List of Articles
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Open Access Article
1 - Comparisons of intelligent systems and empirical equation results in permeability prediction: a case study in one of the southern Iranian carbonate reservoirs
Amir Mola jan Hoseyn MemarianPrediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The mo MorePrediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The most reliable data of permeability are taken from laboratory analysis of cores. Since coring is a costly and time consuming operation, researchers have tried to predict this parameter from other methods. Empirical equation is one of these methods, but results of these equations are not satisfied for all lithology and reservoirs. So far, several studies have been carried out for the estimation of reservoir parameters using intelligent systems. These studies indicate the successful role of these methods such as fuzzy logic, neuro-fuzzy and genetic algorithms for reservoir characterization. In this study, we try to compare results of these two methods (empirical equations and intelligent systems) for permeability prediction in a carbonate reservoir. For this purpose, petrophysical and core data of four well in a carbonate reservoir in the Southern Iran were used. At first, using empirical equations permeability was calculated for the test well; then using data of three wells, intelligent models were constructed. A forth well (test well) from the field was used to evaluate the models. The results show that fuzzy logic result (with R2= 0.88) is the best method for prediction of permeability in the studied reservoir. Also between empirical equations, result of Wyllie-Rose equation is better than others. Finally we offer the constructed fuzzy model (as a best predictor) for permeability prediction in the studied reservoir. Manuscript profile -
Open Access Article
2 - Comparisons of intelligent systems and empirical equation results in permeability prediction: a case study in one of the southern Iranian carbonate reservoirs
Prediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The mo MorePrediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The most reliable data of permeability are taken from laboratory analysis of cores. Since coring is a costly and time consuming operation, researchers have tried to predict this parameter from other methods. Empirical equation is one of these methods, but results of these equations are not satisfied for all lithology and reservoirs. So far, several studies have been carried out for the estimation of reservoir parameters using intelligent systems. These studies indicate the successful role of these methods such as fuzzy logic, neuro-fuzzy and genetic algorithms for reservoir characterization. In this study, we try to compare results of these two methods (empirical equations and intelligent systems) for permeability prediction in a carbonate reservoir. For this purpose, petrophysical and core data of four well in a carbonate reservoir in the Southern Iran were used. At first, using empirical equations permeability was calculated for the test well; then using data of three wells, intelligent models were constructed. A forth well (test well) from the field was used to evaluate the models. The results show that fuzzy logic result (with R2= 0.88) is the best method for prediction of permeability in the studied reservoir. Also between empirical equations, result of Wyllie-Rose equation is better than others. Finally we offer the constructed fuzzy model (as a best predictor) for permeability prediction in the studied reservoir. Manuscript profile -
Open Access Article
3 - Evaluation of hydrocarbon potential of Gadvan Formation in Binak, Gachsaran and Marun fileds by geochemical methods and thermal modeling
نغمه مرتاضیانInvestigation on hydrocarbon source rock potentiality of Gadvan Formation in Marun, Gachsaran and Binak oil fields using Rock-eval pyrolysis shows that Gadvan Formation in Marun and Gachsaran oil fields is an effective source rock and is capable of generating hydrocarbo MoreInvestigation on hydrocarbon source rock potentiality of Gadvan Formation in Marun, Gachsaran and Binak oil fields using Rock-eval pyrolysis shows that Gadvan Formation in Marun and Gachsaran oil fields is an effective source rock and is capable of generating hydrocarbon (oil and gas) , whereas the same formation in the Binak oil field has no hydrocarbon generation potential. The presence of organic matter in Gadvan Formation from Marun and Gachsaran oil fields suggests a mixture of kerogen type II/III and in Binak oil field kergen type III is dominant. Based on Tmax values derived from Rock-Eval pyrolysis, Gadvan Formation in Marun and Gachsaran oil fields is thermally mature and entered oil window stage but in Binak oil field this formation is immature and has not entered oil window yet. The results obtained from pyrolysis and virtinite reflectance measurements are in good agreement with thermal history modeling using PBM software program. Organic facies curve plotted for the Gadvan Formation indicates organic facies BC for Marun and Gachsaran oil fields and organic facies CD for Binak oil field suggesting marine persistent anoxic to oxidizing conditions prevailed Manuscript profile -
Open Access Article
4 - Comparisons of intelligent systems and empirical equation results in permeability prediction: a case study in one of the southern Iranian carbonate reservoirs
