It has been a great challenge for the oil and gas industry to timely identify any electrical submersible pump (ESP) abnormal performance to avoid ESP failure. Given the high cost of the ESP failure, more and more real-time surveillance systems are applied to monitor ESP performance to generate alarms in the case of failures. This paper presents a robust principal component analysis (PCA) model to perform fault detection for ESP systems continuously. A three-dimensional plot of scores of principal components was used to observe different patterns during the stable and failure periods. 47 cases of actual failure events and 40 cases of stable operating events were tested on the robust PCA model to generate prediction results. The testing results demonstrate that the robust PCA model has managed to identify 20 failure events before the actual failure time out of the 47 failure cases and has successfully distinguished all the 40 stable operating wells. This study has concluded that PCA has the potential to be used as a monitoring platform to recognize dynamic change and therefore to predict the developing failures in the ESP system.

Trips and failures are common occurrences in the Electric Submersible Pump (ESP) systems. Considering the high maintenance cost of the intervention and workovers post failures, ESPs connected to real-time monitoring and surveillance systems are used to fulfill proactive responses and early detection of incidents. Large amounts of dynamic and historical production data acquired from the surface and downhole sensors can help perform diagnostics and prognostics on ESPs. This study applies the Hotelling T-square statistic (T²) and Squared Prediction Error (SPE) equations combined with Principal Component Analysis (PCA) method to construct a predictive model based on the collected ESP data. This predictive model provides a solution that enables to detect the developing ESP trips and failures in real time. A new input matrix corresponding to any period in the whole ESP life cycle is fed to the predictive model to obtain prediction results. The model validation analysis demonstrates that the predictive model can identify the stable operating regions of ESP and detect the potential ESP trips and failures before the actual failure time. This study concludes that this proposed diagnostic model can serve as a real-time platform to identify dynamic changes and therefore monitoring potential ESP trips and failures in real-time.

Polymer flooding is an effective enhanced oil recovery (EOR) technology used in Daqing Oilfield. Microscopic distribution of remaining oil in polymer-flooded reservoirs is more complicated in comparison with waterflooded reservoirs. In this paper, UV excitation, frozen section-laser confocal technology, and three-dimensional reconstruction technology were employed to investigate the distribution law and occurrence state of the microscopic remaining oil in polymer-flooded Daqing Oilfield. With these methods, the occurrence states of the microscopic remaining oil distribution in different washing degrees and displacement locations were analyzed, and the remaining oil distribution before and after polymer flooding was compared quantitatively. The changes and microscopic distribution characteristics of crude oil components in the process of polymer flooding were clarified, and the relationship between day minerals and the microscopic remaining oil distribution was discussed. Based on the statistical analysis of experimental results, the remaining oil of the free state decreases gradually, while the remaining oil of the bound state increases as the washing degree increases. In addition, the remaining oil in the distributary line is more enriched than the mainstream line after polymer flooding. Compared with waterflooding, the remaining oil of the free state becomes more, while the remaining oil of the bound state becomes less after polymer flooding. The frozen section-laser confocal experimental results also indicate that the proportion and distribution characteristics of the remaining oil components have been changed, and heavy components increase while light components decrease in the polymer-flooded stage. This research performs the quantized characterization and detailed analysis of remaining oil systematically and lays the foundation for remaining oil prediction and potential tapping in polymer-flooded reservoirs.

With the increasing demands on energy and environmental domains, not only high oil production but also its accurate quantification has become one of the most important topics in academia and industry. This paper initially proposes a comprehensive workflow in which an integrated hierarchy–correlation model is used to thoroughly evaluate the influences of all relevant reservoir parameters on the ultimate oil recovery for water-flooding oil reservoirs. More specifically, the analytic hierarchy process, grey relation, and entropy weight are combined through the multiplicative weighting method to quantitatively describe the production parameters. Accordingly, novel multivariable linear and nonlinear correlations are developed to predict the production performance and validated through comparisons with numerical reservoir simulations. Seven factors, including five reservoir parameters, namely, permeability and its contrast, porosity, thickness, and saturation, and two production parameters, namely, the injection–production ratio and the operating pressure, have been identified as the most influential factors on recovery performances and thus are employed in the proposed correlations to predict the ultimate oil recovery factor. The results obtained by the proposed method are quite close to the real-time simulation data, while the accuracy is retained. The numerical results show that the recovery factors of water-flooding oil reservoirs are about 33.5–59.5%, and the corresponding linear and nonlinear correlation coefficients are 0.903 and 0.789, respectively. In comparison with the numerical simulation, the approximation error by the linear correlation is about 0.5%, which is lower than that of nonlinear correlation, for example, 12.3%. This study will be beneficial to analyze the reservoir-related parameters and provide a useful tool for rapid production performance evaluation of the water-flooding production scenario.

Trilinear flow model is an effective method to reproduce the flow behavior for horizontal wells with multistage hydraulic fracture treatments in unconventional reservoirs. However, models developed so far for transient analysis have rarely considered the inflow performance of wells. This paper introduces a new composite dual-porosity trilinear flow model for the multiple-fractured horizontal well in the naturally fractured reservoirs. The analytical solution is derived under a constant rate condition for analyzing transient pressure behaviors and generating the transient inflow performance relationships (IPRs). The plots of pressure profiles with time could provide insightful information about various flow regimes that develop throughout the entire production cycle. Sensitivity analysis of pressure and pressure derivative response was also performed by varying different parameters (such as hydraulic fracture width and permeability, reservoir configurations, etc.) and by which the impacts of different parameters on the durations of regimes as well as the productivity index can be confirmed. The main outcomes obtained from this study are as follows: (1) the ability to characterize naturally fractured reservoirs using a new composite dual-porosity trilinear flow model; (2) the application of analytical solutions of transient analysis to generate transient IPR curves for different flow regimes; (3) understanding the effect of reservoir configurations, fractures, and matrix characteristics on pressure distribution, flow regime duration, and transient IPR. More specifically, the pressure drop increases and the productivity index decreases with the decrease of the hydraulic fracture conductivity and the increase of matrix permeability and the skin factor. Also, the larger hydraulic fracture spacing and drainage area result in the later onset of the pseudo-steady-state regime. (4) A comprehensive study on transient pressure behaviors and transient inflow performance can provide valuable information to characterize the multifractured complex systems as well as some insights into the production.

The key point in coalbed methane (CBM) wells is to pump out the confined water to produce gas. Traditional drainage coalbed methane recovery methods have different limitations and characteristics. A new drainage gas recovery method called double-tube gas lift is applied in the Hancheng block for CBM production. The working mechanism of double-tube gas lift technology is to insert a hollow sucker rod into the original production pipe string to assist CBM production. What is more, the gas production channel and the drainage channel can be distinguished by this method. An improved measure is designed to overcome the double-tube gas lift’s shortcomings by adjusting the entry depth of the smaller tubing to expose the coal seam. In this way, the two-phase liquid of injected gas and produced liquid can be discharged from the annulus between the tubing and the smaller tubing. The produced gas is directly discharged from the annulus between the external tubing and the casing. The analysis of influence factors shows that the gas injection volume, water production, smaller-diameter tubing size, and the well entry depth have a significant influence on the production of the double-tube gas lift technology. The improved double-tube gas lift method has been applied in the Hancheng CBM wells to prove that this method has great production results and has the potential to get more applications in other CBM fields.