Prediction of water saturation in tight sandstone reservoirs from well log data based on the large language models(LLMs)
Prediction of the water saturation in well logging is a key step in the reservoir evaluation and production prediction of tight sand oil and gas reservoirs.The application of machine learning models to predict water saturation has,to some extent,alleviated the problem of large prediction errors in conventional methods.However,existing machine learning methods usually begin model training with limited logging data,which restricts the capacity of the model,hindering its generalization ability.Based on the excellent generalization performance and rich knowledge information of Large Language Models(LLMs),this paper introduces LLMs to predict reservoir water saturation.Then,a realistic relational and tabular transformer network(REaLTabFormer)enhanced LLMs alignment framework model(RTF-LLA)is established and experimentally compared and verified.And the following research results are obtained.First,the RTF-LLA model consists of three core modules,i.e.,data enhancement,knowledge distillation and cross-modal alignment.Second,the data enhancement module captures the intrinsic relationships between well logging parameters and reservoir physical parameters to generate large-information well logging data by using the REaLTabFormer,based on original well logging data.Third,the knowledge distillation module extracts the main knowledge information from LLMs to guide the cross-modal alignment of well logging data and LLMs text knowledge and endow the model with the ability to predict reservoir water saturation accurately.Fourth,the cross-modal alignment module effectively reduces the prediction error of reservoir water saturation through lexical unit alignment,feature alignment and sequence alignment.In conclusion,when the RTF-LLA model is used for the experimental evaluation of reservoir saturation in S Gas Field,its mean absolute error(MAE)and root mean square error(RMSE)are 1.332 and 2.207,respectively,which are at least 3.310 and 3.174 lower than those by other mainstream machine learning algorithms.What's more,the RTF-LLA model can provide effective technical support for the accurate prediction of the reservoir water saturation with small sample logging data,as well as a new idea and a method for the prediction of reservoir water saturation.
Large Language Models(LLMs)Cross-modal alignmentTight sandstone reservoirWater saturation prediction in well loggingGeneralization
万方数据
维普
