Combining Physics and Machine Learning for Multimodal Virtual Flow Metering with Confidence

AL-Qutami, Tareq Aziz; Ishak, Mohd Azmin; Wollebaek, Lars; Ahmad, Wasim

doi:10.2523/iptc-22431-ms

Day 3 Wed, February 23, 2022 2022

DOI: 10.2523/iptc-22431-ms

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Combining Physics and Machine Learning for Multimodal Virtual Flow Metering with Confidence

Tareq Aziz AL-Qutami

Mohd Azmin Ishak

Lars Wollebaek³

et al.

Abstract: This paper introduces a multimodal virtual flow meter (VFM) that merges physics-driven multiphase flow simulations with machine learning models to accurately estimate flow rates in oil and gas wells. The combining algorithm takes advantage of the confidence decay and historical performance factors to assign confidence and contribution weights to the base estimators and then aggregates their estimates to arrive at more accurate flow rate estimates. Furthermore, the proposed multimodal VFM provides an indication… Show more

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Cited by 3 publications

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A New Approach to Creating a Digital Twin of Well for Production Monitoring in Western Siberia Fields

Kobzar,

Mosyagin,

Gudilov

et al. 2023

Day 3 Wed, October 04, 2023

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The purpose of this work is to create a stable and scalable digital twin of oil well for virtual flow metering task capable of predicting the flow rate of wells equipped with ESPs for many fields in Western Siberia at various stages and conditions of development. The basis of the suggested approach is to use the best aspects of classical petroleum engineering methods and machine learning algorithms. There is a digital twin of the well, which includes a hydraulic and electrical parts. The adaptation of the well model is carried out through the calculation of the degradation coefficients of the ESP and then a regression task is set to predict these correction coefficients using a gradient boosting model on decision trees. The final element is the prediction of the flow rate according to the well physical model based on correction coefficients predicted by machine learning methods. The described approach proved its effectiveness after testing at several fields in Western Siberia for various operating conditions. The algorithm was especially useful for estimating the flow rate of a well in severe cases: unstable well operation, joint measurements, a new well after drilling, a broken flow rate measuring system. A comparison was also made with the classical approach of forecasting the flow rate for one well - the area of applicability. An assessment of the sufficiency of data for the construction of the model, the degree of degradation of the approach in the absence of data, the ability to scale and increase computational costs has been tested. Metrics have been obtained and the method of assessing the quality of forecasting in relation to the problem of virtual flow metering has been improved. Practical recommendations on the implementation of this approach are given. The novelty of this work lies in the method of combining physical and statistical calculations - the sequence of calculations according to the scheme: white-box - black-box - white-box. As well as working with telemetry time series along with machine learning algorithms.

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A New Approach to Creating a Digital Twin of Well for Production Monitoring in Western Siberia Fields

Kobzar,

Mosyagin,

Gudilov

et al. 2023

Day 3 Wed, October 04, 2023

View full text Add to dashboard Cite

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Streamlining Selection and Surveillance: An End-To-End Lifecycle Tool for Well Test Optimization