الهام عزیز ابادی فراهانی Kazemzadeh Ezatolah ELham Aziz Abadi FarahaniPrediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The mo MorePrediction of permeability that is one of the most important parameters in oil and gas reservoirs is probably the most challenging issue geologists, petrophysicists, and reservoir engineers have to deal with. This parameter control fluid flow in production stage. The most reliable data of permeability are taken from laboratory analysis of cores. Since coring is a costly and time consuming operation, researchers have tried to predict this parameter from other methods. Empirical equation is one of these methods, but results of these equations are not satisfied for all lithology and reservoirs. So far, several studies have been carried out for the estimation of reservoir parameters using intelligent systems. These studies indicate the successful role of these methods such as fuzzy logic, neuro-fuzzy and genetic algorithms for reservoir characterization. In this study, we try to compare results of these two methods (empirical equations and intelligent systems) for permeability prediction in a carbonate reservoir. For this purpose, petrophysical and core data of four well in a carbonate reservoir in the Southern Iran were used. At first, using empirical equations permeability was calculated for the test well; then using data of three wells, intelligent models were constructed. A forth well (test well) from the field was used to evaluate the models. The results show that fuzzy logic result (with R2= 0.88) is the best method for prediction of permeability in the studied reservoir. Also between empirical equations, result of Wyllie-Rose equation is better than others. Finally we offer the constructed fuzzy model (as a best predictor) for permeability prediction in the studied reservoir. Manuscript profile -
Open Access Article
5 - Sedimentological studies and Petrophysical interpretation: An approach to reservoir characterization of Sarvak formation in the Dalpari oil field
Abolhasan Ahan kar Abolhasan Ahan karThe Ilam and Sarvak Formations of Bangestan Grouop are the second important potential reservoirs after Asmari formation in the zagros basin. Integration of petrographical factors and Petrophysical parameters resulted in better understanding of reservoir qualities of t MoreThe Ilam and Sarvak Formations of Bangestan Grouop are the second important potential reservoirs after Asmari formation in the zagros basin. Integration of petrographical factors and Petrophysical parameters resulted in better understanding of reservoir qualities of these formations. In this study , 250 thin sections were collected and subjected to microfacies and petrography studies. As a result eight microfacies from three sedimentary environments were identified: Lagoonal (L1 ,L2 ,L3),Barrier (B1 ,B2) and Open marine (O1 ,O2 ,O3) and in three different of digenetic environments such as marine, meteoric and burial. This Study shows Sarvak Formation is being formed on Carbonate Rimmed shelf platform. Petrophysical interpretations by using of IP software shows Sarvak reservoir is divided into 3 zones (4, 5, 6). This study reveals that zone 4 contains the best reservoir quality in compare with the others by having (17m) oil column. porosity (more than 8%) and water saturation less than(14%) in this field. There is also decreasing of water saturation in Pay zone 4, but no significant changes has been observed through out zones 5&6. In zone 4, shale volume increases in zones 5&6 (Vsh more than 25%). Crossplot K-Th and K-Pe reveals that Chlorite_Montmorilonite are the dominant clay minerals in this reservoir. Manuscript profile -
Open Access Article
6 - Biomarker study of Asmari Reservoir oil in the oil fields situated in N.E. Dezful Embayment
Mahmud Memariani Ali reza Bani asadMasjid-e-Solyman, Haft kel, Par-e-Siah and Naft Safid are productive oil fields which are located in mountain front of NE Dezful Embayment. In this research, in order to Geochemical correlation and Petroleum Systems determination of Asmari oils, a few oil samples were s MoreMasjid-e-Solyman, Haft kel, Par-e-Siah and Naft Safid are productive oil fields which are located in mountain front of NE Dezful Embayment. In this research, in order to Geochemical correlation and Petroleum Systems determination of Asmari oils, a few oil samples were subjected to biomarker studies by GC and GC-MS techniques. Review of biomarkers fingerprints indicate two petroleum systems probably are active in studied oilfields. A major petroleum system that has controlled the hydrocarbon generation, migration and accumulation in all studied oilfields and a younger petroleum system, which has caused mixture of oils with another source in Masjed-Soleyman and Par-e-Siah oilfields, Biomarkers fingerprints, Steranes, Hopanes in addition to the main petroleum system. parameters, Pristane to Phytane ratios and also n- alkane's distributions among the studied oils, indicate that the Asamri oils were produced mainly from a marine and marine-carbonate source rock(s), which has been deposited in an anoxic conditions, with kerogen mainly of Type II with little contribution of terrestrial Kerogen (Type III) and oil samples has a maturity about early oil window without any severe biodegradation. 13C isotope values distribution, presence of Oleannane biomarker and slightly differences - mainly from lithological aspects and maturation levels of oils - of Masjid-Soleyman and Par-e-Siah Oils, reveal that, the mixed oils in these two reservoirs have been probably produced from two source rocks, a younger source rock namely Pabdeh Formation (Middle Eocene and Early Oligocene) with less importance of Kazhdumi Formation (Albian) which is the main source rock. Manuscript profile -
