Utilizing Neural Networks to Predict Production Decline in Horizontal Wells in the Eagle Ford Shale Formation

Abstract

This study incorporates neural networks to evaluate the production decline in horizontal wells in the Eagle Ford Shale Formation, which presents a revolutionary method in petroleum engineering. The critical issue that affects the determination of the appropriate rate of production decline in this case is the heterogeneity of the reservoir, operational issues, and technological constraints. This significance of this research lies in enhancing the resource management, financial management and technology areas concerning big data and non-linear causality analysis. The approach involves obtaining a large and suitable database of the production history, geological information, and operational specifications and the use of a deep neural network to optimize hyperparameters. The study shows that neural networks provide better and more accurate results than the conventional decline curve analysis methods and thus better production forecasts. This approach also improves the decision-making and operational strategies as well as stressing the significance of data quality and its preparation in the model. Thus, further improvement of neural network models and their testing on other shale formations are suggested to confirm their efficiency and applicability across different cases and contribute to the enhancement of the use of big data analytics for effective and sustainable management of resources in the oil and gas industry.