Development of a new hydraulic electric index for rock typing in carbonate reservoirs.

Rock typing techniques have relied on either electrical or hydraulic properties. The study introduces a novel approach for reservoir rock typing, the hydraulic-electric index (HEI), which combines the strengths of traditional electrical and hydraulic rock typing methods to characterize carbonate reservoirs more accurately. By normalizing the ratio of permeability and formation resistivity factor (K/FRF) with respect to porosity, the HEI method is applied to two datasets of carbonate core samples: dataset 1 consists of 112 carbonate core samples from the Tensleep formation in the Bighorn basin of Wyoming and Montana, and dataset 2 includes 81 carbonate core samples from the Asmari formation in the south-west of Iran.

An ensemble-based machine learning solution for imbalanced multiclass dataset during lithology log generation.

The lithology log, an integral component of the master log, graphically portrays the encountered lithological sequence during drilling operations. In addition to offering real-time cross-sectional insights, lithology logs greatly aid in correlating and evaluating multiple sections efficiently. This paper introduces a novel workflow reliant on an enhanced weighted average ensemble approach for producing high-resolution lithology logs.