Jianlei Sun

Title: Innovations of Jianlei Sun in Well Forecasting Technologies

Introduction

Jianlei Sun is an accomplished inventor based in Katy, TX, known for his significant contributions to the field of well forecasting technologies. With a total of four patents to his name, he has developed innovative systems and methods that enhance the efficiency and accuracy of well productivity predictions.

Latest Patents

One of Jianlei Sun's latest patents is titled "Systems and methods for forecasting well interference." This invention discloses systems and methods for obtaining input data related to properties associated with both parent and child wells. The process involves dividing the input data into subsets for training, validation, and testing, selecting machine learning models, tuning hyperparameters, and generating learning outputs that indicate test and training root-mean-square errors (RMSE).

Another notable patent is "Systems and methods for forecasting well productivity." This invention outlines a method for obtaining an input sequence of data features associated with a well, including production rates and operational constraints. The data is divided into subsets, and a well production model is built using machine learning to generate forecasts for future production rates, including oil, gas, and water rates.

Career Highlights

Jianlei Sun is currently employed at Halliburton Energy Services, Inc., where he applies his expertise in machine learning and data analysis to improve well forecasting methodologies. His work has been instrumental in advancing the capabilities of the energy sector in predicting well performance.

Collaborations

Throughout his career, Jianlei has collaborated with notable colleagues, including Dwight David Fulton and Jianfu Ma. These partnerships have contributed to the development of innovative solutions in the field of well forecasting.

Conclusion

Jianlei Sun's innovative work in well forecasting technologies demonstrates his commitment to enhancing the efficiency of energy production. His patents reflect a deep understanding of machine learning applications in the oil and gas industry, positioning him as a key figure in this evolving field.