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 ...
Read More
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.
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 ...
Read More
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.
N. Fouladi Moghaddam; A. A. Matkan; M. R. Sahebi; M. Roustaei
Abstract
Hydrocarbon fluid extraction from high compactable and low permeable reservoirs resulted in gradual surface deformation that causes significant costs due to overburden failures. However, surveying benchmarks make it possible to compare the repeated leveling measurements at the specific locations, then ...
Read More
Hydrocarbon fluid extraction from high compactable and low permeable reservoirs resulted in gradual surface deformation that causes significant costs due to overburden failures. However, surveying benchmarks make it possible to compare the repeated leveling measurements at the specific locations, then it is necessary to introduce an effective method that is more real time and cost-effective. Differential SAR interferometry (DInSAR) is a new technology in which satellite images are used for field surface displacement monitoring. In this method, the high resolution images derived from Radar measurements are used for surface deformation rates assessment to improve the management and mitigation of traditional production costs. In this study, surface displacements caused by fluid withdrawal in Aghajari oil field are presented using Radar observations as the InSAR data reveal both subsidence and uplift signals for each production and observation wells distributed over the site. A number of production site inspections in a time series of interferograms reveal that the surface deformation signals developed due to extraction in several months as well as different subsidence or uplift rates and deformation styles occur locally depending on the geological conditions and excavation rates.
