How Artificial Intelligence can Guide Marcellus Development

Abstract

The application of Artificial Intelligence for planning has received increased attention in the energy industry in the past few years, particularly for the increased production efficiency requirements and environmental standards. The objective of this paper is to show the successful integration of production, completion, subsurface and spatial data using machine-learning algorithms to predict production performance for future development wells. The internal Marcellus Business Unit (MBU) well database, populated with data of 500+ historical wells, has been used in this study. Production data, treated as timeseries, has been processed using functional Principal Component Analysis (PCA) to allow removal of outliers and mode detection. Utilizing this data, a suite of machine-learning algorithms has been applied to reconstruct gas production from available and target well data. Uncertainty quantification has been provided for production curves to identify the quality of prediction. During the study, the sensitivity analysis on input variables has been performed iteratively to screen and rank the most important variables for prediction. The workflow, Unconventional Reservoir Assistant (URA), has been implemented in a proprietary cloud-based platform providing the necessary means for data upload, integration, pre-processing, and finally model training and deployment. This allows the user to focus on the evaluation of model output quality, data filter and workspace generation for continuous model testing and improvement. The full well dataset, split into trained and tested data, has been used for prediction as a preliminary guide to where the most prolific areas of development are located. Results were ranked based on production expected by pad and based on normalized performance. The information was then used to compare with type curves and original development order. In parallel, economic evaluation of break-even was performed to rank all future pads. Consequently, integration of the model prediction and breakeven ranking were used to generate the final development order for the MBU. The URA tool has shown preliminary success in predicting production performance for the pilot development area. Multiple case studies have been run achieving blind test results with high accuracy for historical prediction. Results show some dependency of predictor variable ranking on the field development area, providing insight on how subsurface may affect well dynamic behavior. This paper describes how the integration of URA can complement the development workflow for unconventional reservoirs and be used to predict performance based on complex data integration. The methodology used is superior to standard machine learning models providing only production indicators, as it gives the user the possibility to evaluate economics and completion design sensitivity for future well activities. The methodology can be further extended as a proxy model for well schedule optimization in planning or for better insight into well refrac selection.