تعیین الکتروفاسیس های مخزن آسماری با استفاده از شبکه عصبی som در میدان نفتی قلعه ‌نار

تعیین الکتروفاسیس های مخزنی نقش مهمی در ارزیابی پتروفیزیکی زون های یک مخزن بمنظور بهره برداری بهینه از مخازن و توسعه میادین نفتی دارد. الکتروفاسیس بر مبنای خوشه بندی داده ها تعریف می شود، که بر مبنای خوشه بندی نمودارهای پتروفیزیکی مشابه در گروه های یکسان و تمایز آنها از سایر گروه ها می باشد. پژوهش حاضر در سازند آسماری میدان نفتی قلعه نار صورت پذیرفته است. در ابتدا با استفاده از روش های مختلف خوشه سازی نظیر som، mrgc و dynclust در تعدادی از چاه های میدان، مدل اولیه الکتروفاسیس ها تعیین گردید. الکتروفاسیس های تعیین شده با واحد های جریانی حاصل از تخلخل و تراوایی نمودارمغزه تطابق داده شد. از بین آنها روش som که دارای بیشترین تطابق بود جهت خوشه سازی انتخاب گردید. الکتروفاسیس ها بر اساس پارامترهایی از قبیل نمودارهای تخلخل و گاما ایجاد شده و به کل میدان بسط داده شد و در نتیجه مدلی ایجاد گردید که توانایی جدایش بخش های مختلف مخزنی را از همدیگر دارا بود. این مدل نشان داد که زون های 1 و 3 دارای کیفیت مخزنی مطلوبی است و زون 4 نیز دارای کیفیت متوسط تا خوب می باشد، اما زون های 2 و5 شرایط نا مطلوبی را دارا هستند.

electrofacies determination of the asmari reservoir using neural net som method in qaleh nar oil field

determining reservoir electrofacies plays an important role in the petrophysical evaluation of the zones of a reservoir in order to optimally exploit reservoirs and develop oil fields. electrofacies is defined on the basis of data clustering, which is based on the clustering of similar petrophysical logs in the same groups and their differentiation from other groups. the current research was carried out in the asmari formation of the ghalenar oil field. at first, using different clustering methods such as som, mrgc and dynclust in a number of field wells, the initial model of electrofacies was prepared. in examining the initial clusters of all three methods, similar clusters were merged in terms of sonic wave velocity parameters, shale amount and gamma log, and finally, based on the reservoir quality, 6 final facies were created. the determined electrofacies were matched with the flow units obtained from the porosity and permeability of core analysis data. among them, the results of the som method, which had the most agreement, were chosen as the optimal clustering method. although the porosity in these facies is mainly inter granular, inter crystalline, mold and cavity or fractures, but these facies have some differences in texture. cementation and dolomitization processes are more common in facies 1 than in facies 2. in facies 3, compared to facies 4, the process of dolomitization, micritization, cementation, and pressure dissolution (stylolitization) process has also occurred. facies 5 has better reservoir conditions due to the decrease in intensity of cementation and dolomitization. facies 6 is characterized by dominant grain fabric and scattered dolomitization. the provided electrofacies model was extended to the whole field. this model has the ability to separate different parts of the reservoir from each other. based on the results of this model and changes in electrofacies, zones 1 and 3 have favorable reservoir quality, zone 4 has medium to good quality, and zones 2 and 5 generally have unfavorable reservoir conditions. in general, the reservoir quality becomes unfavorable towards the base of the reservoir in terms of facies changes.