Application of Deep Learning Methods in Evaluating Well Production Potential Using Surface Measurements

Abstract

This work investigated deep learning (DL) algorithms to forecast wells' economic potential using wellhead surface measurements. The results of DL algorithms were compared to existing solutions in the literature that aim at solving the same problem. The performance of algorithms trained on multiple input representations, resulting from applying unsupervised DL feature extraction methods, was compared to algorithms trained on raw inputs. In this work, available wellhead measurements that can be collected at surface were used in modeling their relationship to well production rate. A search method was used to select the best combination of measurements for modeling. In addition, features were extracted automatically using autoencoders to generate additional inputs. The algorithms used for forecasting were designed using standard and customized DL layers. The results of both DL and existing solutions from the literature were compared systematically to determine the usefulness of the DL methodology developed. Previous work has shown the limitations of correlation-based methods in offering a reliable solution when applied to data outside of the historically observed range. In addition, multiple models are sometimes required depending on operational settings, e.g. choke opening, making the modeling process inefficient. Moreover, mass balance-based methods require information, such as phase density, that might not be readily available when modeling is performed, affecting the reliability of the solutions. We found that using DL algorithms offers a flexible solution that can help overcome these limitations and opens a new path to solve similar problems. We found that algorithms trained properly on relevant features are useful tools to use in assessing the economic potential of wells, using already available surface measurements. The study developed a novel methodology for forecasting economic potential using wellhead surface measurements and demonstrated the application to real field data. The method was found to be an efficient utilization of data that are readily available and do not require well intervention to measure.

Real-Time Liquid Rate and Water Cut Prediction From the Electrical Submersible Pump Sensors Data Using Machine-Learning Algorithms

“…In this article, CNN and LSTM are just two examples of the multivariate prediction issues offered for a generic framework. Before the LSTM network layer, CNN is used to extract the horizontal correlations between multidimensional variables, and LSTM is used to learn the temporal relationships of these features and make predictions based on them. − …”