Data-Driven Production Forecasting for Shale Gas Wells Using Production Characteristics Analysis

Abstract

Abstract Several studies have used machine learning-based techniques to improve the production behavior prediction in existing shale gas wells. However, few studies have investigated production prediction in new wells wherein no prior information is available. This is challenging because these predictions are generally based on the analysis of data available on existing wells. Therefore, in this study, data-driven analytics is utilized to analyze the production characteristics of existing wells and improve the predictive performance of the production for new wells. Field data on the Marcellus Shale wells with production histories exceeding 48 months were collected from Enverus’ Drillinginfo. We derived production-related attributes of these wells and identified the key factors using principal component analysis to establish the production dependency on them. Subsequently, the prediction reliability was improved by classifying different production characteristics into groups, and using their trend lines to estimate the cumulative production of the existing wells. For new wells, we developed a model to classify groups based on key factors, and utilized probabilistic values from the classified groups to predict stochastic ranges of cumulative production using an artificial neural network. The field data were normalized with respect to lateral length or the number of stages to enable comparison between multiple wells. Outliers of each input factor were excluded during pre-processing. An analysis of production characteristics was performed by classifying the existing wells into three groups. Results indicated that Group 2 was highly productive, with evident influence of normalized fluid volume during the middle and late phases of production. Further, initial variations in production tendency were observed in Groups 1 and 3 by spacing. Trend lines of classified groups were used to forecast the cumulative production per unit length (NP) of the existing wells. The observed error was less than 10 % in the prediction of NP 48 based on the analysis of NP 6 and NP 12. Additionally, high production variability in shale play is known to induce a rapid reduction in the production trend after the initial production. Therefore, a prediction model with NP of 6, 12, and 48 months was developed. To validate the model, probabilistic values of spacing and decline factors were used in the predictions of NP 6, 12, and 48, yielding an accuracy exceeding 80% and an error of approximately 10%. The proposed multi-well productivity analysis is a trial-and-error process based on data-driven analytics, which can be used to predict shale production in any shale play. In addition, the range of the predicted probabilistic production includes the actual values; therefore, the prediction errors are small compared to those of previous methods for new wells. Consequently, time and resources expended for data acquisition are reduced, and the reliability of productivity forecasts in shale development is improved.