Singh,

Ahmad,

Nordin

2024

Day 2 Tue, April 23, 2024

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A multi module and evolving cloud based well test equipment selection, surveillance and monitoring tool has been developed to ensure optimum field specific selection of well testing equipment's for field life-cycle operability and can be utilized for real time monitoring on accuracy and performance of installed MPFMs during operations. The tool consists of three modules each used at different project phase namely early sizing and selection (FEL 0/1), verification (FEL 2/3), commissioning and surveillance (Operation). This paper covers the methodology of the tool to demonstrate fit for purpose selection catered for full field life reliability for well test equipment by ensuring calculations from process simulations or Heat and Material balance taking into consideration full field life scenarios and cases as well as well counts. Results of each case and simulation is then analyzed via two phase flow map, GVF vs WLR plots as well as flow regime predictions. Apart from that, another module serves the purpose of creating and monitoring well test performances to ensure deviations are monitored and mitigation plans can be made to ensure improved acceptance rates and confidence with well testing facilities which is always questioned. Fourteen project cases had been evaluated utilizing this tool and the evaluation results from tool usage has demonstrated incorrect and not fit for purpose selection to last for full field life as per intension, reverification during FEED stage proved vital in correcting the selection as well as setting the baseline for health checks during operation and surveillance provided rectification plans with assured results. All in all, the simplicity of tool usage as well as being hosted in a sustainable cloud-based environment, contributed towards significant cost savings in manhours to expedite tedious manual calculations and perform flow regime predictions in order to develop the optimum operating envelope for the field life of the wells. In addition, during operations, this tool has proven to demonstrate higher well tests acceptance rate due to the ability to carry out real time surveillance which enabled well test engineers to carry out tuning or rectification on well test parameters to improve accuracy of measurements. Results depicted in Table 1 showcases tool strengths in selection, verification, and monitoring of well test equipment. The tool namely "PETRONAS Well Test Equipment Selection and Surveillance Tool" nullifies requirements for third party testing and verification by Operators, removes tedious manual calculations of each well stream and case to select equipment which would satisfy all cases of a well prediction. Whilst also providing real time well data monitoring and recommendations to resolve issues with well test equipment's specific to field requirements and reservoir characteristics. Its current deployment has managed to resolve issues at hand with realized cost savings however there is room for further development as well as potential commercialization in the future to resolve pain points and eventually becoming a virtual advisor.

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Hybrid Virtual Flow and Pressure Metering Approach Integrating Physics and Machine Learning Models for Accurate and Efficient Rates and Bottom Hole Pressure Prediction

Abudeeb,

Idris,

Abdullah

2024

Day 1 Mon, April 22, 2024

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Summary Data collection is crucial in the Oil and Gas business. Reliable production and pressure measurements for oil and gas operations can be acquired from Multiphase Flow metering and bottom hole pressure metering. Accurate and continuous production and pressure measurements are crucial not only for field surveillance, but also for effective production optimization. Virtual Flow & Pressure Metering (VFPM) is an alternative to the conventional physical meters and can exist in two forms, physics-based VFPM and Machine learning-based VFPM. The physics-based VFPM relies on multiphase flow and pressure simulations including thermodynamics, fluid dynamics, fluid modeling, and optimization techniques. Machine learning VFPM however, uncovers the relationship between target variables and sensor data. Both methods have strengths but also have some limitations. The proposed algorithm is the hybrid VFPM that merges both types, the physics-based VFPM and the Machine Learning VFPM. The approach first tests each VFPM method independently and checks for time consumed and accuracy. Then, it tests for the hybrid model option. In the hybrid model case, the framework starts with the basic well data going into the Physics-based Model to simulate the well behavior and create sensitivity analysis. The generated data is then fed to the Machine Learning model. The process then considers correlation between input features and removes highly correlated features. Data is split into train and test and then, the regression model is built using multiple model options to choose the most suitable model based on accuracy metrics and elapsed time. The best model is picked and the target variables are generated. The model here was decision tree-based Machine Learning Model as it represented the data best and needed few hyper-parameters to tune. The effectiveness of the algorithm was tested and validated, achieving a high accuracy of 90% in predicting Multiphase Flow Rate and 98% accuracy in predicting bottomhole pressure, and a reduction of 45% in time consumed to generate the data. The algorithm's prediction reduces the time needed to generate data using solely physics-based VFPM while increasing the accuracy of the solely Machine Learning-based VFPM. In addition, this approach can translate into a huge cost saving since it eliminates the need for a physical meter in each well.

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Combining Physics and Machine Learning for Multimodal Virtual Flow Metering with Confidence

Cited by 3 publications

References 5 publications

A New Approach to Creating a Digital Twin of Well for Production Monitoring in Western Siberia Fields

A New Approach to Creating a Digital Twin of Well for Production Monitoring in Western Siberia Fields

Streamlining Selection and Surveillance: An End-To-End Lifecycle Tool for Well Test Optimization

Hybrid Virtual Flow and Pressure Metering Approach Integrating Physics and Machine Learning Models for Accurate and Efficient Rates and Bottom Hole Pressure Prediction

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