual Technical Conference and Exhibition. – San Antonio, Texas [6] Gregg J M, Rey T L and Wang J J 2009 Constructing A Well Engineered Simulation Model//PSIG (Pipeline Simulation Interest Group) Annual Meeting. – Galveston, Texas [7] Lomeland F, Ebeltoft E and Hammervold Thomas W 2005 A New Versatile Relative Permeability Correlation//Reviewed Proceedings of the 2005 International Symposium of the SCA (Securities and Commodities Authority). – Abu Dhabi, United Arab Emirates [8] Hemmati M and Kharrat R 2007 A Correlation Approach for Prediction of Crude Oil PVT Properties//SPE Middle East Oil and Gas Show and Conference. – Manama, Bahrain [9] Litvak M L, Gane B R and Angert P F 2007 Field Development Optimization Technology//SPE Reservoir Simulation Symposium. – Houston, Texas [10] Yildiz T 2005 Multilateral Horizontal Well Productivity//SPE EAGE (European Association of Geoscientists and Engineers) Annual Conference. – Madrid, Spain  PGON2016 IOP PublishingIOP Conf. Series: Earth and Environmental Science 43 (2016) 012057 doi:10.1088/1755-1315/43/1/0120574

Integral analysis of geological and field data for selection of oilfield development strategy

  СЕКЦИЯ 18. ГЕОЛОГИЯ, ГОРНОЕ И НЕФТЕГАЗОВОЕ ДЕЛО  (ДОКЛАДЫ НА АНГЛИЙСКОМ И НЕМЕЦКОМ ЯЗЫКАХ) 1189  1  1  1  1  1  1  1  1   6. Novikov A.P. et al. Colloid transport of plutonium in the far-f ield of the Mayak Production Association,  Russia // Science. – 2006. – Vol. 314. P. 638-641. 7. Novikov A.P. Migration and concentration of artif icial radionuclides in environmental objects // Geochemistry Int. – 2010. – Vol. 48 (13). – P. 1263-1387. 8. Novikov A.P., Kalmykov S.N., Tkachev V.V. The forms of existence and migration of actinides in the environment // Ros. Chem. J. - 2005. - T. XLIX. - №2. - P. 119-126. (In Russian). 9. Priest N. et al. Investigation of Radiological Situation in the Sarzhal Region of the Semipalatinsk Nuclear Test Site. – Final report, 2003. – NATO SfP-9760046(99). 10.  Salbu B., Skiperrud L. Speciation of radionuclides in the environment // J. Environ. Radioact. – 2009. – Vol. 100(4). – P. 281-282. 11.  Sanitary regulations "Sanitary-epidemiological requirements to ensure radiation safety"//approved by Decree of the Government of the Republic ofKazakhstan. – № 261 from 27.03.2015. (In Russian). 12.  Vintró L. et al. Americium, plutonium and uranium contamination and speciation in w ell waters, streams and atomic lakes in the Sarzhal region of the Semipalatinsk Nuclear Test Site, Kazakhstan // J. Environ. Radioact. – 2009. – Vol. 100. – № 4. – P. 308–314.  INTEGRAL ANALYSIS OF GEOLOGICAL AND FIELD DATA FOR SELECTION OF OILFIELD DEVELOPMENT STRATEGY K.V. Tsivelev, A.A. Milke Scientific advisors associate professor O.S. Chernova National Research Tomsk Polytechnic University, Tomsk, Russia  The purpose of the research is selection of optimum development system and economic calculations for studied oilfield A. The oilfield A is located in the Kaymysovky oil region next to Dvurechenskoe and Krapivenskoe  oilfields. There are 9 exploration wells drilled in field A, five of which have core data. The base properties, such as porosity, permeability and water saturation, are determined on the bas is of core data. As the main material the following logs data are used: gamma ray log (GR), spontaneous potential log (SP), resistivity logs, neutron logs, potential logs, and so on. There were 270 samples taken from core of different production intervals. All data were analyzed separately for two 1- 2  3-4 production intervals: U1 and U1 . The properties distribution was constructed on the basis of core data. First of all, the intervals, which could be potential sources of hydrocarbons, were determined on the basis of SP and GR logs for all wells [3]. The shaliness should be calculated for the effective porosity calculations. Shale normally contains radioactive bearing minerals and gamma ray log could be used for shale identification. Shaliness was analyzed using different models, the best result was achieved by the Larionov model. On the basis of this method the effective porosity curve was calculated for each well. The comparison of core porosity and log porosity for U 1-2 and U 3-4 was made separately. Then on the basis of the «base well» concept, the effective porosity curves were built for wells without core data. The «base well» was determined