Intelligent Prediction of Sampling Time for Offshore Formation Testing Based on Hybrid-Driven Methods

Author:

Nie Yiying1,Li Caoxiong2,Zhou Yanmin3,Yu Qiang3,Zuo Youxiang3,Meng Yuexin3,Xian Chenggang2ORCID

Affiliation:

1. College of Artificial Intelligence, China University of Petroleum, Beijing 102200, China

2. Unconventional Petroleum Science and Technology Institute, China University of Petroleum, Beijing 102200, China

3. China Oilfield Services Limited, Langfang 065201, China

Abstract

Formation testing is widely used in offshore oil and gas development, and predicting the sampling time of pure fluids during this process is very important. However, existing formation testing methods have problems such as long duration and low efficiency. To address these issues, this paper proposes a hybrid-driven method based on physical models and machine learning models to predict fluid sampling time in formation testing. In this hybrid-driven model, we establish a digital twin model to simulate a large amount of experimental data (6000 cases, totaling over 1 million data points) and significantly enhance the correlation between features using physical formulas. By applying advanced machine learning algorithms, we achieve real-time predictions of fluid sampling time with an accuracy of up to 92%. Additionally, we use optimizers to improve the model’s accuracy by 3%, ultimately reaching 95%. This model provides a novel approach for optimizing formation testing that is significant for the efficient development of offshore oil and gas.

Funder

project “Research on Formation Testing Pressure Interpretation and Sampling Methods Using Mini-DST”

Publisher

MDPI AG

Reference41 articles.

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