Maximizing Efficiency in Giant Gas Fields Harnessing Virtual Flow Meters to Optimize Testing Frequency and Enhance the Data Reliability

Abstract

Abstract The objective of this paper is to develop and validate a virtual flow meter (VFM) utilizing machine learning techniques to provide accurate, real-time flow rate measurements. Traditional flow meters, which rely on physical sensors, often face challenges such as high maintenance costs, susceptibility to harsh environmental conditions, and calibration issues. This paper aims to address these limitations by leveraging data-driven approaches to predict flow rates based on easily measurable parameters, thereby enhancing reliability, reducing operational costs, and improving overall efficiency in flow measurement systems. This paper encompasses the design, implementation, and evaluation of a machine learning-based virtual flow meter. The scope includes: Data Collection and Preprocessing: Gathering historical flow / production data and associated parameters (e.g., pressure, temperature, and rates) from existing physical flow meters. This includes data cleaning and normalization to ensure high-quality input for the machine learning models. Model Development: Developing various machine learning models to predict flow rates. This involves feature selection, model training, and hyperparameter tuning to optimize performance. Model Validation: Evaluating the accuracy and robustness of the developed models using cross-validation techniques and comparing their performance against traditional flow meters. Deployment and Integration: Implementing the virtual flow meter within the official system for back allocation. This includes real-time data integration, user interface design, and system calibration. Performance Monitoring: Continuously monitoring the performance of the virtual flow meter in live conditions to ensure accuracy and reliability. Cost-Benefit Analysis: Conducting a high-level analysis of the economic and operational benefits of the virtual flow meter compared to traditional methods. By addressing these areas, the paper aims to demonstrate the feasibility and advantages of using machine learning for flow measurement in oil & gas industrial applications. The implementation of a virtual flow meter in the gas reservoir aims to significantly improve back allocation accuracy and overall system efficiency. The virtual flow meter provided real-time production estimation capabilities, leading to better-informed decision-making and reduced discrepancies in gas volume measurements. During the evaluation period, the virtual flow meter demonstrated high reliability and consistency in flow measurements. Observations & conclusions are highlighted below: VFM can provide reliable and continuous data for gas wells, which can improve reservoir management and production optimization. VFM can reduce the testing frequency by up to 50%, which can save operational costs and minimize production losses. VFM is a valuable tool for maximizing the efficiency of giant gas fields, especially in complex and challenging environments. VFM data-driven measurement methods have the potential to be applied to different types of wells. With appropriate integration into the existing IT environment, they can provide enterprise-wide visibility of current production of VFM-facilitated wells.