Flow behavior of a coupled model between horizontal well and fractal reservoir

Many research findings have proven that the system of porous medium reservoirs exhibits different heterogeneous structures at various scales,demonstrating some form of self-similarity with fractal characteristics.In this paper,fractal theory is incorporated into the reservoir to investigate coupled flow between reservoir and horizontal well.By examining the pore structure of highly heterogeneous reservoirs,the fractal dimension can be determined.Analytical methods are utilized to solve the Green function of a point source in a reservoir with fractal characteristics.Employing Green's function and the principle of spatial superposition,a finite flow model for a horizontal well coupled with a fractal reservoir is developed to calculate the flow rate and flow profile of the horizontal well.The model also accounts for the impact of wellbore friction and is solved numerically.A specific example is used for calculation to analyze the influence of fractal parameters on the production and flow rate of the horizontal well.When considering the fractal characteristics of oil reservoirs,the flow rate of the horizontal well is lower than that in Euclidean space.As the fractal dimension increases,the connectivity of pores in the reservoir improves,making it easier to drive the fluid into the wellbore,and the flow distribution along the wellbore becomes more uniform.Conversely,as the anomalous diffusion index increases,the connectivity between pores deteriorates,thus the distribution of flow rate along the wellbore becomes more uneven.

Identification of carbonate sedimentary facies from well logs with machine learning Author links open overlay panel

Sedimentary facies identification is critical for carbonate oil and gas reservoir development.The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long time.Identifying carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers'subjective influence.Although many references reported the application of machine learning to identify lithofacies,but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic movement.This paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models,and the optimal machine learning with the highest prediction accuracy is recommended.First,the carbonate sedimentary facies are classified into the lagoon,shallow sea,shoal,fore-shoal,and inter-shoal five tags based on the well loggings.Then,five well log curves including spectral gamma ray(SGR),uranium-free gamma ray(CGR),photoelectric absorption cross-section index(PE),true formation resistivity(RT),shallow lateral resistivity(RS)are used as the input,and the manual identified carbonate sedimentary facies are used as the output of the machine learning model.The performance of four different machine learning algorithms,including support vector machine(SVM),deep neural network(DNN),long short-term memory(LSTM)network,and random forest(RF)are compared.The other two wells are used for model validation.The research results show that the RF method has the highest accuracy of sedimentary facies prediction,and the average prediction accuracy is 78.81%;the average accuracy of sedimentary facies prediction using SVM is 77.93%.The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM,and the average accuracy is 69.94%and 73.05%,respectively.The predicted carbonate sedimentary facies by LSTM are more continuous compared with other machine learning models.This study is helpful for identifying compelx sedimentary facies of carbonate reservoirs from well logs.