Using data mining to stop or mitigate lost circulation
استفاده از داده کاوی برای متوقف کردن یا کاهش گردش خون از دست رفته-2019
Lost circulation materials (LCMs) are available in a great variety of materials, sizes, and can vary in the method of application. Selecting the right LCM and treatment for the job is critical in achieving the successful control of the lost circulation event. After reading a good number of papers and case histories regarding lost circulation materials and treatments and comparing the current classification to actual real field data, a large gap was found in the current calcification of lost circulation materials and treatments. An updated classification of lost circulation materials and treatments was provided based on the type of losses and applications. Data were collected on both LCM and applications from various number of sources in the Basra area of Iraq where drilling operations are highly susceptible to lost circulation in the Dammam, Hartha and Shuaiba formations. After analyzing the data, the best lost circulation treatments and materials to treat seepage, partial, severe, and complete losses in Basra oil fields were provided as a flowchart accomplished by practical guidelines that can serve as a reference material for the drilling personnel when responding to lost circulation in the field. This paper will also discuss methods that are used to ameliorate lost circulation without the use of traditional lost circulation materials. Example of the alternative approaches include discussions of blind drilling and floating mud cap drilling using case histories from the Basra fields. The results of this analysis provide a path forward for effectively and systematically using lost circulation materials and treatments in the Basra area. The methodologies presented in this work can be adapted and used to treat lost circulation worldwide.
Keywords: Drilling fluid | Lost circulation | Updated classification | Basra oil fields | Iraq
Intelligent decisions to stop or mitigate lost circulation based on machine learning
تصمیمات هوشمند برای متوقف کردن یا کاهش گردش خون از دست رفته بر اساس یادگیری ماشین-2019
Lost circulation is one of the frequent challenges encountered during the drilling of oil and gas wells. It is detrimental because it can not only increase non-productive time and operational cost but also lead to other safety hazards such as wellbore instability, pipe sticking, and blow out. However, selecting the most effective treatment may still be regarded as an ill-structured issue since it does not have a unique solution. Therefore, the objective of this study is to develop an expert system that can screen drilling operation parameters and drilling fluid characteristics required to diagnose the lost circulation problem correctly and suggest the most appropriate solution for the issue at hand. In the first step, field datasets were collected from 385 wells drilled in Southern Iraq from different fields. Then, fscaret package in R environment was applied to detect the importance and ranking of the input parameters that affect the lost circulation solution. The new models were developed to predict the lost circulation solution for vertical and deviated wells using artificial neural networks (ANNs) and support vector machine (SVM). The using of the machine learning methods could assist the drilling engineer to make an intelligent decision with proper corrective lost circulation treatment.
Keywords: Lost circulation | Intelligent decision | Artificial neural networks | Support vector machine