Development And Implementation Of Oil Well Production Prediction System Based On Machine Learning

Petroleum resources are the core driving force of China’s industrial development and urban operation,affecting all aspects of people’s lives.However,because of the limited and non-renewable reserves of oil resources,it is important to improve oil field production efficiency and accurately predict oil well production.In this paper,machine learning theory is introduced into the production prediction of oil wells.Taking the production of oil wells in Changqing Oilfield as the object of study,an algorithm for extracting oil well production features based on improved noise reduction self-encoder is presented.In view of the large amount of data,many influencing factors and complexity of oil well production dataset,an algorithm for predicting oil well production based on improved sparrow search is presented,and a system for oil well production prediction is implemented.It has certain guiding significance to the development decision-making of water drive oilfield.The main work and research are as follows:(1)In view of the features of many missing values in well production data,the data collected is easily interfered by human factors and environmental factors,data cleaning and missing value filling are carried out first,and an algorithm for extracting well production features based on improved noise reduction self-encoder(MHSA-SDA)is proposed.This algorithm introduces a multi-head self-attention mechanism in noise reduction self-encoder and uses stack connection to construct a depth neural network,which can extract the characteristics of production data of oil wells and lay a data foundation for oil well production prediction.The validity of the algorithm is validated by the simulation of production data in a certain operation area of Changqing Oilfield.(2)In view of the characteristics of complex influencing factors and large amount of data,this paper presents an improved sparrow search based oil well production prediction algorithm(ISSA-Cat Boost).Chaotic mapping mechanism was introduced to form chaotic mapping sparrow individuals at the initial stage of population.Secondly,the firefly disturbance mechanism is introduced in the algorithm update,which solves the problems that the algorithm is prone to fall into local optimization and "premature".Finally,an ISSA-Cat Boost oil well production prediction algorithm is constructed by using the improved sparrow search algorithm to optimize the Cat Boost algorithm with superparameters.The simulation results show that the algorithm improves the accuracy of oil well production prediction.(3)Design and implementation of an oil well production yield prediction system.Adopting software engineering development technology,the development of an oil well production yield prediction system is achieved.The proposed MHSA-SDA algorithm and ISSA-Cat Boost algorithm are applied to the preprocessing of oil well production data and oil well production yield prediction,providing a reference basis for oilfield development decision-making and having certain application value.