FedRBF-SVM: A Federated Support Vector Machine for IIoT-Based Anomaly Detection in Upstream Oil and Gas Wells

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Abnormal operating conditions and equipment malfunctions in upstream oil and gas wells can compromise safety, disrupt production, and cause substantial economic losses. This study proposes FedRBF-SVM, a federated radial basis function (RBF) support vector machine (SVM) approach for collaborative anomaly classification across Industrial Internet of Things (IIoT)-enabled wells. Each participating well, represented as a client, trains and retains a local RBF-SVM using labeled samples and returns only a scalar decision-function score for each inference sample. The server averages these scores and applies a validation-calibrated decision threshold. The approach was evaluated on the 3W oil-well dataset in a simulated federated setting using different numbers of locally stored training patterns, three target false-positive rate (FPR) operating points, and a centralized RBF-SVM reference. FedRBF-SVM performance generally improved as the number of locally stored training patterns increased. With 2500 training patterns per client and a target FPR of 0.05, FedRBF-SVM achieved an F1-score of approximately 0.79, a precision of approximately 0.97, and a recall of approximately 0.67. These results demonstrate the feasibility of federated SVM for distributed oil-well monitoring while keeping training data and models at the clients.