This study presents a hybrid physics/machine-learning (ML) framework for virtual flowmetering in electrical-submersible-pump (ESP)-lifted oil fields, with a focus on practical deployment and real-world applicability. Unlike conventional approaches that evaluate performance on randomly split data, the proposed workflow employs a well-based data-partitioning strategy to ensure that model performance reflects true generalization to unseen wells. In addition, the learning task is reformulated in a normalized space relative to pump best efficiency point (BEP), enabling consistent scaling across different pump sizes and operating conditions.
Methodology
Data Set and Study Scope. The data set employed for the purpose of the current study contains approximately 35,000 samples acquired from approximately 50 ESP-lifted wells. In each sample, a combination of surface measurements, pump conditions, and completion attributes is accompanied by an observed gross fluid-production rate.
Data Cleaning and Standardization. To achieve uniformity across all wells, categorical features such as formation type, pump type, and operating mode were made uniform by trimming excess white space and making text uniform.
