Data driven techno-economic framework for the development of shale gas resources
Document Type
Article
Publication Date
12-1-2019
Abstract
This work presents a data driven techno-economic framework that combines upstream, midstream and downstream operations to optimize profitability of shale projects. This framework is illustrated using a shale gas supply chain structure planned for development and integration in an over-supplied gas market. Field development strategies are developed based on applying machine learning techniques to an existing field. Alternative development strategies are implemented on the integrated production-modeling platform RESOLVE–REVEAL to simulate hydrocarbon/water production. Long-short term memory (LSTM) neural networks are developed to predict gas demand and freshwater availability. Long-term strategic planning is achieved using a mixed-integer non-linear programming (MINLP) formulation. Results indicate a net present value (NPV) of 205.56 MMUS$ for optimal infrastructure design, gas and liquids transportation and distribution, and water management after 54-well integration. Additionlly, results provide an optimal gas storage schedule that supports shale asset profitability. Application of this techno-economic approach improves profitability projections for shale enterprises.
Publication Source (Journal or Book title)
Journal of Natural Gas Science and Engineering
Recommended Citation
Chebeir, J., Asala, H., Manee, V., Gupta, I., & Romagnoli, J. (2019). Data driven techno-economic framework for the development of shale gas resources. Journal of Natural Gas Science and Engineering, 72 https://doi.org/10.1016/j.jngse.2019.103007