Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs

A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out.This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis.The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series.Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting.The analysis of history production data of waterflooding reservoirs shows that,compared with history matching results of numerical reservoir simulation,the production forecasting results from the machine learning model are more accurate,and uncertainty analysis can improve the safety of forecasting results.Furthermore,impulse response analysis can evaluate the oil production contribution of the injection well,which can provide theoretical guidance for adjustment of waterflooding development plan.

A new method for predicting injection multiples of extreme displacement in waterflood reservoirs

The theoretical relationship between water injection multiple(i.e.injected pore volume)and water saturation is inferred from theoretical concepts of reservoir engineering.A mathematical model based on core displacement tests is established for the entire injection process that satisfies both initial displacement and extreme displacement,simultaneously.The results show that prior to the flooding,the water injection multiple has a linear relationship with the water saturation,and the utilization rate of the injected water is the highest.As water breakthrough at the production end,the water-cut increases,and the injection multiple increases exponentially while the utilization efficiency of the injected water gradually decreases.When the injection multiple approaches infinity,the utilization efficiency of the injected water gradually decreases to 0,by which time the water-cut at the production end is always 1.At this time,the water saturation no longer changes,and the water flooding recovery rate reaches its limit.Based on the experimental test data,a mathematical model of the entire process of injection multiple and water saturation is established,which has high fitting accuracy that can predict the injection multiple in the different stages of development of a mature oil reservoir.The dynamically changing index of the injection water utilization efficiency in reservoir development by reactive water flooding can be obtained through reasonable transformation of the mathematical model.This is of great significance in guiding evaluations of the effects of reservoir development and formulating countermeasures.

Production Dynamic Prediction Method of Waterflooding Reservoir Based on Deep Convolution Generative Adversarial Network(DC-GAN)

The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has been the basis of production optimization of water-flooding reservoirs.Considering that the construction of geological models with traditional numerical simulation software is complicated,the computational efficiency of the simulation calculation is often low,and the numerical simulation tools need to be repeated iteratively in the process of model optimization,machine learning methods have been used for fast reservoir simulation.However,traditional artificial neural network(ANN)has large degrees of freedom,slow convergence speed,and complex network model.This paper aims to predict the production performance of water flooding reservoirs based on a deep convolutional generative adversarial network(DC-GAN)model,and establish a dynamic mapping relationship between well location deployment and output oil saturation.The network structure is based on an improved U-Net framework.Through a deep convolutional network and deconvolution network,the features of input well deployment images are extracted,and the stability of the adversarial model is strengthened.The training speed and accuracy of the proxy model are improved,and the oil saturation of water flooding reservoirs is dynamically predicted.The results show that the trained DC-GAN has significant advantages in predicting oil saturation by the well-location employment map.The cosine similarity between the oil saturation map given by the trained DC-GAN and the oil saturation map generated by the numerical simulator is compared.In above,DC-GAN is an effective method to conduct a proxy model to quickly predict the production performance of water flooding reservoirs.