Stratigraphic subdivision-based logging curves generation using neural random forests

Comprehensive logging curves are necessary for the accurate characterisation of unconventional hydrocarbon formations. However, the logging curves for some wells remain unavailable. Traditional methods of generating the missing logging curves (e.g. multiple regression techniques) have low accuracy, and it is difficult to represent the complex nonlinear relationships between the logging curves of unconventional reservoir using them. The neural random forest (NRF) is a new robust and fault-tolerant machine learning algorithm with high precision. Therefore, we used the NRF model to generate the missing logging curves of a shale gas reservoir in China for the first time. Specifically, four models for generating compensated neutrons, compressional slowness, gamma ray, and density curves were developed based on the NRF framework, incorporating stratigraphic subdivision information. Subsequently, considering the subnetwork connectivity characteristics of the NRF model, the joint and independent methods were separately used to train the model. Finally, the performance of the NRF model was evaluated by comparing it with neural network (NN) and random forest (RF) models. Results revealed that the NRF model achieved superior performance, with R2 > 0.85 on average. Compared to the NN and RF models, the NRF model demonstrated higher prediction accuracy. In addition, the prediction performance of the jointly trained NRF model was slightly superior to that of the independently trained NRF model. Moreover, stratigraphic subdivision information was proved to be important in reducing the model prediction errors and improving the accuracy by almost 20%. In summary, the proposed model provides a cost-effective method for generating the missing logging curves of horizontal wells in the shale gas reservoirs, which will further facilitate the exploration and development of unconventional reservoirs. •Neural random forests (NRF) was proposed for generating the missing well-logging curves in shale gas reservoirs.•The stratigraphic subdivision added in dataset contributed great improvement in prediction accuracy.•Hyperparamters of the NRF model were optimized.•Comparion of traditional machine learning and the NRF model was developed.