Demystifying Data-Driven Neural Networks for Multivariate Production Analysis

Rastogi, Ayush; Agarwal, Karn; Lolon, Ely; Mayerhofer, Mike; Oduba, Oladapo

doi:10.15530/urtec-2019-247

Cited by 1 publication

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“…), dimensionality reduction techniques, and sometimes unsupervised algorithms. Based on Hair et al (2013), − this project’s exploratory data analysis includes univariate study that focuses on the dependent variables flow rate and water cut. Then, a multivariate study is likewise introduced to focus on the relationship between dependent and independent variables.…”

Section: Literature Studymentioning

confidence: 99%

Real-Time Liquid Rate and Water Cut Prediction From the Electrical Submersible Pump Sensors Data Using Machine-Learning Algorithms

et al. 2023

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This study presents a novel data-driven approach for calculating multiphase flow rates in electrical submersible pumped wells. Traditional methods for estimating flow rates at test separators fail to identify production trends and require additional costs for maintenance. In response, virtual flow metering (VFM) has emerged as an attractive research area in the oil and gas industry. This study introduces a robust workflow utilizing symbolic regression, extreme gradient boosted trees, and a deep learning model that includes a pipeline of convolutional neural network (CNN) layers and long short-term memory algorithm (LSTM) layers to predict liquid rate and water cut in real time based on pump sensors' data. The novelty of this approach lies in offering a cost-effective and accurate alternative to the usage of multiphase physical flow meters and production testing. Additionally, the study provides insights into the potential of data-driven methods for VFM in electrical submersible pumped wells, highlighting the effectiveness of the proposed approach. Overall, this study contributes to the field by introducing a new, data-driven method for accurately predicting multiphase flow rates in real time, thereby providing a valuable tool for monitoring and optimizing production processes in the oil and gas industry.

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Section: Literature Studymentioning

confidence: 99%