摘要：Measuring oil well production is a fundamental task in oilfield operations and development. Precise measurement results are vital for diagnosing well anomalies, assessing field development, and optimizing well operating conditions. Currently, the majority of oilfields depend on metering stations for production measurement, a costly and labor-intensive approach with limited automation. Similarly, oil measurement methods based on indicator diagrams also face challenges due to substantial measurement errors. To improve the efficiency and accuracy of oil production measurement for pumping wells, we propose an intelligent measurement approach based on multi-source information fusion, integrating electrical parameters, indicator diagram data, and well production parameters. The intelligent measurement model incorporates two distinct feature extraction channels, taking electrical parameters and indicator diagram data from a single well cycle as inputs. The first channel uses a convolutional neural network (CNN) enhanced by a squeeze-and-excitation (SE) attention mechanism to extract 2D image information of load and displacement during the pumping well＇s operation. The second channel employs a gated recurrent unit (GRU) to capture 1D temporal information from the electrical parameter signals. Subsequently, well production parameters, including stroke length, stroke rate, and pump diameter, are combined with features extracted from both channels. This fused data is then analyzed by a fully connected network to enable intelligent oil measurement. Validation results using actual production data from 5,317 data samples across 13 wells indicate that the proposed intelligent measurement method achieves favorable accuracy in liquid production measurement, with a root mean square error (RMSE) of 0.3512 on the test set, a mean absolute percentage error (MAPE) of 4.51%, and a Mean Absolute Error (MAE) reaching 0.0299 under typical conditions, including normal operation, gas interference, and insufficient liquid supply. Compared to models based solely on electrical parameters or indicator diagrams, the multi-source information fusion model demonstrates significantly higher accuracy and robustness across various operational scenarios. Copyright 2025, Society of Petroleum Engineers.

ISSN号：9781959025733

卷、期、页：/

发表日期：2025-01-01

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）

发表期刊名称：Society of Petroleum Engineers - GOTECH 2025

通讯作者：杨丁丁,马赫,陈华鹏,吴庆霞,朱志勇

第一作者：韩国庆

论文类型：会议论文

论文概要：杨丁丁,韩国庆,马赫,陈华鹏,吴庆霞,朱志勇，Oil Production Measurement Method for Pumping Wells Based on Multi-Source Information Fusion，Society of Petroleum Engineers - GOTECH 2025，2025，/

论文题目：Oil Production Measurement Method for Pumping Wells Based on Multi-Source Information Fusion