Research On The Causes And Predictionmethods Of Casing Damage In Complex Faulted Basin

With the world’s major oilfield has been exploited more and more complex oilfieldgeological conditions cause the Increasingly high Frequency of the casing damage of Oilwells. It takes huge economic losses to oilfield by causing accidents and making wellsabandoned. So, It’s great significance to find a reasonable prediction method for predictingcasing situation, the extension of the working time of oil wells and reduce losses.Casing damage is a systems with many affecting factors and complex mechanism, whichhas the characteristics like complexity, uncertainty, ambiguity and qualitative analysis isdifficult to quantify. In this paper, through statistical analysis of data from Hailar oilfieldproduction and the existing data on the basis of the well casing damage. research InfluencingFactors each block to find the major factor. Research shows that the casing damage of Hailarblock is a variety of factors influence the outcome such as water guiding tomographic,swelling shale, poor reservoir properties, serious pressure, the high oil pressure, the highcontent of clay minerals, high watery.Therefore, this paper introduces the basic casing damage research methods andcommonly used prediction methods withsystem. Through comparative analysis of FuzzyMath and Artificial Neural Networks, find Fuzzy Math is suitable for solve complex factors,evaluation criteria fuzzy and difficult to qualitative analysis. Artificial Neural Networks issuitable for solve the nonlinear relationship between the data and Individual data errors. Thencombine Hailar oilfield production data, studies different blocks, comprehensive analysis ofthe factors affecting the development process of Casing damage. Direct factors of Casingdamage and limitations of the evaluation criteria are fuzzy and difficult quantitative analysis.Propose a fuzzy comprehensive evaluation to determine the weight of the forecasting methodbased on AHP and the establishment of appropriate Casing damage prediction model, achievea quantitative analysis on the basis of the qualitative analysis of the casing failure, calculationand verification examples to the well known the Casing damage. Evaluation results show that fuzzy comprehensive evaluation model with high credibility;Direct factors of Casing damagehave limitation of error value and dfficult to establish a linear function, proposed Casingdamage prediction method based on BP neural network method, and the establishment ofappropriate casing damage prediction model. Data show the results of learning and trainingsamples to construct a reasonable model, sample forecasts comply with the actual situation.On the basis of theoretical research, in MapInfo Professional, Map Basic platform,developed a practical and reliable Casing damage prediction and analysis system,combinedwith actual production data of three blocks Hailar oilfield to predict. The prediction resultsshow that, using Fuzzy Math forecasting system, it has Feature as easy to use, highadaptability parameter selection, Flexibility to determine the critical value. Using ArtificialNeural Networks forecasting system has feature as sample Selection intelligent learning andtraining, strong generalization ability. Provides a new way for prediction of oilfield Casingdamage and has good prospects.