Study On Water Injection Optimization Method Of X Oilfield Based On Data Mining

The X oilfield has experienced over 40 years of water injection development,resulting in a comprehensive water cut exceeding 96%.The oilfield is confronted with challenges such as a significant production decline,rapid increase in water content,and difficulties in unlocking potential.Hence,it is essential to systematically manage the X oilfield to stabilize production and reduce water content.With the swift advancement of big data,data mining,and artificial intelligence technologies,their efficient,accurate,and universal features have facilitated their extensive implementation in the oil and gas industry,yielding satisfactory outcomes in domains like reservoir identification,production forecasting,and production optimization.This study integrates traditional reservoir engineering methodologies with data mining and artificial intelligence technologies to investigate water injection development effect evaluation,production parameter optimization,and production and water content prediction for the X oilfield,yielding the following findings:1.Assessment of water injection development effects in the X oilfield,encompassing evaluation of water storage rate,water consumption rate,water content rate,water flooding control degree,water flooding drive degree,and pressure system evaluation.It is acknowledged that the X oilfield has entered a high-water-cut phase and requires adjustments to development measures.2.Utilizing fuzzy mathematics and the analytic hierarchy process,an oil well production development effect evaluation system is established,considering development,geology,and economic benefits.The development effects of all oil wells in the area are assessed.Based on the evaluation outcomes,dynamic and static production data of oil and water wells are extracted and analyzed,examining the reasons for suboptimal oil well development effects and categorizing the primary production contradictions of the block oil wells into six classes.The training data sample library is expanded through a combination of manual identification and active learning technology.3.Developing a Light GBM oil well production contradiction identification model and an LSTM production and water content prediction model.The Light GBM model is employed to identify the main production contradictions of oil wells,selecting the corresponding production parameters based on the identification results and optimizing them using the MOPSO algorithm.The optimized production parameters are input into the LSTM model to assess the impact of the optimization scheme on increasing production.According to the prediction results,the model exhibits high accuracy,and the optimization schemes have achieved production increase and water reduction to a certain extent.The optimized target block maintains stable production,with the overall water cut decreasing by approximately 1%.