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

Gas Lift operation involves the injection of compressed gas into a low producing or non-performing well to maximize oil production. The oil produced from a gas lift well is a function of the gas injection rate. The optimal gas injection rate is achieved by optimization. However, the gas lift, which is an artificial lift process, has some drawbacks such as the deterioration of the oil well, incorrect production metering, instability of the gas compressor, and over injection of gas. This paper discusses the various optimization techniques for the gas lift in the Oil and Gas production process. A systematic literature search was conducted on four databases, namely Google Scholar, Scopus, IEE Explore and DOAJ, to identify papers that focused on Gas lift optimizations. The materials for this review were collected primarily via database searches. The major challenges associated with gas lift were identified, and the different optimization strategies available in the literature reviewed. The strategies reviewed were found to be based on artificial intelligence (AI) and machine learning (ML). The implementation of any of the optimization strategies for the gas lift will enhance profitability, reduce operational cost, and extend the life of the wells

A novel chaotic time series prediction method and its application to carrier vibration interference attitude prediction of stabilized platform

Aiming at the problems existing in previous chaos time series prediction methods, a novel chaos times series prediction method, which applies modified GM(1, 1) model with optimizing parameters to study evolution laws of phase point L1 norm in reconstructed phase space, is proposed in this paper. Phase space reconstruction theory is used to reconstruct the unobserved phase space for chaotic time series by C-C method, and L1 norm series of phase points can be obtained in the reconstructed phase space. The modified GM(1, 1) model, which is improved by optimizing background value and optimizing original condition, is used to study the change law of phase point L1 norm for forecasting. The measured data from stabilized platform experiment and three traditional chaos time series are applied to evaluate the performance of the proposed model. To test the prediction method, three accuracy evaluation standards are employed here. The empirical results of stabilized platform are encouraging and indicate that the newly proposed method is excellent in prediction of chaos time series of chaos systems

Research on a Novel Kernel Based Grey Prediction Model and Its Applications

The discrete grey prediction models have attracted considerable interest of research due to its effectiveness to improve the modelling accuracy of the traditional grey prediction models. The autoregressive GM(1,1) model, abbreviated as ARGM(1,1), is a novel discrete grey model which is easy to use and accurate in prediction of approximate nonhomogeneous exponential time series. However, the ARGM(1,1) is essentially a linear model; thus, its applicability is still limited. In this paper a novel kernel based ARGM(1,1) model is proposed, abbreviated as KARGM(1,1). The KARGM(1,1) has a nonlinear function which can be expressed by a kernel function using the kernel method, and its modelling procedures are presented in details. Two case studies of predicting the monthly gas well production are carried out with the real world production data. The results of KARGM(1,1) model are compared to the existing discrete univariate grey prediction models, including ARGM(1,1), NDGM(1,1,k), DGM(1,1), and NGBMOP, and it is shown that the KARGM(1,1) outperforms the other four models

A novel servo control method based on feedforward control – Fuzzy-grey predictive controller for stabilized and tracking platform system

Through analysis of the time-delay characteristics of stabilized and tracking platform position tracking loop and of attitude disturbance exciting in stabilization and tracking platform systems, a compound control method based on adaptive fuzzy-grey prediction control (CAGPC) is proposed to improve the disturbance suppression performance and system response of stabilized and tracking platform system. Firstly, the feedforward controller which is to improve disturbance suppression performance of stabilized and tracking platform servo system and aiming at the external disturbances is introduced. Secondly, aiming at the disadvantages of conventional fixed step size of Fuzzy-grey prediction and the prediction error forecast model has, an adaptive adjustment module adjusting the prediction step and comprehensive error weight at the same time is proposed, according to the actual control system error and the prediction error, the Fuzzy-grey prediction step and the prediction error weights are regulated while to improve the control precision and the adaptability of the system prediction model; At last, Numerical simulation results and the stabilized and tracking platform experimental verification illustrate that the compound control method can improve the stable platform servo system response and the ability of suppress external disturbances and the CAGPC control method has better performance in the stabilized and tracking platform system