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

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.