کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5484696 1522996 2017 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improvement of zonal isolation in horizontal shale gas wells: A data-driven model-based approach
ترجمه فارسی عنوان
بهبود انزوا زون در چاه های گاز افقی شیل: یک رویکرد مبتنی بر مدل مبتنی بر داده ها
کلمات کلیدی
گاز شیل، چاه های افقی جداسازی زون، حفاری خوب مدل سازی مبتنی بر داده ها، مدل سازی طبقه بندی
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات علوم زمین و سیاره ای (عمومی)
چکیده انگلیسی
Shale gas production from horizontal wells may encounter potential problems because of gas leakage from various zones of a well into the air and ground water reserves. Stopping such leaks is a serious challenge faced by the shale gas industry. To stop gas communication between various zones of the well, lifelong integrity of the cemented annulus between the metal casing and the borehole wall must be ensured. Various physical factors i.e. casing properties (internal diameter, centralizers and casing-hole relationship), cement and drilling mud properties (density, viscosity, additives), and other variables, such as temperature and pressure, affect the quality of cement job. The quality of cement job was analyzed in terms of sustained casing pressure. Sustained casing pressure or SCP results from sustained pressure on an annulus seen at surface from fluid or gas leaking from a lower formation as a result of poor zonal isolation. The SCP value was used in categorizing horizontal shale gas wells as leaking or not leaking. The statistical classification model was built to predict whether a well will leak or not, under the effect of various physical factors. The multivariate statistical technique PLS-DA (Partial Least Square Discriminant Analysis) was used to build this model and as the input (predictor) variables, the model used dimensionless groups of the physical factors. The VIP (variable importance in projection) variable selection method was used to identify highly impacting physical factors and the optimal model structure was determined using the 10-fold cross validation method. The model was able to correctly classify 81% of the classified wells in cross-validation tests for intermediate casing. The types of data-driven models developed are helpful in predicting whether annular gas leakage will occur under the influence of physical factors and based on the model feedback, the responsible factors can be regulated to perform better cement job, which would result in reduced gas leakage and less remedial cementing cost.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Natural Gas Science and Engineering - Volume 47, November 2017, Pages 101-113
نویسندگان
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