using the following criteria: wells lithology similarity and the lateral distance between the wells (well A2 was chosen). The permeability was measured with the use of nitrogen gas, so the core permeability data were corrected on  the Klinkinberg effect, provided that there was a slippage effect of gas molecules along the grain surface. Then the relationship between porosity and permeability was calculated based on core data. The effective porosity curves were  used for this reason. It was decided to use unique relationship for each development object. Despite the fact that the exponential type of correlation was obtained, the determination coefficient was high. This is due to log and core porosity similarity. The average log derived parameters for U   1-2 : φ=15%, k=7.7 mD, Sw=0.48; for  U  3-4 : φ=17.2%, k=176 mD, Sw=0.43. The average core derived parameters for U   1-2 Sw=0.37. : φ=13.2%, k=8.8 mD, Sw=0.56; for U   3-4 : φ=18%, k=162  mD, One of the main objectives of property estimation is correct calculation of Stock Tank Oil Initially in Place (STOIIP). However, additional step should be made in order to eliminate the values which are of minor importance for reservoir field development. The criteria for this elimination include several steps: water saturation, porosity,  permeability and shaliness cut-off criteria. The cut-off criteria are estimated separately for each productive formation. The cut-off criteria are defined for  U  1-2 k=1.06 mD, Vsh=0.27. : Sw=0.76, φ=0.116, k=0.88 mD, Vsh=0.24 and for  U   3-4 : Sw=0.73, φ=0.1202  ,  The interpreted parameters such as porosity, lithology logs and well picks of formation boundaries were used as an input data. The structures of geological model were constructed by offsetting Bazhen bottom structure. The next step was to build up of 3D structural grid. Using modeling software, separate grids   were constructed 1- 2  3-4 3-4 1-2 for U1 and U1 .  Geological  model  was  build using cell  size  of 100 m by 100 m for  U1 and U1 .  The  size was chosen to optimize calculating time as well as obtain accurate model. The number of layers were selected so that the model fully describes vertical heterogeneity typical for regional depositional environment. The STOIIP (Stock Tank   Oil 1-2 3  3-4 3 Initially In Place), which estimated by geological model, was U1 =7.92 mln m  and U1  =34.81 mln m . The lithology was distributed by means of indicator modeling. In terms of lateral trends variogram from similar surrounding deposit was used and vertical lithology was distributed by vertical proportional curves for each layer separately. Variogram parameters for lateral distribution were: azimuth – assumed direction of sediment deposition, long 1- 2  3-4 section rank – 4000 m, cross-section rank – 2000 m. Vertical variogram parameters: 1 m for U1      and up to 3 m for U1      . In terms of input data the pointwise interpretation of porosity log was used and then it was scaled into cells. Then the porosity parameter was distributed by  kriging interpolation method. The same azimuth of variogram was used for lithology distribution.   1190 ПРОБЛЕМЫ ГЕОЛОГИИ И ОСВОЕНИЯ НЕДР  1  1  1  1  The estimation of hydrocarbons volume is based on statistic data correlation results of petrophysics and core analysis. The STOIIP estimation is conducted by three primary methods: deterministic, stochastic or probabilistic, and geo modeling [1]. Using the deterministic method, the STOIIP for U  1-2 is 7.49*106  m3 and for the U 3-4 is 33.9*106 m3. Using the stochastic method the STOIIP for U   1-2 is 7.6*106 m3  and for  the U 3-4 is 34.2*106 m3. Using the geological model, the STOIIP estimation is 7.92*106 m3 and 34.81*106 m3, respectively. The simulation model was based on geological model. Upscaling process was implemented to reduce the number of cells and optimize calculating timing. The lateral dimension of a cell remained unchanged; however vertical cell thickness was scaled up from 0.8 m to 2 m. The reservoir properties were scaled up to a coarser cell. The STOIIP of geological and simulation model were 42.73 mln m3 and 41.39 mln m3,  respectively, for both layers. The dynamic processes were defined by single relative permeability result provided with the core data [2]. Simulation modeling was produced by Tempest «Roxar» software. Static parameters, such as geological