Machine learning-based models for predicting calibration cost of flowmeters in the oil industry

Awsan Mohammed, Abdullah Javaid, Ahmed M. Ghaithan, Osamah AlDhafer, Maged Saeed AlShaibani, Adel Alshibani

Abstract: Accurate measurement of liquid volume in pipelines is a crucial and sensitive process in the petroleum industry. Flowmeter is a common device used to measure the volume of the liquid passing through the pipeline and to invoice clients. Improving flowmeter calibration preserves functionality, identifies issues, and ensures accurate market valuation and pricing. Estimating the cost of third-party calibration is a challenging task for decision-makers in the petroleum industry. Thus, this paper proposes machine-learning algorithms to predict and analyze the cost of flowmeter calibration in the petroleum industry. These techniques include artificial neural networks, SGDRegressor, XGBoost, AdaBoost, support vector machine, K-nearest neighbors, and random forest. Prior to developing the model, the factors impacting the cost calibration of the flowmeters are identified from the literature and finalized by oil and gas experts. In addition, the relationship between the input factors and the output is examined to ensure the quality of the data. The analysis indicated that the most crucial factors affecting the calibration cost are the flowmeter size, flange class, flowmeter type, calibration method, and calibration factor. Furthermore, the developed machine learning-based models are validated by using 153 new additional data. The results revealed that the random forest is the best technique for estimating calibration costs, with an accuracy of 99%, followed by AdaBoost with an accuracy of 97%, and artificial neural network with an accuracy of 96%. The proposed models provide superior accuracy and efficiency which will significantly contribute to estimating calibration costs, assisting decision-makers in establishing a budget for flowmeter calibration.

External IDs:dblp:journals/nca/MohammedJGAAA25