Open Access Article
7 - Application of Artificial Intelligence during History matching in One of fractured oil Reservoirs
ناصر اخلاقی Reyaz kharata Sedigheh MahdaviNowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with u MoreNowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with usual methods is too difficult and has either no single response or their response finding need more time and cost due to their nonlinear of the related problems. Because of much uncertainty on information which used in simulators, the results of these simulation models may have lot errors so production data (Pressure, Production Rate, Water Oil Ratio (WOR), Gas Oil Ratio (GOR) and etc.) during reservoir life is used to historical accommodation between simulator results and actual data. The main purpose of this study is investigation and feasibility study of a usual method of artificial intelligence in oil industry, which is based on the soft computing. In this study, Artificial Neural Network (ANN) is used to make a predicting model for bottom hole pressure and for one of the fractured oil reservoirs with the seven years history of production. Some unconditional parameters such as fracture porosity, horizontal and vertical fracture permeability, height of matrix and matrix-fracture dual porosity were applied as input data of the networks, and pressure was applied as an output in network making. Applied data in network making is achieved from the 50 runs with simulator. The conclusion of this study showed that predicting model of ANN with error less than 4% and reduces the time of process, has a good ability to history matching. Manuscript profile -
Open Access Article
8 - Geochemical investigation of gas condensate from South Pars field in Persian Gulf, Iran.
Mahmud Memariani Roya khezrlo Hadi kermanshahiIn this study, in order to determine the geochemical properties of condensates from South Pars Field, 4 samples from Kangan (Early Triassic) and Dalan (Middle- Late Permian) reservoir were subjected to geochemical analyses. Concentration and Identification of biomark MoreIn this study, in order to determine the geochemical properties of condensates from South Pars Field, 4 samples from Kangan (Early Triassic) and Dalan (Middle- Late Permian) reservoir were subjected to geochemical analyses. Concentration and Identification of biomarkers and their fingerprint were achieved by successive treatments of condensate samples. These analyses were; i) Mild evaporation of light hydrocarbons, ii) Mild oil topping of samples and iii) Urea adduction. Based on different biomarkers fingerprints, the accumulated condensates were generated from a carbonate-clastic source rock containing organic matters with mainly kerogen type II and little terrestrial inputs, with marine origin, which has been deposited in anoxic conditions. Maturity of condensate indicates, hydrocarbon generation from a source rock with late oil window and early gas generation stage. Further investigations revealed that, gas and condensates were originated from highly reach organic matter, Silurian shales (Sarchahan Formation) deposited in the Fars and offshore of Persian Gulf region. Manuscript profile -
Open Access Article
9 - Porosity estimation with data fusion approach (Bayesian theory) in wells of Azadegan oil field, Iran
عطیه مظاهری طرئی Hoseyn Memarian Behzad Tokhmchi Behzad MoshiriPorosity is one of the main variables in evaluating the characteristics of an oil field. Petrophysical data are normally used to determine these variables. Measurements obtained from well logs, containes some errors and uncertainty. This porosity is influenced by differ MorePorosity is one of the main variables in evaluating the characteristics of an oil field. Petrophysical data are normally used to determine these variables. Measurements obtained from well logs, containes some errors and uncertainty. This porosity is influenced by different factors, such as temperature, pressure, fluid type, clay content and the and amount of hydrocarbons. One of the best, and yet most practical ways to reduce the amount of uncertainty in porosity measurement is using various sources of data and data fusion techniques. Data fusion increase certainty and confidence and reduce risk and error in decision making. In this research, the porosity is estimated in 4 wells of Azadegan oil field, with data fusion method (Bayesian theory). To check the ability of generalization of the method, the porosity was also estimated in one other well of this field. A maximum of 7 input variables were used to estimate porosity in this new approach. The results showed that data fusion technique is more powerfull than traditional tecniques for porosity estimation. According to the results, this method has higher credibility than traditional techniques that show 0.7 to 0.8 regressions with log data but data fusion technique showed solidarity over 0.9 with log data. Manuscript profile