New Artificial Neural Network Model for Predicting the TOC from Well Logs

Sultan, Abdullah (King Fahd University of Petroleum & Minerals)

OnePetro 

The key factor for characterizing unconventional shale reservoirs is the total organic carbon (TOC). TOC is estimated conventionally by analysis cores samples which requires extensive lab work, thus it is time-consuming and costly. Several empirical models are suggested to estimate the TOC indirectly using conventional well logs. These models assume the TOC and well logs are linearly related, this assumption significantly reduces the TOC estimation accuracy. In this work, the design parameters of the artificial neural network (ANN) were optimized using selfadaptive differential evolution (SaDE) method to effectively predict the TOC from the conventional well log data. A new correlation for TOC calculation was developed, which is based on the optimized SaDE-ANN model.

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