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Productivity Prediction Integrating Data-Driven Method, Deep Neural Network and Exploratory Data Analysis in Montney Shale Plays
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, First EAGE Digitalization Conference and Exhibition, Nov 2020, Volume 2020, p.1 - 5
Abstract
Summary
This paper presents a novel approach of productivity prediction in Montney shale formation by integrating data-driven method, exploratory data analysis (EDA) and deep neural network (DNN). In this study, a total of approximately 1500 wells. First, the EDA discovered a distribution of un-refined data and null data. In the above analysis, in order to avoid overfitting the proposed DNN model, an outlier analysis of the dataset was performed and an 1143 well was selected as a training data set. Second, in the DNN model, the applicability of categorical variables through one-hot encoding was verified. Hyperparameters optimization of the DNN model also resulted in dropout layer application (without), number of hidden layers (3), number of neurons (200), activation function (ReLU), and learning rate (0.002). Comparisons with optimized DNN model and other supervised learning models, random forest and support vector machine, showed that the DNN model had a minimum of 3.2% lower the mean absolute percentage error values and a minimum of 0.025 lower the root mean squared error values. The proposed DNN model was found to have superior predictive performance.
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