Application of predictive data analytics to model daily hydrocarbon production using petrophysical, geomechanical, fiber-optic, completions, and surface data: A case study from the Marcellus Shale, North America

• Published in: Journal of petroleum science & engineering 2019-05, Vol.176 (C), p.702-715
• Main Authors: Bhattacharya, Shuvajit, Ghahfarokhi, Payam Kavousi, Carr, Timothy R., Pantaleone, Scott
• Format: Article
• Language: English
• Subjects: 02 PETROLEUM; Data analytics; Fiber optic; Machine learning; Marcellus Shale; Predicting gas production
• ISSN: 0920-4105; 1873-4715
• DOI: 10.1016/j.petrol.2019.01.013

Predicting gas production from stimulated unconventional reservoirs has always been a challenge to oil and gas companies because the physics of fluid flow through such reservoirs is not well understood. On the other hand, modeling gas production from shale reservoirs is a complex multi-variate problem that requires proper integration of data from multiple disciplines, such as geology, petrophysics, geomechanics, completions, and surface-based measurements, etc. This study demonstrates the application of data-driven machine learning algorithms, integrating geoscientific, distributed acoustic sensing (DAS), distributed temperature sensing (DTS) fiber-optic, completions, flow scanner production log, and surface data to model daily gas production from a 28-stage stimulated horizontal well drilled in the Marcellus Shale of the Appalachian basin in North America. In addition, this study aims to utilize the data from a fiber-optic monitoring system, such as DAS and DTS to evaluate the well performance. We build supervised data-driven machine learning models using Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM) algorithms to understand the well performance and forecast the daily gas production. The study compares the forecasted results, tests the level of accuracy, and addresses different issues with these machine learning algorithms. A spatio-temporal database is constructed and used to perform sensitivity analyses to identify the key drivers for gas production. The results show that RF and ANN algorithms can be used to predict daily gas production with significantly high accuracy; however, RF algorithm is the best predictor in terms of highest accuracy (96%), less computational time and cost. Based on sensitivity analysis, it appears that Poisson's ratio, minimum horizontal stress, DAS, casing pressure, gamma log are the most important parameters to predict gas production. •Application of machine learning algorithms to forecast daily gas production from unconventional reservoirs using large multi-sensor dataset.•Random Forest and Artificial Neural Network can model daily gas production from each stage efficiently.