Study On Well Logging Evaluation Methods For Coalbed Methane Reservoir

With today's increasing demand for new energy sources, coal bed methane development has been got more attention. Well longing data, with its accuracy, continuation, and reliability characteristics, plays an important role in coalbed methane reservoir evaluation.Be aimed at the problem that the information on coal formation is seriously affected by the hole enlargement, an editing correction method based on expanding rate is proposed in this paper, which can eliminate the impact of logging data to some extent. By analyzing and summarizing the log response characteristics of coalbed methane reservoir in the study area, a coal-bearing formation lithology identification method was established combined with cutting logging data. Based on well logging data, cutting logging data and coal-core test data, this paper analyzes and summarizes the characteristics and influencing factors of coal quality, coal rank, gas content and permeability in the area, and establishes regional statistical models, which can calculate parameters of coal quality and divide coal rank using log data. In addition, coal strata volume model and non-coal strata volume model is proposed to calculate the strata component content. The coalbed methane reservoir has complex space structure, and the dual lateral resistivity log was selected from routine logging to decide the coal fracture porosity and crack width with a iterative method, and then evaluate the coal seam permeability. This paper uses Langmuir adsorption isotherm equation adjusted by temperature and pressure to predict coal gas content, and a multi-parameter nonlinear regression predicting model of gas content is established through analyzing the influencing factors of coal bed gas content. Moreover, this paper also tried to apply artificial intelligence processing technology, such as BP neural networks, support vector machines and genetic algorithms, to the evaluation of coalbed methane reservoir, which can simplify modeling process, avoid reasonably selecting interpretation parameters, and improve the prediction accuracy of reservoir parameters.Based on the above method, corresponding computer program was developed, and coalbed methane reservoir was evaluated of a dozen of wells in the study area using well logging data, which has achieved good practical results.