Well Log Prediction Using Deep Sequence Learning

Abstract Sonic logs including compressional and shear travel time logs (DTC and DTS, respectively) are important measurements for subsurface elastic and geomechanic property characterization. However, these log types are not always measured in practice or incomplete in many oil and gas wells for economic and other practical reasons. We propose to accurately predict these types of log data from the traditional common types of well log measurement data using deep sequence learning methods. After anomalous data removal and augmenting the ratio DTC/DTS as a new lithology differentiating feature, the preprocessed inputs were fed into a bidirectional Long Short-Term Memory (LSTM) model for training to minimizing the error in predicting the sonic logs. Once trained, the model is then applied at the target wells to predict the sonic logs. The predicted sonic logs match the actual values with good accuracy. This entirely data-driven approach significantly reduces dependency on prior domain knowledge, providing a generalization advantage that enables automated large-scale well log prediction across fields. Introduction Well-log data analysis and interpretation are commonly conducted and play a central role in quantitative reservoir characterization, formation and completion evaluation (Ellis 2007, Glover 2014). Various borehole measurements provide information to determine reservoir rock composition such as solid and fluid volume fractions, as well as rock types, etc. When integrated with seismic data, multiple types of well log data can help reduce geologic interpretation ambiguity and improve hydrocarbon reservoir models. Certain well logs, like GR, resistivity, density, and neutron, are considered as “easy-to-acquire” conventional well logs and are deployed in most wells. However due to cost consideration or access limitation, other types of well logs, like nuclear magnetic resonance (NMR), dielectric dispersion, elemental spectroscopy, and dipole/shear sonic, are deployed in a limited number of wells and not as commonly available, or missing at certain depth intervals in an area of interest. On the other hand, sonic or acoustic logs, which measures the travel time of an elastic wave through the formation, are important in many applications. For instance, seismic-well tie requires sonic and density logs as inputs. Geomechanical parameters, very challenging to directly measure, may be derived from sonic logs aided with additional information such as rock types (Chen and Zhang 2020). Additionally, sonic logs also provide information to derive formation porosity, for stratigraphic correlation, and identification of lithologies, facies, fractures and compaction (Glover 2014).