인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract This study proposes the use of different machine learning techniques to predict the estimated ultimate recovery (EUR) as a function of the hydraulic fracturing design. A set of data includes 200 well production data, and completion designs were collected from oil production wells in the Niobrara shale formation. The completion design parameters include the lateral length, the number of stages, the total injected proppant and slurry volumes, and the maximum treating pressure measured during the fracturing operations. The data set was randomly split into training and testing with a ratio of 75:25. Different machine learning methods were to predict EUR from the completion design including linear regression, random forest (RF), and decision tree (DT) in addition to gradient boosting regression (GBR). EUR prediction from the completion data showed a low accuracy. As result, an intermediate step of estimating the well IP30 (the initial well production rate for the first month) from the completion data was carried out; then, the IP30 and the completion design were used as input parameters to predict the EUR. The linear regression showed some linear relationship between the output and the inputs, where the EUR can be predicted with a linear relationship with an R -value of 0.84. In addition, a linear correlation was developed based on the linear regression model. Moreover, the other ML tools including RF, DT, and GBR presented high accuracy of EUR prediction with correlation coefficient ( R ) values between actual and predicted EUR from the ML model higher than 0.9. This study provides ML application with an empirical correlation to predict the EUR from the completion design parameters at an early time without the need for complex numerical simulation analysis. Unlike the available empirical DCA models that require several months of production to build a sound prediction of EUR, the main advantage of the developed models in this study is that it requires only an initial flow rate along with the completion design to predict EUR with high certainty.
#Completion (oil and gas wells)
#Linear regression
#Oil shale
#Hydraulic fracturing
#Correlation coefficient
#Multicollinearity
#Gradient boosting
#Coefficient of determination
#Regression
#Petroleum engineering
#Data set
#Random forest
#Geology
#Computer science
#Mathematics
#Statistics
#Artificial intelligence
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
