Natural fractures are effective reservoir spaces and the main seepage channel for tight
sandstones, which control the migration, enrichment, and productivity of oil and natural gas …

Lithofacies identification in carbonate reservoirs using conventional well logs is a typically
complex nonlinear problem due to influences of multiple factors, such as fluids and fractures …

Detecting fractures using well logs can be difficult due to the complex response of
conventional logs. To address this issue, a novel method called Fracture Identification by …

Reservoir characterization struggles with identifying fractures, a typical imbalance
classification problem. To handle this issue, a novel approach called fracture identification …

Identifying reservoir fractures has been a challenge due to its significant influence in drilling
and production, especially in highly complex carbonate reservoirs. In this paper, one of the …

An intelligent prediction method for fractures in tight carbonate reservoir has been
established by upgrading single-well fracture identification and interwell fracture trend …

How to fit a properly nonlinear classification model from conventional well logs to lithofacies
is a key problem for machine learning methods. Kernel methods (eg, KFD, SVM, MSVM) are …

Typically, relationship between well logs and lithofacies is complex, which leads to low
accuracy of lithofacies identification. Machine learning (ML) methods are often applied to …

Identification of natural fractures in continental shale is a problematic task by conventional
logging. To address this issue, a method known as Frac-gPCC that combines graph pooling …

Identification of reservoir types in deep carbonates has always been a great challenge due
to complex logging responses caused by the heterogeneous scale and distribution of …