Machine Learning Applied to Multiwell Test Analysis and Flow Rate Reconstruction

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Abstract Permanent downhole gauges (PDGs) can provide a continuous record of flow rate and pressure, which provides us rich information about the reservoir and makes PDG data a valuable source for reservoir analysis. In previous work, it has been shown that kernel ridge regression based machine learning is a promising tool to interpret pressure transients from a single PDG. Kernel ridge regression denoises and deconvolves the pressure signal efficiently and recovers the full reservoir behaviors. In this work, the machine learning framework was extended to two applications: multiwell testing and flow rate reconstruction. The multiwell testing was formulated into the machine learning algorithm using a feature-coefficient-target model. The features were nonlinear functions of flow rate histories of all the wells. For each well, the features and the pressure target of this well were used to train its coefficients, which implicitly contain the information about the reservoir model and well interactions. The reservoir model can then be revealed by predicting the pressure corresponding to a simple rate history with the trained model. The multiwell machine learning model was demonstrated to be useful in several different ways, including artificial interference testing and reservoir pressure prediction at various well locations based on flow rate data only. Flow rate reconstruction aims at estimating any missing flow rate history by using available pressure history. This is a very useful capability in practical applications in which individual well rates are not recorded continuously. A set of new features were developed as functions of pressure to model the flow rate. Coupled with kernel ridge regression, the developed features were tested on both synthetic and real data sets and demonstrated high prediction accuracy. The success of the rate reconstruction modeling also illustrates the flexibility of machine learning to different kinds of modeling, by adapting features and targets. The models for both applications maintained the advantages of the machine learning based single well pressure interpretation in terms of the accuracy of prediction, computational efficiency and tolerance to noise. This work further demonstrates machine learning as a promising technique for PDG data analysis.