M Javid; H Memarian; S.M Mazhari; R Zorofi; B Tokhmechi; F Khoshbakht
Abstract
In the oilreservoirsof the ZagrosBasin, fractures play a major role in hydrocarbon migration and production. Borehole image log is a powerful tool to study and identify fractures around the wells. These logs provide critical information about orientation, depth and type of natural fractures. Since thereis ...
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In the oilreservoirsof the ZagrosBasin, fractures play a major role in hydrocarbon migration and production. Borehole image log is a powerful tool to study and identify fractures around the wells. These logs provide critical information about orientation, depth and type of natural fractures. Since thereis noaccuratealgorithmfor automaticidentification of fracture parametersonimage logs of the carbonatereservoirsin Iran, interpretation of theselogsisoftendone manually. This process may become erroneous if the interpreter is not sufficiently experienced. Aimed at automatic detecting of fractures in image logs, this paper presents a new implemented method, which is based upon image processingandoptimization techniques,as well as Artificial Bee Colony Algorithm. According to this approach, points related to fractures arefirst extracted from images using classification methods. Then, the Artificial Bee Colony Algorithmis used to determine the number, depth, dip and dip directionof fractureson extracted points. The proposed method is performed on FMS image log ofonewell located in an oilfield in southernIran. Results areshownindensity log, rose diagramandstereogramfor the identified fractures, and the obtained resultsshow efficiency of the proposedmethod.
M Shademan; B Tokhmechi
Abstract
Nowadays GIS techniques are used as a conventional tool for integrating geographic information datasets. In these methods, integration is done according to quality and quantity of datasets and using appropriate weighting approaches. Finally, with Classification methods like Geometric Distances, Mineral ...
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Nowadays GIS techniques are used as a conventional tool for integrating geographic information datasets. In these methods, integration is done according to quality and quantity of datasets and using appropriate weighting approaches. Finally, with Classification methods like Geometric Distances, Mineral Potential Maps (MPM) is produced. By increasing application in data processing, clustering methods classify samples into groups by similarity between them. In this paper, K_ Means and Fuzzy K_ Means clustering methods are discussed for mapping potential zones of Gold mineralization, then the results are compared with GIS method, Index Overlaying, for the Barika area in 1:100,000 Alut Sheet in South of Azerbaijan. In the Barika area, information of drilling points aren’t available, so it’s not possible to determine number of classes and boundaries of each class for final score map, but in clustering methods, the optimum number of class for output map is done automatically and is tested for the Barika Anomaly data. The results show that clustering methods need no weighting and it’s possible to use it with low information than GIS_ based method. The results also show that both of approaches, clustering methods and Index overlaying, display almost an equal area for the most potential zone, however clustering methods need low information for analyzing, but in Index overlaying, it is needed to have more information for weighting and determining threshold for classification of final scores.
H Nikoogoftar; A Bahroodi; B Tokhmchi; G.H Norouzi; B Mehrgini
Abstract
Identifying and interpreting subsurface heterogeneities, especially Litofacies, plays definitely an important role in assessing and managing hydrocarbon resources. Variety of methods have been developed in order to model discrete features of hydrocarbon reservoirs, as Litofacies, which the majority of ...
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Identifying and interpreting subsurface heterogeneities, especially Litofacies, plays definitely an important role in assessing and managing hydrocarbon resources. Variety of methods have been developed in order to model discrete features of hydrocarbon reservoirs, as Litofacies, which the majority of them have focused on intra-well modeling, and are not applicable for 2D or 3D modeling between oil wells. Furthermore, developing a novel methodology to bring a more factual reservoir facies has always been a matter of attraction, and is effective in lowering risk of decision making in different exploratory stages. These days, Markov Chains is used as a powerful tool for facies modeling. This method is based on conditional probabilistic and providing transitional matrix of states. This study is carried out on an oil field, South-West Iran; where the Asmari Formation is its main reservoir. Here, interval of the Asmari Formation and its cap rock in a 12 kilometers long section, 110 meters width, is classified into three main parts, by the means of Markov Chains modeling. The best result of modeling was obtained with nine wells and four seismic horizons that brought 87% accuracy in average.
B Tokhmechi; H Azizi; H Memarian
Abstract
Estimation of rock type, porosity and saturation are the main applications of petrophysical logs. Several equations are presented for mentioned estimations, and deficiencies of these equations are widely investigated. In this paper, general deficiency of well logs processing methods is discussed. In ...
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Estimation of rock type, porosity and saturation are the main applications of petrophysical logs. Several equations are presented for mentioned estimations, and deficiencies of these equations are widely investigated. In this paper, general deficiency of well logs processing methods is discussed. In general, because of smoothing trait of estimators, variability of estimated data is less than raw data. Since rock type, porosity and saturation are estimated from various well logs, it is anticipated that they have less variability in comparison with raw well logs. Therefore, it seems that energy (equal to information) of Fourier transform of estimated well logs in low frequency bands have to be more than similar energy of raw well logs. This study has been done on raw and estimated well logs of more than 100 wells of Iranian south and southwest oil fields. The results showed that estimated well logs have more variability, which confirms a fundamental deficiency in current well log processing methods.
