Identifying the controlling geological and engineering factors of shale gas production using deep learning models: a case study from Weiyuan, China

Abstract

Predicting shale gas production is challenging due to varying and unclear influencing factors. In this work, we explore the average daily production rate (ADPR) and its determinants by analyzing data from 119 horizontal wells in the Weiyuan block. Initial data analysis revealed weak Spearman correlations between ADPR and geological and engineering parameters. Then, we develop four feed-forward deep learning models to predict ADPR and compare them. One of the proposed models utilizes geological and engineering data while the other three models utilize additional early-stage production data. The model with test-stage gas production can reach while the model without production data can also reach indicating ADPR can be efficiently predicted using only geological and engineering parameters. Moreover, the multiplication of the thickness of the target formation and the drilled length in the high-quality reservoir, as well as total organic carbon content (TOC), are the two most important influencing factors of ADPR apart from test-stage gas production. In contrast, the first-month flowback ratio is not important and cannot improve model performance. These findings can provide experts with theoretical suggestions for shale gas development. The workflow is also beneficial to future research by repeating it on larger datasets with more parameters.