model porosity, permeability, and saturation were used as initial parameters and also PVT properties (Pressure, Volume, Temperature) were used being approximated by specific correlations. Economics of the project was based on evaluation of several potential scenarios of field development. The main variation parameters were drilling pattern and distances between wells, rate of fluid extraction and water injection, changing pattern orientation, hydraulic fracturing, horizontal wells, separate and unified development of both production intervals. All these scenarios were evaluated by the economic model and the most profitable scenario was 5-point pattern with 500*500 in low permeability-thickness product (kh) zones and 1000*1000 in high kh zones. 1- 2  3-4 There were two development objects identified at field A,  U1 and U1 , which were of different thicknesses. Therefore, there were different volumes of geological resources. The reservoir pressure (262.1 atm) and bubble point pressure (60 atm) were the same for both formations. The choice of formation pressure maintenance was defined by type of formation, the size of the formation and its oil-bearing zone, the presence of gas cap, formation oil viscosity, type of reservoir rock and its permeability, the level of formation heterogeneity, the presence of tectonic failure, and others. The presence of two objects of development with similar properties resulted in evaluating two distinct variants of development: joint and separate. Two zones of different kh values were defined during reservoir properties evaluation. The kh varies from 1000 to 5000 mD*m at north-west block, whereas at the south-east block the kh is not exceeding 1000 mD*m. The largest oil recovery index of 51.1% was demonstrated by 5-spot with the 500 meter spacing between production and injection wells. However, comparing economic interpretations, it was shown that, according to kh maps, the most efficient approach was to develop two production zones separately: 500*500 between production and injection wells within zones of low kh and less concentrated pattern of 1000*1000 m in zones of higher kh values. The case was considered to be the most economically viable with recovery factor 50.6%, which was less than previously ment ioned pattern (with recovery factor 51.1%) by only  0.5%. Also, the variant with natural depletion mechanism was simulated. Initially, the case had shown recovery factor 2%, whereas after simulation modeling it revealed recovery factor 9%, which indicated that the aquifer was not included in calculations. As one of the potential pattern of development, horizontal well pattern was simulated.  The  main challenge in this case was to justify the bottomhole pressure on production wells. Basic economic inputs were obtained from “Social economic development forecast of the Russian Federation for 2015”, tax law and custom law. The construction is assumed to start in late 2015 and it is to be continued to first quarter of 2018 when the production commences. The estimate economic life of the field is 8 years with a payback occurring between year 3 and 4. The total production of oil recovered during the project life is 21.2 mln tons of oil and 712.1 mln m3 of gas (used for power generation). The economic oil recovery index is 0.45 achieves in 8 years and technical oil recovery index is 0.50 achieves in 25 years. Depreciation of assets was performed using Declining Balance method with 25% rate. There were additional funds accounted for miscellaneous (5% from total Capital Expenditures (CAPEX), excluding drilling cost) and for environmental reclamation (5% from total CAPEX). Also, to account for uncertainties, the contingency fund of 25%  from total CAPEX was established. There was some exploration cost included in Well development section of capital expenditure.  The revenue will be generated from sales of oil. Contained gas volumes are not in sufficient marketable quantities. Taxation represents 78%, a significant portion of total expenses on the project. Tax model consists  of various federal and regional, labor taxes and royalties. Sensitivity analysis was carried out on the following parameters by changing one parameter at a time   between ±30% at 10% intervals while maintaining the rest of the following parameters constant. The Net Present Value (NPV) of field A is the most sensitive to the taxes and exchange rate and less sensitive to Operating Expenditures (OPEX). The Internal Rate of Return (IRR) period of project was the most sensitive to taxes and CAPEX and, secondly, to oil price and less sensitive to OPEX. The project will produce marketable Urals brand oil which will be sold to local transfer oil pipeline located 30 km from field A. The oil will be treated and analyzed on site before releasing for sale.   