Modeling Early Time Rate Decline in Unconventional Reservoirs Using Machine Learning Techniques

Abstract

Abstract Decline curves are fast methods to predict production behavior in oil and gas wells. Some of the notable decline curve methods are Arp’s, SEDM (Stretched Exponential Decline Model), Duong’s Model and Weibull decline curves. Available production history data can be used to fit any of these equations and future production decline can thus be extrapolated. However, when limited production data is available during early periods of well history, these equations could be fitted using inaccurate parameters leading to erroneous predictions. Also, the traditional decline curve analysis approach does not account for the complexities related to reservoir description and well completions. This study utilizes publicly available databases of the Eagle Ford formation to develop a novel predictive modeling methodology linking decline curve model parameters to well completion related variables that allows for the rapid generation of synthetic decline curves at potential new well locations. Modern machine learning algorithms such as Random Forests (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) can then be used to model the well decline behavior. Cross-Validation technique such as k-fold cross-validation can be used to quantify the predictive accuracy of these models when applied to new wells. First, production data are fitted to decline curve models to estimate the corresponding model parameters. Next, machine learning models are built for these parameters as a function of initial flow rate, various well completion parameters (i.e., number of hydraulic fracture stages, completed lengths, proppant and fracturing fluid amounts) and well location/depth parameters (i.e., well latitudes, longitudes, total vertical depth of heel and difference between total vertical depths of heel and toe of horizontal wells). These models are used to rapidly predict the decline curves for new or existing wells without the need for costly reservoir simulators. It has been found that accurate prediction of rate decline of new wells can be predicted using this methodology. This method can also predict ultimate recovery of a new well based on data collected from previous wells. To our knowledge, this is the first time machine learning algorithms have been used to predict the decline curve parameters and examine the relative performance of various decline curve models. The power and utility of our approach are demonstrated by successful prediction of the decline behavior of blind wells that were not incorporated in the analysis. We also examine the relative influences of various well design and location variables to determine the hidden correlations or interactions among them which are hard to decipher with other methods.