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Study On Well Logging Evaluation Technology For Coalbed Methane Of Southern Yanchuan

Posted on:2014-06-12Degree:MasterType:Thesis
Country:ChinaCandidate:Y QiFull Text:PDF
GTID:2180330452962361Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
Coalbed methane (CBM) has huge development potential due to its abundantresources.To meet the enlarging desire of the market,the exploration and development ofCBM resources has aroused extensive attention both abroad and at home. Well longing data,with its large amount of information, accuracy, continuation, low-cost and reliabilitycharacteristics, plays an important role in coalbed methane reservoir evaluation.Based on the coal composition,physical properties, gas content,acoustic,electrical andother rock physics experiments results of southern Yanchuan CBM reservoir, this papersummarizes and analyzes the log response characteristics of the2ndand10thseam,establishesthe coal seam log identification plates.Using the method of normal distribution trendaverage,this paper standardized the logging curve and corrected the coal core depth of10wells in southern Yanchuan.Based on the thought of coal core calibrating logging,this paperstudies and analyzes the relationship between the well logging parameters and the physicalparameters, and establishes the logging evaluation methods for CBM reservoir parameters;The dual lateral resistivity log and numerical simulation method were selected from routinelogging to predict the coal seam fracture porosity.Using the method of Darcy’s law and thecore calibration model predict the coal seam permeability.As to the coal gas content,this paperestablished gas content evaluation model of the2ndand10thcoal seam in southern Yanchuanthrough analyzing the correlation between the gas content of coal core test and the loggingparameters. This paper also uses Langmuir adsorption isotherm equation and multi-parameternonlinear regression predicting model to predict coal gas content. Moreover, this paper alsotried to apply artificial intelligence processing technology, such as SVM and neural networksto the evaluation of CBM reservoir, which can mprove the prediction accuracy of reservoirparameters.This paper formed a logging qualitative and quantitative evaluation method for CBMreservoir in southern Yanchuan using the above method and CBM reservoir was evaluatedwhich has achieved good practical results.
Keywords/Search Tags:coalbed methane, well logging evaluation, coal analysis, cleat porosity, gascontent
PDF Full Text Request
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