Identification and logging evaluation of poor reservoirs in X Oilfield

Abstract

The reservoirs of X Oilfield have the characteristics of fine lithology particles, strong pore structure heterogeneity, and high argillaceous reservoirs and thin layers are generally developed. Conventional logging interpretation cannot make a fine evaluation, which results in serious discrepancies between the interpretation results of some reservoirs and actual production performance, and reserves are underestimated. Improving poor reservoir identification and logging evaluation accuracy is of great significance to oilfield development. The flow zone indicator (FZI) is used to classify the reservoirs into three types, I, II, and III, and the classification results are combined to establish a reservoir type identification chart based on logging curves; the resolution matching method and the deconvolution method are used to improve the accuracy of thin-layer recognition. Finally, the logging interpretation model is reestablished. Logging evaluations were conducted on 20 wells in X oilfield, and Y core wells were used for verification. The application results show that this method can effectively improve the identification accuracy of thin oilfields and high argillaceous reservoirs; the results of fine logging interpretation of poor reservoirs are consistent with core analysis conclusions and actual production conditions, which are typical of the successful application of poor reservoir technology.

Keywords

• reservoir classification
• fzi method
• reservoir type identification chart
• resolution matching method
• deconvolution method