인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
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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 Accurate prediction of depletion-induced effective stress is essential to reservoir engineering decisions across the field life cycle, influencing well instability, completion and stimulation design, production management, and geomechanical risk assessment. Although fully coupled flow–geomechanics finite-element (FE) simulations provide high-fidelity stress estimates, the associated computational cost and long runtime limit routine operational use. Therefore, this study develops an artificial neural network (ANN) proxy to rapidly predict 3D effective stress distributions driven by production-induced pore-pressure changes. The proposed workflow integrates field-scale coupled simulations with large-scale supervised learning and independent spatial validation to improve both data scale and evaluation rigor relative to many published ANN proxies. A field-scale workflow was implemented using data from 10 wells in the Buzurgan oilfield. High-resolution stress responses were generated using a fully coupled simulator (CMG-GEM 2021) and converted into supervised input–target pairs through an automated Python-based pipeline, yielding 11.26 million training samples. A compact ANN (one hidden layer with three neurons) achieved strong agreement with simulator outputs (avg. R ² = 0.94) and reproduced spatial effective-stress patterns in a structurally independent full-field case after 10 years of production, with most grid cells exhibiting deviations within ± 200 psi. The proxy reduced turnaround time from approximately hours per coupled FE run to minutes per prediction, enabling near real-time stress screening and sensitivity analysis for operational decision-making.
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.
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