Deep Learning for Downhole Data Prediction: A Cost-Effective Data Telemetry Through Data Analytics
Abstract Accurate real-time downhole data collection provides a better understanding of downhole dynamics and formation characteristics, which can improve wellbore placement and increase drilling efficiency by improving the rate of penetration (ROP) and reducing downtime caused by tool failure. High-speed telemetry through wired drill string has enabled real-time data acquisition, but there are significant additional costs associated with the technology. Data-driven techniques using recursive neural networks (RNN) have proven very efficient and accurate in time-series forecasting problems. In this study, we propose deep learning as a cost-effective method to predict downhole data using surface data. Downhole drilling data is a function of surface drilling parameters and downhole conditions. The downhole data acquired using relatively inexpensive methods usually have a considerable lag time depending on the signal travel length. So, the first step in the proposed method is syncing the downhole data and surface data. After the data are synced, they are then fed into an RNN-based long-term short memory (LSTM) network, which learns the relationship between the surface parameters and downhole data. LSTM networks can learn long-term relationships in the data, thus making them ideal for time-series forecasting applications. The trained model is then used to make predictions for downhole data using the given surface data. The median error for the prediction of downhole data using surface data was as low as 3% in this study. The study suggests that the developed model can accurately predict downhole data in real-time. The model is also very robust to the amount of noise or outliers present in the data and can predict downhole conditions 50–60 ft ahead with reasonable accuracy. It was observed that the prediction accuracy varied from well to well and drilling depths. The results demonstrate how deep learning can be cost-effectively employed for downhole data prediction. This paper presents a novel method for using surface data to predict downhole data by employing deep learning. The method can be deployed in real-time to aid in wellbore placement and improve drilling performance.