توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Investigation of Microbial Enhance Oil Recovery’s MEOR performance as a candidate In Iranian Southern Oil Fields
اطلاعات انتشار: اولین کنفرانس بین المللی نفت، گاز، پتروشیمی و نیروگاهی، سال
تعداد صفحات: ۵
Enhanced Oil Recovery (EOR) process is used to recover additional oil left in place after primary recovery. The prediction of its performanceis of great importance in selection and design ofcertain EOR process and future planning for oil production. Microbial Enhance Oil Recovery(MEOR) is friendly with environment, and it isapplied as ex–situ and in–situ in oil reservoirs. In microbial flooding, in the water oil contact themicroorganisms consume the nutrition and producebioacid, biopolymer, biosurfactant, biogas and solvent, which improve the oil recovery and yield theless harmful product for a green environment with respect to other types of EOR methods. This study was investigated potential of applying MEOR by oilrecovery prediction in five different carbonate reservoirs. The study is conducted utilizing 100 laboratory data with valid references. In all of thesereferences, MEOR processes are obtained based on porosity, permeability, salinity, temperature, pressure and PH. Clostridium Acetobutylicum are also used asmicrobe. From this laboratory data different data clusters are tested by Adaptive Neuro Fuzzy Inference System (ANFIS). The best modeling (fouror five clusters) obtained based on Mean Square Error (MSE) and correction factor (R–Value) by employing reservoir parameters as inputs and oilrecoveries as outputs. Five different reservoirs selected from Iranian southern oil fields, which have not experienced any EOR processing before.Reservoir properties entered as inputs in obtained ANFIS model, which result five output as oil recovery prediction. Results reveal 36.71– 40.68% oilrecovery, which conform to previous studies. Besides, considering green technologies, it is shownthat MEOR can be one of the best options among EOR techniques for carbonate reservoirs<\div>

۲Soft–Computation with Virtual Intelligence and Genetic Algorithms to Optimize Drilling Bit Selection
نویسنده(ها): ،
اطلاعات انتشار: اولین کنفرانس بین المللی نفت، گاز، پتروشیمی و نیروگاهی، سال
تعداد صفحات: ۸
Drilling industry encounters various challenges during planning and drilling a new well. There are numerous parameters related to drilling operations that are planned and adjusted as drillingadvances. Among them, bit selection is one of the most influential considerations for planning and constructing a new borehole. Conventional bit selections are mostly based on drillers’ experiences in the field or mathematical equations, which standmore on recorded performances of similar bits from offset wells. It is evident that these sophisticated interrelations between parameters never can be stated in a single mathematical equation. In such intricate cases, utilizing virtual intelligence and Artificial Neural Networks (ANNs) is proven to be worthwhilein understanding complex relationships between variables. In this paper, two models are developedwith high competence and utilizing ANNs. The first model provides appropriate drilling bit selection based on desired ROP to be obtained by applying specific drilling parameters. The second model uses proper drilling parameters obtained from optimizing procedure to select drilling bit, which provides maximum achievable ROP. Meanwhile, Genetic Algorithm (GA), as a class of optimizing methods for complex functions, is applied. The proposed methodsassess the current conditions of drilling system to optimize the effectiveness of drilling, while reducing the probability of early wear of the drill bit. The correlation coefficients for predicted bit types and optimum drilling parameters in testing the obtainednetworks are 0.95 and 0.90, respectively. The proposed methodology opens new opportunities for real–time and in–field drilling optimization that can be efficiently implemented within the span of the existing drilling practice.<\div>
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