7343

6848

كارشناسي ارشد

اكتشاف

اصفهان: دانشگاه صنعتي اصفهان، دانشكده معدن

1391

نه، 84ص.: مصور، جدول، نمودار

ص.ع. به فارسي و انگليسي

نادر فتحيان پور، احمدرضا مختاري

جمشيد مقدسي، مرتضي طايي

11/9/91

حسن طباطبائي، كيقباد شمس

1396/09/21

كتابنامه

معدن

مهندسي معدن

ID6848

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

A Comparison Study Between in situ oil reserve estimation using Multiple Point And Indicator Kriginig Approaches of non linear Geostatistical Methods Hamed Naderi h naderi@mi iut ac ir Date of Submission 2012 09 18 Department of Minnig Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language FarsiSupervisor Nader Fathianpour fathian@cc iut ac ir Ahmadreza Mokhtari ar mokhtari@cc iut ac irAbstract Accurate in situ oil reserve estimation is one of the main goals in developing reservoirs in petroleumupstream industry To achieve such goals the newly developed algorithms named under multiple pointgeostatistics MPG is becoming industry standard among petroleum geoscientists The main advantageof applying MPG is to integrate all possible and available data for simulating unsampled nodes thorough thereservoir spatial extent The main purpose of this study is to reconstruct the spatial distribution ofpetrophysical parameters from one of the well known Iranian south oil fields using modern geostatisticaltools specially MPG algorithms in order to maximize the correspondence of geological patterns existing inrelated hard and soft data in the estimation process Therefore at the first step using 32 wells taken fromone of oil reservoir of south IRAN oil fields the lithologic model was constructed through conventionalSequntial Indicator Simulation algorithm SISIM and Single Normal Equation Simulation of Multiple point algorithm SNESIM Comparison between reproduced lithologic models obtained from SNESIMrealizations are better reconstruction of actual well core data Next the Direct Sequential Simulationalgorithm DSSIM was used to recover the spatial distribution of petrophysical parameters The E typeestimation of DSSIM realizations were used as Training Images TI for Filter based Simulation algorithm FILTERSIM In the second step the petrophysical parameters were estimated through adopting twodifferent approaches 1 in the first approach the SNESIM E type model was inputed as soft data to theFILTERSIM algorithm and 2 in the second approach the SISIM E type Model was set as Soft data forFILTERSIM algorithm Also petrophysical parameters was simulated on the whole reservoir domainthrough Sequential Gaussian Simulation 2 point algorithm SGSIM To assess the performance of the above mentioned approach in simulating lithology and petrophysicalparameters the E Type estimation of simulated values out of 50 realizations were compared to that of actualcore sample data from preset aside 4 wells as validation data The results showed that the second approachoutperformed the first giving better correlation between the observed and predicted values This result isbelieved to be caused by imposing the geological constraints and in particular lithological distributionthroughout reservoir Finally the in situ oil reserve is estimated using petrophysical parameters estimated out of bothapproaches giving 8 29 and 6 44 billion barrels of oil reserve Based on better agreement between knowngeology and simulated SNESIM approach it is believed that the 8 29 billion figure is more accurate andcloser to the real condition of the studied reservoir Keywords Geostatistical Simulation Lithilogical model Petrophysical parameters SNESIM FILTERSIM E type estimation MPG Training Image DSSIM SGSIM SISIM

نادر فتحيان پور، احمدرضا مختاري

جمشيد مقدسي، مرتضي طايي

حسن طباطبائي، كيقباد شمس