A regression-based model for prediction of flowmeters calibration cost in oil and gas industry

Abstract

Measurement of liquid flowmeters is one of the most expensive processes in the oil and gas industry. Estimating calibration costs for such flowmeters in the oil and gas industry is complicated task for the decision-making team. The difficulties arise as a result of the presence of numerous uncertain factors that influence calibration charges such as the fabrication of special tools and spools. Consequently, this paper proposes a data-driven approach for estimating the calibration costs of flowmeters in oil and gas industry. A regression-based model is developed to predict the future calibration costs of flowmeters. The factors that affect the costs of calibrating flowmeters are identified from literature and interviewing local experts. The results indicated that the most important factors influencing the cost of liquid flowmeter calibration include flowmeter size, calibration method, flowmeter type, flowmeter class and calibration factor. The developed model is validated using 577 new data points of flowmeters calibration costs. The findings showed the uncertainty of the proposed model within 98% confidence level. An accurate calibration cost for liquid flowmeter will help to manage the operational and services costs.