Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

Okorocha, Ikenna Tobechukwu; Chinwuko, Chuka Emmanuel; Mgbemena, Chika Edith; Mgbemena, Chinedum Ogonna

doi:10.12928/ijio.v1i2.2470

Cited by 2 publications

References 32 publications

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Production Forecasting in Gas Lifted Wells using Interpretable Machine Learning Techniques

Isehunwa,

Awojinrin,

Oluwatayo

2024

SPE Nigeria Annual International Conference and Exhibition

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Traditional techniques, such as decline curve analysis (DCA) and material balance, are industry-accepted standards for evaluating reservoir performance and estimating developed reserves. However, while these methods are reliable for estimating well productivity under natural flow conditions, accurate evaluation of gas lifted wells can be challenging due to the additional energy injected to augment mthe natural flow. Although attempts have been made to model gas lifted wells using machine learning (ML) techniques, these models provide little or no interpretability, thereby offering no clues to the underlying physical interactions behind their predictions. To address this issue, an empirical correlation was developed to predict gas lifted oil production rates using interpretable non-linear regression ML models. Production data from four wells were obtained from literature, encompassing production periods both with and without gas lift. A Python program was written to train the ML models. A ridge regression model was trained on production data from three of the wells using this program. The coefficients and intercepts of the resulting model were extracted to generate an empirical correlation and its predictive performance was then statistically analyzed. The performance of the derived empirical correlation was validated using production data from a new well, exhibiting a correlation coefficient of 0.96 and a root mean squared error of 27.55 STB. Analysis of predicted production rates under both natural and gas lifted flow conditions indicated that proper utilization of gas lift can increase developed reserves under natural flow by as much as 200%. The predicted production life of the well also increased significantly from 28 months under natural flow to 48 months with gas lift. A non-linear empirical correlation has been developed and tested to predict the oil production rate in gas lifted wells. With slight modifications, this correlation and the workflow implemented here can be applied to other wells, providing a practical tool for forecasting oil production rates not only in gas lifted scenarios but for other artificial lift methods as well.

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Production Forecasting in Gas Lifted Wells using Interpretable Machine Learning Techniques

Isehunwa,

Awojinrin,

Oluwatayo

2024

SPE Nigeria Annual International Conference and Exhibition

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A Comprehensive Review of Gas Lift Optimization Methods

Qin

2024

srb

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This paper provides a comprehensive review of the methods and techniques developed over the past two decades for optimizing continuous gas lift systems. It traces the evolution from isolated single-well analysis to real-time, multivariate optimization approaches that consider the interactions of all wells in an oilfield. While some methods are limited by their failure to account for the interdependencies among well sharing common streamlines, others struggle with scalability and the quality of solutions when applied to large-scale networks of hundreds of wells. The aim of this paper is to offer insights into the development of gas lift optimization methods and highlight the key challenges that remain unresolved in the field.

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Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

Cited by 2 publications

References 32 publications

Production Forecasting in Gas Lifted Wells using Interpretable Machine Learning Techniques

Production Forecasting in Gas Lifted Wells using Interpretable Machine Learning Techniques

A Comprehensive Review of Gas Lift Optimization Methods

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