Research On The Construction Of Oil Well Submergence Depth Prediction Model And The Algorithm Of Curve Fitting

The petroleum industry is a pillar industry of national economy and plays an important role in my country's economic and social development.In the process of oil exploitation,the submergence depth is an important indicator to measure the production status of oil well,and its height directly affects the working efficiency of oil well pump.When the pumping speed does not match the submergence depth,the production efficiency of the oil well will be reduced,resulting in waste of electric energy.Therefore,real-time understanding of the submergence depth of oil wells provides a theoretical basis for formulating reasonable oil pumping plans,and is of great significance to enhancing the international competitiveness and sustainable development of my country's oil fields.In this dissertation,comprehensive utilization of oil well's oil layer seepage characteristics and other factors have constructed an oil well submergence depth prediction model reflecting oil layer seepage conditions.On this basis,three application methods are studied,and the monotonic increasing characteristic of the characteristic function is used to prove the unique existence of the understanding,so that the model can be better applied to actual oilfield production.The main content of this dissertation is as follows:(1)According to the reservoir percolation characteristics of the oil layer and combined with the oil well permeability,the non-homogeneous linear differential equation for oil well pressure is derived by using Darcy's law,and the prediction model of oil well submergence depth is constructed.(2)The parameter merging method is used to reduce the parameter dimension of the submergence depth prediction model,which greatly reduces the parameter dimension,reduces the application complexity,and effectively improves the scope of its application,so that the model not only conforms to the reservoir percolation law of the oil layer,but also suitable for the balance laws of other substances with potential energy balancing ability.(3)Aiming at the parametric dimensionality reduction model,the extraction of the greatest common divisor method is used to solve the model to obtain the accurate solution of submergence depth.In order to improve the accuracy of submergence depth prediction,the weighted dichotomy method is used to weight the multi-point measurement results,which effectively reduces the error amplification caused by measurement error.For multi-point measurement values,the nonlinear least squares curve fitting is used to solve the problem,so that the submergence depth error reaches the minimum value of the 2-norm,and the number of iterations is accurately given by the convergence speed of halving the numerical calculation error in every iteration.This method is suitable not only for the zero submergence depth case in the initial state but also for non-zero submergence depth cases.The experimental results show that the change law of submergence depth can be grasped in real time by using the proposed submergence depth prediction model;Combined with the proposed application methods,the corresponding submergence depth can be obtained at any time point.The model and application methods provide strong support for formulating scientific oil production plans and implementing reasonable production methods for oil wells.