References  1. Gross J.G. The Philosophy of Reserve Estimation//SPE Hydrocarbon Economics and Evaluation Symposium.  – Dallas, Texas, 1997.   1191 СЕКЦИЯ 18. ГЕОЛОГИЯ, ГОРНОЕ И НЕФТЕГАЗОВОЕ ДЕЛО  (ДОКЛАДЫ НА АНГЛИЙСКОМ И НЕМЕЦКОМ ЯЗЫКАХ)   2. Lomeland F., Ebeltoft E. and Hammervold Thomas W. A New  Versatile Relative Permeability Correlation // Review ed Proceedings of the 2005 International Symposium of the SCA (Securities and Commodities Authority). – Abu Dhabi, United Arab Emirates, 2005. 3. Merkulov V.P. Production w ell logging. – Tomsk Polytechnic University, 2008. – p. 139.   MODERN TECHNOLOGIES IN SHALE OIL EXTRACTION V.V. Vasiliev, Yu.Yu. Mikhailina  Scientific advisor associate professor G.P. Pozdeeva National Research Tomsk Polytechnic University, Tomsk, Russia  The relevance of the research is that the total volume reserves of oil shale far exceed the reserves of conventional oil. The object of research presents consideration of future production and use of shale oil. The subject of the research is scientific articles, experts' forecasts in the oil and gas industry. The research implies accomplishment of the objective which to study the development prospects of shale oil extraction while performing the following set of tasks: • To conduct a literature review for investigated theme; • To investigate the positive and negative factors of shale oil extraction; • To compare the different methods in terms of their resource intensity, efficiency and borders of applicability. The theoretical basis of our research is based on the general scientific methods of research: information,  logic. The use of these methods of research allowed showing the way and prospects of shale oil extraction. Oil shale is a sedimentary rock that is also a fossil fuel. Shale oil extraction is commonly conducted above ground (ex situ processing) by mining the oil shale and then treating it in processing facilities. Other contemporary methods conduct the processing underground (on-site or in situ processing) by using heat and extracting the oil by means of oil wells.  The earliest description of the process dates to the 10th century. There are three major methods of in shale oil extraction.  1 . Horizontal Drilling (Fig.) is the key compared to vertical methods; horizontal drilling puts the well casing in contact with a much greater percentage of the oil reservoir’s surface area, making recovery from shale faster and  more efficient. In modern horizontal drilling, producers drill straight down until they hit shale, and then use directional drilling techniques to bend the drill string in parallel to the shale layer. Downhole motors and advanced measurement using drilling sensors at selected intervals along the drill string help the crew above ground steer the drill and make real-time adjustments based on directional data [2].    . Fig. Horizontal Drilling  2 . Multistage hydraulic "fracking" increases the speed and precision of the fracturing process, increasing cost- effectiveness and production output. Area residents and environmental groups have raised concerns about possible effects of this method on groundwater and air quality, but environmental impact studies suggest that horizontal drilling effects are comparable to conventional techniques [1].  3 . Situ technologies boil up oil shale underground by injecting hot fluids into the rock formation, or by using linear or planar heating sources followed by thermal conduction and convection to distribute heat through the target zone. Shale oil is then regained through vertical wells drilled into the formation. These technologies are potentially able to extract more shale oil from a given area of land than conventional ex situ processing technologies, as the wells can reach 

Integral analysis of geological and field data for selection of oilfield development strategy

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