Journal of Engineering Geology

fa انتخاب متۀ حفاری بهینه با استفاده از الگوریتم‌های داده‌کاوی-مطالعۀ موردی Optimum Bit Selection Using Data Mining Algorithms-A Case Study ژئوتکنیک (مکانیک خاک و سنگ) Geotecnic مقاله پژوهشی Original Research انتخاب بهترین مته در شرایط پیچیده حفاری متناظر بـا آن، یکی از مهم‌ترین موضوعاتی است که در حوزۀ حفاری وجود دارد. زیرا با وجود این‌که قیمت مته 2 تا 3 درصد هزینه‌های تکمیل یک چاه را در بر می‌گیرد، اما بر 75 درصد هزینه‌های کلـی حفـاری به‌طور غیرمستقیم تأثیرگذار است. در این تحقیق به مدل‌سازی انتخاب مته حفاری بهینه با استفاده از چاه ‌نمودارهای (لاگ) مختلف 7 چاه نفتی موجود در منطقه‌ای در ترکیه پرداخته شد. برای مدل‌سازی از روش‌های داده‌کاوی شامل درخت تصمیم، قوانین انجمنی، احتمال بیز، مبتنی بر تشابه و سیستم استنتاجی نروفازی تطبیقی استفاده شد. بدین‌ترتیب که از داده‌های شش چاه به‌عنوان آموزش مدل‌ها و داده‌های یک چاه دیگر به‌عنوان داده‌های آزمون برای ارزیابی صحت و دقت مدل‌ها استفاده شد. در نهایت نتایج مدل‌های مختلف در کنار یک‌دیگر مقایسه و تحلیل شد. نتایج نشان داد مدل ایجاد شده به‌وسیلۀ سیستم استنتاجی نروفازی تطبیقی با اختلاف معنا‌داری از مدل‌های ایجاد شده به‌وسیلۀ سایر روش‌ها کاراتر و دقیق‌تر است. اما بدین معنی نیست که سایر روش‌ها کارا نیستند بلکه تحلیل نتایج نشان می‌دهد دیگر روش‌ها نیز می‌توانند مدلی هرچند در کیفیتی پایین‌تر از مدل سیستم استنتاجی نروفازی تطبیقی اما سودمند و قابل اعتماد ایجاد کنند.     Introduction Drill-bit selection is one of the most important aspects of well planning due to the bearing it can have on the overall cost of the well. Bit selection in conventional and slightly inclined wells is a very delicate and complex process. In high angle and horizontal wells it is even more difficult. Historically, drilling engineers have selected bits on the basis of what has been worked well in the area and what has been determined to have the lowest cost run from offset bit records. Often the best bit records were not available for evaluation, because the best bit may not yet have been run, may have been run by a competitor or the engineer was new to the area. As a result the bit program was generally developed by trial and error and at significant additional costs for a large number of wells. In most cases the optimum program was never reached because there was nothing to predict that a bit selection change could further reduce the cost of the well. In this study, an alternative solution approaches using the concept of the power of data mining algorithms to solve the optimum bit program for a given field is proposed. Material and methods It has been considered an offset well to be drilled outside the known boundaries of a known field. For this purpose, the seventh well (X-7) of the same field was used as a verification point. The data was trained using the well log and rock bit data of six wells located in the field and the real well log data of well 7 was input as unknown data. These depths are selected based on reported rock bit program. When compared to the real data, it could be observed that the models (adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules) estimate<s>s</s> the formation hardness accurately. This minor discrepancy was also present with the company’s suggested rock bit program, which was based on the previous wells’ rock bit data. Results and discussion In this paper, data mining algorithms for optimum rock bit program estimation is proposed. The accuracy and efficiency of the developed data mining algorithms (adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules) that requires sonic and neutron log data input was tested for several real and synthetic cases. In the case of a development? well to be drilled outside the known boundaries of a field the model estimated rock bits with properties that consider the formation hardness correctly but slightly underestimated further rock bit details. The models also produced reasonable rock bit programs for an advance well to be drilled within the known boundaries of a field and a wildcat well drilled in a nearby field with similar rock properties to the training field. Thus it was concluded that the developed adaptive neuro fuzzy inference system is suitable as a front-end system for rock bit selection that could help engineers in decision-making analysis. Conclusion Optimum bit selection is one of the important issues in drilling engineering. Usually, optimum bit selection is determined by the lowest cost per foot and is a function of bit cost and performance as well as penetration rate. Conventional optimum rock bit selection program involves development of computer programs created from mathematical models along with information from previously drilled wells in the same area. Based on the data gathered on a daily basis for each well drilled, the optimum drilling program may be modified and revised as unexpected problems arose. The approaches in this study uses the power of data mining algorithms to solve the optimum bit selection problem. In order to achieve this goal, adaptive neuro fuzzy inference system, K-nearest neighbors, decision tree, Bayesian classification theory and association rules were developed by training the models using real rock bit data for several wells in a carbonated field. The training of the basic models involved use of both gamma ray and sonic log data. After that the models were tested using various drilling scenarios in different lithologic units. It was observed that the adaptive neuro fuzzy inference system model has provided satisfactory results.     انتخاب مته حفاری, چاه‌نمودارهای (لاگ) مختلف, سیستم استنتاجی نروفازی تطبیقی, درخت تصمیم, قوانین انجمنی, احتمال بیز, مبتنی بر تشابه Bit selection, Adaptive neuro fuzzy inference system, K-nearest neighbors, Decision tree, Bayesian classification theory, Association rules 487 522 http://jeg.khu.ac.ir/browse.php?a_code=A-10-807-3&slc_lang=fa&sid=1 Hadi Fattahi هادی فتاحی h.fattahi@arakut.ac.ir 10031947532846004761 10031947532846004761 Yes دانشگاه صنعتی اراک، دانشکدۀ مهندسی علوم زمین، گروه مهندسی معدن Younes Afshari یونس افشاری iron.azar@gmail.com 10031947532846004762 10031947532846004762 No دانشگاه صنعتی اراک، دانشکدۀ مهندسی علوم زمین، گروه مهندسی معدن